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Molecular Mechanisms of Embryonic Stem Cell Pluripotency
Embryonic stem (ES) cells isolated from the inner cell mass (ICM) of blastocysts possess the defining pluroptency: unlimited self-renewal and giving rise to all cells of the organ‐ ism[1, 2]. Thus, ES cells hold great promise for regenerative medicine to treat many dis‐ eases including heart failure, diabetes, Alzheimer’s and Parkinson’s disease by replacing the damaged cells with ES cell-derived healthy ones. The recent advent of induced pluri‐ potent stem (iPS) cells reprogrammed from somatic cells has the potential to revolution‐ ize the field of regenerative medicine since patient-derived iPS cells, in principle, circumvent the ethical problems and immune rejection associated with human ES cells[3]. Nevertheless, the future clinical translation of ES cells and iPS cells is facing nu‐ merous hurdles. Understanding the molecular mechanisms that impart ES cells with plu‐ ripotency may help address some of these challenges. The past few years have seen tremendous progress in understanding of mechanisms which govern ES cell pluripoten‐ cy. In this chapter, we will review critical signaling and transcription factor networks that have been identified to maintain ES cell pluripotency.
Introduction
Embryonic stem (ES) cells isolated from the inner cell mass (ICM) of blastocysts possess the defining pluroptency: unlimited self-renewal and giving rise to all cells of the organism [1,2]. Thus, ES cells hold great promise for regenerative medicine to treat many diseases including heart failure, diabetes, Alzheimer's and Parkinson's disease by replacing the damaged cells with ES cell-derived healthy ones. The recent advent of induced pluripotent stem (iPS) cells reprogrammed from somatic cells has the potential to revolutionize the field of regenerative medicine since patient-derived iPS cells, in principle, circumvent the ethical problems and immune rejection associated with human ES cells [3]. Nevertheless, the future clinical translation of ES cells and iPS cells is facing numerous hurdles. Understanding the molecular mechanisms that impart ES cells with pluripotency may help address some of these challenges. The past few years have seen tremendous progress in understanding of mechanisms which govern ES cell pluripotency. In this chapter, we will review critical signaling and transcription factor networks that have been identified to maintain ES cell pluripotency.
Signaling pathways of ES cells
ES cells require extrinsic growth factors to maintain their pluripotency in culture. These extrinsic growth factors act on different signaling pathways to regulate intrinsic transcription factor networks to sustain ES cells in the undifferentiated state. The signaling pathways required to support pluripotency in mouse ES cell are distinct from those in human ES cells (Figure 1).
LIF/JAK/STAT3 pathway
Mouse ES cells were originally cultured on feeder layers derived from mouse embryonic fibroblasts (MEF). Later it was found that Leukaemia Inhibitory Factor (LIF), a member of the Interleukin-6 cytokines produced by MEFs, was the key factor to maintain pluripotency of mouse ES cells by inhibiting their differentiation [4]. Upon LIF binding, the LIF receptor recruits gp130 to form a heterodimer which subsequently activates Janus kinase (JAK) through transphosphorylation [5]. Activated JAK then phosphorylate gp130, creating a docking site to bind the SH2 domain of Signal Transducers and Activators of Transcription 3 (STAT3) [6][7][8][9]. Once STAT3 binds to the gp130 docking site, JAK then phosphorylates the recruited STAT3. Phosphorylated STAT3 forms a homodimer, which subsequently translocate into the nucleus, where it binds to gene enhancers to regulate target gene expression [10][11][12].
Although the LIF/JAK/STAT3 pathway has been well documented to maintain pluripotency of mouse ES cells in the presence of serum, the mechanisms by which activated STAT3 functions in this regard is poorly understood. Recently, studies in identification of STAT3 target genes have improved our understanding of activated STAT3 in maintaining pluripotency. Chen et al identified 718 STAT3-bound genomic sites that were co-occupied by pluripotency transcription markers (Oct4, Sox2 and Nanog) by using chromatin immunoprecipitation sequencing (ChIP-seq) [12]. In addition, Kidder and colleagues found that STAT3 target genes enriched in ES cells were downregulated in differentiated cells by mapping STAT3 binding targets in mouse ES cells and differentiated embryoid bodies (EBs) [13]. Along with these results, it has been demonstrated that knocking down STAT3-target genes induces activation of endodermal and mesodermal genes, supporting the conclusion that STAT3 prevents mESC differentiation by suppressing lineage-specific genes [14].
Interestingly, the LIF receptor and gp130 are also expressed in human ES cells and human LIF can induce STAT3 phosphorylation and nuclear translocation in human ES cells. However, human LIF is unable to maintain the pluripotent state of human ESs, suggesting that mouse and human ES cells require distinct signaling mechanisms to govern their pluripotency [15].
TGF-β signaling
TGF-β superfamily consists of more than 40 members, including TGF-β, Activin, Nodal, and bone morphogenetic proteins (BMPs). The TGF-β members transduce signals by binding to heteromeric complexes of serine/threonine kinase receptors, type I and type II receptors, which subsequently activate intracellular Smad proteins. Smads 2 and 3 are specifically activated by activin, nodal and TGF-β ligands, whereas Smads 1, 5 and 8 are activated by BMP ligands [16,17] (Figure 1). The TGF-β-related signaling pathways play complex roles in regulating the pluripotency and cell fate of ES cells.
BMP signaling pathway
Bone Morphogenetic Protein (BMP) is a subset of the TGF-β superfamily [18]. When BMP ligands bind to type II BMP receptors (BMPRII), BMPRII then recruits and phosphorylates type I BMP receptors (BMPRI). Activated type I receptors subsequently phosphorylate BMPresponsive SMAD1/5/8 which then forms a complex with SMAD4 and translocates into nucleus to regulate target gene expression ( Figure 1). In mouse ES cells, LIF can substitute MEF feeder layers in maintaining pluripotency in the presence of animal serum by activating the transcription factor STAT3. However, in serum-free cultures, LIF is insufficient to block neural differentiation and maintain pluripotency. Recently, Ying et al reported that BMP was able to replace serum to maintain pluripotency of mouse ES cells in the presence of LIF. BMP has been shown to phosphorylate SMAD1/5 and activate inhibitors of differentiation (Id) genes, which block neural differentiation by antagonizing neurogenic transcription factors [19]. In the absence of MEF and serum, exogenous LIF, in combination with BMP4 proteins, can sufficiently maintain the pluripotency of mouse ES cells derived from "permissive" mouse strains.
In contrast to a maintenance role in mouse ES cell pluripotency, BMP has been shown to promote human ES cells differentiation to trophoblasts, and inhibiting BMP signaling with the BMP antagonist, Noggin, sustains the undifferentiated state of human ES cells [20,21]. In consistence, dorsomorphin and DMH1, small molecule BMP inhibitors previously identified in our lab, were shown to promote long-term self-renewal an pluripotency of human ES cells, presumably by inhibiting BMP induced extraembryonic lineage differentiation [22][23][24][25].
TGF-β/activin/nodal signaling pathway
Although MEFs feeder layers were initially used to co-culture both mouse and human ES cells, signal factors secreted from MEFs to maintain pluripotency of the two types of ES cells are fundamentally different. Sato et al first discoveried that TGF-β and Nodal genes were highly expressed in undifferentiated human ES cells [26]. Beattie et al later reported that Activin A, a member of the TGF-β superfamily, was secreted by MEFs, and medium enriched with activin A can replace MEF feeder-layers or MEF-conditioned media to maintain human ES cells in an undifferentiated state [27]. In consistence, James et al demonstrated that the TGF-β/Activin/Nodal pathway was activated through the transcription factors Smad2/3 in undifferentiated human ES cells [28]. The notion that TGF-β/Activin/Nodal signaling supports human ES self-renewal and pluripotency is further supported by the fact that recombinant Activin or Nodal stimulation induces higher levels of pluripotent protein expression (Oct4 and Nanog), while inhibition of TGF-β/Activin/Nodal signaling with Lefty or Follistatin decreases expression of these pluripotent proteins in human ES cells [29,30].
Recent studies have focused on understanding the molecular mechanisms of TGF-β/Activin/ Nodal signaling in retaining human ES cells pluripotency. Xu and colleagues showed that TGF-β/Activin/Nodal signaling activated Smad2/3 which subsequently binds to the Nanog promoter in undifferentiated human ES cells to induce expression of Nanog, a pluripotent transcription factor [31]. Additionally, mutating the putative Smad-binding sites reduced the response of Nanog to modulation of TGF-β signaling [31]. Nanog was also shown to coordinate with Smad2 in a negative-feedback loop to inhibit human ES cell differentiation [32]. In contrast to its important role in maintaining human ES cell pluripotency, the TGF-β/Activin/ Nodal signaling is not essential for pluripotency of mouse ES cells. Although this pathway was shown to be active in undifferentiated mouse ES cells as assessed by phosphorylation of smad 2/3, inhibition of smad 2/3 phosphorylation by SB431542 had no effect on the undifferentiated state of mouse ES cells [28]. However, the TGF-β/Activin/Nodal signaling may play a role in mouse ES proliferation. A recent study showed that Inhibition of TGF-β/Activin/ Nodal signaling by Smad7 or SB-431542 dramatically decreased mouse ES cell proliferation without effect on their pluripotency[33].
Growth and Differentiation factor 3 (GDF-3)
GDF-3 is another TGF-beta superfamily member that plays opposite roles in mouse and human ES cells. GDF-3, which acts as a BMP antagonist by direct binding to BMP-4, is specifically expressed in the pluripotent state of both mouse and human ES cells [34]. Ectopic expression of GDF-3 leads to the maintenance of pluripotency in human ES cells, whereas a similar effect is observed in mouse ES cells when GDF-3 levels are decreased. In the absence of LIF, GDF-3-deficient mouse ES cells can still sustain pluripotent markers[34]. These results are consistent with previously discussed BMP signals which can promote pluripotency of mouse ES cells, but cause differentiation of human ES cells. Thus lower concentrations of BMP antagonists, such as GDF-3, may enhance pluripotency in mouse ES cells, whereas higher levels of GDF-3 may favor pluripotency of human ES cells by abrogating BMP signaling.
FGF/MEK signaling
The importance of Fibroblast growth factor (FGF) signaling for human ES cells pluripotency is highlighted by the facts that human ES cells are traditionally cultured in the presence of Fibroblast growth factors (FGFs) either on fibroblast feeder layers or in fibroblast-conditioned medium [35,36]. Studies have demonstrated that all four FGF receptors (FGFR1, FGFR3 and FGFR4) and several components (SOS1, PTPN11 and RAF1) of their downstream activation cascade are significantly upregulated in undifferentiated human ES cells, in comparison to differentiated human ES cells [37][38][39]. In consistence, withdrawal of FGFs or inhibition of FGF signaling by a FGFR inhibitor, SU5402, rapidly induces human ES cell differentiation [40][41][42].
Although the pluirpotency maintenance role of exogenous FGFs in human ES cell has been known for a long time, the molecular mechanisms by which they function remain unclear. FGFs signal by binding to FGF receptors (FGFRs), and activate multiple signaling cascades, including Mitogen-Activated Protein Kinases (MAPKs), the Janus kinase/signal transducer and activator of transcription (Jak/Stat), phosphatidylinositol 3-kinase (PI3K) and phosphoinositide phospholipase C (PLCg) pathway [43]. Several studies have highlighted the FGF contribution to the maintenance of human ES cells mainly through the FGF/MEK pathway (Figure 1), [44,45]. Studies have showed that FGF2 induces feeder layer cells to secret TGFβ1 and insulin-like growth factor 2 (IGF2), which can subsequently promote the undifferentiated state of human ES cells [46,47]. Bendall et al further reported that the function of exogenous FGFs in promoting ES self-renewal could be replaced by addition of IGF2 alone, suggesting an indirect role of FGFs for human ES cell growth. However, this model was challenged in subsequent publications from Wang et al who reported that exogenous IGF2 alone was insufficient to maintain undifferentiated growth of human ES cells, and they proposed that FGFs may play a direct role in blocking caspase-activated apoptosis through anoikis in human ES cells [48]. Recently, Eiselleova and colleagues postulated a new model whereby endogenous FGF-2 signaling maintained the undifferentiated state and survival of human ESCs, while exogenous FGF-2 mainly suppress cell death and apoptosis genes, thus indirectly contributing to the maintenance of human ES cell pluripotency [49].
FGF signaling in mouse ES cells has also been extensively investigated. Mouse ES cells genetically deficient in Fgf4 and extracellular-signal regulated kinase 2 (Erk2) differentiate inefficiently. These results can be reproduced using inhibitors of FGF receptor and ERK, suggesting blockage of the FGF/MEK signaling pathway promotes mouse ES cell pluripotency [50][51][52]. Indeed, serum-free mouse ES cell medium supplemented with FGF/MEK inhibitors and LIF permits the derivation of mouse ES cells in the absence of feeders from strains normally considered non-permissive [53]. In addition, a recently identified compound, Pluripotin/SC1, has been shown to maintain mouse ES pluripotency by inhibiting ERK1 and activating the phophoinositide-3 kinase (PI3K) pathway through blocking RasGAP [54][55][56] [57,58]. Although inhibition of FGF/MEK pathway can attenuate ES cell differentiation, it is insufficient to support mouse ES cell self-renewal. Combination of the MEK inhibitor PD0325901 with the Glycogen synthase kinase-3 (GSK-3) inhibitor CHIR99021 (known as 2i) can efficiently sustain the pluripotency of mouse ES cells in the absence of exogenous cytokines [59,60]. Several groups dem-onstrated that improvement of mouse ES cell pluripotency by inhibition of GSK-3 occurred via Wnt/β-catenin signaling, whereas many others argued that GSK3 was likely to exert β-catenin independent effects in ES cells [59,[61][62][63][64][65][66][67].
As demonstrated above, human and mouse ES cells are both derived from blastocyst-stage embryos, but they require different biological signals for maintaining pluripotency. In general, mouse ES cells maintain their pluripotency by activating LIF/STAT3 and BMP signaling, while human ES cells require TGF-β/Nodal and FGF/MEK pathways. Interestingly, several pathways, such as BMP and FGF/MEK, have completely oppositing effects on maintaining the pluriotency of mouse and human ES cells. Activation of BMP signaling and inhibition of the FGF/MEK pathway promote mouse ES self-renewal, whereas inhibition of BMP signaling and activation of FGF/MEK pathway sustain human ES cell pluripotency. These distinct signaling effects on pluripotency may reflect intrinsic differences between mouse and human ES cells. Recent studies have demonstrated that conventional human ES cells do not represent the "ground or naïve state" of stemness, but rather a more developmentally mature "primed state" resembling mouse epiblast stem cells (mEpiSCs) found in the post-implantation, pre-gastrulation stage of embryos [68][69][70][71][72][73][74]. Conventional human ES cells exhibit numerous similarities to the mouse EpiSCs over mouse ES cells (Table 1). For instance, conventional human ES cells and mouse EpiSCs display flattened cell colonies and epigenetic X-chromosome inactivation (XiXa), and require Activin and FGF for pluripotency maintanince. In contrast, mouse ES cells exhibit dome-shaped colony morphology and epigenetic activation of both X-chromosome (XaXa), and require LIF/STAT3 signaling to promote self-renewal. Subsequent studies have demonstrated that the medium containing "2i" (MEK inhibitor and GSK-3 inhibitor), when supplemented with other factors (such as forskolin), can efficiently convert conventional human ES cells into a ground or "naïve" state with display of hallmark features of mouse ES cells. This medium can also maintain human ES cell pluriptoency at the naïve state [69,70,72,[75][76][77][78].
The regulatory network of pluripotency factors
ES cell pluripotency is conferred by a unique transcriptional network [79]. Early global transcriptional profiles and genetic studies have identified several critical transcription factors that are required for the pluripotency of ES cells, such as Oct4, Sox2, Nanog, Foxd3 and Id, etc [80][81][82][83][84][85][86][87][88]. Here we will mainly focus on Oct4, Sox2 and Nanog, three key transcription factors of the core pluripotency transcriptional network.
OCT4 and SOX2
OCT4 (also known as Oct3), a POU domain-containing transcription factor, was one of the first transcription factors identified as essential for both early embryo development and pluripotency maintenance in ES cells [84,89]. The expression of Oct4 is activated at the 8-cell stage and is later restricted to the inner cell mass (ICM) and germ cells in early mouse embryogenesis in vivo [89][90][91][92]. Oct4 is highly expressed in both human and mouse ES cells, and its expression diminishes when these cells differentiate and lose pluripotency. Oct4 regulates a broad range of target genes including Fgf4, Utf1, Opn, Rex1/ Zfp42, Fbx15, Sox2 and Cdx2 [93][94][95]. Repression of Oct4 activity in ES cells upregulates Cdx2 expression, leading to ES cell differentiation into trophectoderm [96]. Oct4 is also known to activate downstream genes by binding to enhancers carrying the octamer-sox motif (Oct-Sox enhancer), for synergistic activation with Sox2. In contrast with its target genes, little is known about Oct4 upstream regulators. The Oct4 promoter contains conserved distal and proximal enhancers that can either repress or activate its expression depending on the binding factors occupying these sites [97,98]. The precise level of Oct4 is important for ES cell fate determination. Loss of Oct4 causes inappropriate differentiation of ES cells into trophectoderm, whereas overexpression of Oct4 results in differentiation into primitive endoderm and mesoderm [99,100].
Sox2 is an HMG-box transcription factor that is detected in pluripotent cell lineages and the nervous system [101][102][103]. Inactivate Sox2 in vivo results in early embryonic lethality due to the failure of ICM maintenance [102]. Sox2 can form a complex with the Oct4 protein to occupy Oct-Sox enhancers to regulate target gene expression. Oct-Sox enhancers are found in the regulatory region of most of the genes that are specifically expressed in pluripotent stem cells, such as Oct4, Sox2, Nanog, Utf1, Lefty, Fgf4 and Fbx15 [93,94,[104][105][106][107][108].
Nanog
Nanog is another homeobox-containing transcription factor that is specifically expressed in pluripotent ES cells. The essential role of Nanog in maintaining the pluripotency of ES cells is highlighted by the facts that Nanog-deficient ES cells are prone to differentiation, whereas forced expression of Nanog partially renders ES cells self-renewal potential in the absence of LIF [85,86,109]. How Nanog regulates stem cell pluripotency remains entirely unknown. Studies have indicated that Nanog may maintain ES cell pluripotency by 1) downregulating downstream genes essential for cell differentiation such as Gata4 and Gata6 and 2) activating the expression of genes necessary for self-renewal such as Rex1 and Id [19,85,86]. Although it is widely accepted that Nanog, like Oct4 and Sox2, play a central role in pluripotency maintenance, this dogma has been challenged by a subsequent report that Nanog protein levels are undetectable in a fraction of ES cells that express Oct4, and the pure populations of Nanog−/− ES cells can be propagated without losing expression of other pluripotency markers [110].
Little is known about the mechanism by which Nanog is regulated in ES cells. Recently, Suzuki et al showed that Nanog expression was upregulated by BrachyuryT and STAT3 in mouse ES cells [111]. In human ES cells and in mouse EpiSCs, Vallier et al reported that Activin/Nodal signaling stimulated expression of Nanog, which in turn prevents FGF-induced neuroectoderm differentiation [112]. In addition, several studies indicated that the Oct4/ Sox2 complex was directly bound to the Nanog promoter to regulate target gene expression [106,107,113]. Genomic studies have revealed that Oct4, Sox2, and Nanog frequently bind the same regulatory regions in undifferentiated mouse and human ESCs, and that these binding sites are often in close proximity to one another [113][114][115][116]. These results indicate that Oct4, Sox2, and Nanog may physically interact with each other and coordinately regulate target genes in some cases. Additionally, Goke and colleagues reported that combinatorial binding sites of the Oct4/Sox2/Nanog were more conserved between mouse and human ES cells than individual binding sites were [113,114,[117][118][119].
Summary
Understanding the molecular mechanism of pluripotency can greatly expand our knowledge of ES cell biology and facilitate future stem cell clinical applications. In the past few years, we have seen tremendous advances in understanding ES cell pluripotency. Although mouse ES cells and conventional human ES cells require distinct signaling pathways to maintain pluripotency, they display similar gene expression profiles, activities of transcription factors (such as Oct4, Nanog and Sox2) and transcription factor networks. Our understanding of pluripotency has been further expanded by the advent of iPS cells and the very recent discovery that conventional human ES cells are more equivalent to mouse EpiSCs, but rather "naïve state" of mouse ES cells. Nevertheless, our knowledge of the molecular mechanisms of ES cell pluripotency is still very limited. For instance, it remains unknown how growth factors establish and control transcriptional networks to regulate pluripoency and how ES cells respond so precisely to exogenous cues. Given the rapid advance in ES cell biology, we anticipate the molecular mechanisms underlying pluripotency of ES cells will soon be uncovered and pluripotent stem cells, such as ES cells and iPS cells, will be widely used for clinical applications in the near future.
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Domain: Biology
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Synergistic Antitumor Activity of Vitamins C and K 3 on Human Bladder Cancer Cell Lines
Exponentially growing cultures of human bladder tumor cells (RT4 and T24) were treated with Vitamin C (VC) alone, Vitamin K3 (VK3) alone, or with a VC:VK3 combination continuously for 5 days or treated with vitamins for 1 h, washed with PBS and then incubated in culture medium for 5 days. Co-administration of the vitamins enhanced the antitumor activity 12to 24-fold for the RT-4 cells and 6to 41-fold for the T24 cells. Flow cytometry of RT4 cells exposed to the vitamins revealed a growth arrested population and a population undergoing cell death. Growth arrested cells were blocked near the G0/G1-S-phase interface, while cell death was due to autoschizis. Catalase treatment abrogated both cell cycle arrest and cell death which implicated hydrogen peroxide (H2O2) in these processes. The H2O2 production resulted in a moderate increase in lipid peroxidation and depletion of cell thiol levels. Analysis of cellular ATP levels revealed a transient increase in ATP production for VC and the VC:VK3 combination, but decreased ATP levels following VK3 treatment. Lipid peroxidation, thiol depletion and ATP modulation occurred at a 17-fold lower concentration in the vitamin combination than with either vitamin alone. These results suggested that the increased cytotoxicity of the vitamin combination was due to redox cycling and increased oxidative stress.
Introduction
Bladder cancer is the second most common urological malignancy in the United States of America with an estimated 72,570 new cases and 15,210 deaths in 2013 [1]. Unlike most epithelial tumors, divergent pathways of tumorigenesis are involved in urothelial carcinoma [2]. These separate mechanisms produce at least two distinct types of neoplasms: non-invasive, low-grade tumor and high-grade, often invasive, carcinoma [3]. Patients with low grade tumors usually undergo transurethral tumor resection (TUR). While the prognosis for these patients is usually good, they exhibit a lifelong risk of recurrence (50% -70%) with occasional progression to invasion. Given the relatively high rates of recurrence and progression, it is necessary to consider adjuvant intravesical therapy in most patients [4,5]. Since the mid 1980s, the standard treatment for metastatic bladder cancer has been methotrexate, vinblastine, doxorubicin and cisplatin [6]. However, even with this regimen, the prognosis for patients with metastatic disease is poor with a median survival being approximately 12 -14 months [7]. Furthermore, addition of new drugs, such as gemcitabine, to the standard cisplatin-based regimens has not improved clinical outcomes [8,9]. In addition, the use of several targeted agents, such as, antiangiogenics, anti-epidermal growth factor receptor agents, and immunomodulatory agents did not result in a major breakthrough [7]. Finally, because of the lifelong need for monitoring for recurrence, the typical cost incurred by a bladder tumor patient from diagnosis to death has been reported to be the highest among all cancers [4,10]. Taken together, these facts demonstrate the need for the development of agents that are more effective, less toxic, and more cost effective agents that are more effective, less toxic and affordable [7].
Due to their low systemic toxicity, several vitamins have been evaluated for their chemopreventive and therapeutic potential abilities against bladder cancer [11]. Vitamins A, B 6 , C, E, and K 3 have all demonstrated activity in the prevention or treatment of bladder cancer [12]. In addition, Lamm et al. [12] performed a double blind, randomized trial in patients with bladder cancer who were treated with transurethral resection plus megadose vitamins daily vs the recommended daily allowance of multivitamins. The overall recurrence rate was 80% in the recommended daily allowance arm and 41% in the megadose vitamin arm (p = 0.0011). This vitamin treatment not only was nontoxic, but also produced a greater reduction in the rate of tumor recurrence than BCG immunotherapy which is the gold standard for the treatment of superficial bladder cancer. In addition, there is a growing body of evidence demonstrating the benefit of combining vitamins C and K 3 for the treatment of: acute lymphoblastic leukemia [13], acute myelogenous leukemia [14][15][16], bladder [17][18][19][20][21][22][23][24][25][26][27][28], breast [29], glioblastoma [30], glioma [31], kidney [32], liver [33][34][35][36][37], lung [38], ovarian [39][40][41][42] and prostate cancers [43][44][45][46][47][48][49][50][51][52][53][54][55]. Unlike the majority of chemotherapeutic agents which target rapidly dividing cells, VC:VK 3 appears to target tumor cells by inflammation [50]. Tumor cells possess a greater need for glucose than normal cells and express facilitative glucose transporters (GLUTs) to achieve this task. Because of the structural resemblance of dehydroascorbic acid (DHA, the oxidized form of vitamin C) to glucose, DHA can also enter the tumor cells and bio-accumulate. Epithelial tumors appear to rely on superoxide (inflammation) which is produced constitutively via NA DPH oxidase of non-neoplastic stromal cells to oxidize the ascorbic acid [56,57]. Once dehydroascorbic acid enters the cells, it is reduced and retained as ascorbic acid (AA) which is not transportable through the bidirectional GLUTs [58,59]. The purpose of the current study is to evaluate VC, VK 3 and the VC:VK 3 combination for their antitumor activity against two human bladder cancer cell lines and to make an initial attempt to elucidate the mechanism(s) of action of the VC:VK 3 combination.
Culture Conditions
Human bladder cancer cell lines (T24 and RT4) were purchased from the American Type Culture Collection (ATTC, Rockville, MD, USA) and were grown in culture medium according to ATTC instructions. All media was supplemented with 10% fetal bovine serum (FBS, Gibco, Grand Island, NY) and 50 µg/mL gentamicin sulfate (Sigma, St Louis, MO). All incubations were performed at 37˚C and with 5% CO 2 unless other conditions are stated. Vitamin C (VC) and menadione bisulfite (VK 3 ) were purchased from Sigma Chemical Company (St Louis, MO, USA) and were dissolved in phosphate-buffered saline (PBS) to create 8000 µM VC, 500 µM VK 3 and 8000 µM VC:80 µM VK 3 test solutions. To prevent photodegradation of the vitamins, all the vitamin solutions were prepared and experiments were performed in a darkened laminar flow hood.
Protein Concentration Assay
In all experiments total protein concentration was determined using the method of Bradford [60]. Sham-treated cells served as controls in all experiments.
Cytotoxicity Assay
The cytoxicity assay was performed using the microtetrazolium assay[MTT,3-(4,5-dimethylthiazol-2-yl)-2,5diphenyl-diphenyltetrazolium bromide] assay as described previously [23]. Corning 96-well titer plates were seeded with tumor cells (5 × 10 3 per well) and incubated for 24 hr. Vitamin test solutions were serially diluted with media in twelve 2-fold dilutions. Each dilution was added to seven wells of the titer plates and co-incubated with the tumor cells for 5 days. After vitamin treatment and the incubation period, cytotoxicity was evaluated using the MTT assay. Following linear regression, the line of best fit was determined and the CD 50 was calculated. The fractional inhibitory concentration index (FIC) was employed to evaluate synergism.
Flow Cytometry
Determination of cell DNA content and ploidy were performed according to our previously published procedure [45]. Briefly, titer dishes were seeded with 1.0 × 10 6 RT4 cells suspended in MEM (10% FCS). Following 24 hours of incubation, the MEM was removed and the cells were washed twice with 3 ml of PBS. The cells in each titer dish were then overlaid with 2 ml of MEM containing the vitamins. Human foreskin fibroblast cells served as diploid internal standard cells in flow cytometric studies. After a one hour incubation period with vitamins, the cultures were washed free of vitamin and overlaid fresh MEM. Following a 24-hour of incubation period, the cells were harvested from the titer dishes and suspended in 0.1% NP-40 in a Tris-citrate solubilization buffer which contained propidium iodide (PI, 5 mg/ml) and 0.1% RNase A. Following a 30-minutes incubation, DNA ploidy and cell cycle analysis was performed on a Ortho Cytoron flow cytometer. The data from 2 × 10 4 cells were collected (when possible), stored, and analyzed using ModFit Cell Cycle Analysis.
Addition of Catalase
Titer plates were seeded and incubated as described in the cytotoxicity assay. After 24 h, the appropriate nontoxic concentrations of catalase and the vitamin test solutions were added to the wells and the titer plates were incubated at 37˚C in 5% CO 2 for 1 h. The cells were subsequently washed with PBS, overlain with culture media and incubated for 5 days. Cytotoxicity was evaluated using the MTT assay.
Analysis of Lipid Peroxidation
Lipid peroxidation was evaluated using the thiobarbituric acid (TBA) method [61]. RT4 cells were treated and harvested as described in the thiol assay. After centrifugation, the cell pellets were resuspended in 6.0% TCA (trichloroacetic acid), mixed with 1 ml of 0.25 N HCl containing 0.375% TBA and 15% TCA, heated in a water bath for 15 min at 95˚C and then allowed to cool. Following centrifugation the supernatant was monitored fluorimetrically for malondialdehyde (MDA) production using an excitation wavelength of 532 nm and an emission wavelength of 555 nm. Data was expressed as nM MDA per mg of protein, calculated on the basis of an MDA standard curve generated using 1,1,3,3-tetramethoxypropane.
Analysis of ATP
RT4 cells (1.0 × 10 6 ) were seeded and then incubated at 37˚C and 5% CO 2 . After 24 h, the culture medium was removed and the cells were exposed for 1 h to culture media containing the vitamins at their CD 90 concentrations. The cells were then washed with PBS, overlaid with vitamin-free culture media and solubilized in somatic cell ATP releasing reagent (Sigma Chemical Co, St Louis, USA) at 1-h intervals for 6 h and intracellular ATP content was assayed using an ATP bioluminescent assay kit (Sigma, St Louis, U. S. A.) [62]. Bioluminescence was measured using a Beckman LS 9000 scintillation counter set for single photon counting. Data was expressed as nM ATP per mg of protein, calculated on the basis of an ATP standard curve.
Analysis of Protein Thiols
Thiols were assayed using the method of Nagelkerke and co-workers [63]. RT4 cells were exposed for 1 h to culture media containing the vitamins at their CD 90 concentrations. The cells were then washed with PBS, overlaid with vitamin-free culture media, trypsinized at 1-h intervals for 6 h and centrifuged for 5 min at 1000 rpm. The cell pellets were washed twice with 6.5% TCA and resuspended in 1 ml of 0.5 M Tris-HCl (pH 7.6). Subsequently, 50 µl of 10 mM methanolic Ellman's Reagent was added and the solution was incubated at room temperature. After 20 min, the solution was centrifuged for 5 min at 1000 rpm and the absorbance of the supernatant was measured at 412 nm. Data was expressed as µM thiols per mg of protein, calculated on the basis of a reduced glutathione (GSH) standard curve.
Statistics
Three-way ANOVA was performed using BMDP statistical software. In the three-way ANOVA, the two-way interactions were tested at the 0.005 level of significance, while all other effects were tested at the 0.0022 level of significance. A summary of the experimental design is given in Figure 1.
Cytotoxicity
VC, VK 3 and the VC:VK 3 combination with a VC:VK 3 ratio of 100:1 have been evaluated for their cytotoxicity against two human bladder carcinoma cell lines following continuous 5-day vitamin exposure or 1-h vitamin exposure followed by a 5-day incubation in media (Table 1). A continuous 5-day exposure to VC:VK 3 treatment of the RT-4 cells resulted in a 22-fold decrease of the CD 50 of VC (2430 to 110 µM) and a 12-fold decrease in the CD 50 of the VK 3 (12.8 to 1.10 µM) and a 12-fold decrease in the CD 50 of the VK 3 (12.8 to 1.10 µM) with an FIC value of 0.136 indicating that the combination was synergistic. T24 cells treated continuous for 5 days with VC:VK 3 resulted in a 41 fold decrease in VC (1,490 µM to 13.1 µM) and a 6-fold decrease in VK 3 (212 µM to 2.13 µM) with an FIC value of 0.158 also demonstrating a significant synergism after only 1hr of VC:VK 3 treatment.
Taper and his associates [34] have shown that the VC:VK 3 combination exhibited antitumor activity with exposure times as short as 1 h. We sought to determine if the vitamins would exert significant antitumor activity against RT-4 and T24 cells following a 1 h exposure (Table 1). A 1 h VC:VK 3 treatment of the RT-4 cells resulted in an 18-fold decrease in the CD 50 value of VC (4740 µM reduced to 267 µM) and a 22-fold decrease in VK 3 CD 50 values (60.7 µM reduced to 2.68 µM) with a FIC value of 0.100. The same VC:VK 3 1hr treatment on T24 cells resulted in a 41-fold decrease in the CD 50 of VC (4970 µM reduced to 120 µM) and a 59-fold decrease in the CD 50 values of VK 3 (73.2µM reduced to 1.21 µM) with an FIC of 0.093.
Flow Cytometry
Flow cytometry was also employed to determine whether vitamin treatment effects the cell cycle of RT4 cells. Human foreskin fibroblasts were mixed with RT4 cells and then analyzed by flow cytometry in an effort to determine the channel number of the true diploid G 0 -G 1 peak. The mean channel of the fibroblast G 0 -G 1 peak is 59 and the mean channel of the G 2 -M peak is 118. The mean channel of the RT4 cell G 0 -G 1 is 108, and the mean channel of the G 2 -M peak is 216. The DNA index (mean channel of RT4 G 0 -G 1 /mean channel of fibroblast G 0 -G 1 ) is 1.83. This reading indicates that RT4 is n aneuploid cell line. In fact it is hypotetraploid. The a distribution of the cells within the phases of the cell cycle can be found in Table 2. Like untreated RT4 cells, VC-treated RT4 cells exhibited a G 0 -G 1 peak in channel 115 and a G 2 -M peak in channel 231. An aneuploid shoulder was visible on the G 0 -G 1 peak in channel 130 and the trace also showed a small amount of sub-G 0 -G 1 "multi-cut debris". When compared to control cells, the VC-treated cells exhibited 34% of the cells in G 0 -G 1 phase and 60% of cells in the S phase as opposed to 76% of the cells in G 0 -G 1 phase and 18% of the cells in S phase in control cells. VK 3 -treated cells showed a G 0 -G 1 peak in channel 121 and a G 2 -M peak in channel 241. Sub-G 0 -G 1 multi-cut debris was also evident. As a consequence of this treatment, the proportion of RT4 cells in the G 0 -G 1 phase decreased to 30% while the number of cells in S phase increased to 64%. VC:VK 3 -treated cells RT4 cells were exposed to the vitamins at their 90% cytotoxic doses for 24 h and then harvested. DNA ploidy and cell cycle analysis was performed on a FACSscan flow cytometer and analyzed using MofFit Cell Cycle Analysis. Sham-treated RT4 cells served as the negative control. Human foreskin fibroblasts served as diploid controls.
showed a G 0 -G 1 peak in channel 107 and a G 2 -M peak in channel 214. Sub-G 0 -G 1 multi-cut debris was also present. As a consequence of this treatment, the proportion of RT4 cells in the S phase and G 2 -M phase were 37 and 10% respectively, compared with 18% and 6% for control cells.
Hydrogen Peroxide
In our previous studies, catalase administration to DU145 and T24 cells was shown to abrogate the antitumor activity of the vitamins at catalase doses as low as 100 µg/ml [17,45]. Therefore, administration of exogenous catalase has been employed to elucidate the role of hydrogen peroxide (H 2 O 2 ) in the antitumor activity of vitamins.
Catalase administration to RT4 cells abrogated the antitumor activity of VC at catalase doses as low as 100 µg/ml (Table 3). The majority of the antitumor activity of the vitamin combination was lost at a catalase concentration of 100 µg/ml. However, the antitumor activity of VK 3 could not be completely neutralized by the administration of catalase even at concentrations as high as 1000 µg/ml. Conversely, the antitumor activity of VC:VK 3 was lost following administration of as little as 300 µg/ml of catalase. These results demonstrated that H 2 O 2 production was necessary for the antitumor activity of the vitamins.
Lipid Peroxidation
Exposure of tumor cells to VC, VK 3 or the VC:VK 3 combination has been shown to generate hydrogen peroxide (H 2 O 2 ) and other reactive oxygen species (ROS) that may initiate membrane lipid peroxidation [17,45]. Therefore the effect of vitamin treatment on cellular lipid peroxidation (Table 4) was examined using the thiobarbituric acid method. The lipid peroxidation of shamtreated RT-4 cells displayed an average value of 3.17 nM(MDA)/mg of protein. However, this is only a measure of the lipid peroxidation that occurs during the heating 90 doses, harvested at one hour intervals for 5 h and assayed for lipid peroxidation using the thiobarbituric acid method. Malondialdehyde (MDA) production was monitored fluorimetrically and data was expressed as nM MDA per mg of protein, calculated on the basis of a MDA standard curve. Values are the mean ± standard error of the mean of three experiments with three readings per experiment and were compared to the control. of samples to 95˚C during the assay and can, therefore, be considered as a baseline for MDA production. Lipid peroxidation values following VC treatment peaked at 4.27 nM/mg with an average value of 3.67 nM/mg, while lipid peroxidation of VK 3 -treated cells was significantly higher at 5.84 nM/mg with an average of 4.98 nM/mg. Lipid peroxidation values for VC:VK 3 peaked at 6.7 nM/mg with an average value of 5.58 nM/mg of protein.
The treatment of the cells with the vitamins resulted in a statistically significant alteration in lipid peroxidation (p < 0.005). This lipid peroxidation was vitamin related because lipid peroxidation values rapidly returned to control levels when the vitamins were removed (data not shown).
ATP Production
Transmission electron microscopy has shown that mito-Copyright © 2013 SciRes. JCT chondrial architecture was altered by vitamin treatment [36,63]. In the following experiments, intracellular levels of ATP synthesis was measured to determine if vitamininduced cell death was related to mitochondrial damage and subsequent "ATP-less" cell death (Figure 2). The ATP content of sham treated RT4 cells displayed averaged of 59.
Thiols
Administration of VK 3 to hepatocytes is known to induce a variety of effects including: depletion of GSH and oxidation of protein sulphydryl groups in cytoskeletal proteins [35,65]. Therefore, the effect of vitamin treatment on cellular thiols has been examined (Table 5). The thiol content of sham-treated RT4 cells averaged 1.39 µM/mg of protein. VC treatment resulted in a decrease in cellular thiol levels to 0.92 ± 0.31 µM/mg during the first hour. Subsequently, the thiol level remained constant during the second hour, dropped precipitously to 0.47 ± 0.03 µM/mg during the third hour, rebounded to 0.73 ± 0.12 µM/mg of during the fourth hour and then returned to 0.45 ± 0.03 µM/mg during the final hour. VK 3 treatment lowered thiol levels to 0.62 ± 0.5 µM/mg during the first hour. Thiol levels remained constant during the next three hours and then dropped slightly to 0.54 ± 0.1 µM/mg during the final hour. The VC:VK 3 combination produced a decrease in thiol concentration to 0.63 ± 0.05 µM/mg during the first hour. Thiol levels gradually decreased to 0.45 ± 0.03 µM/mg during the second hour and then remained constant. VC:VK 3 treated cells induced significant depletion of cellular thiols, The treatment of the cells with the vitamins resulted in a significant alteration in thiol levels (p < 0.005).
Discussion
VC exhibits selective toxicity against a plethora of tumor cell lines as well as experimental tumors [64,65]. In addition, VC is a chemosensitizing agent [66,67] and radiosensitizing agent [68]. The mechanism(s) responsible for the antitumour activity of VC appears to be related to the prooxidant properties of ascorbate and dehydroascorbate, the oxidative product of ascorbate, which generate intracellular H 2 O 2 and other reactive oxygen species (ROS) which may deplete cellular thiol levels and initiate membrane lipid peroxidation [45,66].
Likewise, VK 3 is a synthetic derivative of phylloquinone (VK 1 ) that exhibits in vitro cytotoxic activity against a variety of tumor cell lines [69] as well as in vivo antitumor activity [70]. VK 3 is also a chemosensitizer for most of traditional chemotherapeutic agents [70]. Administration of VK 3 to tumor cells induces depletion of glutathione, reduction of nicotinamide adenine dinucleotide phosphate and adenosine triphosphate pools, oxidation of sulfhydryl groups in cytoskeletal proteins, and induction of single-stranded DNA breaks [71][72][73].
When VC and VK 3 were combined in a ratio of 100:1 and administered to three human tumor cell lines, the vitamin combination exhibited a synergistic inhibition of cell growth at concentrations that were 10 to 50 times lower than for the individual vitamins [74]. The vitamin combination also potentiated the in vitro growth inhibitory activities of several chemotherapeutic agents 3-to 14-fold [75]. The VC:VK 3 combination also inhibited tumor growth, increased lifespan and decreased metastasis of C3H mice implanted with a murine transplantable liver tumor (TLT) cells [34][35][36][37]49]. Additional studies indicated that the VC:VK 3 combination was an effective chemosensitizer [34,35] and radiosensitizer that induced little systemic or major organ pathology [35,36]. The potentiation and specificity of the antitumor activity was attributed to the possible generation of hydrogen peroxide followed by membrane lipid alteration, DNase activation and DNA destruction by the VC:VK 3 combination in the catalase-deficient cancer cells [35]. When VC and VK 3 were combined in a 100:1 ratio, the VC:VK 3 interaction not only fostered single-electron reduction to produce the long-lived semiquinone and ascorbyl radicals and increased the rate of redox cycling of the quinone to form H 2 O 2 and other ROS, but also fostered two-electron transfer, which ensured that ascorbic acid (AA) and dehydroascorbic acid (DHA) were present at pharmacologic levels for a protracted period of time [24,50,76,77]. Single electron redox cycling generated a moderate increase in oxidative stress [24,50,76] and induced a complex stress response that resulted in structural damage to the catalase-deficient cancer cells. A number of cellular processes were effected by the presence of AA and especially DHA, including: modulation of signal transduction, cell cycle arrest and inhibition of glycolytic respiration, inhibition of metastasis [15,23,24,26,35,49,50,78,79]. Together the two redox cycles reactivated DNase I and II and induced autoschizic cell death [20,23,46]. A number of other laboratories have subsequently have modified or added more components to the VC: VK 3 combination. Modification of one of the constituents (i.e.brominated VK 3 ) or replacement of the VK 3 with any number of constituents (arsenic trioxide, benzoquinones, coenzyme Q10, doxorubicin, lipoic acid, resveratrol, vitamin B12, Vitamin E and others) [67,[80][81][82][83][84][85].
In the current study, VC, VK 3 and the VC:VK 3 combination were evaluated for their antitumor activity against two human bladder cancer cell lines (RT4 and T24). An MTT assay was employed to demonstrate that a VC:VK 3 combination with a VC:VK 3 ratio of 100:1 exhibited synergistic antitumor activity against both the RT4 and the T24 cell lines following continuous 5-day vitamin exposure or a 1-h vitamin exposure followed by a 5-day incubation in culture media. The fact that there was a close agreement between the CD 50 values of the VC:VK 3 combination for the 1-h and 5-day vitamin exposures indicated that the antitumor activity generated during a 1-h vitamin treatment was almost as effective as the antitumor activity generated during a continuous 5-day vitamin treatment. These results were consistent with those found in both prostate cancer (DU145) and bladder cancer (T24) cell lines and suggested some of the events responsible for triggering tumor cell death occurred during the first hour of vitamin treatment [17,45]. Characterization of the 5 day vitamin treatment is presented in this report while characterization of the 1-h vitamin treatment will be presented in a future report. In a previous study with T24 cells, flow cytometry revealed that VC:VK 3 treatment resulted in a growth arrested population of cells and a population of cells undergoing autoschizic cell death [24].
Likewise, in the current study, a cell cycle block was observed at G 0 -G 1 /S. From previous studies,it is known that enhanced population of cells in early S phase represent both growth arrested and autoschizic cells [24]. The disposition of these autoschizic cells was described in a previous publication [22]. Similar results were seen in an in vivo study when UMUC-14 tumorigenic urothelial carcinoma cells were implanted into the subcutis of nude mice and the mice were treated with gemcitabine GEM), VC:VK 3 or a GEM-VC:VK 3 combination. In vivo analysis suggested that synergistic antitumor activity was due to antiproliferative effects rather than to enhanced apoptosis. However, there was significant necrosis in combination group tumors which was most likely autoschizis [28]. From our experience with VC:VK 3 , these effects can be attributed to the combined effects of H 2 O 2 and the vitamins [26].
When RT4 cells were co-incubated with catalase and VC, VK 3 or the VC:VK 3 combination, the antitumor activity of the vitamins was negated. The fact that a greater amount of catalase was required to destroy the antitumor activity of VK 3 than was required to destroy the antitumor activity of the vitamin combination, suggested that while H 2 O 2 was involved in the mechanism of action of these vitamins, the enhanced antitumor activity of the vitamin combination was not simply due to an excessive increase in H 2 O 2 production. Furthermore, the fact that the majority of VC is known to enter tumor cells through Glut channels as DHA coupled with the observation that antitumor activity of the vitamins was abrogated by exogenous catalase suggested that at least a portion of lost antitumor activity of VC and VC:VK 3 in the presence of catalase may be a function of diminished extracellular conversion of VC to DHA and a decreased uptake of the vitamin by the tumor cells [86].
The observation that H 2 O 2 generation was essential for the antitumor activity of the vitamin combination suggested that lipid peroxidation may have been responsible for the vitamin-induced cytotoxicity. When the RT4 cells were co-incubated with the vitamins for 1 -5 h, the level of lipid peroxidation increased 1.1-to 1.3-fold for VC, 1.3-to 1.9-fold for VK 3 and 1.5-to 2-fold for VC:VK 3 . These results suggested that, while lipid peroxidation increased, the modest increase in lipid peroxidation was not the primary cause of the cytotoxicity of the vitamin combination. These results were consistent with those of McGuire and co-workers [87] who demonstrated that when the vitamins were removed from the RT4 cells following a 1-h exposure, lipid peroxidation values returned to near control levels while the cells continued to undergo autoschizis.
Since VC:VK 3 treatment of RT4 cells was shown to produce severely damaged mitochondria [26], intracellular levels of ATP were measured to determine if vitamin induced cell death was related to the depletion of ATP. Treatment of RT4 cells with VK 3 decreased intracellular ATP levels to approximately 60% to 80% of control levels, while following exposure to VC, intracellular ATP levels increased 2.4-fold during the first hour, decreased to about 1.5-fold greater than control during the second hour and remained constant for the next two hours. Following VC:VK 3 exposure, intracellular ATP levels decreased 25% during the first hour, rose to a maximum of about 2.3-fold greater than control by 3 hours and then began to decrease. Interestingly, the fact that the level of intracellular ATP levels for all treatments decreased to control levels by the fifth hour may be indicative of the duration of the pharmacological activity of the vitamins in RT4 cells. These results are consistent with those seen in prostate cancer cells (DU145) and another bladder cancer cell line (T24) and suggested that ATP depletion was not the proximal cause of VC:VK 3 -induced tumor cell death in RT4 cells [17,45]. While the cause of these ATP spikes has not yet been elucidated, they may reflect the ability of the VC:VK 3 to form a shunt around a defective region of complex III of the electron transport chain by having menadione accept electrons from coenzyme Q (ubiquinone), shuttle them to ascorbate and then to cytochrome c. Impaired oxidative phosphorylation has been observed in a variety of cancers including: prostate tumors, and bladder (infiltrating bladder urothelial) carcinomas due to alterations in the protein complexes, especially in complex III [88,89]. Such a shuttle was observed in a patient with a defect in electron transport at complex III in skeletal muscle [91]. The shunt was able to bypass the antimycin-a-sensitive site in both forward and reversed electron transport (had two intact phosphorylation sites) and produced a shift from glycolytic activity to increased mitochondrial oxidative phosphorylation and a dimunition of lactic acidosis.
The effect of vitamin treatment on cellular thiols was examined because redox cycling was shown to draw down cell thiols [71][72][73]77,78,87]. Following a one hour VC treatment, the intracellular thiol levels of RT4 decreased to 67% of control levels. Thiol levels remained at this level for another hour and then decreased to 34% of control levels. Subsequently, thiol levels oscillated between 34% and 50% of control levels. Exposure of RT4 cells to VK 3 led to a drop in intracellular levels to 45% of that of control levels. Following a one hour VC:VK 3 treatment, the intracellular thiol levels of RT4 decreased to 46% of control levels. In the second hour, intracellular thiol levels decreased to approximately 35% of control levels and then remained constant for the remaining 3 hours. These results suggested that the cells were only affected by vitamin treatment when the oxidative stress of the vitamins surpassed the reducing ability of the cellular thiols and cellular or genetic damage occurred. Tumor cells appeared to be particularly susceptible because they have reduced levels of catalase, superoxide dismutase, and/or glutathione peroxidase as well as other ROS detoxifying enzymes. Therefore, they have difficulty in metabolizing hydrogen peroxide and other ROS that can accumulate, alter cellular processes, and induce cellular damage or cell death [74].
While the morphologic trends and defects observed following combined vitamin treatment were the composite of the biochemical and cytological damage induced by ascorbate or the menadione-treated alone, the damage induced by VC:VK 3 was seen at a VC concentration of 520 µM and a VK 3 concentration of 5.2 µM concentrations which were 17-fold less than the concentrations of 8750 and 90 µM when VC and VK 3 were administered individually. The values indicated a synergistic interaction between VC and VK 3 . The vitamin combination was shown to be safe and effective against human prostate cancer cell lines in vitro [44,45], against androgen-independent prostate cancer in nude mice [47] and in two clinical trials with endstage prostate cancer patients [51,55]. Likewise, VC:VK 3 was shown to be safe and effective against human bladder cancer cell lines [17][18][19][20][21][22][23][24][25][26][27] and in a murine bladder cancer model when administered alone or in conjunction with gemcitabine [28].
Conclusion
While the results described in the current study provide information concerning the effect of VC:VK 3 administration on tumor cells death, cell cycle arrest, cellular ATP Copyright © 2013 SciRes. JCT levels, the effect of redox cycling on cellular thiol levels and confirm the role of H 2 O 2 in lipid peroxidation and tumor cell death, the major limitation of these techniques is that they provide no information concerning the genes and signal transduction mechanisms involved these processes. The authors concluded that, based on their results, a clinical trial was warranted to examine the efficacy and toxicity of VC:VK 3 . Because of these results, on July 31, 2007, the combination of sodium ascorbate and menadione sodlium bisulfite (tradename Apatone ® , designation request #06-2366) was granted orphan drug status for the treatment of metastatic or locally advanced, inoperable transitional cell carcinoma of the urothelium (stage III and IV bladder cancer). Efforts are underway to conduct a phase II clinical trial for this indication.
Figure 1 .
Figure 1.(A) An MTT assay was employed to measure VC:VK 3 induced cell cytotoxicity. Since VC:VK 3 was equally potent following a 1hr or 5 day exposure both values were reported.(B) Since the MTT value may be a composite measure of metabolic arrest, cell death and cell cycle arrest, cell cycle arrest was measured by flow cytometry and cell death by autoschizis was reported earlier.(C) Electron microscopy (Gilloteaux et al. 2010) showed that mitochondrial architecture was damaged by the VC:VK 3 combination, therefore changes in ATP levels were used to evaluate cellular energy content and to determine if cell death was ATP-dependant or ATP-independent.(D) The role of hydrogen peroxide in the activity of VC:VK 3 was determined by exogenous catalase titration.(E) If H 2 O 2 was not responsible for the antitumor activity other mechanistic activities were performed (not shown).(F) If H 2 O 2 were involved in the mechanism of action lipid peroxidation should occur. Therefore, malondialderayde production was monitored.(G) VC:VK 3 have been reported to form a redox pair. If redox was involved there should be a concomitant decrease in cellular thiol content.
64 nM ATP/mg of protein. VC exposure resulted in an increase in ATP levels to 147 ± 8.64 nM during the first hour. Subsequently, the ATP levels decreased to 86.0 ± 4.73 nM during the second hour and remained relatively constant during the third and fourth hours and then fell to 56.1 ± 4.09 nM during the final hour. VK 3 treatment lowered ATP levels to 39.9 ± 0.99 nM during the first hour. ATP levels rose slightly to 48.8 ± 4.52 nM during the second hour, remained relatively constant for the next 3 hours and increased to near control levels during the final hour. The VC:VK 3 combination produced a slight decrease in ATP concentration to 46.7 ± 2.13 nM during the first hour. ATP levels increased during the second and third hours to 134 ± 1.46 nM and decreased gradually to near control levels during the final two hours. These results demonstrate that pulse treatment of RT4 cells with VC alone or with the VC: VK 3 combination resulted in a transient increase in intracellular ATP levels following vitamin treatment. The treatment of the cells with the vitamins resulted in a significant alteration in ATP levels (p < 0.005).
Figure 2 .
Figure 2. Cultures of exponentially growing RT4 cells were treated for 1 hour with the vitamins at their CD 90 doses and then harvested at one hour intervals for 5 h. ATP content was assayed using a bioluminescence assay. Data has been expressed as nM ATP per mg of protein, calculated on the basis of an ATP standard curve. Values are the mean ± standard error of the mean of three experiments with three readings per experiment and were compared to the control (p << 0.05 between all groups relative to control).
± 0.27 0.45 ± 0.03 0.54 ± 0.01 0.47 ± 0.02 †µM of Thiol/mg of protein. RT4 cells were treated for 1 hour with the vitamins at their CD90 doses, harvested at one hour intervals for 5 h and assayed for cellular thiol content by monitoring absorbance following reaction with Ellman's Reagent. Data has been expressed as µM thiol per mg of protein, calculated on the basis of a GSH standard curve. Values are the mean ± standard error of the mean of three experiments with three readings per experiment and were compared to the control (p < 0.005).
Table 1 . Antitumor activity of vitamins against bladder carcinoma cells.
cytopathic doses of the vitamins administered together. Antitumor activity was measured by a MTT assay following a 1 h exposure and 5 day incubation or a 5 day exposure to VC, VK 3 or a vitamin combination with a VC:VK 3 ratio of 100:1. Values are the mean ± the standard error of the mean of three experiments with six readings per experiment.
Table 3 . Effect of catalase treatment of RT-4 cells antitumor activity.
RT4 cells were incubated with the vitamins and increasing doses of catalase for 1 hour. The tumor cells were subsequently washed with PBS and overlain with culture media. Cytotoxicity was evaluated after 5 days using a MTT assay. Values are the mean of three experiments with three readings per experiment.
Table 4 . Vitamin-induced lipid peroxidation in RT-4 cells.
† nM of MDA/mg of protein. RT-4 cells were treated for with the vitamins at their CD
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Domain: Biology
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The Single Nucleotide Polymorphisms of Myostatin Gene and Their Associations with Growth and Carcass Traits in Daheng Broiler
Myostatin (MSTN) is a negative regulator of skeletal muscle growth. In order to investigate whether there is a correlation between MSTN polymorphisms and chicken production performance, in this study, single nucleotide polymorphisms (SNPs) in MSTN gene were examined across 180 Daheng broilers by direct sequencing of PCR product, and the correlations between the genotype and body weight at the age of 1-10 weeks and carcass traits at the age of 73 day were analyzed. Five SNPs (rs313622770, rs313744840, rs316247861, rs314431084, rs317126751) of MSTN gene were identified across Daheng broiler samples, and four haplotypes were reconstructed based on the five SNPs. Results of association analysis showed that four (rs313622770, rs313744840, rs316247861 and rs317126751) of these SNPs had significant association with some growth traits (p<0.05), but there were no significant effect on carcass traits and the four SNPs were strong linkage. For rs314431084, there was no significant correlation between different genotypes and growth or carcass traits. The AA genotype of rs313622770, GG genotype of rs313744840, CC genotype of rs316247861, TT genotype of rs317126751 were good for chicken growth. Diplotypes were significantly associated with chest muscle and leg muscle weight (p<0.05). Overall, these results provide evidence that polymorphisms in MSTN gene are associated with growth traits in chicken. The SNPs in MSTN gene could be utilized as potential markers for marker-assisted selection (MAS) during chicken breeding.
INTRODUCTION
Meat production is one of the most important economic traits in chicken, and how to improve meat production is one of the most important objectives of breeding researchers. The growth traits are regulated by multiple genetic loci. Recently, researchers have selected lots of candidate genes associated with growth traits, Myostatin (MSTN) is one of these genes identified as a negative regulation factor of skeletal muscle growth (Wehling et al., 2000).
MSTN, also known as growth differentiation factor 8 (GDF-8), is a member of the transforming growth factor beta (TGF-β) family. It has been widely investigated in livestock, poultry, rodents and humans (Schiffer et al., 2011;Varga et al., 2003;Wang et al., 2014). A number of evidence has shown that MSTN acts as a negative regulator of skeletal muscle growth, and loss or decrease of its activity will cause excessive development of animal muscle (Clop et al., 2006). In the embryo stage, MSTN controls embryonic myoblast proliferation to regulate skeletal muscle size, Kocamis et al. (1999) investigated the developmental pattern of MSTN gene in chicken embryonic development and found that the expression of MSTN gene has been detected as early as the blastoderm stage, and they suggested MSTN gene eRBCA-2018-0808 eRBCA-2018-0808 plays an important role in skeletal muscle development and embryogenesis in the chicken embryo. It also plays an important role in muscle regeneration and muscle wasting in adult animals (Sharma et al., 2001). In adult mice, MSTN is mainly expressed in skeletal muscle. Some studies have found that the MSTN knockout mice is 30% heavier than wild-type mice, the skeletal muscle mass in MSTN knockout mice is 86% more than wild-type mice, and individual muscles of MSTN knockout mice weigh 2-3 times more than those of wild-type mice (Mcpherron et al., 1997), it suggests that MSTN is the inhibitory factor of the skeletal muscle growth in adult mice. In addition, some reports also showed that MSTN regulates fat metabolism (Kim et al., 2001;Lin et al., 2002). Langley et al. (2002) have found that MSTN function is related to the MyoD, MSTN down-regulated MyoD to inhibit myoblast differentiation. In humans, SNPs of the MSTN gene are associated with obesity (Pan et al., 2012) and gross muscle hypertrophy (Schuelke et al., 2004). In livestock, the MSTN gene is widely studied for its association with muscular hypertrophy, some mutations of MSTN gene have been associated with double muscling in cattle (Gill et al., 2009), and sheep (Dhakad et al., 2017;Ranjan, 2017). Some SNPs were found in chicken MSTN. Zandi et al. (2013) found that MSTN had a high degree of polymorphism that significantly associated it with body weight in native chickens of Azerbaijan. Paswan et al. (2014) found a SNP in minimal promoter of MSTN that associated it with body weight in chicken. But there was little useful evidence of MSTN SNPs in chicken growth, it is necessary to study the relationship between SNPs of MSTN and chicken production traits.
The Single Nucleotide Polymorphisms of Myostatin Gene and Their Associations with Growth and Carcass Traits in Daheng Broiler
Daheng broiler is a meat-type quality chicken population, it is a commercial broiler by a long-term breeding, and it is popularwith its excellent meat flavor in China. But its growth rate and meat production rate are much lower than those of international commercial broilers, such as Avain broilers. It is important to improve the growth traits of domestic commercial chicken. In this study, MSTN SNPs are identified to explore the relationship between their genotypes and growth, carcass traits in Daheng broiler, which provides the basic information for the marker-assisted selection in chicken.
Experimental population
A total of 180 Daheng broiler from three strains were employed for testing, which were developed by Daheng Poultry Breeding Company (Chengdu, China), including S08 (30 females and 30 males), S07×S06 (30 females and 30 males) and S07×S08 (30 females and 30 males). All chickens were hatched on the same day and developed under the same conditions and diet. The BW (body weight) was measured in grams at hatch, 1wk (week), 2wk, 3wk, 4wk, 5wk, 6wk, 7wk, 8wk, 9wk and 10wk. All individuals were slaughtered at 73 days of age, after slaughtered, the carcass traits including live weight (LW), carcass weight (CW), eviscerated weight (EW), semi-eviscerated weight (SEW), breast muscle weight (BMW), and leg muscle weight (LMW) were measured and recorded on the same day. Before slaughtered, wing venous blood samples were collected, prepared for DNA extraction. Genomic DNA was isolated by the standard phenol/ chloroform method, the purity and concentration of DNA samples were measured by Nucleic Acid Protein Analyzer Nanodrop 2000/2000C (Thermo, Germany). TE buffer was added to DNA samples extracted from the blood to produce a target concentration of 100ng/ μL, then, the DNA samples were stored at -20°C.
All experimental procedures involving animals were approved by the Animal Care and Use Committee of College of Animal Science and Technology, Sichuan Agricultural University (No. YYS130125), and were carried out in accordance with the National Research Council's Guide for the Care and Use of Laboratory Animals.
Amplification and genotyping
Primers for the chicken MSTN gene amplification and sequencing (Table 1) were designed in NCBI (National Center for Biotechnology Information) (Boschiero et al., 2013) based on the complete DNA sequence of Gallus gallus MSTN gene (EMBL ID: ENSGALG00000039458).
Statistical analysis
The general linear model (GLM) procedure of SAS 6.12 (Statistical Analysis Systems Institute Inc. Cary, NC) was built to test associations between the genotype and growth traits, significant associations were declared when p<0.05, the mixed model is as follows: Y = μ + G + S+ B+ F + e ijkf Where Y = the dependent variable, μ= the population mean, G = genotype value, S = fixed effects of sex, B = fixed effects of breed, F = family effect, and e ijkf = random error.
The identified SNPs in this MSTN gene were tested for Hardy-Weinberg equilibrium, when p>0.05 indicated the genetic balance of population gene (Wigginton et al., 2005). The linkage disequilibria D' and r 2 value of the SNPs were estimated by Haploview (Barrett et al., 2005). Significance of the least squares means was tested with the Duncan's Multiple Range test. The polymorphism information content (PIC) was established (PIC>0.5 is high polymorphism, 0.25<PIC<0.5 is intermediate polymorphism, and PIC<0.25 is low polymorphism) (Elston, 2005).
Haplotypes were constructed based on each SNP of MSTN in all experimental animals by use of the PHASE program v. 2.0. The function of this program is to reconstruct haplotypes from the population data. The genetic status of the subjects was expressed as the combination of two haplotypes. The SAS 6.12 (Statistical Analysis Systems Institute Inc. Cary, NC) was used to analyze the associations between the Haplotypes and growth traits. Significant associations were declared when p<0.05.
Sequence polymorphism in chicken MSTN gene
In this study, the exons sequence of the MSTN gene were examined, a total of five SNPs (Table 2) have been detected in MSTN exon1 of Daheng broiler. They were genotyped in Daheng broiler to evaluate their genetic association with chicken growth and carcass traits by direct sequencing of PCR product. For each SNP(SNP1-SNP5), three genotypes were found in the total population. The genotypes, allele frequencies and the genetic information of the 5 SNPs are showed in Table 3. PIC test results indicate that SNP1, SNP2, SNP3 and SNP5were intermediate polymorphism
Association of 5 MSTN SNPs with chicken growth and carcass traits
The factor analysis results indicated that the SNP1 was significantly associated with BW (body weight) at hatch (p=0.033), 1wk (p=0.042) and 8wk (p=0.044) but was not associated with other growth traits. The SNP2 was significantly associated with BW at hatch (p=0.031) and1wk (p=0.036) of age but was not associated with other growth traits. The SNP3 was only significantly associated with BW at hatch (p=0.048). The SNP4 was not associated with any growth traits. And the SNP5 was significantly associated with BW at hatch (p=0.041), 1wk (p=0.037) and 8wk (p=0.048) of age (Table 4). In addition, the results showed that there were no significant association of each SNP with any carcass traits (p>0.05) (Table 5).
Meanwhile, SNP1 chickens with AA genotype had a higher BW athatch at 1wk and8wkthan those with genotypes AG and GG (p<0.05). In SNP2, chicken with the GG genotype had significant higher weight athatch and 1wk than those with AG and AA genotypes (p<0.05). In SNP3, the CC genotype had significant higher hatch weight than those with GG genotype (p<0.05), and there was no difference between chickens with CC and CG genotypes (p>0.05). And in SNP5, chicken at hatch, 1wk and 8 wk had significantly higher weights with the TT genotype than those chickens with the CC and CT genotypes (p>0.05) ( Table 6).
The Single Nucleotide Polymorphisms of Myostatin Gene and Their Associations with Growth and Carcass Traits in Daheng Broiler
eRBCA-2018-0808
The association analysis indicated that there were significant associations between diplotypes and carcass traits (Table7), but no significant results were obtained for growth traits. Diplotypes were significantly associated with LMW and BMW (p<0.05). The H1H4 diplotype had significantly higher LMW and BMW than other diplotypes (p<0.05). "*" means there is significant difference between Least mean squares for a certain trait; Bold represents the advantageous diplotype; Underline represents the negative diplotype a,b means no common superscript differ significantly (p<0.05).
DISCUSSION
Myostatin is a negative regulator of skeletal muscle growth, and loss of myostatin function will lead to a dramatic and specific increase in skeletal muscle mass (Lee & Mcpherron, 1999). The mutations that lead to loss of myostatin function have been found in these double-muscled cattle breeds, which is one of the reasons that myostatin accounts for double-muscling in cattle (Karim et al., 2015). Therefore, it is important to investigate the associations and roles of MSTN SNPs in improving chicken growth performance.
A total of five SNPs have been detected in MSTN exon of Daheng broiler, all of them are associated with some growth traits, except SNP4, but there was no significant association of each SNP with any carcass traits, MSTN gene not only regulates muscle growth, but is also involved in fat metabolism. Lin et al. (2002) studied the muscle and fat growth in the myostatin knockout mice and found that myostatin knockout increased muscle growth, but decreased fat depots at 12 weeks, compared with wild type mice. Next, it is necessary to study the effect of MSTNSNPs in adipose tissue. Previous research also found that SNP2 was associated with body weight in chicken (Mitrofanova et al., 2017). All the four SNPs (except SNP4) have significant effect on hatch. It has been reported that MSTN controlled embryonic myoblast proliferation to regulate skeletal muscle size (Dushyanth et al., 2016). Zhang et al. (2012) found SNP4 was significantly associated with body weight in Bian chicken, the genotypes AA and GA had significantly higher body B A
The Single Nucleotide Polymorphisms of Myostatin Gene and Their Associations with Growth and Carcass Traits in Daheng Broiler
eRBCA-2018-0808 weights than those of genotype GG, but in this study, there is no significant correlation between different genotypes and body weight in SNP4. It is likely to be caused by the lower genotype frequency of GG (1.12%) in Daheng broiler (Table 3) the commercial broiler compared with Bian chicken-the native breed. Commercial broiler is generated with a long-term breeding, the disadvantaged genotype was eliminated gradually during the breeding process.
Both SNP1 and SNP5 have a significant effect on body weight athatch, 1wk and 8wk. Linkage disequilibrium (LD) analysis indicate that SNP1 and SNP5 are a close LD pair (D'=1 and r 2 =97) (Fig. 1A, B). Meanwhile, SNP1, SNP2, SNP3 and SNP4 have strong linkage (D>0.8 and r 2 >0.33), which suggested that these four mutations are associated with some specific traits of interest, these results showed that the four SNPs have a significant effect on body weight at hatch which supports this conclusion. And the mutant AA genotype of SNP1 and mutant TT genotype of SNP5 are good for chicken growth, the mutant GG genotype of SNP2 and CC genotype of SNP3 are good for chicken early growth ( Table 6). All of these SNPs are synonymous mutants, which were found to be significant associated with some growth traits in chicken, although synonymous SNPs do not cause any change in the amino acid and protein that they encode, they could affect mRNA stability, structure, splicing, or protein folding, which significantly affect protein function (Sauna, 2009).
It is reported that haplotype (diplotypes) determined the usefulness of closely link markers in identifying genetically superior individuals and was an essential part of the genetic architecture (Wang et al., 2014). Kim et al. (2013) suggested that diplotypes were useful for identifying more precise and distinct signals over single-locus. Thus, it is necessary to analyze the effect of diplotypes on chicken growth and carcass traits and further find the application in markerassisted selection. In this study, a total of 7 diplotypes were constructed to study their associations with growth and carcass traits, the results indicated that H1H4 diplotype had significantly higher LMW and BMW. There were some studies that revealed that MSTN haplogroups had a significant effect on body weight and carcass traits in chicken (Bhattacharya & Chatterjee, 2013;Dushyanth et al., 2016). But in this study, H1H4 diplotype frequency is 3.33% within the limited sample population, we cannot conclude H1H4 was the most advantageous diplotype for chest muscle and leg muscle growth in Daheng broiler. It needs to be further verified.
In summary, five SNPs were identified in the chicken MSTN exon, four of those (SNP1, SNP2, SNP3 and SNP5) showed significant association with some growth traits in Daheng broiler. And they were strong linkage, except SNP4. Diplotypes were significantly associated with chest muscle and leg muscle weight, but the most advantageous diplotype needs to be further verified. Anyhow, MSTN SNPs could be the genetic markers for future MAS of chicken muscle development.
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Domain: Agricultural And Food Sciences Biology
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Antimicrobial Resistance and Molecular Characterization of Salmonella Enterica Serotypes Isolated from Poultry Sources in Brazil
Salmonella spp. remain among the most important agents of foodborne diseases worldwide. The importance of Salmonella spp. in public health is linked to their wide range of antimicrobial resistance and to their pathogenicity and virulence in both human and animal hosts. The aim of this study was to determine the antimicrobial resistance patterns for Salmonella serotypes isolated from poultry sources in Brazil and to detect virulence-associated genes and verify their association with specific serotypes. A total of 163 strains of Salmonella enterica isolated from poultry sources in Southern Brazil were selected, and each belonged to one of 11 different serotypes. They were tested against ten antibiotics and examined for the presence of 26 virulence-associated genes by PCR. S. Typhimurium, S. Bredeney, S. Schwarzengrund and S. Tennessee showed the highest overall resistance rates. Approximately 18% of Salmonella strains were classified as multidrug-resistant strains. Our results indicate associations between antimicrobial resistance and specific serotypes. Most of the investigated genes presented a high frequency and a regular distribution, regardless of the serotype. Eight genes are positively or negatively associated with at least one serotype. The observed associations between antimicrobial resistance and specific serotypes are useful in developing specific control and treatment measures for each serotype. Despite the virulence genes being evenly distributed among the serotypes, some of these genes are associated with specific serotypes, and sefA, sopEand lpfA were selected as possible markers of Salmonella serotypes.
INTRODUCTION
Salmonella spp.remain one of the main pathogens responsible for foodborne disease worldwide, and salmonellosis outbreaks are commonly associated with the consumption of poultry and poultryderived products (Centers for Disease Control, 2015;European Food Safety Authority 2017b;Brasil, 2018). In the US, Salmonella serotypes are responsible for approximately 34% of reported infections (Centers for Disease Control, 2015). In Europe, the authorities reported Salmonella as the second most important agent of foodborne diseases, with more than 94,530 salmonellosis cases (European Food Safety Authority, 2017b). In Brazil, Salmonella is responsible for more than 30% of foodborne disease, according to the Brazilian Ministry of Health (Brasil, 2018).
The importance of Salmonella spp. in public health is not only due to the high frequency of salmonellosis outbreaks but also because of the wide range of antimicrobial resistance that this microorganism presents (Tondo & Ritter 2012). Recent studies have demonstrated increasing resistance of Salmonella strains isolated from humans and animals to the most commonly used antibiotics (European Food Safety Authority, 2017a). Recently, many studies in this area have occurred because the resistance of Salmonella in animal food products may present the potential to be transmitted to humans through the food chain (Wang et al., 2013). According to the US Food and Drug Administration, the use of antibiotic in foodproducing animals in the United States has increased approximately 20% between 2009 and 2013. Less than 30% of antibiotics sold for veterinary use were exclusively intended for therapeutic treatments (Food and Drug Administration, 2014). In 2016, 60% of the domestic sales of all antimicrobials approved for use in food production corresponded to the medically important antimicrobials (Food and Drug Administration, 2017). Resistance has appeared since the introduction of antimicrobial agents in medical and veterinary areas. However, the resistance of some microorganisms, such as Salmonella spp.and Campylobacter jejuni, might have started in food-producing animals (Koluman & Dikici 2013). Multidrug-resistant strains of Salmonella spp.are associated with increased hospitalization as well as deaths and the cost of treatment (World Health Organization, 2011a). The emergence of multidrugresistant Salmonella has aroused the attention of governments all over the world (Brasil, 2012;Pulido-Landínez et al., 2014;Proroga et al., 2015;European Food Safety Authority, 2017a;National Antimicrobial Resistance Monitoring System for Enteric Bacteria, 2017). Therefore, monitoring Salmonella resistance in the poultry chain is essential due to the potential spread of antimicrobial-resistant Salmonella isolates to humans (Wang et al., 2013).
The way that the pathogen adapts to the conditions inside the host depends on the virulence of the strain (Madigan et al., 2010). For many pathogens, virulence is conferred by a single region of the genome. However, Salmonella pathogenesis and its interaction with the host are a complex and multifactorial phenomenon that depends on several virulence factors (Wallis & Galyov, 2000;Skyberget al., 2006;). These factors are encoded by many virulenceassociated genes that are distributed along its chromosome and/or in mobile genetic elements such as plasmids (Wallis & Galyov, 2000). Some virulence factors are related to the components of the bacterial structure such as fimbriae and play an important role in the virulence of the strains (Clouthier et al., 1993). Salmonella Pathogenicity Islands (SPI) are large genetic elements with pathogenic properties (Hacker & Carniel, 2001). SPI-1 encodes the components of a Type III Secretion System (TTSS), a complex protein secretion system, and other proteins required for the invasion of non-phagocytic cells and the activation of the inflammatory response (de Jong et al., 2012;Wisner et al., 2012). The islands are also involved in Salmonella recognition and multiplication within macrophages, in iron metabolism, and in endotoxin production (Álvarez, 2007).
In this context, the aim of this study was to determine the antimicrobial resistance patterns for different Salmonella serotypes isolated from poultry sources and to detect virulence-associated genes and verify their association with specific serotypes.
Detection of virulence-associated genes
DNA extraction was carried out by heat treatment as described by Borges et al. (2017a). PCRs for the invA gene were carried out to confirm the presence of Salmonella DNA in the extracted samples. Individual or multiplex PCR protocols were conducted to detect the presence of 26 virulence-associated genes (hilA, lpfA, lpfC, sefA, agfA, spvB, spvC, pefA, sopE, avrA, sivH, orgA, prgH, spaN, tolC, sipB, sitC, pagC, msgA, spiA, sopB, cdtB, iroN, sifA, sseL, and stn) in Salmonella strains. Gene function, primer sequences, amplicon sizes, cycling conditions and reaction mixtures (25 µL) were previously described by Borges et al.(2017b). The cycling program was performed in the Esco Swift MaxPro thermal cycler (Esco, Singapore). The amplified products were separated by electrophoresis in a 1.5% agarose gel and stained with ethidium bromide. Fragments were transilluminated with UV light. Mannheimia haemolytica ATCC 29694 and Salmonella Enteritidis ATCC 13076 were used as negative and positive controls, respectively, for all PCRs except that of the cdtB gene, for which a strain of Salmonella Senftenberg (from our laboratory stock collection) was used as a positive control. In all PCRs, a mixture of all constituents of the PCR except the extracted DNA were mixed and used as a PCR control.
Statistical analysis
Chi-square (χ2) and Fisher's tests were used to analyse the susceptibility of the strains to the different antimicrobials tested, to compare the resistances and to analyse the presence of virulence genes among Salmonella serotypes. Discriminant analysis was used to build decision tree and identify possible serotype marker genes.
Antimicrobial susceptibility test
The antimicrobial resistances of Salmonella strains regardless of the serotype are described in Figure 1. Among the 163 analysed strains, only 5 (3.1%) were susceptible to all tested antimicrobials. No antimicrobial agent was efficient in inhibiting the growth of 100% of tested strains. Amoxicillin, ceftiofur, chloramphenicol, gentamicin and sulfamethoxazole with trimethoprim inhibited the growing of more than 90% of the strains. Ciprofloxacin and sulfafurazole were the antimicrobial agents that presented the significantly (p<0.05)highest numbers of non-susceptible strains. There were important differences in antimicrobial resistance among Salmonella serotypes, as described in Table 1. S. Typhimurium, S. Bredeney, S. Schwarzengrund and S. Tennessee showed the highest overall resistance rates. However, this result can be influenced by the reduced number of samples of the three last serotypes. Statistical associations between each serotype and its resistance for specific antibiotics were determined considering only the serotypes S. Enteritidis, S. Heidelberg, S. Hadar and S. Typhimurium. Amoxicillin resistance was associated with S. Heidelberg, ciprofloxacin with S. Enteritidis and S. Typhimurium, spectinomycinwith S. Heidelberg
Antimicrobial Resistance and Molecular Characterization of Salmonella Enterica Serotypes Isolated from Poultry Sources in Brazil
eRBCA-2019-0827 and S. Typhimurium, sulfafurazole withS. Enteritidis, tetracycline with S. Hadar and S. Heidelberg, and chloramphenicol and sulfamethoxazole with trimethoprim with S. Typhimurium.
The maximum and minimum MAR indices of isolates were 0.1 and 0.6, respectively, and the average MAR was 0.2. The MAR distribution according to serotype is described in Table 1. Approximately 18% (30/163) of Salmonella strains were classified as MDR strains. The majority of MDR strains belonged to the serotypes S. Enteritidis (9/30), S. Typhimurium (7/30) and S. Bredeney (5/30).
Detection of virulence-associated genes
Most of the investigated genes presented a high frequency and a regular distribution regardless of the serotype. The frequencies for the twenty-six genes are described according to serotype in Table 2. Serotype S. Enteritidis presented the highest average (24) number of detected genes (of the 26 virulence-associated genes analysed), followed by S. Heidelberg ( 21 For statistical analyses of the association between a given gene and serotypes, only S. Enteritidis, S. Heidelberg, S. Hadar and S. Typhimurium were used in the comparison because they had the highest numbers of samples. Eight genes were positively associated (p<0.05) with at least one serotype, and one gene was negatively associated (p<0.05) with the four serotypes. This negative association indicates that this gene was restricted to some groups of strains and was not usually related to one of the four analysed serotypes. Based on the distribution of virulence-associated genesin these serotypes, a decision tree was constructed (Figure 2) considering the sefA, sopE and lpfA genes.
DISCUSSION
Salmonella spp.are considered priority bacteria by the World Health Organization (WHO) and the World Organisation for Animal Health (OIE) in monitoring the emergence of resistant strains in animals due to the increase in their antimicrobial resistance over the years. Thus, in vitro tests are important not only for the choice of antimicrobial for the treatment of infections but also for the monitoring of resistance (Jorgensen & Ferraro, 2009). Unfortunately, Brazil does not have integrated programmes for monitoring the antimicrobial resistance of the main pathogens of humans and production animals, such as Salmonella spp.and Campylobacter jejuni. The analysis of the behaviour of these pathogens in these populations would allow the adoption of new measures to control and restrict the use of antimicrobials. Resistance to sulfonamides is common in production animals, and it has been widely described in the literature (Benacer et al., 2010;World Health Organization, 2011a;Proroga et al., 2015;European Food Safety Authority 2017a). These high rates of resistance are possibly related to the wide use of these substances, which would result in an increase in selective pressure (Grave et al., 2010;Mąka et al. 2015;Food and Drug Administration, 2017). More than 70% of the strains resistant to ciprofloxacin also showed resistance to enrofloxacin. This fact can be explained by the similar structures of these antimicrobials (Marshall & Levy, 2011). Fluoroquinolones are considered the preferred antimicrobial agents for the treatment of salmonellosis in humans (World Health Organization, 2011b;European Food Safety Authority, 2017a). The WHO classifies these antimicrobials as extremely important and recommends special attention be paid to the surveillance of antimicrobial resistance in animals, as resistance may be the result of the transfer of strains from non-human sources. The WHO also supports the interruption or the reduction of their use in production animals (World Health Organization, 2011a).
Official data show that the potential for antimicrobial resistance acquisition may vary among serotypes (Canadian Integrated Program for Antimicrobial Resistance Surveillance, 2013; Centers for Disease Control, 2015; European Food Safety Authority, 2017a). Thus, the relative contribution of each serovar may also influence the overall level of resistance in the genus Salmonella (European Food Safety Authority, 2015). S. Typhimurium strains presented the highest overall resistance, and almost all strains were classified as MDR in our study. This serotype has shown high resistance rates to the most commonly used drugs, regardless of the source of isolation (Ahmed et al., 2016;Almeida et al., 2016;Lopes et al., 2016;Wang et al., 2017). Recently, S. Heidelberg strains have become more resistant to antibiotics, limiting therapeutic options (Center for Infectious Disease Research and Policy, 2017). In addition, the frequency of finding MDR S. Heidelberg has increased dramatically in the last few years (Centers for Disease Control, 2014). However, our strains did not present a higher frequency of multidrug resistance.
Although the frequency of MDR strains found in this study was lower than previously reported frequencies (Pulido-Landínez et al., 2014;Proroga et al., 2015), these results indicate that the increase in antimicrobial resistance is a matter of worldwide concern, even though there are differences between the methodologies used (Lertworapreechaet al., 2013). Almost all strains of S. Typhimurium, frequently isolated from human salmonellosis, were classified as MDR, which is of great concern to public health.
Although there is evidence that the use of antimicrobials in production animals is responsible for resistance in human to some pathogens such as Salmonella spp., control has not been effectively adopted in all sectors of the poultry production chain (World Health Organization, 2011a;World Health Organization, 2011b;Collignon, 2012). In addition, globalization and the consequent trade in animal products between countries allow MDR strains to be disseminated to different regions (World Health Organization, 2011b;European Food Safety Authority, 2015). Some factors such as foreign travel, international trade in food, the breeding of different species in the same environment and the vertical structure of some animal production systems may also influence the propagation of resistant strains (European Food Safety Authority, 2015).
The presence of the sefA gene was restricted to S. Enteritidis, since the gene had positive association (p<0.05) with this serovar and negative association (p<0.05) with the others. This gene is a marker of this serotype (Amini et al., 2010). A positive association (p<0.05) of lpfA and lpfC with S. Enteritidis, S. Heidelberg and S. Hadar serotypes was also observed, demonstrating that despite being considered conserved within the genus Salmonella (Bäumler & Heffron, 1995;Doran et al., 1996), the operon lpfABCDE is more frequent in some serotypes. The plasmidial genes spvB, spvCand pefA were positively associated (p<0.05) with S. Enteritidis. A negative association (p<0.05) between these genes and the serotypes S. Hadar and S. Heidelberg was also found. According to Rychlik et al. (2009), S. Enteritidis and S. Typhimurium tend to present plasmids, whereas other serotypes such as S. Typhi, S. Hadar and S. Infantis usually do not. The sopE gene is positively associated (p<0.05) with S. Enteritidis and S. Heidelberg. The frequency variation of this gene among Salmonella serotypes may be related to its location, since it is found in a bacteriophage. Phage have predilections for certain serotypes, and they facilitate the horizontal transmission of bacterial genes (Mirold et al., 1999). The iroN gene showed a positive association (p<0.05) with S. Typhimurium, S. Heidelberg and S. Hadar and a negative association (p<0.05) with S. Enteritidis. This result differs from the results published by Skyberg et al.(2006), which A decision tree computes binary classifications based on univariate divisions of categorical predictors. It finds the best data partition and discards variables that do not fully explain the categories of the variable response. In this context, classification trees are useful in determining serotype marker genes. In our study, sefA, sopE and lpfA are potentially markers for S. Enteritidis, S. Typhimurium, S. Heidelberg and S. Hadar. Although sopE and lpfA may be present in all serotypes and sefA is exclusively detected in S. Enteritidis, simultaneous analysis of the presence or absence of these genes through the construction of decision trees can significantly predict the probable involved serotype.
The observed association between antimicrobial resistance and specific serotypes is useful in developing specific control and treatment measures for each serotype. Despite the virulence genes being evenly distributed among the serotypes, some of these genes are associated with specific serotypes. Further studies are needed to understand how the molecular patterns of each serotype influence pathogenicity and virulence in vivo. In addition, sefA, sopE and lpfA are possible markers of Salmonella serotypes.
Figure 1 -
Figure 1 -Antimicrobial susceptibility (%) of Salmonella strains to ten antimicrobial agents by disc diffusion tests, regardless of serotype.
Figure 2 -
Figure 2 -Classification tree of S. Enteritidis, S. Heidelberg, S. Hadar and S. Typhimurium based on the distribution of sefA, sopE and lpfA genes.
Table 1 -
Antimicrobial susceptibility and multiple antibiotic resistance (MAR) indices of Salmonella enterica serotypes isolated from poultry sources.
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Domain: Agricultural And Food Sciences Biology
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Effect of cysteine, insulin-like growth factor-1 and epidermis growth factor during in vitro oocyte maturation and in vitro culture of yak-cattle crossbred embryos
ABSTRACT Some antioxidants and growth factors play an important role in promoting oocyte maturation and embryo development in many mammalian species, but there is little information about the yak (Bos grunniens). Therefore, the objective of this study was to evaluate Cys, insulin-like growth factor-1 (IGF-1) and epidermis growth factor (EGF) on yak oocyte maturation, cleavage and blastocyst rates after in vitro fertilized with Jersey sperm. A single or different combination of Cys, EGF and IGF-1 was added to in vitro maturation (IVM) and in vitro culture (IVC) media. The results showed that a single addition of Cys and IGF-1 increased oocyte maturation and blastocyst rates (p < .05), but did not increase cleavage rate; EGF or IGF-1 + EGF increased oocyte maturation, cleavage and blastocyst rates (p < .05) compared with the control. A combination of IGF-1 + EGF + Cys could have a beneficial effect (p < .05). These results indicated that supplementation of IVM and IVC media with Cys, IGF-1, EGF and their combinations could improve in vitro production efficiency of yak-cattle crossbred embryos.
Introduction
The yak (Bos grunniens) is one of the world's most remarkable domestic animalsan herbivore living in and around the Himalayas and further north at altitudes ranging from 2500 to 5500 m. They are very important to local herders by providing milk and meat, as few other domestic animals can survive in such cold, hypoxic ecological conditions, but their production performance is much inferior to the improved cattle breeds. The meat and milk performance of hybrids derived from that yaks crossbred with the improved bovine breeds were greatly improved, however, sterility of F1 males prevents successful inter-se matings (Wiener et al. 2003). With the current development of in vitro production (IVP) and embryo transfer, the F1 females are possible not only give additional milk, but also produce valuable offspring (F1) if they are used as recipients of yak-cattle crossbred embryos. The desirable IVP efficiency of crossbred embryos is prerequisite for F1 producing F1, but it is considerably low at present (Zi et al. 2009). Cysteine (Cys), insulin-like growth factor-1 (IGF-1) and epidermis growth factor (EGF) have been shown to promote oocyte maturation and embryo development in many mammalian species (Pawshe et al. 1998;Sirisathien et al. 2003;Choe et al. 2010;Neira et al. 2010;Shabankareh and Zandi 2010;Nabenishi et al. 2012;Yu et al. 2012;Toori et al. 2014;Thongkittidilok et al. 2015;Zhou et al. 2016;Sato et al. 2018), but there is little information about the yak (Pan et al. 2015;Chen et al. 2017). Therefore, the objective of this study was to investigate the effects of Cys, IGF-1 and EGF on yak oocyte maturation and development of yak-cattle crossbred embryos in vitro.
Ethics statement
All animal procedures were approved by the Institutional Animal Care and Use Committee of the Southwest Minzu University and all methods were performed in accordance with the relevant guidelines and regulations.
Jersey frozen semen was thawed and incubated in Sperm-Rinse™ at 38.5°C for 50 min allowed the motile sperm to swim up. Groups of 30 COCs were inseminated with sperm that had been prepared by swim-up procedure at a final concentration of 2 × 10 6 sperms/ml in 70-μl drops of the IVF TM , and after a period of 24 h post-insemination (hpi), presumptive zygotes were cultured in a four-well dish containing 500 μl of SOF medium (IVC medium) consisting of different concentrations of IGF-1, IGF-2 and EGF with an overlay of mineral oil in a humidified incubator with 90% N 2 , 5% CO 2 and 5% CO 2 at 38.5°C. The culture medium was changed at 96 hpi. Cleavage and blastocyst formation were assessed on days 2 and 7 of culture, respectively.
Experimental design
IVM and IVC media were supplemented with Cys, IGF-1 and EGF to final concentrations of 0.6 mM, 100 ng ml −1 and 10 ng ml −1 , respectively, and supplemented with none of them in the control. The experiment contained eight groups: Cys, IGF-1, EGF, IGF-1 + EGF, IGF-1 + Cys, EGF + Cys, IGF-1 + EGF + Cys, and control. These concentrations were chosen because they had previously been shown to be the most effective dosage for IVM and IVC in some studies (Shabankareh and Zandi 2010;Lott et al. 2011;Chen et al. 2017)
Statistical analysis
All data were subjected to ANOVA followed by Tukey-Kramer test. Analyses were carried out using the GLM procedure of Statistical Analysis System (SAS; SAS institute, Cary, NC, USA).
Results and discussion
The effects of supplementation of Cys, IGF-1, EGF and their combinations in IVM and IVC media on maturation cleavage, and blastocyst rates of yak oocytes are listed in Table 1. The results showed that a single addition of Cys or IGF-1 increased oocyte maturation and blastocyst rates of yak-cattle crossbred embryos (p < .05), but did not increase cleavage rate compared to control. EGF increased oocyte maturation, cleavage and blastocyst rates (p < .05) compared to control. No additive effect of combining EGF and IGF-I was seen when results were compared to those following supplementation of the media with EGF alone, but the cleavage rate was greater than those supplemented with IGF-I alone (p < .05). IGF-1 + Cys and EGF + Cys did not give a significantly more beneficial effect compared to Cys, IGF-1 or EGF alone, however, the combination of IGF-1 + EGF + Cys could greatly improve oocyte maturation (84.44%), cleavage (80.45%) and blastocyst rates (38.67%).
Our results are in agreement with other reports showing that significant improvements in the proportion of oocytes undergoing cleavage and blastocyst development were achieved when cysteine (0.6 mM) was added to the bovine maturation medium as compared to control medium without antioxidant supplementation (Ali et al. 2003;Lott et al. 2011), however, this concentration did not have favourable effects in porcine oocytes under low oxygen tension (Viet Linh et al. 2009) and bovine oocytes exposed to heat stress. The addition of 1.2 mM cysteine during IVM could alleviate the influence of heat stress for oocyte developmental competence by increasing GSH content and inhibiting the production of oocyte ROS followed by apoptosis of cumulus cells (Nabenishi et al. 2012).
There are studies showing that the positive effect of growth factors on embryo development may vary depending on the maturation and culture medium component like granulosa cell co-culture (Herrler et al. 1992), bovine serum (Palma et al. (Pawshe et al. 1998;Quetglas et al. 2001). In cattle, 100-200 ng/ml EGF and 50-100 ng/ml IGF-I were suggested by Sakagami et al. (2012), but 50 ng/ml EGF and 100 ng/ml IGF-I were suggested by Arat et al. (2016). It appears that the optimal concentrations of EGF and IGF-1 were some different among different animal species (Sirisathien et al. 2003;Choe et al. 2010;Shabankareh and Zandi 2010;Thongkittidilok et al. 2015;Zhou et al. 2016;Sato et al. 2018).
In this study, IVM and IVC media of yak were supplemented with only one dose for each Cys (0.6 mM), IGF-1 (100 ng ml −1 ) and EGF (10 ng ml −1 ). There was no difference between the rate of embryo development obtained by the addition of similar growth factors to the maturation medium and the rate of blastocyst growth obtained by the addition of growth factors to both the maturation medium and culture medium in cattle (Arat et al. 2016). Since Cys, IGF-1 and EGF were added to both the maturation medium and the culture medium in this study, it was not clear at which stage they contribute to the development of yak embryo. This should be investigated in the future studies.
Conclusion
Cys, IGF-1, EGF or their combinations can improve yak oocyte maturation and/or development to blastocyst competence after in vitro fertilized with cattle sperm. This provides important information to improve IVP efficiency of yak-cattle crossbred embryos. However, there is a need to study the optimal concentrations of Cys, IGF-1 and EGF in IVM and IVC media that are the most effective for IVP of yak-cattle crossbred embryos.
Disclosure statement
No potential conflict of interest was reported by the authors.
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Domain: Agricultural And Food Sciences Biology
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This below document has 3 sentences that start with 'Heme synthesis enhances globin formation', 2 sentences that start with 'ALA induced erythroid differentiation of', 2 sentences that start with 'This feedback mechanism enabled partial', 2 sentences that start with 'It is conceivable that', 2 sentences that start with 'The graphic presentation of', 2 sentences that end with '( Figure 1)', 2 paragraphs that start with 'We investigated the'. It has approximately 2129 words, 91 sentences, and 28 paragraph(s).
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ALA Induced Heme Synthesis: Fine Tuning Mechanisms of PBG Deaminase and ALA Dehydratase
During erythroid differentiation and maturation, three critical components α-globin, β-globin, and heme are critical for the formation of stable α2β2 hemoglobin (Hb) complexes. Heme synthesis enhances globin formation, not only because it is the precursor of Hb but also by modulating the machinery of gene expression and globin synthesis and coordinating erythroid cell maturation. Exogenous 5-aminolevulinic acid (ALA), the first precursor in the heme biosynthesis pathway, circumvents the rate limiting enzyme ALA synthase, and accelerates the heme synthesis pathway. ALA induced erythroid differentiation of human myelogenous leukemia K562 and murine erythroleukemia MEL cells, leads to hemoglobinization and cellular typical maturation. Only two key enzymes where known as regulator of the accumulation of PpIx: Porphobilinogen deaminase and Ferrochelatase. Recently we demonstrated that downregulation or over expression of ALA dehydratase (ALAD) and porphobilinogen deaminase (PBGD), the second and the third enzymes of the pathway, by specific shRNAs caused a marked alteration in PpIX synthesis in K562 erythroleukemic cells. PBGD down regulation induced an elevation in ALAD activity, while over expression of PBGD reduced ALAD activity, indicating a novel regulation feedback of PBGD on ALAD activity. This feedback mechanism enabled partial PpIX synthesis under PBGD silencing, whereas ALAD silencing reduced PpIX production to a minimum, only ALAD loss resulted in reduced PpIX and Hb synthesis. Hemoglobin synthesis and assembly is largely dependent on coordinated gene expression control, it is well established that the histone deacetylase inhibitor butyric acid activates transcription of heme synthesis an globin mRNA expression, leading to changes in cell morphology and induction of erythroid differentiation of erythroleukemic cell lines. We have demonstrated that the activity of the multitargeting ALA prodrug, AlaAcBu in the induction of protoporphyrin IX (PpIX) and anti-cancer activity following light irradiation. The prodrug AlaAcBu undergos enzymatic intracellular hydrolysis releasing ALA, butyric acid and acetaldehyde and these active components induce accelerated heme and hemoglobin synthesis. Erythroid differentiation was characterized by cellular maturation characterized by cytoplasm hemoglobinization, expression of the marker glycophorin A and polar arrangement of mitochondria and a developed central vacuolar system preceding nuclear extrusion. The ability of AlaAcBu to promote differentiation along the erythroid lineage and to dramatically induce hemoglobin synthesis is presented in this report.
Introduction
Heme is a central component of hemoglobin (Hb), myoglobin, cytochromes, catalase and peroxidase. α-Globin, ß-globin, and heme are essential components of erythroid differentiation and maturation. Heme synthesis enhances globin formation, not only because it is the precursor of Hb but also because it modulates the eIF2α kinase activity HRI, leading to enhanced translation of erythroid precursors [1]. During erythroid differentiation and maturation, three critical components α-globin, ß-globin, and heme accumulate at a ratio of 2:2:4 to form stable α2β2 hemoglobin complexes. Heme synthesis enhances globin formation, not only because it is the precursor of Hb but also because it modulates the eIF2a kinase activity of the hemeregulated eIF2a kinase, HRI, leading to the translation of erythroid precursors [2]. Rutherford et al., showed that K562 cells display clear erythroid properties and could be used to analyze the regulation of hemoglobin differentiation, since they synthesize glycophorin, heme, globin chains and ferritin [3].
Experimentally, it was shown that exogenous 5-aminolevulinic acid (ALA), the first precursor in the heme biosynthesis pathway, circumvents the rate limiting enzyme ALA synthase, and accelerates heme synthesis. ALA induced erythroid differentiation of human myelogenous leukemia K562 and murine erythroleukemia MEL cells, leads to hemoglobinization and cellular typical maturation [4].
Heme Biosynthesis
The heme biosynthesis pathway is one of the most studied enzymatic tracks in cellular anabolism. 5-Aminolevulinate synthase (ALAS) is the first and rate-limiting mitochondrial enzyme producing ALA ( Figure 1). Two ALAS isoenzymes, the housekeeping in the liver,
Journal of Hematology & Thromboembolic Diseases
and ALAS-N in erythroid cells, by a negative feedback mechanism are regulated by the end product heme. In erythroid tissues both ALAS-E and ALAS-N are expressed, and regulated by distinctive mechanisms. In differentiating erythroblasts ALAS-E is upregulated by heme as well as globin synthesis followed by cellular maturation. Figure 1: The major enzymatic steps of the heme biosynthesis pathway. ALA, produced by ALA synthase in the mitochondria is exported to the cytosol and condensed by ALA dehydratase (ALAD) to form the first pyrrole, porphobilinogen. Porphobilinogen deaminase (PBGD) condenses 2 molecules of porphobilinogen to its cofactor dipyrromethane (DPM), before the final product, the linear tetrapyrrole hydroxymethylbilane (HMB), is released. HMB is converted to uroporphyrinogen III (Uro'gen) by Uro'gen synthase. Uro'gen decarboxylase and copro'gen III oxidase catalyze the formation of protoporphyrinogen IX (Proto'gen). In the last step Ferrochelatse inserts iron into the tetrapyrrole ring to produce heme The intermediate precursor ALA is transferred from mitochondria to the cytosol, where the second, very abundant enzyme ALA dehydratase (ALAD) condenses two ALA molecules to form the primary pyrrole ring, porphobilinogen (PBG). PBG, the first fluorescent product of this pathway binds to PBG deaminase (PBGD) and activates two separated enzymatic reactions (I) to produce the unique dipyrromethane (DPM) cofactor, consisting of two linked PBG molecules covalently attached to the enzyme; and (II) synthesis of hydroxymethylbilane by attaching 4 PBGs to form a linear tetrapyrrole which is then closed to uroporphyrinogen III by uroporphyrinogen III synthase. DPM is part of the enzyme active site PBGD and acts as a reaction primer. The uroporphyrinogen III is decarboxylated by mitochondria bound URO decarboxylase to form coproporphyrinogen III ( Figure 1). The next enzyme, coproporphyrinogen oxidase (CPO), the second mitochondrial bound enzyme catalyzes the oxidative decarboxylation of coproporphyrinogen III to produce protoporphyrinogen IX. Subsequently, the next mitochondrial bound protoporphyrinogen oxidase [5] removes six hydrogen atoms from the protoporphyrinogen ring to create protoporphyrin IX (PpIX). The final and rate-limiting mitochondrial enzyme, ferrochelatase (FeCh), inserts a ferrous ion into the center of the PpIX molecule, thus forming heme ( Figure 1) [6,7]. This last step is highly sensitive to the iron pool and to heavy metal intoxication.
In erythroid systems silencing of ALAS-E mRNA using anti-sense RNA results in a decrease in mRNA levels of ALAS, ALAD, PBGD, FeCh and β-globin, as well as heme formation in differentiating cells. Compensation by ALA or hemin treatments circumvented ALAS silencing and partially restored FeCh and β-globin expression and the heme levels in these cells. In conclusion, heme formation in erythroid cells is largely dependent on ALAS-E expression [8].
Cytosolic Enzymes Play Regulatory Roles in Heme Synthesis
Several mechanisms involved the regulation of the biosynthesis pathway such as feedback inhibition, proteasomal degradation and gene expression involvement [1,9] were suggested. It is conceivable that unnoticed cytosolic regulatory mechanisms may affect porphyrin synthesis. Recently we described cytosolic mechanisms of fine tuning that affect the heme biosynthesis pathway.
PBGD expression and enzymatic activation following ALA treatments
We investigated the effect of ALA pre-treatment on PBGD expression and activity. K562, cells were treated with ALA for 4 or 24 h ( Figure 2A). The results show that ALA incubation for 24 h activated PBGD activity by 250% in comparison to control. The PBGD expression level was examined by Western blot analysis ( Figure 2B) and clearly showed no significant difference in the PBGD expression level following ALA administration. Moreover, ALA pre-treatment resulted in a significant PpIX elevation, presumably due to PBGD activation.
In several studies we have shown that PBGD has a dual subcellular localization, as expected in the cytosol and in addition 50% of total enzyme is imported into the nucleus without a known function [10]. We attempted to examine whether the nuclear fraction of PBGD will change following enzymatic activation. Following ALA pre-treatment no notable change in PBGD localization could be seen. PBGD is constantly localized both in the nucleus and in the cytoplasm ( Figure 2C).
PBGD silencing
Two cytosolic expression checkpoints that may affect tetrapyrrole biosynthesis -ALA dehydratase (ALAD) and porphobilinogen deaminase (PBGD), were investigated. We manipulated PBGD expression levels by PBGD siRNA (Feuerstein et al., 2009). PBGD down-regulation reduced PBGD activity by 80% activity, while overexpression of PBGD induced an elevation of 140% in activity ( Figure 3B). Only a partial decrease in PpIX synthesis was measured under PBGD silencing. The graphic presentation of this silencing cascade is shown in Figure 3A.
ALAD silencing
We down-regulated the expression of ALAD and PBGD by siRNAs ( Figure 4B); or over expressed PBGD by using siPBGD plasmids. Figure 4B shows that ALAD siRNA down regulated ALAD expression and reduced markedly the activity of the enzyme. However transfection of the K562 cells with PBGD siRNA induced a marked increase in ALAD enzymatic activity. The graphic presentation of these studies are summarized in Figure 4A, it depicts the unexpected result of fine tuning of the pathway via PBGD levels, reduction of PBGD induce increased levels of ALAD. This feedback mechanism enabled partial PpIX synthesis under PBGD silencing, whereas ALAD silencing reduced PpIX production to a minimum ( Figure 5).
Heme Synthesis and Erythroid Differentiation Induced by a Multifunctional 5-aminolevulinic Acid Derivative
Several studies have shown that anti-cancer drugs, very diverse in their chemical structure, can induce erythroid differentiation of K562 cells at low non-toxic concentrations.
The specific histone deacetylase inhibitor butyric acid (BA) activates transcription of globin mRNA, leading to changes in cell morphology and induction to erythroid differentiation of erythroleukemic cell lines [11][12][13][14]. It was previously shown that combination of ALA and BA treatment of K562 cells enhances hemoglobin synthesis to a substantially higher level than does each compound separately [15]. We have recently shown that the activity of ALA prodrugs in the induction of PpIX and anti-cancer activity following light irradiation [16,17] was superior to ALA in inducing photo-dynamic cell death.
The concept of stimulation of erythropoiesis by the multifunctional 5-aminolevulinic-acid (ALA) derivative, 1-(butyryloxy)ethyl-5amino-4-oxopentanoate, (AlaAcBu Figure 6), was tested on the human erythroleukemia K562 cell system. We have shown that the multifunctional AlaAcBu derivative undergoes metabolic hydrolysis yielding two erythroid differentiation inducers, ALA and BA, each acting through a different mechanism. ALA, the first precursor in the heme biosynthesis, accelerates heme synthesis and BA, a histone deacetylase inhibitor (HDACI) that activates the transcription of globin mRNA. Our study [18] showed that the AlaAcBu mutual prodrug is a potent chemical differentiation inducer of K562 cells manifested by augmentation of heme synthesis.
Moreover, AlaAcBu stimulated globin synthesis and assembly of hemoglobin in the erythroleukemic cells (Figure 7). It is conceivable that the treatment with AlaAcBu increased heme synthesis and globin expression on the gene expression level and on the enzymatic level as well. We propose that stimulation of the heme pathway evident by activation of PBGD and ferrochelatase as seen by heme accumulation. Cellular erythroid differentiation was revealed by the expression of the marker glycophorin A and cellular maturation characterized by cytoplasm hemoglobinization, polar arrangement of mitochondria and a developed central vacuolar system preceding nuclear extrusion . Thus the synthetic ALA derivative AlaAcBu, upon hydrolysis promotes superior stimulation of erythroid differentiation by two independent mechanisms. The first released product ALA, stimulates heme synthesis being its natural precursor, The second product butyric acid, induces erythroid gene expression by its histone deacetylation activity, and an additional effect of supplemented ALA is the induced DPM synthesis and thus activation of the rate limiting enzyme PBGD. Figure 7: Hemoglobin synthesis by K562 cells induced by AlaBuAc or ALA. Hb absorbance was determined at 540 nm in cell lysates following 96 h treatment. K562 cells were exposed to 0.5 mM of the indicated compounds and hemoglobin content was measured in a 0.1 mM KCN solution.
Conclusion
We investigated the possibility to stimulate PpIX synthesis in an erythroleukemia K562 cell model, by elevating PBGD activity due to ALA supplementation, which serves as the precursor for PBGD cofactor DPM on one hand, and in parallel enables synthesis of PBG. The elevated PBG availability enabled further efficient PpIX synthesis. We have previously shown that ALAD silencing in K562 cells caused a dramatic reduction in PpIX synthesis, significantly more than PBGD silencing. While ALAD reduced PpIX by two orders of magnitude, almost as control cells, the PpIX level following PBGD silencing caused only one order of magnitude decrease. This phenomenon may arise from the fact that ALAD silencing prevented PBG formation and therefore PBGD, which is rate limiting, could not be active.
The fine-tuning of the porphyrin synthesis pathway was depicted under silencing of PBGD. While PBGD was silenced by a specific siRNA, unexpectedly the enzymatic activity of ALAD became enhanced markedly under these conditions. This positive feedback mechanism enabled partial PpIX synthesis under PBGD silencing, as the increased activity of ALAD enabled compensated production of tetrapyrroles and PpIX. Conversely, ALAD reduced activity under gene silencing resulted in reduced PBG synthesis and consequently failure in PpIX synthesis.
Moreover the ALA derivative AlaAcBu induced the expression and activity of heme biosynthesis, the key enzymes porphobilinogen deaminase (PBGD), ferrochelatase and the expression of glycophorin A (the erythroid cell marker), and led to a dramatic increase in heme
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Domain: Biology Chemistry
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Studying the pathogenesis of BCR–ABL+ leukemia in mice
Animal models of BCR–ABL+ leukemias have provided important new knowledge about the molecular pathophysiology of these diseases, and answered questions that are difficult or impossible to address using BCR–ABL-expressing cell lines or primary Ph+ leukemia samples from patients. The power of mouse models lies in their ability to recapitulate precisely the phenotypes of BCR–ABL+ leukemias in vivo, but this comes at the price of significant complexity. Here I review recent studies of leukemias induced in mice by BCR–ABL with an emphasis on the intricate nature of these diseases and the need for careful pathological and molecular analysis.
Although much has been learned about the biology of BCR -ABL through these studies, a complete understanding of the pathophysiology of BCR -ABLassociated leukemias requires the expression of the oncogene in the hematopoietic system of a living organism. This is because the complex nature of leukemia cannot be adequately modeled in any currently existing cell culture system. Although cell lines do exist that recapitulate some aspects of hematopoietic differentiation in vitro, these may not be appropriate systems for the analysis of BCR -ABL activity. For example, although the hallmark of human chronic myeloid leukemia (CML) is an overproduction of myeloid cells with preservation of myeloid differentiation, expression of BCR -ABL in 32D cells, which can undergo terminal granulocytic differentiation in response to G-CSF, blocks the ability of these cells to differentiate (Laneuville et al., 1991). Furthermore, while murine embryonic stem (ES) cells can undergo differentiation in vitro to all myeloid lineages, studies of BCR -ABL activity in ES cells have not provided major insights into leukemogenesis. Expression of BCR -ABL in ES cells alters the balance of in vitro erythroid differentiation towards myeloid and multipotential progenitors (Era and Witte, 2000) and permits multilineage engraftment of irradiated recipient mice by differentiated ES cells , but the hematologic disease that develops in recipients is not an accurate representation of human CML (Peters et al., 2001).
In order to express BCR -ABL directly in the hematopoietic system of mice, both transgenic and retroviral transduction approaches have been employed. Despite a large effort, there is no realistic transgenic model of BCR -ABL-induced CML (Van Etten, 2001). However, the retroviral expression system does provide such a model, and will be the focus of this review.
An accurate and quantitative model of CML in mice Human CML can be faithfully modeled in mice by retroviral transduction of the BCR -ABL gene into mouse bone marrow cells, followed by transplantation into irradiated syngeneic mice (Daley et al., 1990;Kelliher et al., 1990). When high-titer virus stock is employed, this procedure induces CML-like myeloproliferative leukemia in all recipients within 4 weeks after transplantation (Li et al., 1999;Pear et al., 1998;Zhang and Ren, 1998). Mice with BCR -ABL-induced CMLlike disease exhibit massive polyclonal expansion of maturing myeloid cells, principally neutrophils, which express Bcr -Abl protein and infiltrate spleen, liver, and lungs. Although neutrophils are the predominant hematopoietic lineage overproduced in murine CMLlike disease, macrophages, erythroid progenitors, Blymphocytes and sometimes T-lymphocytes from diseased mice carry the same spectrum of BCR -ABL proviral clones as the granulocytes, demonstrating that the cells initiating the CML-like leukemia are early multipotential progenitors (Li et al., 1999). The CMLlike disease is efficiently transferred by transplantation of bone marrow or spleen cells from a primary animal to irradiated secondary recipients (Li et al., 1999;Pear et al., 1998;Zhang and Ren, 1998). Interestingly, of the many different BCR -ABL-transduced clones that contribute to the leukemia in the primary mouse, only a small subset are capable of generating day 12 spleen colonies and of engrafting and inducing CML-like disease in secondary recipients (Li et al., 1999;Zhang and Ren, 1998), suggesting that the cells initiating CML-like disease are heterogeneous for self-renewal. Serial transplantation leads to evolution of the leukemic process into clonal acute myeloid or more often lymphoid leukemia, representative of blast crisis Pear et al., 1998). Murine CMLlike leukemia is therefore an accurate and faithful model of human CML that has proven useful for analysis of the molecular pathophysiology of this disease Li et al., 1999;Million and Van Etten, 2000;Roumiantsev et al., 2001).
Multiple distinct leukemias originate from BCR -ABL-transduced marrow All recipients of BCR -ABL-transduced marrow develop CML-like disease when bone marrow donors are pretreated with 5-fluorouracil (5-FU) before harvest. This is consistent with the origin of these leukemias from early progenitor/stem cells, whose transduction is favored by 5-FU treatment. However, other BCR -ABL-transduced progenitors are present in the bone marrow population and can induce other forms of leukemia if recipients do not first succumb to CMLlike disease. This can be seen with decreases in virus titer (Daley et al., 1990), alterations in transduction conditions (Elefanty and Cory, 1992), or by employing marrow from donors not treated with 5-FU (Li et al., 1999). These other malignancies include acute Blymphoid and T-lymphoid leukemia/lymphoma, erythroleukemia, and histiocytic tumors (sarcomas) arising from the myelomonocytic lineage. The latter disease most closely resembles one of the human histiocytoses such as malignant histiocytic reticuloendotheliosis (Groopman and Golde, 1981), which does not typically have a Ph chromosome.
These multiple leukemias can compete within the bone marrow of recipient mice and lead to confusing clinicopathological syndromes under certain conditions. In recipients of BCR -ABL-transduced marrow from non-5-FU-treated donors, a mixture of CML-like myeloproliferative disease, B-cell acute lymphoblastic leukemia (B-ALL), and histiocytic sarcoma develops, with some recipients having two or even all three diseases simultaneously (Li et al., 1999). Such multileukemic mice have the cardinal clinicopathological features of each disease independently and can be recognized by careful histological analysis and by the demonstration of distinct proviral clones in the different leukemic cells. The effect of competing leukemias can also be observed with mutations in BCR -ABL that attenuate the induction of CML-like disease, such as point mutations in the Src homology 2 (SH2) domain. The R1172K mutation in p210 BCR -ABL eliminates phosphotyrosine binding by SH2 (Ilaria and Van Etten, 1995) and recipients of p210 R1172K-transduced marrow all succumb to B-ALL (Roumiantsev et al., 2001), suggesting that the SH2 domain is absolutely required for induction of CMLlike disease by BCR -ABL.
However, the cells initiating the B-ALL have restricted differentiation potential (Li et al., 1999) and phenotypic characteristics of early B-lymphoid progenitors (D Krause and RA Van Etten, unpublished observations). Depletion of these progenitors from the p210 R11722K-transduced marrow allows CML-like disease to reemerge in recipients after a delay, demonstrating that loss of Bcr -Abl SH2 function merely attenutates the development of CML-like leukemia but does not eliminate it (Roumiantsev et al., 2001). Defining the nature of the bone marrow progenitors that initiate distinct BCR -ABL-induced leukemias and developing methods to model each disease separately are major goals for future work. Importantly, B-ALL can be efficiently induced in the absence of CML-like disease by direct transduction and transplantation of marrow from non-5-FU-treated donor mice (Roumiantsev et al., 2001), allowing this BCR -ABL-induced disease, which is an accurate representation of human Ph + acute Blymphoblastic leukemia, to be quantitatively modeled in mice.
Autocrine and paracrine effects in BCR -ABL leukemogenesis
Another intricacy of BCR -ABL-induced leukemia is that BCR -ABL can induce the secretion of multiple cytokines. This was appreciated initially from studies in the myeloid cytokine-dependent FDCP-1 cell line, where expression of BCR -ABL induces secretion of IL-3 in an SH2-dependent fashion (Anderson and Mladenovic, 1996;Hariharan et al., 1988). However, the ability of BCR -ABL to transform IL-3-dependent hematopoietic cell lines to become independent of exogenous IL-3 for survival and growth does not involve an autocrine mechanism (Daley and Baltimore, 1988;Hariharan et al., 1988;Ilaria and Van Etten, 1995).
In bone marrow transplant experiments, cytokine secretion by BCR -ABL-expressing cells can lead to expansion of hematopoietic cell populations in recipient mice that do not express the oncogene. A good example is the histiocytic sarcoma induced by BCR -ABL. Small areas of perivenular histiocytic infiltration are frequently observed in livers of mice with CML-like leukemia (Figure 1a), and analysis of proviral integration patterns demonstrates these cells are derived from the same multilineage progenitors that generate the neutrophils in this disease (Li et al., 1999). However, BCR -ABL-induced histiocytic malignancies can exist independently of classic myeloproliferative disease (Figure 1b), where they are characterized by slow accumulation of malignant macrophages that involve liver, mesentery, peritoneum and often associated with ascites (Daley et al., 1990;Elefanty et al., 1990). Mice with primary histiocytic sarcoma can exhibit increased levels (from 10 -50610 3 per mm 3 ) of neutrophils in the peripheral blood, suggestive of myeloproliferative disease; however, molecular analysis demonstrates that these neutrophils do not contain the retroviral provirus and hence are not a direct part of the malignant process (Daley et al., 1990;Elefanty et al., 1990;Scott et al., 1991). Mice with histiocytic sarcoma contain increased levels of circulating G-CSF and GM -CSF that are likely produced directly by these tumors and may responsible for the secondary increase in neutrophils (Elefanty et al., 1990). This illustrates that one must be extremely careful about diagnosing myeloproliferative disease in mice that harbor histiocytic sarcoma.
This phenomenon was responsible for some initial confusion about the leukemogenic properties of v-abl, the transforming gene of Abelson murine leukemia virus, when expressed in murine bone marrow. It was first reported that v-abl also induced CML-like disease in recipients of transduced marrow (Kelliher et al., 1990), and others subsequently described chronic myeloproliferative disease induced by v-abl in similar experiments Han et al., 1991). However, these mice develop a complex mixture of Blymphoid, mast cell, and histiocytic tumors and although some recipients have increased circulating neutrophils, genomic DNA from these cells lack the retroviral provirus and hence likely arise from paracrine stimulation by secreted cytokines (Scott et al., 1991). The inability of v-abl to induce CML-like myeloproliferative disease in the current high-efficiency retroviral bone marrow transduction/transplantation model system was confirmed by two recent studies (Gross and Ren, 2000;Million and Van Etten, 2000).
To complicate matters further, mice with BCR -ABL-induced CML-like disease exhibit a modest increase in circulating interleukin 3 (IL-3) (Li et al., 1999;Zhang and Ren, 1998), and perhaps in granulocyte-macrophage colony-stimulating factor (GM -CSF) as well (Zhang and Ren, 1998). The increase in IL-3 is particularly interesting because of the recent observation that primitive Ph + progenitors from human chronic phase CML patients express aberrant transcripts for IL-3 and exhibit autonomous in vitro growth that is partially inhibited by anti-IL-3 antibodies (Jiang et al., 1999). These observations suggested that autocrine production of IL-3 might contribute to the pathogenesis of both human and murine CML. However, when mice with homozygous inactivation of the Il3 or both the Il3 and Gmcsf genes are used as donors in the retroviral bone marrow transduction/transplantation model, recipients of BCR -ABL-transduced marrow efficiently develop myeloproliferative disease , demonstrating that neither cytokine is required for the pathogenesis of CML-like disease in this model system. Interestingly, increased circulating IL-3 is still observed in wild-type recipients of BCR -ABL-transduced marrow from Il3 7/7 donors but not when the host is of Il3 7/7 genotype (Figure 2), demonstrating that the source of increased IL-3 is the recipient, not the BCR -ABL-expressing donor cells . Levels of circulating IL-3 are higher in recipients of marrow transduced with BCR -ABL retroviral vectors that coexpress A. victoria green fluorescent protein (GFP) at high levels from an internal ribosome entry site than with vectors expressing a neomycin phosphotransferase gene at low levels from an internal promoter ( Figure 2), while there is no increase in IL-3 in recipients of marrow transduced with an empty retrovirus. Collectively, these results suggest that the elevation in IL-3 represents an immunological reaction of the recipient to transplantation of bone marrow expressing a foreign protein.
The use of such bicistronic retroviral vectors, which co-express BCR -ABL and GFP from a single mRNA via an internal ribosome entry site (IRES), facilitates titering of retroviral stocks and allows the identification by flow cytometric analysis of transduced hematopoietic cells in diseased mice (Pear et al., 1998). The presence of a large fraction of GFPmyeloid cells in mice with BCR -ABL-induced CMLlike disease has been taken as evidence of a significant paracrine effect in this model system (Zhang and Ren, 1998), but the persistence of this GFP 7 CD11b + myeloid population in Il3 -/ -Gmcsf -/recipients of BCR -ABL-transduced marrow from donors of the same genotype demonstrates that IL-3 or GM -CSF are not responsible. The majority of these GFPcells are probably accounted for by mechanisms other than paracrine stimulation of normal marrow progenitors. While it is fairly certain that GFP + cells also express BCR -ABL, the converse is not true, and a large fraction of the GFPcells must contain the retroviral provirus and may also express BCR -ABL. This follows from the observation that myeloid cells from these mice contain the BCR -ABL provirus at levels that are greater than or equal to one proviral copy per diploid genome (Li et al., 1999), and indeed Southern blotting of genomic DNA from purified GFP + and GFPmyeloid cell populations from mice with myeloproliferative disease induced by an oncogenic receptor tyrosine kinase, activated FLT3, demonstrates equivalent levels of the provirus in the two populations (Kelly et al., 2002). The explanation for the lack of detection of GFP in provirus + cells may involve loss of GFP due to cell damage during in vitro manipulation ) and possibly to genetic or epigenetic mechanisms that impair IRES function after proviral integration.
These examples demonstrate that autocrine and paracrine effects of BCR -ABL expression are an inevitable complication of in vivo leukemogenesis model systems that must be considered during the analysis of hematologic malignancies induced by BCR -ABL in mice.
Not all myeloproliferative disease is created equal: the case of Tel -Abl
Fatal myeloproliferative leukemia develops in recipients of bone marrow transduced with retroviruses expressing a wide variety of dysregulated tyrosine kinases in addition to Bcr -Abl, including Tel -Jak2 (Schwaller et al., 1998), Tel-PDGFbR (Tomasson et al., 2000), and activated FLT3 (Kelly et al., 2002). However, the human hematologic diseases associated with these different kinases have some distinct features from classical Ph + CML, and careful histopathological and molecular analysis of the disease process in mice can yield valuable insights into differences in patho- Figure 2 Recipients of BCR -ABL-transduced marrow produce IL-3 in reaction to GFP. Plasma IL-3 levels in transplanted mice were measured with an ELISA assay that detects nanogram quantities of this cytokine in biological fluids . Leukemic recipients of BCR -ABL-transduced marrow (BCR -ABL (combined)) exhibit increased levels of circulating IL-3 relative to mice transplanted with untransduced marrow (control BMT) or with marrow transduced with empty vector (MSCVneo). Recipients of marrow transduced with retrovirus co-expressing high levels of A. Victoria green fluorescent protein (MSCV-IRES/ GFP) exhibit significantly greater increases in circulating IL-3 than with BCR. Use of donors (BCR -ABL IL3-to WT) or recipients (BCR -ABL WT to IL3-) with homozygous inactivation of the Il3 gene demonstrate that the source of IL-3 is the recipient, not the BCR -ABL-expressing donor cells physiology. The leukemogenic activity of the Tel -Abl fusion tyrosine kinase provides a good illustration of this.
Fusion of the ABL gene to TEL (also called ETV6) on chromosome 12p13 has been reported in six patients with leukemia, three of whom had acute leukemia of B-lymphoid (Papadopoulos et al., 1995), T-lymphoid (van Limbergen et al., 2001) and undifferentiated myeloid origin, the other three with atypical (Brunel et al., 1996) or typical (Andreasson et al., 1997;van Limbergen et al., 2001) CML. TEL encodes a ubiquitously expressed 452 amino acid protein with homology to the Ets family of transcription factors (Golub et al., 1994). Two different TEL -ABL fusions have been observed; in the patients with B-ALL and atypical CML, the first four exons of TEL were fused to ABL exon 2, while the other four patients had TEL exons 1 -5 fused to ABL exon 2. The resulting chimeric Tel -Abl proteins contain Tel amino acids 1 -154 or 1 -336, respectively, fused to the same 1104 COOH-terminal amino acids of c-Abl that is found in the Bcr -Abl fusion proteins. Both Tel -Abl fusion proteins share an NH 2 -terminal region of Tel (the PNT homology domain) that mediates homooligomerization Jousset et al., 1997). The fact that Bcr contains a coiled-coil oligomerization domain that is also required for activation of Bcr -Abl kinase activity and transformation has led to the suggestion that oligomerization of Abl is the critical event in the pathogenesis of these leukemias, and that other functions of the NH 2 -terminal Abl fusion partner are unimportant. Consistent with this, Tel -Abl has been shown to transform Rat-1 fibroblasts , primary bone marrow B-lymphoid cells , and cytokine-dependent Ba/F3 hematopoietic cells Hannemann et al., 1998) in vitro in a manner indistinguishable from Bcr -Abl. Furthermore, Tel -Abl and Bcr -Abl activate similar intracellular signaling pathways in cultured hematopoietic cells (Okuda et al., 1996;Voss et al., 2000).
However, when the ability of the larger Tel -Abl fusion protein to induce myeloid leukemia in mice was tested in the retroviral bone marrow transduction/ transplantation model, several distinct differences with Bcr -Abl-induced myeloproliferative disease were noted (Million et al., 2002). Some recipients of TEL -ABL-transduced bone marrow succumbed to CML-like leukemia that was very similar to that induced by BCR -ABL but with a significant increase in disease latency. However, most TEL -ABL recipients died abruptly around 4 -5 weeks posttransplantation with moderate leukocytosis and splenomegaly (Figure 3a), but without evidence of the pulmonary myeloid infiltrates and hemorrhage that are the cause of morbidity and death in mice with classic CML-like disease. Histopathological evaluation of these mice revealed acute fatty liver and extensive neutrophilic infiltration and necrosis of the small bowel villi (Figure 3b,c). The hepatic picture is suggestive of endotoxin-induced injury, and indeed analysis of serum cytokine and chemistry profiles from premorbid mice revealed significant elevations in circulating endotoxin and TNFa with evidence of fulminant hepatic and renal failure (Figure 4). This distinctive fatal illness has been named small bowel syndrome (SBS). The precise pathophysiological mechanism of SBS and its distinct association with Tel -Abl are under investigation, but it is possible that abnormal homing of Tel -Abl expressing neutrophils to the gut and/or direct induction of TNFa expression by Tel -Abl are responsible ( Figure 5). The relevance of Tel -Abl-induced SBS to human disease is suggested by the presence of ulcerative bowel disease in a patient with Tel -Abl-associated CML (van Limbergen et al., 2001).
A second distinct difference between BCR -ABL and TEL -ABL-induced leukemias is observed upon adoptive transfer of leukemic cells from primary diseased mice into secondary recipients. Similar to BCR -ABL, TEL -ABL-induced CML-like disease arises from multilineage progenitors capable of generating day 12 spleen colonies in secondary transplants, but neither TEL -ABL-induced CML-like disease nor SBS could be transferred to lethally irradiated recipient mice despite transplantation of large numbers of viable bone marrow and/or spleen cells (Million et al., 2002). In contrast, BCR -ABL-induced CML-like disease is successfully transferred to secondary recipients over 80% of the time under identical conditions. Most TEL -ABL secondary recipients succumbed to early or delayed graft failure, with others developing Tlymphoid or histiocytic tumors after long latent periods ( Figure 6). Many of the former secondary mice had evidence of provirally marked cells in the spleen 2 -3 weeks post-transplantation but these clones failed to radioprotect the recipients. These results suggest that TEL -ABL may act to expand a hematopoietic progenitor that lacks self-renewal as measured by secondary transplantation, or that TEL -ABL directly inhibits self-renewal of stem cells. Distinguishing between these possibilities will require further experiments and may have important implications for the treatment of these diseases by autologous stem cell transplantation.
Conclusions
The BCR -ABL + leukemias are perhaps the most thoroughly understood of human malignancies, in part because of the development of accurate animal models for these diseases. In this review, I have tried to illustrate that the pathophysiology of these diseases must be understood in the most minute detail if the experimental results are to provide information useful for understanding human leukemia. A common mistake in analysis of murine leukemia experiments is to use survival as the primary endpoint of the study but fail to define precisely the cause of morbidity or death. As demonstrated by TEL -ABL, similar survival curves can result from very different pathological processes. Conversely, the case of BCR -ABL SH2 mutants shows that minor changes in the physiology of what is essentially the same leukemic process can result in disparate survival outcomes. Careful and creative application of these model systems should continue to provide important new knowledge about the pathophysiology of BCR -ABL + leukemia that cannot be obtained from analysis of cell lines or primary leukemic cells. (c) Photomicrograph of H&E-stained liver from mouse with SBS, demonstrating lack of cellular infiltrate but with extensive microvesicular change and hepatocellular apoptosis. Similar findings are present in all mice with SBS but to a varying degree. Insert shows oil red staining of this liver, demonstrating that the vaculolar change is due to the accumulation of neutral lipids such as triglycerides. Magnification 4006 Figure 4 Increased circulating endotoxin and TNFa and hepatic and renal failure in mice with TEL -ABL-induced small bowel syndrome (SBS). Plasma endotoxin (far left) and TNFa (second from left) levels in mice with BCR -ABL-and TEL -ABL-induced CML-like disease were determined by commercial ELISA assays and compared with TEL -ABL-induced SBS and control recipients of untransduced marrow (normal Balb/c). Similarly, plasma glucose (third from left) and blood urea nitrogen (BUN, far right) levels were measured in these mice. Mice with SBS exhibit variable but significant increases in circulating endotoxin and TNFa, with marked hypoglycemia and increased BUN characteristic of severe liver and kidney dysfunction Acknowledgements I thank the members of my laboratory, past and present, for their essential contributions to many of the papers discussed herein, and apologize to colleagues whose work was not cited due to space limitations. Special thanks to Drs George Daley, Warren Pear, and Gary Gilliland for many helpful discussions. This work was supported by NIH grant CA90576 and grants from the Leukemia and Lymphoma Society. RA Van Etten is a Scholar of the Leukemia and Lymphoma Society and the Carl and Margaret Walter Scholar in Blood Research at Harvard Medical School. Figure 5 Possible pathophysiological mechanism of TEL -ABL-induced small bowel syndrome. Transplantation of TEL -ABLtransduced bone marrow into irradiated recipient mice (BMT) is followed by homing of Tel -Abl-expressing neutrophils to the small bowel with infiltration and necrosis. Because mice with TEL -ABL-induced CML-like disease do not exhibit significant infiltration of the small bowel despite large numbers of circulating Tel -Abl + neutrophils, it is possible that transient alterations in expression of leukocyte homing receptors in the gut from the radiation employed in the conditioning regimen contribute to the disease. Destruction of the bowel mucosal barrier leads to endotoxemia and stimulates TNFa production by monocytes, which is directly responsible for hepatic fatty change and apoptosis, leading to shock and renal acute tubular necrosis (ATN). It is also possible that direct induction of TNFa expression by Tel -Abl-expressing hematopoietic cells contributes to the pathogenesis of SBS Figure 6 Neither TEL -ABL-induced CML-like disease nor SBS can be transferred to secondary recipients. Pie diagrams representing outcomes of transplantation of bone marrow and spleen cells from primary mice with BCR -ABL-induced CML-like disease (left), TEL -ABL-induced CML-like disease (middle panel), or TEL -ABL-induced SBS (right panel) into lethally irradiated secondary recipient mice. The phenotype of disease that developed in secondary recipients is indicated by the color code at the bottom\===
Domain: Biology Chemistry. The above document has 4 sentences that end with 'et al., 1991)',
3 sentences that end with 'et al., 1990)',
2 sentences that end with 'al., 1998;Zhang and Ren, 1998)',
4 sentences that end with '(Li et al., 1999)',
2 sentences that end with 'et al., 1998)',
2 sentences that end with '(Kelly et al., 2002)',
2 sentences that end with '(Million et al., 2002)',
2 sentences that end with 'small bowel syndrome (SBS)',
3 paragraphs that end with 'et al., 2001)'. It has approximately 3931 words, 118 sentences, and 22 paragraph(s).
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Ceramide-1-phosphate transfer protein enhances lipid transport by disrupting hydrophobic lipid–membrane contacts
Cellular distributions of the sphingolipid ceramide-1-phosphate (C1P) impact essential biological processes. C1P levels are spatiotemporally regulated by ceramide-1-phosphate transfer protein (CPTP), which efficiently shuttles C1P between organelle membranes. Yet, how CPTP rapidly extracts and inserts C1P into a membrane remains unknown. Here, we devise a multiscale simulation approach to elucidate biophysical details of CPTP-mediated C1P transport. We find that CPTP binds a membrane poised to extract and insert C1P and that membrane binding promotes conformational changes in CPTP that facilitate C1P uptake and release. By significantly disrupting a lipid’s local hydrophobic environment in the membrane, CPTP lowers the activation free energy barrier for passive C1P desorption and enhances C1P extraction from the membrane. Upon uptake of C1P, further conformational changes may aid membrane unbinding in a manner reminiscent of the electrostatic switching mechanism used by other lipid transfer proteins. Insertion of C1P into an acceptor membrane, eased by a decrease in membrane order by CPTP, restarts the transfer cycle. Most notably, we provide molecular evidence for CPTP’s ability to catalyze C1P extraction by breaking hydrophobic C1P–membrane contacts with compensatory hydrophobic lipid–protein contacts. Our work, thus, provides biophysical insights into how CPTP efficiently traffics C1P between membranes to maintain sphingolipid homeostasis and, additionally, presents a simulation method aptly suited for uncovering the catalytic mechanisms of other lipid transfer proteins.
di erences described by PCs 1-4 and the distributions of PC values sampled in solution-phase and membranebound simulations of both apo and C1P-bound forms of CPTP: S1 PC1 describes a concerted rotation about helix -6, which approximates the surface of the membrane. Negative values of PC1 correspond to structures in which helix -2 is located closer and more parallel to the membrane surface, whereas positive values correspond to structures with helix -2 orientated more perpendicular to the membrane surface. We find that structures of the C1P-bound form of CPTP bound to the membrane have increased values of PC1 on average compared to either the C1P-bound form in solution or the apo form. Thus, membrane binding of the C1P-bound form promotes a concerted reorientation that aids the opening of gating helix -2 and that positions a widened entrance to CPTP's hydrophobic cavity at the membrane surface.
PC2 describes an internal reorganization of CPTP's helices that rotates the sides of its sandwich-like structure relative to each other (as if two stacked planar sheets were rotated relative to each other). Such changes captured by PC2 are evocative of a cleft-like gating mechanism (Fig 1 and S1 Fig) [16]. Of the first four PCs, variation along PC2 best captures di erences between the apo and C1P-bound forms of CPTP, regardless of if CPTP is in solution or bound to the membrane. Indeed, C1P uptake (or release) results in substantial rearrangement of the sides of CPTP's sandwich-like structure relative to each other (Fig 1 and S1 Fig). Membrane-bound structures of both the apo and C1P-bound forms of CPTP have increased values of PC2 on average compared to their respective solution-phase structures. Thus, membrane binding promotes a consistent change in the cleft to CPTP's hydrophobic cavity.
PC3 describes a concerted rotation orthogonal to that of PC1; if motion along PC1 were described as 'rocking side-to-side', then motion along PC3 would be described as 'rocking forward-and-backward'. Conformational ensembles of the apo and C1P-bound forms sampled in both solution-phase and membranebound simulations exhibit similar variation along PC3.
PC4 describes an internal reorganization of CPTP's helices di erent from that of PC2. While solutionphase structures of CPTP have similar average values of PC4, the membrane-bound structures of the apo and C1P-bound forms have average values di erent from each other and from the solution-phase structures.
Overall, PCA indicates that structures of the apo and C1P-bound forms di er both in solution and bound to the membrane, and that membrane binding can promote opening of gating helix -2 and conformational changes suggestive of a cleft-like gating mechanism. S2 Note B: Definition of Q. The fraction of contacts C1P makes with CPTP when fully inside its hydrophobic cavity, Q, was used as a second order parameter (or collective variable) for biased simulations. Q was chosen to reliably identify configurations with C1P inside CPTP versus configurations with C1P outside CPTP and to enhance the sampling of CPTP-C1P interactions. Since r LxS only describes C1Pmembrane interactions, it accomplishes neither of these things, while Q does. Specifically, CPTP-C1P contact pairs used to calculate Q were selected to capture: 1. Hydrophobic contacts between carbons of C1P and carbons of residues lining CPTP's hydrophobic cavity. Carbon-carbon (CC) pairs were selected based on their average distance, d C1P≠CPTP , in solution-phase simulations of the C1P-bound form of CPTP (Fig B panel A). To minimize the computational expensive of calculating Q during biased simulations, which increases with the number of CC pairs considered, while accurately identifying configurations with C1P inside CPTP's hydrophobic cavity, a cuto of d C1P≠CPTP AE 7.8Å was used to select these CC pairs. Residues with these CC pairs used to calculate Q are shown in Fig B panel C. 2. Polar contacts between C1P's headgroup and sphingoid backbone and residues at the entrance to CPTP's hydrophobic cavity. Heavy atom pairs were selected based on d C1P≠CPTP (Fig B panel B).
To minimize the computational expense of calculating Q during biased simulations while capturing all important polar contacts, a cuto of d C1P≠CPTP AE 5.5Å was used to select these pairs. Residues with these polar atom pairs used to calculate Q are shown in Fig Based on these criteria for selecting contact pairs, 1,176 atom pairs are used to calculate Q using Eq. 4 given in the Methods. We confirmed that Q reliably identifies configurations with C1P fully inside CPTP's hydrophobic cavity and orientated as in crystal structures from configurations with C1P outside, partially inside, or improperly orientated within CPTP's cavity. To do so, we monitored the value of Q during all-atom simulations in which C1P enters into CPTP's hydrophobic cavity from the solvent. While the relaxation process that occurs during these simulations is not cellularly relevant, it is su ciently rapid to observe in unbiased simulations. Thus, we are able to harvest multiple all-atom trajectories of C1P entry from solvent and use them to benchmark Q. Five simulations, each initialized with C1P randomly placed in the solvent around the apo form of CPTP, were performed using the same parameters as the all-atom solution-phase simulations described in the Methods. Each simulation was run for a maximum of 2 µs or until both tails of C1P were inserted into CPTP and no longer exposed to solvent based on visual inspection. Fig C shows the value of Q during each of these simulations and the final configuration of C1P bound to CPTP. In three of the simulations (shown in green, cyan, and blue in Fig C) created by helices -N and -4. These trajectories are not representative of C1P uptake from a membrane since insertion through helices -N and -4, which are fully exposed to solvent when CPTP is bound to a membrane (Fig 2), would require C1P to become fully solvated before entering CPTP's hydrophobic cavity. Thus, they serve as valuable tests of using Q to accurately identify configurations with C1P properly housed in CPTP's cavity. Q = 1 when C1P is properly housed inside CPTP's cavity, whereas Q never surpasses 0.5 in these trajectories. In the other two trajectories (shown in magenta and red in Fig C), C1P inserts through the entrance to CPTP's hydrophobic cavity as occurs when it's extracted from a membrane. In both trajectories, C1P's tails enter individually. In the trajectory shown in magenta in Fig C, the second tail fails to enter within 2 µs. In the trajectory shown in red in Fig C, both tails enter within 700 ns. In both trajectories, Q rapidly changes from Q ¥ 0.1 to Q ¥ 0.6 when the first tail enters, and, in the red trajectory, then changes rapidly again when the second tail enters. Thus, Q distinguishes di erent ways that C1P can bind to CPTP and can be used to enhance the sampling of interactions between C1P and CPTP. We note that these trajectories do not provide any evidence that Q is the reaction coordinate [32-34] (or necessarily a component of the reaction coordinate) for CPTP-mediated C1P transport.\===
Domain: Biology Chemistry. The above document has 2 sentences that start with 'We find that', 2 sentences that start with 'Thus, membrane binding', 2 sentences that start with 'Residues with these', 2 sentences that start with 'In both trajectories', 2 sentences that start with 'In the trajectory shown in', 2 sentences that end with 'CPTP-mediated C1P transport', 3 sentences that end with 'CPTP's hydrophobic cavity'. It has approximately 1386 words, 58 sentences, and 8 paragraph(s).
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Temporal mixture modelling of single-cell RNA-seq data resolves a CD4+ T cell fate bifurcation
Differentiation of naïve CD4+ T cells into functionally distinct T helper subsets is crucial for the orchestration of immune responses. Due to multiple levels of heterogeneity and multiple overlapping transcriptional programs in differentiating T cell populations, this process has remained a challenge for systematic dissection in vivo. By using single-cell RNA transcriptomics and computational modelling of temporal mixtures, we reconstructed the developmental trajectories of Th1 and Tfh cell populations during Plasmodium infection in mice at single-cell resolution. These cell fates emerged from a common, highly proliferative and metabolically active precursor. Moreover, by tracking clonality from T cell receptor sequences, we infer that ancestors derived from the same naïve CD4+ T cell can concurrently populate both Th1 and Tfh subsets. We further found that precursor T cells were coached towards a Th1 but not a Tfh fate by monocytes/macrophages. The integrated genomic and computational approach we describe is applicable for analysis of any cellular system characterized by differentiation towards multiple fates. One Sentence Summary Using single-cell RNA sequencing and a novel unsupervised computational approach, we resolve the developmental trajectories of two CD4+ T cell fates in vivo, and show that uncommitted T cells are externally influenced towards one fate by inflammatory monocytes.
Introduction
T helper (Th) cells, also known as CD4 + T cells, are key instructors of the immune system (1). They display extensive functional and phenotypic diversity in response to a spectrum of immune challenges, including viral, bacterial, fungal and parasitic infection, immunogenic cancers, and autoimmune and allergic stimuli. Th cell subsets are distinguished from each other most frequently by the cytokines they secrete. Th1 cells produce interferon-γ, leading to macrophage activation and enhanced killing of intracellular pathogens. Th2 cells produce IL-4, IL-5, and IL-13, prompting eosinophils to act against extracellular parasites and venom. Th17 cells produce IL-17 and IL-22, promoting neutrophilic responses against extracellular bacteria and fungi. Follicular T helper (Tfh) cells, a more recently defined Th subset, secrete IL-21, and drive somatic hypermutation of immunoglobulin genes in germinal centre B cells. This produces high affinity antibodies, upon which many licensed vaccines depend for efficacy. Since Th subsets can both control infections and drive immune-mediated diseases there remains tremendous interest in the molecular mechanisms that control their in vivo development.
In order for Th cells to develop, CD4 + T cells must first be raised from an immunologically naïve state by antigenic stimulation of their highly diverse T cell receptors (TCR), which is followed by processes of clonal proliferation and differentiation. Recent in vivo data suggested that the unique TCR sequence of a single naïve CD4 + T cell imparts a genetically programmed 3 preference towards a particular Th fate (2). However, co-stimulatory and cytokine signals can also profoundly influence both the magnitude of the response, and skewing towards particular Th fates. Several master transcription factors have been described in CD4 + T cells that drive and stabilize Th fates, which supports a view of Th development as a choice between clearly distinct states. However, the relationship between Th subsets, particularly between Tfh and other Th fates remains unclear in vivo.
In many cases, immune challenges, such as infection or vaccination, induce concurrent differentiation into two or more Th fates within the same individual. Indeed, by performing a limiting dilution single-cell adoptive transfer of naïve CD4 + T cells, it was suggested that daughter cells from a particular clone could bifurcate phenotypically to give rise to both Th1 and Tfh cells (2). However, it was not possible to visualize and pinpoint the bifurcation of Th1/Tfh cell fates in vivo.
Resolving Th cell fate decision-making in vivo using population-level approaches has been challenging, mainly due to extensive heterogeneity amongst differentiating cells. More specifically, CD4 + T cells at any given time point display a distribution of intermediate and transitional states, which blurs the dynamics of Th cell developmental progression (3). Tfh differentiation, in particular, has been difficult to elucidate since it involves multiple stages with potential overlap with transcriptional programs of other Th subsets. Of particular note, computational tools for modelling bifurcations in cellular decision-making have not been available.
Th cell fate decisions are driven by both intrinsic factors and external signalling cues from other cells. Conventional dendritic cells (cDCs) are important cellular sources of antigenic stimulation, co-stimulation and cytokines for Th differentiation in secondary lymphoid tissues. Intra-vital imaging in lymph nodes has demonstrated that cDCs make long-lasting stable contacts with naïve CD4 + T cells in order to initiate T cell priming (4). Once activated, CD4 + T cells continue to require antigenic stimulation via their TCR to optimize their proliferation and Th differentiation (5)(6)(7). Continued signalling has been reported to be important for Th1 responses, although the cell types providing this signal remain unknown (4). A recent report suggested that CXCR3 expression by activated CD4 + T cells facilitated continued interaction with adoptivelytransferred CXCL9 and CXCL10-expressing cDCs (8), however, interactions with endogenous myeloid cell populations, including cDC subsets and monocytes have not been studied in vivo. While Tfh cells are sustained, once generated, via multiple molecular interactions with B cells in developing germinal centres (9,10), possible roles for myeloid cells in providing early instruction towards a Tfh fate remain relatively unexplored. A recent study targeted antigens to two different cDC-subsets in vivo, and suggested that CD8α -cDCs displayed the greater propensity for generating Tfh responses (11). Whether Th1/Tfh fate bifurcation can result from differential interactions with cDC subsets or activated monocytes currently remains unknown.
Herein we have used single-cell RNA sequencing (scRNA-seq) to study the various transcriptional states of individual CD4 + T cells during blood-stage Plasmodium chabaudi infection in mice. This is an experimental model of malaria in which CD4 + T cells are essential 4 for controlling parasite numbers, and which is characterized by concurrent development of Th1 and Tfh cells (12). We have used Plasmodium-specific TCR transgenic CD4 + T (PbTII) cells to minimise the effects of TCR diversity on Th fate decisions.
Crucially, our approach builds on scRNA-seq profiling coupled with new computational strategies to reconstruct the differentiation trajectories of Th1 and Tfh cells at a single-cell resolution. Our data reveals, for the first time, the molecular detail of how a single antigenspecific CD4 + T cell clone can undergo parallel development into Th1 and Tfh states in vivo, and reveals the hierarchical regulation of genes involved in this cell fate decision. Finally, we investigated intercellular interactions using scRNA-seq, and predicted roles for inflammatory monocytes, after cDC-dependent T cell activation, in coaching uncommitted CD4 + T cells, specifically towards a Th1 fate.
scRNA-seq resolves Th1 and Tfh cell fates during Plasmodium infection in mice
To study concurrent progression towards Th1 and Tfh fates, and to characterize the heterogeneity associated with this process during an in vivo CD4 + T cell response, we performed scRNA-seq of PbTII cells during PcAS infection ( Figure 1A, Figure S1). We transferred naïve, proliferative dye-labeled PbTII cells into congenically marked wild-type mice, and recovered them at days 2, 3, 4, and 7 post-infection (p.i.) by fluorescence-activated cell sorting (FACS) of those expressing the early activation marker, CD69, or displaying dilution of the proliferative dye ( Figure S2). Flow cytometric measurements of the canonical Th1 markers, T-bet (coded by Tbx21) and Interferon-γ, and Tfh markers, CXCR5 and Bcl6, indicated that these subsets emerged in parallel by day 7 p.i. (13,14) ( Figure 1B-D). Notably, markers of Th2, Th17 or Treg subsets were not upregulated on the PbTII cells ( Figure S3).
We initially used Principal component analysis (PCA) to assess the overall heterogeneity of the PbTII cells ( Figure 1E, Figure S4A). In all time points, the first principal component was strongly associated with the number of detected transcripts, which is reflective of changes in cellular RNA content and, in general, is linked to proliferative status ( Figure S4B). As expected, the variability related to previously established Th1 and Tfh gene expression signatures became more prominent with the progression of time (15) (Figure S4C). Notably, at day 7 p.i., a PCA using these signature genes alone recapitulated the results of the genome-wide PCA (Spearman correlation -0.87) ( Figure S5). Amongst the cells from day 7 p.i., two distinct subpopulations were apparent, separated along PC2 ( Figure 1E). Notably, many of the genes associated with these subpopulations have been identified as associated with either Th1 or Tfh fates ( Figure 1F, Table S1). Results from a global PCA of the entire dataset were largely in accordance with the time point information, with the Th1/Tfh signature genes showing separation along multiple PCs ( Figure S6). Taken together, these results suggested a progressive commitment to Th1 and Tfh fates, and indicated that single-cell transcriptomes could be used for estimating both proliferative states and degrees of differentiation of individual cells. 5
Unbiased delineation of Th1 and Tfh trajectories using a Mixture of Gaussian Processes model
The results from the PCA suggest that gene expression variation in PbTII single-cell transcriptomes permit reconstruction of the transcriptional programs underlying Th1 and Tfh differentiation. To more explicitly model the temporal dynamics of the differentiation process, we developed and applied GPfates, a temporal mixture model that builds on the Gaussian Process Latent Variable Model (GPLVM) and Overlapping Mixtures of Gaussian Processes (OMGP) (16). This approach first reconstructs the differentiation trajectory from the observed data ("pseudotime", Figure 2A-B), thereby establishing an order for the cells. While our model uses the sample time as prior information, the inferred temporal orderings did not strictly adhere to these experimental time points ( Figure S7). For example, cells from day 4 p.i. were mixed with some of the cells from day 3 and day 7 at either end of the day 4 pseudotime distribution. This was consistent with the idea that bulk assessments of cells at specific time points fail to take into account the heterogeneity and differential kinetics of responses made by single cells. We also repeated this analysis without supplying the experimental sampling times to the model, finding overall consistent results (Comp. Supp. Figure 8).
In a second step, GPfates uses a time series mixture model, which we adapted from a model that was initially developed to deconvolve temporal data into independent separate trends, and which is related to previous time series models for bulk gene expression time series (16). Using this approach, we identified two simultaneous trends ( Figure 2C-D). These two alternative trajectories were in agreement with the Th1/Tfh signature genes identified by Hale et al. (15) ( Figure 3A-D), indicating that the fitted mixture components correspond to cells with Th1 and Tfh phenotypes. Notably, these trends could not be identified by other published methods for reconstructing single-cell trajectories (17,18) (Figure S8). Furthermore, the mixture modelling in GPfates could also successfully resolve bifurcation events in two other recently published scRNAseq datasets, which examined lung epithelial development in mice (Comp. Supp. Figure 11) (19) and primordial germ cell development in human embryos (Comp. Supp. Figure 12) (20). This suggests that pseudotime inference coupled with time series mixture modelling is applicable more generally for studying cellular differentiation in scRNAseq data.
Next, we sought to more clearly characterize the bifurcation time point. Using a change point model to annotate the inferred trajectories (see section 4.2 of the Computational Supplement), we could divide pseudotime into before and after bifurcation. We sought to characterize single cells that existed at the Th1/Tfh bifurcation point. Firstly, bifurcation initiated amongst cells from day 4 p.i. (see section 6.2 of computational supplement for a robustness analysis), specifically at a relatively early point in pseudotime compared with all day 4 p.i. cells ( Figure 4A). Bifurcating PbTII cells also expressed the largest number of genes compared to those at all other points in pseudotime.
High transcriptional activity correlated with upregulation of Mki67 and other known proliferation marker genes (21) (confirmed at the level of Ki-67, Figure 4B-C and S9A). It also correlated 6 with cell cycle activity, based on computational allocation of cells into cell cycle stages (22), and flow cytometric confirmation of DNA content and cell size ( Figure 4D-E). Bifurcating PbTII cells also had increased expression of genes associated with aerobic glycolysis (data not shown), an indication of increased metabolic requirements being met by glucose metabolism and increased mTORC1 activity. Consistent with this was the observed elevated levels of ribosomal protein S6 phosphorylation by day 4 p.i. (Figure 4F).
Taken together, our data indicate that bifurcating PbTII cells exhibit a highly proliferative and metabolically active state, coupled with the upregulation of thousands of genes. Importantly, progression from the Th1/Tfh bifurcation point to either fate was marked by widespread silencing of gene expression across the genome. Although this decrease in gene expression can be partially explained by a deceleration in cell cycle speed, it is also consistent with other cellular differentiation processes characterized at a single-cell resolution (19).
Detectable expression of endogenous T cell receptor loci reveals breadth of clonotype fates
Since previous reports have suggested a role for TCR sequences in determining Th cell fate (2), our TCR transgenic approach was designed to minimize this potential source of variability. Importantly however, PbTII cells were generated in mice with functional Rag1 and Rag2 genes, and therefore, retained natural expression of highly diverse endogenous TCR chains in addition to the transgenic TCR. Sequence analysis of TCR transcripts in single PbTII cells confirmed universal expression of the PbTII Vα2 and Vβ12 chains in all cells (Supplementary Tables 2 & 3). Moreover, it confirmed highly diverse, though lower levels of expression of endogenous TCRα chains in many cells ( Figure S10).
Given the vast combinatorial diversity of endogenous TCR sequences, we employed these as unique molecular barcodes to scan for PbTII cells that could be inferred with high confidence to have derived from a single common PbTII progenitor clone. Notably, we identified six clones comprising two or more sibling cells, while all other PbTII cells were individually unique. Of these six clones, two consisted of sibling cells that mapped close to the bifurcation point. For the remaining four clones, siblings exhibited highly diverging patterns of gene expression, with three sibling groups falling at the extremities of the Th1-Tfh phenotype spectrum ( Figure 5A). These results demonstrate that during an in vivo infection, the progeny of a single CD4 + T cell clone can differentiate into both Th1 and Tfh cells.
Transcriptional signatures associated with bifurcation of Th1 and Tfh fates
Next, we sought to identify genes whose expression followed the pattern of branching. We derived bifurcation statistic to estimate the concordance with the bifurcation for individual genes (see section 4.2 of the Computational Supplement text for details, Figure 5B). Among the highest-ranking bifurcating genes, the most common pattern was an increase in expression during progression to the Th1 fate. These genes were positively correlated with both pseudotime 7 and the Th1 trend assignment ( Figure 5B). This suggests that Tfh cells are in fact developmentally closer to the highly proliferative progenitor state than Th1 cells as the Th1 fate involves up-regulation of numerous genes not expressed in either the progenitor or Tfh states.
The highest-ranking transcription factors were Tcf7 for the Tfh fate, and Mxd1, Bhlhe40, Hopx, Pgs1 and Id2 for the Th1 fate ( Figure 5C). In addition, the hallmark Tfh transcription factor Bcl6 was strongly associated with the Tfh fate. Tcf7 is required for T cell development, and has been recently shown to be instrumental for Tfh differentiation (23,24). Notably, it represented one of the rare genes defined by a decrease in expression when moving towards the Th1 fate. Of the Th1-associated transcription factors, Mxd1 is a negative regulator of the proliferation-associated, proto-oncogene, Myc (25) and Bhlhe40 has been recently identified as a cofactor of T-bet (coded by Tbx21) (26). Id2 is known as an antagonist of Tcf7 (27) and as a regulator of effector CD8 + T cell responses. Notably, while this manuscript was under revision, the role of Id2 as a key driver of Th1 responses was independently shown by another study (28).
Our results strongly support reciprocal regulation of Id2 and Tcf7 as a key feature of the Th1/Tfh bifurcation process. Expression of Id2 and Ifng were highly correlated in the later stages of Th1 differentiation, and negatively correlated with Tcf7, both at a transcriptional and protein level ( Figure 5D-F, Figure S11). Notably, the hallmark Th1 transcription factor Tbx21 was induced before the bifurcation point, and showed only modest separation after bifurcation ( Figure S12).
To validate the robustness of these gene signatures and the timing of the bifurcation, we repeated the infection, and at days 0, 4 and 7 sequenced additional single PbTII-cells using the Smart-seq2 protocol (29) ( Figure 1A & S13A). Consistent with the original data, the cells from day 7 (but not day 4) segregated into two subpopulations correlating with Th1 and Tfh gene signatures ( Figure S13A). Subset-characteristic co-expression patterns of the bifurcating genes identified by GPfates emerged by day 7 ( Figure S13B). Notably, at this time, the cells from the different mice could be equally separated into distinct Th1-and Tfh-subpopulations using the top bifurcating genes ( Figure S13C). Taken together, this indicated that the gene expression patterns associated with the cell fate bifurcation were reproducible across experiments and sequencing platforms.
In Th1 cells, a large fraction of the bifurcating genes were cytokine and chemokine receptors, including the top-ranked gene, Cxcr6, confirmed at protein level (Fig S14A and S14B), other established Th1 markers, Ifngr1 and Il18rap (30, 31), and the chemokine receptors Ccr2 and Ccr5 ( Figure 5C). These data were consistent with the idea that Th1 cells can migrate to peripheral tissues and remain receptive to external signals. In contrast, the only bifurcating chemokine receptor associated with a Tfh fate was Cxcr5, a gene established to mediate migration of Tfh cells into B cell follicles (32, 33).
Cxcr5 was among an early wave of chemokine receptor genes, including Cxcr3 and Ccr4 ( Figure 5G) whose expression and translation into protein ( Figure S14C) was initiated before the Th1/Tfh bifurcation point had been reached. We hypothesized that differences in the timing of expression of receptors reflected their roles in controlling differentiation or effector function. We reasoned, for instance, that while Cxcr6, Ccr2 and Ccr5 served to mediate trafficking and 8 effector function of Th1 cells, others such as Cxcr3 and Cxcr5 controlled Th cell fate via interactions with other immune cells ( Figure 5H). Indeed, Cxcr5 allows T cell trafficking towards B cells (34, 35), while Cxcr3 has been associated with cDC-driven Th1 fates (8).
Myeloid cells support a Th1 but not Tfh fate
After activation and proliferation, PbTII cells reached an uncommitted state around the bifurcation and expressed chemokine receptors that indicated receptiveness to other chemokineexpressing cells. Given that B cells were essential, as expected, for supporting a Tfh fate in PbTII cells (Fig S15), we hypothesized that myeloid cells provided alternative, competing signals to promote a Th1 fate.
To study this, we performed scRNA-seq on splenic cDCs and inflammatory monocytes when activated PbTII cells were yet to bifurcate. We sorted CD8α + and CD11b + cDCs and Ly6C hi monocytes from naïve and infected mice ( Figure S16) and subjected these to single-cell analysis. PCA of cDCs firstly distinguished between the two naïve cell types, separating them along PC2 ( Figure 6A & S17) with an efficiency consistent with recent data (36), and further highlighting a number of expected and previously unknown cDC subset-specific genes (Figure S18A-C). We next compared naïve cDCs with those from infection ( Figure 6A & S16), and separated these along PC6 ( Figure 6A). Analysis of differential gene expression between cDCs from naive and infected mice identified 30 genes, 29 upregulated ( Figure 6B & S19), including interferonassociated transcription factors, Stat1 and Irf1, and CXCR3-attractant chemokine genes, Cxcl9 and Cxcl10. Notably, gene expression patterns amongst individual cDCs varied according to the gene. For example, Stat1 and Irf1 were heterogeneously expressed amongst individual naïve cDCs, and further upregulated during infection ( Figure 6C). This was similar for Cxcl9, which was expressed by CD8α + cDCs in naive mice, while Cxcl10 was induced only upon infection ( Figure 6C). These data revealed interferon-associated gene expression amongst individual cDCs, and also suggested interactions between cDCs and uncommitted CXCR3 + PbTII cells, consistent with a recent study (8). Next, PCA of Ly6C hi monocytes from naïve and infected mice distinguished them from each other along PC2 ( Figure 6D & S20). Differential gene expression analysis between naïve and infected groups uncovered ~100 genes, both up-and down-regulated during infection ( Figure 6E & S21). This illustrated a fundamental difference in the directionality of transcriptional changes in individual monocytes compared to cDCs during Plasmodium infection, with only monocytes exhibiting down-regulation of gene expression ( Figure 6B-C & E-F). Interestingly, a high proportion (~40%) of genes upregulated in cDCs were also induced in Ly6C hi monocytes, including transcription factors Stat1 and Irf1, and the chemokine Cxcl10 ( Figure 6E & F), suggesting possible overlapping biological functions between these cell types. In addition, monocyte-specific chemokines were also observed, including Cxcl2, Ccl2 and Ccl3 ( Figure 6E & F). Furthermore, specific examination of all immune cellular interaction genes ( Figure S22) revealed emerging variable expression of Tnf, Cd40, Pdl1, Ccl4, Ccl5, Cxcl16, Cxcl9, and Cxcl11 in monocytes, thus suggesting complex interactions and multiple roles for Ly6C hi monocytes during infection . 9 Given that Cxcl9-11, Ccl2, Ccl3 and Ccl5 signal through either Cxcr3 or Ccr4, which were expressed by activated but uncommitted PbTII cells, we next hypothesized that Ly6C hi monocytes, in addition to cDCs, might interact with PbTII cells, thereby influencing Th1/Tfh fate (8). To begin testing this, we first confirmed chemokine expression at protein level by Ly6C hi monocytes, focussing on CXCL9 ( Figure 6G). Kinetics of CXCL9 production was similar in cDCs and Ly6C hi monocytes, consistent with a possible role in interacting with CXCR3 + PbTII cells. To test whether monocytes could influence Th1/Tfh bifurcation in vivo, we employed LysMCre x iDTR mice, in which Ly6C hi monocytes could be depleted after PbTII cell activation, but before bifurcation ( Figure 6H, Figure S22A). We also noted a modest reduction in CD68 + macrophages using this approach, with no evidence for depletion of cDCs or marginal zone macrophages ( Figure S23). In this transgenic approach, Th1 fates, but not Tfh fates, were supported by monocytes/macrophages ( Figure 6H). Together, these data supported a model in which progression of activated, uncommitted PbTII cells towards a Tfh fate was dependent upon B cells ( Figure S14), and a Th1 fate was promoted by chemokine-expressing myeloid cells, including Ly6C hi inflammatory monocytes.
Discussion
By capturing single CD4 + T cell transcriptomes over time, and using a novel analysis approach to reconstruct the continuous course of events, we have resolved the bifurcation of naive CD4 + T cells into Th1 and Tfh cells at an unprecedented level of molecular detail, and illustrated that external cellular signals influence Th fate around the point of bifurcation. Importantly, the GPfates modelling of scRNA-seq data is not limited to immune cells or single bifurcation events. The mixture of time series model we used can also be combined with existing computational workflows (17, 37) (see section 5.2 of the Computational Supplement). Therefore, it provides the means for high-resolution analysis of differentiation in any cellular system, mainly towards two fates, as shown by our examination of existing embryonic development and lung tissue regeneration data (Comp. Supp. Figure 11), and, in principle, also for differentiation into multiple cell types (Comp. Supp. Figure 12), for example, during haematopoiesis. The filtered expression data and gaussian process models presented in this study can be found on our interactive web application at data.teichlab.org, where users can visualise their own genes of interest.
Our data reveals the developmental relationship between Th1 and Tfh cells on a genomic scale, and shows that the same naïve precursor can give rise to both fates simultaneously. It provides insights for the early stages of differentiation, and describes the order of transcriptional events before and after the bifurcation of Th1 and Tfh fates. To date, this process has remained incompletely characterised. Here, we use pseudo-temporal ordering of cells to reveal the hierarchy of transcriptional regulation of these events at an unprecedented resolution. Our data highlight the importance of stochastic expression of transcription factors as well as chemokine receptors, suggesting a role for noisy gene expression in Th development.
Transcriptomic profiling previously suggested developmental similarities between Tfh and Th1 cells (38), with in vitro studies suggesting relatively late bifurcation of Tfh and Th1 cells (39). However, highly immunogenic viral or bacterial infections induced CD4 + T cells to segregate into Bcl6 + (Tfh) or Blimp-1 + (Th1) subpopulations within two days, and by three days, fatecommitted Tfh cells had developed (40-42). In our parasitic model, single CD4 + T cell transcriptomes remained remarkably similar until four days of infection. Although it is difficult to directly compare viral or bacterial systems with our parasitic model, we speculate that due to infection-related differences in antigen-presenting cell function, antigen load and availability, Plasmodium infection in mice does not drive Th bifurcation as early as observed with highly immunogenic viruses or bacteria. Evidence of sub-optimal MHCII antigen-presenting cell function early during Plasmodium infection (43, 44) raises the hypothesis that Th bifurcation is sensitive to immune-suppression. Our data indicate that uncommitted, activated CD4 + T cells are heterogeneous, but nevertheless closely related at a transcriptional level, suggesting considerable flexibility throughout the proliferative phase of their response. Such plasticity during Th differentiation has been proposed to be beneficial as a means of countering evolution of immuneevasion strategies by pathogens (3).
As CD4 + T cells progress from immunological naivety towards a Th fate, they may experience different cellular microenvironments, even within the confines of secondary lymphoid tissue. The observation that bifurcation towards Th1 and Tfh cells was preceded by upregulation of chemokine receptors prompted us to investigate possible interactions with chemokine-expressing myeloid cells. Previous studies have highlighted the potential for cDCs in lymph nodes to produce Th1-associated chemokines (8). Our study, which focused on the spleen, was consistent with this concept, and, furthermore, implicated inflammatory monocytes in Th1 support. However, since our transgenic approach for depleting monocytes also removed a portion of splenic red pulp macrophages, we cannot discount the possibility that red pulp macrophages may partly contribute to a Th1 fate. Nevertheless, our data support a model in which myeloid cells in the spleen influence bifurcation, and support a Th1 fate during Plasmodium infection. Moreover, our studies emphasise that although cDCs are the predominant professional antigen-presenting cell for initiating CD4 + T cell activation in the spleen, other myeloid cells also exhibit a capacity to influence towards a Th1 fate. In contrast, Th bifurcation towards a Tfh fate was not supported by monocytes/macrophages. Instead, given that CXCR5 was the only chemokine receptor significantly associated with bifurcation towards a Tfh fate, cellular interaction with B cell follicles may be the primary mechanism for supporting activated CD4 + T cells towards a Tfh fate. Our model suggests that activated, uncommitted CD4 + T cells become receptive to competing chemoattractant signals from multiple cell types in different zones of the spleen. This model focuses on intercellular communication as the main driver of bifurcation. However, upstream of these processes, internal stochasticity in uncommitted CD4 + T cells may control the balance of chemokine receptor expression (45), thus mediating differential trafficking and variation in intercellular interactions. Future experiments combining our integrated single-cell genomics and computational approach with in vivo positional and trafficking data may reveal molecular relationships between internal stochasticity, migratory behaviour, Th fate and perhaps immunological memory. (D) Flow cytometry data indicate concurrent differentiation of Th1 (IFNγ + ) and Tfh (CXCR5 + ) PbTII CD4 + T cells within the spleen of PcAS-infected mice (n=4). Index expression is the product of MFI and proportion IFNγ + or CXCR5 + . These data are representative of two independent experiments. MFI, mean fluorescence intensity.
(E) PCA of single PbTII cells at 7 days post-infection with PcAS. The arrows represent the Pearson correlation with PC1 and PC2. Cell size refers to the number of detected genes. The size of the data points also represents cell size. "Th1 signature" and "Tfh signature" refer to cumulative expression of genes associated with Th1 or Tfh phenotypes (15). PC, Principal Component.
(F) Expression of top 50 genes with largest PC2 loadings of day 7 cells (D). The genes were annotated as Th1-or Tfh-associated based on public datasets (15,38,46,47). *Cdk2ap2 appears twice because two alternative genomic annotations exist. PC, Principal Component (A) Overview of analysis abilities from the framework of Gaussian Processes. Data is modelled and interpreted on the cellular level using the global genomic level data. Through downstream analysis from these models, it is possible to investigate individual genes to explain the drivers of the different models.
(B) Sketch of the analysis workflow. A low-dimensional model of the non-linear highdimensional data is inferred by Bayesian GPLVM. The low-dimensional representation is then modelled as an Overlapping Mixture of Gaussian Processes. This gives us a data-trend assignment per cell which can be used for interpretation. Since the models are all predictive, the low-dimensional model can be interpreted in the original high-dimensional space.
(C)
The low-dimensional representation of our data. The blue line depicts the progression of pseudotime. The text labels illustrate features of typical cells on that region of the pseudotime, and are provided purely as a visual aid. (A) Th1 and Tfh states were defined as cells with assignment probability of ≥0.8 for the respective trend. For each single cell, cumulative expression of Th1 and Tfh signature genes (15) was calculated as in Figure 1E.
(B) The effect of the probability threshold on the cumulative expression of Th1 and Tfh signature genes. The p-values were calculated using Wilcoxon rank sum test. Tables 2 and 3).
(B) Identification of genes associated with the differentiation of Th1 or Tfh cells. For every gene, the correlation of its expression with pseudotime (x-axis) and Tfh trend assignment (y-axis) are shown. Statistical significance was determined using the bifurcating score (methods). Genes satisfying the significance threshold of FDR<0.002 are represented in colours according to the functional classification of the genes (methods and Supplementary Table 4). FDR, False Discovery Rate, estimated by performing the same analysis with permuted data. (C) The genes with strongest association with Th1 (left) or Tfh differentiation (right). The genes were filtered using the bifurcation score as in (B). The genes were then ranked in descending order of association with either Th1 or Tfh trend. Cdk2ap2 appears twice because two alternative genomic annotations exist.
Introduction
GPfates is based on a three-stage approach that first i) infers a low-dimensional representation of single-cell RNA-seq data, then ii) infers pseudotime to iii) model the temporal dynamics of gene expression profiles with a mixture model. These steps build on existing model- Figure 2D of the main text (as well as Supp. Comp. Fig 1). In Sections 2 and 3 we describe the statistical models that underlie the components of GPfates. In Section 4 we describe downstream analysis methods for interpreting the fitted model. Finally, in Section 5, we present additional validation experiments using simulations, robustness analyses and by analyzing multiple existing data sets.
Gaussian Process Regression
A main component of GPfates is to model temporal transitions. We use the Gaussian process (GP) framework, thereby casting this problem as non-parametric regression. Let us begin by assuming that the developmental time t for each cell we observe is known. Then, the output y g (i.e. expression of gene g) is modelled as a continuous function of the input t (i.e. developmental progression) where is Gaussian distributed residual noise and f (t) denotes the unknown regression function. In this work y g is considered to be an N -dimensional vector of N cells with observed expression of the gene g. We denote the expression of g in an individual cell n as [y g ] n .
A GP can be interpreted as a function-valued prior on the elements of f , which is defined by a covariance function that in turn is parametrized by the input (developmental time) t: cov(f (t n1 ), f(t n2 )) = k(t n , t n2 ). Sup. Comp. Fig. 1: Illustration of the analysis workflow. A low dimensional parametrization of the data is found using Bayesian GPLVM. The low-dimensional representation is viewed as a mixture problem, and solved by an Overlapping Mixture of Gaussian Processes. This allows us to represent our cells as members of different smooth processes. But also interpret in terms of the high-dimensional space parametrized by the GPLVM.
The covariance function k(t n1 , t n2 ) encodes prior assumptions on the smoothness and lengthscales of the function f (t). The most widely used covariance function is the Squared Exponential (SE) covariance function, and this is the covariance function we will generally be used in this work. This covariance has the hyperparameters θ = (σ 2 SE , l 2 SE ), which parametrize the amplitude ( σ 2 SE ) and the lengthscale (l 2 SE ) of functions under the prior. Throughout the remainder of the text we will omit the hyperparameters from equations for the sake of brevity. Note that there is a whole compendium of valid covariance functions, which can also be combined using sum or multiplication; see [cite: Rasmussen, GP 2006] for an overview.
We write that a function f is Gaussian Process distributed by (t n1 , t n2 )).
This prior on the function f can be linked to the finite observed data using a Gaussian likelihood: Together with the prior on the corresponding (finite) elements of f , this results in the marginal likelihood p(y g |t) = N (y g |0, K t + σ 2 · I).
Here K t is an N × N matrix of pairwise evaluations of the covariance functions at the observed times t. I.e.
By considering the joint distribution of the observed data y g and an unseen function value f (t ⋆ ), it is possible to derive the predictive distribution for f (t ⋆ ): For a full review on Gaussian Processes, see Williams and Rasmussen [2006].
We use Y to compactly denote the N × G expression matrix of cells × genes, where The assumption that all genes are governed by similar functional relationships with t means we place the same GP prior (with shared covariance function): In the next section we will see the usefulness of considering multiple genes at once.
Pseudotime inference by Bayesian GPLVM with per -cell prior
The Gaussian Process regression framework described above assumes we know the time t of each cell. While in many single-cell RNA-seq experiments record a collection times over some time-course, these are rather sparse, and it has been pointed out [Trapnell et al., 2014] that cells are sampled from a population where responses are unsynchronized. Each cell has reached a certain stage in the differentiation process under investigation, which we do not observe directly. The progress in to this process is referred to as pseudotime. We can however infer this from the data. In the Gaussian Process Latent Variable Model (GPLVM) [Lawrence, 2006], we use the multiple output case of Gaussian Process regression (equation 4), but consider the values of t to be parameters which we wish to infer. The joint probability of the GPLVM is where p(Y |t) is defined in equation 5, and the prior p(t) is such that for cell n, p(t n ) = N (0, 1).
Following Reid and Wernisch [2016], we can also consider the prior p(t) to be informed about the experimental ordering of collection times of the cells, putting the mean of t n to correspond to the time point of cell n. If we use our Malaria time course as and example, we can put the prior on t so that where day n ∈ {1, 2, 3, 4, 5} correspond to the collection order of those cells. The parameter σ 2 prior alters the strength of the prior.
The objective of Bayesian GPLVM [Titsias and Lawrence, 2010], is to find the posterior probability distribution p(t|Y ) ∝ p(Y |t)p(t). This is intractable though, due to the t values appearing non-linearly in the matrix inverse [K t + σ 2 · I] −1 .
In Titsias and Lawrence [2010], a lower bound to the marginal likelihood is calculated by estimating the posterior p(t|Y ) by a variational distribution q(t). The distribution N (t n |µ n , S n ) is described in that paper, and Bayesian training of the model to maximize this lower bound. This is the method we use.
Because the scale of t is ambiguous, in particular if no priors are specified, we prefer to at times scale the inferred t to the range [0, 1] when reporting the pseudotime, to avoid confusion about negative "time". In these cases we refer to pseudotime as scaled pseudotime in the legends.
Dimensionality reduction
In many cases it is useful to work on a reduced representation of cellular expression profiles. For example, when modelling transcriptomic data, fitting a model to a low-dimensional representation can be preferable to fitting it to expression profiles of thousands of genes. Formally, the objective of dimensionality reductions is to find some M -dimensional representation of the Gdimensional expression measurements, where M << G. Typically M is 2 or 3, which aides visual interpretation. Analogous to the pseudotime inference, these latent cell states can also be inferred using the GPLVM. Say X is an M × N matrix so that each cell n correspond to an M -dimensional vector, We want to model the expression matrix Y so that Note that now the covariance function is evaluated as k(X n1 , X n2 ), where, in the Squared Exponential covariance function in equation 2, the operator |·| is evaluated as the Euclidean norm for vectors, rather than absolute value. Just as the t n values are inferred from data above, so can the X n vectors be inferred from the data.
Bifurcation inference using overlapping mixtures of Gaussian processes
In a continuous setting, a bifurcating process can be seen as one function, splitting apart into two functions over time. One approach to model this could be to consider two functions throughout time, but before the bifurcation happens, the two functions are identical. With this in mind, we can use a mixture model to tease apart the shared and bifurcated functions.
Mixture model
Mixture models are hierarchical models where an observation is assumed to be generated from one of C components, each of which is described by its own model. cells. This model was originally developed for the application of missile tracking, and in that setting an observation is e.g. a radar based location at a given real time point. As such, the main focus of the definition of the model is for the case of C completely independent components. The approach presented here is based on the realisation that the model would also be able to handle the case of branching trajectories. There would simply be a time interval where it does not matter which mixture trajectory data is sampled from. In
Original OMGP application
Bifurcating OMGP application Sup. Comp. Fig. 2: Comparison of the original OMGP use case (left) and our use case (right), in both cases where the number of trends K = 2. In the original use case trends are expected to be independent throughout time, albeit with some ambiguity in some locations. In our application, we interpret ambiguous cell assignment to be in a common precursor state.
our setting, an observation is a single cell, and the analog to real time is pseudotime (Supp. Comp. Fig. 2).
As an additional extension, we phrase a version of the OMGP model which is non-parametric in the number of trajectories.
In the original regression case described in equation 1, data is assumed to be generated by a single smooth unknown function. When modeling our gene expression data with the Overlapping Mixture of Gaussian Processes, data is considered to be generated by However, we are lacking information about which latent function f c generated any given observation (t n , X n ) of pseudotime and gene expression for the N observed cells. Here X correspond to some representation of the transcriptional state of the cells. It could be the expression of all genes (X = Y ), a single gene (X = y g ), or an Mdimensional inferred representation as discussed above. This is viewed as a mixture modelling problem, where each cell has a latent variable z i specifying to which component f c the cell should be allocated to. Write F for the collection of all latent functions. The covariance functions k c for each f c can be different from each other, though for the applications we discuss here, we take them as Squared Exponential covariance functions with different hyperparameter values.
In the OMGP formulation, the likelihood is We specify a multinomial prior on the latent variables Additionally, each of the latent functions f c has an independent Gaussian process prior: The covariance matrices K 1 t , . . . , K C t for the latent functions f 1 , . . . , f C are generated from a covariance functions k 1 (t n1 , t n2 ), . . . , k C (t n1 , t n2 ) like in equation 3. Now we rephrase this as a Dirichlet Process Gaussian Process mixture model [Hensman et al., 2015]. Let every latent function f c have an associated "stick-breaking length" v c , based on the "stick-breaking" formulation of the Dirichlet Process. Here V = [v 1 , · · · , v ∞ ] is the collection of stick-breaking lengths for constructing the Dirichlet process for the assignment. The joint distribution of the OMGP model is The value α is a parameter of the model which controls the expected concentrations of mixtures (which we in practice take as α = 1, a common default), and where Beta(·, ·) is the beta distribution. The prior distribution over the collection of Gaussian Processes is Following the stick-breaking formulation, The assignments between observations X and the latent functions F is given by a binary N × C matrix Z. The assignments to latent functions are considered as additional variational parameters. Let φ be an N × C matrix where φ nc is the approximate posterior probability of assigning the nth observation to the cth latent function. The φ parameters are inferred by collapsed variational inference as described in Hensman et al. [2012]. Overall, the likelihood of the model is (It should be noted that everything described generalizes to the case where the latent functions f c are vector valued, as long as all output dimensions of such a function share the same covariance function. In this case, probabilities factorize over output dimensions, but beyond that all calculations are the same.)
Parameter inference
In Lázaro-Gredilla et al. [2012] the latent variables Z in the parametric version of OMGP were inferred using an expectation-maximization scheme. Here we describe how we perform variational inference for the φparameters in the non-parametric version of the model.
To make the inference problem tractable, the variational distribution q(Z) is introduced with variational parameters φ, at a given truncation level C such that q(Z) = The second and third parts of L KL were derived in [Hensman2014] as For optimizing variational mixture assignment parameters we follow Hensman et al. [2012], and use natural gradient descent. For hyperparameters of the kernels, as well as the variance parameter σ 2 of the model, we perform gradient descent.
If we know ∂LKL ∂φ we can calculate the natural gradient by equation (22)
.1 Ranking genes by bifurcation
Once the OMGP model has been fit, it can be used to investigate individual genes in terms of their bifurcating trajectory.
The log-likelihood of the OMGP model depends on the covariance matrices K t = {K c t , c = 1, . . . , C}, the variational mixture parameter matrix φ, and the N observations (t, X). Let us assume that we have mixture parameters φ b which have been found to distinguish a bifurcating trend based on some X response variables. We can now keep the fitted parameters and evaluate the marginal likelihood of a model where the response variables X are replaced by gene expression values y g . We call this new model H bifurcating . We wish to find genes which fit this bifurcating model better than a model where this is no bifurcation. To this end, we make a third model H not bifurcating identical to the precious one, except we replace φ b with ambiguous assignments φ a . To asses whether a given gene g is better described by the bifurcating or the not bifurcating model, we evaluate the Bayes factor: BF g = log p(y g |H bifurcating ) − log p(y g |H not bifurcating ).
We refer to this ratio as the bifurcation statistic.
To estimate p-values, we used a permutation approach where we perform the same analysis for every gene g, except with permuted t values to estimate a null distribution.
As a proxy for effect size of bifurcation, we consider how well the expression values of a gene correlate with the trend assignments to a latent function. Strong positive correlation will mean the gene is particularly upregulated in the cells unambiguously belonging to the trend. Conversely, a strong negative correlation indicates the gene is down-regulated in the strongly assigned cells compared to all cells.
Inferring the bifurcation time point
It is possible to qualitatively appreciate from the GP assignment probability (φ c ) for each trajectory (f c (t)) of the OMGP model, which cells are ambiguous and which cells are exclusive to individual GP's. In the case of two trends, ambiguous cells have assignment probability (φ) close to 0.5. A model where the data can be described by two trends, but not by one, will have a higher likelihood. Similarly, if only a region of the φ parameters over time are replaced by ambiguous cell assignment values, the new model will have a lower likelihood.
For the sake of clarity, we make the assumption that the OMGP will begin as ambiguous, and then become less ambiguous over time, splitting into two trends, in this special case. To investigate these cases, we pick a time-point t b in an OMGP, then replace all φ values prior to t b with 0.5. We define this new φ as φ >t b : Now we can evaluate the model likelihood for this particular t b and define This procedure is repeated for multiple ts over the predictor variable of the OMGP model. In our implementation, we consider 30 evenly spaced bins by default, which has given enough resolution for the data investigated (though the number of bins can easily be changed).
The likelihood has to decrease by definition. However, after the bifurcation the decrease is much more pronounced. We use a break-point heuristic to detect this elbow, which is indicative of the bifurcation time.
To identify the region at which the likelihood decreases more rapidly, we fit a piece-wise linear curve to the log-likelihoods, defined by This curve consists of two linear pieces, broken up at the point p. When the curve is fitted, we consider the breakpoint p to be the point after which we can be confident that a bifurcation has occurred, see Supp. Comp. 5 Implementation and combination with existing workflows
Practical use of GPfates
The basis principle of GPfates is the combination of pseudotime and mixture modelling.
Sup. Comp. Fig. 3: Inferring bifurcation point. The plot illustrates how different points along the pseudotime are sampled. Ambigous assignment probabilities replaces trained assignment probabilities in the observations earler than the sampled points. The breakpoint model identifies the points where a decrease in likelihood differences becomes more extreme.
Input to the GPLVM is an expression table consisting of log scaled relative abundance values Transcripts Per Million, TPM, with a value of 1 added to handle cases where expression is 0. As relative abundance follow a log-normal distribution, the Gaussian likelihood used for Gaussian Process regression should be appropriate.
In practice, the pseudotime should represent the biological process of interest. If this process is clear, the expression data should be usable without pre-processing. In single cell time course experiments where the process of interest is less immediate, a strategy highlighted in Trapnell et al. [2014] is to select the gene set used could be to rank the genes by an ANOVA test over the time points, and select a larger number of significant genes.
Similarly, the low-dimensional representation of the transcriptomic cell state should represent the biological response of interest. It can be beneficial to select the parts of the representation which correspond to this. For example, in the analysis of CD4+ T cell time course, we use the second GPLVM latent variable as a representation of T cell response, and model this factor by the OMGP.
While the pseudotime can be inferred directly from the expression matrix Y , in many cases it helps interpretation to perform an intermediate step of dimensionality reduction. This process could also be beneficial if the data has a very complex structure.
Another practical consideration we must consider is that single cell expression vales can be quite noisy. This limits the time-scale at which we can expect to measure proper functional differences in expression levels. Due to this, we tend to put lower limits on the lengtscale l SE of the squares exponential covariance function.
Integration of existing methods
We have presented use of the GPfates method when pseudotime or low-dimensional representations have been based on the GPLVM. This is because the OMGP follows from this framework, and the statistical assumptions are consistent between the models.
In practice, other methods for inferring pseudotime or low-dimensional representations could also be considered. Here we briefly outline possible strategies for applications of GPfates downstream of popular singlecell analysis methods.
Recall that to perform the GPfates inference, we need pseudotime t and some representation of transcriptomic state X. These variables can be set as the output from other methods.
In Monocle [Trapnell et al., 2014], the low-dimensional representation X is found by independent component analysis, and the pseudotime t for each cell is defined by the path distance to a starting cell through a minimum spanning tree in the coordinates of X.
In Wanderlust, a heuristic is used to build a stable k Nearest Neighbor (kNN) graph of the data in the highdimensional space of protein measurements. The pseu-Sup. Comp. Fig. 4: OMGP is compatible with e.g. Wanderlust and Monocle, as demonstrated with a toy data set dotime t for a cell is then defined as the average shortest path from a known starting cell through the kNN graph. Note that for CyTOF data, which Wanderlust is designed for, only up to 40 analytes can be measured at once, so it could be feasible to take X to be the original expression matrix (Y in our notation).
Another dimensionality reduction technique which have been used for single cell RNA seq data is Diffusion Maps [Haghverdi et al., 2015]. Here X is a spectral embedding of the data manifold, based on the Laplace operator. It has been pointed out that these embeddings preserve branching structure in the data. Taking the pseudotime t as the Diffusion Pseudotime [Haghverdi et al., 2016], an approximation of geodesic distance over the data manifold (from a known starting cell), based on the diffusion map, GPfates modelling could be used downstream to quantify the branching structure of the data.
We list alternative compatible pseudotime methods in table 1.
As a demonstration, we generated a toy data set with three branches, and extracted the pseudotime using both the Monocle method and the Wanderlust method. Then fitted and OMGP with C = 3 on the output. The results can be seen in Comp. Supp. Fig 4, which illustrates the correct identification of the branching processes for either input.
Software availability
We have made a software package for using the GPfates method, which is available at [URL]/ Teichlab/GPfates. It provides guidance and sensible defaults for the kind of analysis we have described here. It makes extensive use of the GPy 1 package, and the GPclust 2 package, where we implemented the nonparametric OMGP model. Sup. Comp. Fig. 5: Robustness of analysis steps by subsampling. Parameters inferred from a subsample of the data are compared to parameters inferred using the full data. The top panel indicates this analysis for independent steps assuming the previous step is known. The lower panel shows the result when running the workflow from start to end.
6 Assessment of GPfates on simulated and real data 6
.1 Sample-size robustness analysis
Our full analysis consists of several independent consecutive steps: first the GPfates method where we are i) finding a low-dimensional representation, ii) smoothing the data over a pseudotime, and iii) finding a trend mixture model. After this we perform downstream analysis where we are iv) dentifying the end states and bifurcation.
How much data do we need to accurately reconstruct trends from all four of the above steps, and how much data is needed for individual steps? We investigated both how stable the full procedure is, as well as the individual steps, by re-running it on sub-sampled datasets with fewer cells than the entire dataset.
To measure the stability of the methods, we consider absolute Pearson correlation of the parameters inferred for sub-sampled data relative to the results obtained from performing the analysis on the full data set.
We found that recovering a low-dimensional representation is extremely stable with respect to the number of cells (Supp. Comp. Fig. 5), with almost perfect correlations between analysis of the sub-sampled data and the original GPLVM values. (For example, the lowest absolute Pearson correlation for a run with 50 cells was 0.96). Similarly, the pseudotime inference is also very stable to sub-sampling.
Finding the entire OMGP mixture model over pseudotime requires a larger number of cells. We don't see Sup. Comp. Fig. 6: Complete reanalysis of our T-cell data excluding cells cellected at day 4. The bifurcation point is identified as being between Day 3 and Day 7, and is not forced in to either of the days.
any higher degrees of consistency until we reach 150 subsampled cells, with correlations around 0.5. It is rare to see single cell studies with so few cells, and in the study accompanying this text we had many more cells (408). Identifying only the end states is rather robust (but in many cases might be best analyzed as a cluster problem rather than a continuous value problem), where we start seeing a correlation of 0.9 at 150 cells.
The individual steps were in general very stable to sub-sampling, relative to the "gold standard" of using the full data set. When running the entire procedure, we see that smaller errors early on in the analysis will propagate and affect later steps.
Predicted bifurcation time is not biased by collection times
We consider the risk that the identified bifurcation point in the CD4+ T cell data potentially just reflects the time points at which we have collected data. We test the robustness of the prediction of the bifurcation as having happened at Day 4 by re-running the analysis after removing cells collected at Day 4. In this analysis we find that the bifurcation happens at some point between Day 3 and Day 7 where we don't have any observed cells. The alternate hypothesis would have been that the bifurcation would be found in either Day 3 or Day 7. This provides confidence both in the bifurcation point identification, and more generally in the meaningfulness of the low-dimensional GPLVM representation of the data (Supp. Comp. Fig. 6).
Assessment of the ability to select the number of trends in OMGP
In principle, the marginal log likelihood of the OMGP model should let us select the C number of trends which optimally explain the data. We investigated this by generating four synthetic data sets with between 1 and 4 underlying trends. For each of the data sets, we optimized OMGP models with the number of trends C varying Sup. Comp. Fig. 7: Attempts detecting number of trends with OMGP. Simulated data with expected numbers of trends where fitted with OMGP, where the C cutoff was set to a range of values. from 1 to 9 (three times per C value). We found that the marginal likelihood of the models corresponded to the correct number of trends in the cases of 3 and 4 ground truth trends, but not for the 1-trend and 2-trend synthetic data. For 1 trend, the likelihood was lowest for a larger number of trends, and for 2 trends, the likelihood was very similar for 2 and 3 trends. This suggests that the OMGP may have a tendency to overestimate the number of trends if there is a single progression. Supp. Comp. Fig. 7.
For our CD4+ T cell data, we found that the marginal likelihood continuously increased with the number C. We elected to keep the model simple and made the assumption that we could sufficiently explain the data with C = 2.
It is possible that the optimal likelihood for K is not well defined when we have trends branching off from a common trend. In the original application of the OMGP model, the assumption is that the trends will be completely independent of each other. As we are already to some extent failing to fit two models in the ambiguous case, this might cause the likelihood to reflect a poor fit. For quantitatively determining the number of trends in the data, further work is needed, probably with a model which explicitly considers branching from a common original trend. The marginal likelihood of the model is an indication, but the choice of C should also reflect the biological system under consideration.
Sup. Comp. Fig. 8: Comparison of pseudotime with and without per-cell priors. The upper left shows the fit of the pseudotime predicted in to the 2D GPLVM with and without priors. Below are the corresponding inferred covariance matrices. The right plot shows the relations between the two versions of pseudotime, clearly indicating that they have an approximate one-to-one mapping.
Sup. Comp. Fig. 9: Investigation of uncertainty of inferred pseudotimes. Left panel, since the Bayesian GPLVM fits the variance of the pseudotime for each cell, we can compare the assignments with each other. The bars correspond to 95% confidence intervals. On the right panel we see how the lengths of the confidence intervals globally decrease as the number of cells used increases.
Comparison of pseudotime inference with and without priors
For the 1-dimensional Bayesian GPLVM which we use to find the pseudotime of the data, we put priors on the cells based on their known time points. This is not strictly necessary, but helps to enhance interpretability as there is intrinsic invariance to the inferred values. If we do not use priors, qualitatively, the same trajectory is identified. Additionally, comparing the two versions of pseudotime against each other, we see that they correspond to a circular shift relative to each other. The covariance matrices inferred using either strategy have a very similar block structure (Frobenius norm ... of difference) indicating that neighbor relations are consistent. Supp. Comp. Fig. 8.
Pseudotime uncertainty
As pointed out in Campbell and Yau [2015], we can use the posterior distribution of pseudotime from the Bayesian GPLVM to assess how meaningful the order-Sup. Comp. Fig. 10: Stability of GPLVM representation, and prediction through GPLVM. Top row: Predicting cells from regions of show higher similarity with left out real cells from corresponding regions than noncorresponding regions. Bottom row: Predicting cells from unobserved regions potentially identifies antagonizing gene combinations.
ing is. By investigating the confidence intervals of the pseudotime for each cell compared to neighboring cells, we see that the ordering is quite meaningful (few cells overlap in confidence interval). (Supp. Comp. Fig. 9) We also investigated how the confidence of the pseudotime depends on the number of cells observed. As the number of observed cells increases, the distribution of variance per cell decreases towards zero. (Supp. Comp. Fig. 9) 6.6 Stability of the circular shape of the GPLVM representation We wanted to rule out the possibility that the latent variable representations of data which appear circular might be artifacts due to random noise, as suggested by Diaconis et al. [2008]. To make sure this was not the case for our CD4+ T cell data, we removed two 'slices' of cells from the circular 2D GPLVM pattern. Following this, we fitted a new GPLVM with this reduced data set. After optimizing the GPLVM, a representation was found which was again missing the same slices, Supp. Comp. Fig. 10A. (The correlation between the two representation for the common cells is very high as well, XX). This control experiment strongly suggests that the GPLVM learns the actual topology of the data.
Assessing the accuracy of imputing virtual cells
Unlike many other dimensionality reduction techniques, the GPLVM creates a model which maps into the high dimensional observed space. It is, however, not clear how meaningful this representation is. We assessed this by taking the "slice-less" model described above, and in the empty areas corresponding to the removed cells, predicting "virtual cells" (Supp. Comp. Fig 10A). Using an independent clustering technique, t-SNE [Van der Maaten and Hinton, 2008], on both the left out slices of cells and the predicted virtual cells, we find that single cell transcriptomes predicted from a given slice coincide with the real cells from the corresponding slice (Supp. Comp. Fig. 10). This indicates that GPLVM prediction in to high-dimensional spaces is not simply producing overfitted results. Following from this, we investigated the "hole" in our CD4+ T cell data. We create number of virtual cells from the hole region and compare which genes would be expressed in these cells compared to genes expressed in all cells (Supp. Comp. Fig. 10C). The underlying reasons for data being non-linear is that particular combinations of gene expression patterns do not occur together. If we find genes which are high in the virtual cells but are not observed at the same time in actual cells, this might indicate that they are incompatible with each other. This might be a good complementary tool for generating hypotheses about regulation. For instance, we identified the genes Hspe1 and Gm29216 which would be co-expressed in the hole, but are generally not co-expressed in observed cells (Supp. Comp. Fig. 10D).
Validating the BGPLVM and
OMGP approach by application to other data sets In order to further corroborate our analysis approach, we considered two recently published single cell data sets produced to investigate progression of single cells in two developmental contexts: mouse fetal lung and human fetal primordial germ cells.
Analysis of lung development data
We downloaded the data from Treutlein et al. [2014] and quantified the expression using Salmon. To smooth the data over pseudotime, we found genes that vary over the a priori known time points by a likelihood ratio test of an ANOVA model of the time points. The expression values for the top varying genes were run on a GPLVM. One of the factors of the optimized GPLVM was used as pseudotime, and the top two factors of the GPLVM were used to represent the entire data set. An OMGP was then optimized on this low-dimensional representation to identify the two trends corresponding to the AT1 and AT2 lung cell lineages without prior annotation. The bifurcation statistic for all expressed genes in these cells reconstituted many of the genes identified in a largely manual manner by Treutlein et al. [2014].
Analysis of human primordial germ cell data
The data from was downloaded and quantified with Salmon as with the other data, but with an index based on the human transcriptome: Ensembl 78 annotationa of GRCh38, together with ERCC sequences and human specific repeats from RepBase. To smooth the time course data, we used a likelihood ratio test to find the top genes which were described linearly along the time points in the data. The expression of these genes were then used to fit a GPLVM. This lowdimensional representation of the data was then used to fit an OMGP, taking one of the latent factors as pseudotime.
In this data set, the ground truth about the sex of the cells is known, and thus we could have use a supervised approach such as GPTwoSample We applied the bifurcation statistic test to identify genes which follow the male and female development differently.
Unlike in the case of the lung development data, the majority of the genes we identify are not discussed in the original study. In the original study, the authors focused on genes specific to given conditions (e.g. Male PGC's from week 11 compared to all other cells). In our analysis, we consider the dynamics of gene expression over development. We find that in the male branch, the GAGE family is highly upregulated over development. Additionally we find the Y-linked gene ENSG00000279950. Also among the top male hits is RHOXF2, a gene linked to male reproduction [Niu et al., 2011]. Further down the list we also interestingly find PIWIL4, a gene with function in development and maintenance of germline stem cells [Sasaki et al., 2003]. On the female side, the top hit is MDK, a gene involved with fetal adrenal gland development (by similariry: UniProtKB P21741). Other top hits include MEIOB, a meiosis related gene, and the satellite repeat GSATII. Surprisingly, we also see upregulation of SPATA22, a gene associated with spermatogenesis.
Discussion
We have demonstrated our GPfates method, where we use latent variable modelling to infer temporal expression dynamics, and Gaussian process mixture modelling to identify diverging global trends. The method has been investigated in terms of robustness, and applied on several simulated and real data sets showing good results.
Of course there is no silver bullet for these sorts of problems, and it would not be surprising if other methods than the ones we have used work better for some biological systems. We have illustrated that the main component, the Gaussian process mixture modelling, is compatible with other methods in these cases.
A benefit from the methods we use is that diagnostics such as marginal likelihood can be used to aide the user with regards to the models to use. Still, the user will need to keep the biological system in mind, and be critical of results.
Infections
Plasmodium chabaudi chabaudi AS parasites were used after one in vivo passage in WT C57BL/6 mice. Mice were infected with 10 5 pRBCs i.v. and blood parasitemia was monitored by Giemsa-stained thin blood smears obtained from tail bleeds.
Flow cytometry and cell isolation Single-cell suspensions were prepared by homogenising spleens through 100 µm strainers and lysing erythrocytes using RBC lysis buffer (eBioscience). Fc receptors were blocked using anti- Single-cell mRNA sequencing Single cell capture and processing with the Fluidigm C1 system was performed as described in (52). The cell suspension obtained from sorting was loaded onto the Fluidigm C1 platform using small-sized capture chips (5-10µm cells). 1 µl of a 1:4000 dilution of External RNA Control Consortium (ERCC) spike-ins (Ambion, Life Technologies) was included in the lysis buffer. Reverse transcription and pre-amplification of cDNA were performed using the SMARTer Ultra Low RNA kit (Clontech). For processing with the Smart-seq2 protocol (29), the cells were sorted into 96-well plates containing lysis buffer using either a MoFlo XDP (Beckman Coulter) or an Influx (Becton Dickinson) instrument. The Smart-seq2 amplification was performed as described in (29), with the lysis buffer containing Triton-X, RNase inhibitor, dNTPs, dT30 primer and ERCC spike-ins (Ambion, Life Technologies, final dilution 1:100 million). The cDNA amplification step was performed with 24 cycles. The sequencing libraries were prepared using Nextera XT DNA Sample Preparation Kit (Illumina), according to the protocol supplied by Fluidigm (PN 100-5950 B1). Libraries from up to 96 single cells were pooled and purified using AMPure XP beads (Beckman Coulter). Pooled samples were sequenced on an Illumina HiSeq 2500 instrument, using paired-end 100 or 125base pair reads.
Processing and QC of scRNA-Seq data Gene expressions were quantified from the paired end reads of the samples using Salmon (41), version 0.4.0. An example command for a one sample would be "salmon quant -i mouse_cdna_38.p3.78_repbase_ercc_index -l IU -p 4 -1 1771-026-195-H4_1.fastq -21771-026-195-H4_2.fastq -o1771-026-195-H4_salmon_out -g mouse_cdna38.78_repbase_ercc_index_gene_map.txt". The parameter libType=IU, and a transcriptome index built on Ensembl version 78 mouse cDNA sequences. We also had 30 sequences from the ERCC RNA spike-ins in the index, as well as 313 mouse specific repeat sequences from RepBase to potentially capture transcribed repeats. For quality control of the single-cell data we assessed the number of input read pairs, and the amount of mitochondrial gene content. For all cells, we considered samples with less than 100,000 reads or more than 35% mitochondrial gene content as failed. For T cells, we additionally considered cells where number of genes was less than 100 + 0.008 * (mitochondrial gene content) as failed. For the data generated using a 96-well plate-based Smart-seq2 protocol, which does not permit visual inspection of the captured cells, we additionally excluded lowquality cells from which fewer than 2000 genes were detected, motivated by negative control wells. To verify that that the cells sorted in the wells were PbTII cells, we only selected cells from which both the transgenic TCR alpha and beta chains were detected (Supplementary Tables 2 and 3). Excluded cells were removed from all further analyses, and the remainder of the samples were taken as individual single cells.
For expression values, the Transcripts Per Millions (TPM's) estimated by Salmon included ERCC spike-ins. Thus, for analysis of the cells, we removed ERCC's from the expression table and scaled the TPM's so they again summed to a million. This way we get endogenous TPM values, representing the relative abundance of a given gene within a cell. We also globally removed genes from analysis where less than three cells expressed the gene at minimum 1 TPM, unless stated otherwise.
Latent Variable Modelling of data We modelled the data using an unsupervised Bayesian Gaussian Process Latent Variable model (BGPLVM) (14) on log10 transformed TPM values (with a scaling factor 1 added). The BGPLVM was run with 5 latent variables. As we used an ARD (Automatic Relevance Determination) squared exponential covariance function, we could infer that two latent factors explained the data. All other parameters to the BayesianGPLVM model in GPy (version 1.0.9) were left as default. Upon inspection we noticed a circular pattern. This corresponds to a 1dimensional topology, which requires two dimensions for a faithful representation. Thus we inferred a new latent variable by 1-dimensional BGPLVM, with priors on the latent variable based on the cell collection times (see the Computational Supplement), where we used the 2D latent variables as input. This way we inferred smoothed "pseudotime" values for the every data point representing the progressive response to the malaria infection. In the 2-dimensional model of the data, we searched for genes that highly correlated with either of the two explanatory latent factors. Performing functional enrichment analysis using gProfiler (42) on the top genes revealed that factor 1 (which explained most of the variation) was related to cell proliferation. The second factor was largely explained by genes involved with immune response. Upon inspection, it seemed as the second latent factor terminated in two groups of cells. We investigated this in terms of a bifurcating time series.
Bifurcating time series analysis To study the cells in terms of a bifurcating time series, we implemented an Overlapping Mixture of Gaussian Processes (OMGP) model (16), see the Computational Supplement for details. The 31 model uses an optimization procedure to associate observed data with a given number of individual independent trends over a time variable. The model was run with pseudotime as input, and the immune response related latent variable as output. For the mixture model, we assumed two trends. The two trends were given squared exponential covariance functions, where we fixed the length scale to 1 based on our prior assumptions on smoothness over pseudotime. We also constrained the model variance to 0.05, which allows trends to share observed data points. Remaining hyperparameters were optimized by gradient descent. (See the Computational Supplement for details) Testing genes for bifurcated expression The output of the OMGP model is a soft assignment to each of two trends for every observed cell. The original model was fitted with the 2nd latent variable from the latent variable analysis. To find genes that significantly drive this bifurcation, we keep all parameters fixed but change the data to be individual genes expression levels and calculate the data likelihood. In order to get a null distribution to assess significance, we performed the same analysis but with randomly permuted pseudotime-values. This is described in detail in the Computational Supplement. To measure in which direction a gene is involved with the bifurcation, we used correlation between expression and trend assignment. For example, a gene's expression being strongly positively correlated with a trends assignment means it is being upregulated on that bifurcated branch.
Monocle
The Monocle analysis was performed with version 1.2.0 of the Monocle package, following the steps outlined in the original vignette (17). In brief, the analysis was performed using the sizenormalized data (TPM) including all genes expressed in ≥10 cells (11439 genes) with default parameters. The genes used for the ordering of cells were defined by carrying out a differential expression analysis of the time points using the differentialGeneTest embedded in the package. Following the original vignette, genes with q-value <0.01 were selected (7738 genes). The num_paths option was set as 2. SCUBA ( [URL]7) was run using 3003 genes and provided information about time point. RNAseq_preprocess.m and SCUBA scripts were run according to instructions. SCUBA did not find any bifurcation points. Similarly, using 1000 most informative genes (SCUBA default), or scaling of the data (to aid the sensitivity), did not result in any bifurcation either. Changing the number of was done by the variable ngene_select in RNAseq_preprocess.m. All other variables were kept at default.
Annotation of cell-surface receptors, cytokines and transcription factors Genes likely to encode transcription factors, cell-surface receptors or cytokines were found by combining information from KEGG ( [URL]/), the Gene Ontology Consortium ( [URL]/, PANTHER ( [URL]/) along with the more specific databases detailed below. (A) Principal Component Analysis of the single cells from the replicate PcAS infection. The single cells were sorted on 96-well plates and cDNA was amplified using the Smart-seq2 protocol (29). The arrows represent the Pearson correlation with PC1 and PC2. Cell size refers to the number of detected genes. "Th1 signature" and "Tfh signature" refer to cumulative expression of genes associated with Th1 or Tfh phenotypes (15). PC, Principal Component.
(B)
The emergence of subset-specific gene patterns at day 7 of infection. For the top bifurcating genes (Fig S5C) pairwise gene-to-gene Spearman correlations were calculated. The rowside colours represent the association of the gene with either Th1 fate (red) or Tfh fate (blue).
(C)
The expression of top 20 Th1 and Tfh associated genes identified using GPfates in single PbTII cells at days 4 and 7. Cdk2ap2 appears twice because two alternative genomic annotations exist.
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Domain: Medicine Biology Computer Science
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Upregulation of chloroplastic pyruvate dehydrogenase genes in rice leaf would potentially drive the in planta photorespiratory bypass for higher biomass
At ambient temperature (25–30 o C) and the prevailing atmospheric CO 2 levels (380 ppm), installing the C 4 photosynthetic machinery in a C 3 plant would potentially drive away the photorespiratory process through a carbon concentrating mechanism (CCM), thereby preventing oxygenation reaction of Rubisco. Development of C 4 rice is a global research priority, for enhanced water use eciency (WUE) and yield. At optimal environment, the difference in the solar energy to biomass conversion between C 3 and C 4 plants is mainly due to photorespiration. So, photorespiratory bypasses are the potential alternatives than conversion to C 4 . Genetically transformed C 3 model plants with photorespiratory bypass had demonstrated higher biomass (under same environmental conditions) than its wild type. Using a transcriptome approach, we report here the differential expression pattern for photorespiratory genes and chloroplastic pyruvate dehydrogenase (plPdc) gene between the leaves, peduncle, and the developing grain tissues in three rice genotypes. In addition to pyruvate, glycolate and glyoxylate also are the substrates for the plPdc gene product and hence a suitable candidate for photorespiratory bypass.
Introduction
At optimal environmental conditions, solar energy to biomass conversion e ciency is roughly 25% higher for C 4 plants than the C 3 ones, with key differences in photorespiration, while loss through respiration in light is unavoidable [1][2][3] . The C 4 trait, a carbon concentrating mechanism (CCM) that drives away photorespiration through preventing oxygenation reaction of Rubisco, is reported to have convergently evolved multiple times, nevertheless, same gene lineages were recruited in the C 4 trait evolution 4,5 .
Converting a C 3 crop plant with a C 4 pathway to enrich the solar energy conversion e ciency for higher biomass is one of the research priorities to improve yield. Installing photorespiratory bypasses is also a viable alternative, demonstrated to improve e ciency with higher biomass through reduced photorespiration in model C 3 plants 6-8 . Also, it is important for plants to metabolize 2-phosphoglycolate (2-PG, formed through the oxygenation process of rubisco) and glyoxylate (key intermediate of photorespiration), to overcome the metabolite toxicity that inhibits photosynthesis and starch biosynthesis 9,10 . Installing a C 4 machinery or a photorespiratory bypass in C 3 plants would help enhance the assimilation rate under optimal environmental conditions 7,8,11 .
In addition to pyruvate, glycolate and glyoxylate -the intermediates of photorespiration -also acts as a substrate for the chloroplastic pyruvate dehydrogenase complex (plPDC) in plants 12 . The PDC constitutes three components, E1 (pyruvate dehydrogenase in heterotetramer state-a2b2), E2 (dihydrolipoyl acetyltransferase, homodimer) and E3 (dihydrolipoyl dehydrogenase, monomer) with copy numbers of these components in the complex is variable 13,14 . Recent study highlights the E2 component's RNA binding activity with psbA mRNA coding for the D1 protein of the PSII reaction center 15 . To understand the expression pattern for genes of photorespiration and the C 4 pathway in leaf and non-leaf (photosynthetic) tissues in rice, transcriptome analysis was performed in the three rice genotypes, with two biological replicates.
Materials And Methods
Three rice (Oryza sativa ssp. indica) genotypes -Apo (EC734333), BAM4234 (EC497171), and Crossa (IC575838) -were grown under eld conditions during Kharif season 2018 in triplicate at the research farm of the Division of Plant Physiology at IARI (New Delhi). Flag leaf, peduncle and developing grains (3-5 days-post-anthesis, dpa) were collected in two replicates, snap frozen using liquid nitrogen (-196 o C) and stored at -80 o C for transcriptome studies. The experiment was planned with two replicates since the study involves three genotypes. The expression levels between genotypes for most of the genes studied were insigni cant (Supplemental File_S1, hence genotypes could equally be considered as biological replicates, totaling to six (two replicates x three genotypes). So, technically, the expression pattern reported in the study for each tissue is supported with an equivalent of six biological replicates. All methods pertaining to this study were performed in accordance with the relevant guidelines / regulations / legislation as applicable.
Total RNA from the samples (80-100mg) was extracted using a RNeasy plant mini kit (Qiagen, USA) following the manufacturer's protocol. The quality and quantity of the RNA was assessed using a Bioanalyzer 2100 (Agilent technologies, USA) and spectrophotometer ND-8000 (Thermo Scienti c, USA).
The RIN values for the 18 samples (3 genotypes and 3 tissues, repeated twice) ranged from 7.0 to 9.5. RNA-seq libraries were sequenced on an Illumina platform (2x150bp paired-end reads). A total of 521 million pairs of reads were obtained. Adapter trimmed reads were quality checked using FastQCv0.11.8 16 . These pre-processed reads were mapped against the indica rice (ASM465v1) genome sequence 17 . Mapping and alignment against the reference were done using Tophatv2.1 18 . Summary statistics on the number and percent reads mapped were provided in the Supplemental File_S2.
Cu inksv2.2.1 was used to assemble the individual transcripts for expression quanti cation 19 . The assembled transcripts were merged for the differential expression studies between each tissue (leaf vs peduncle, leaf vs grain, and peduncle vs grain) in every genotype and vice-versa (to con rm no signi cant differential expression for the genes / transcripts studied, between genotypes for the same tissue) using Cuffmerge 20 . The expression values (in RPKM) were tested for statistical signi cance at FDR 0.01 cutoff value using Cuffdiffv2.2.1 19 .
Including the key eight genes coding for the gene products being involved in photorespiration 21 , totally, 42 transcripts (with gene ids) were identi ed for the 11 genes involved in photorespiration viz., phosphoglycolate phosphatase-chloroplastic (cpPGLP), glycolate oxidase-peroxisomal (pGOX), glutamate:glyoxylate aminotransferase-peroxisomal (pGGT), serine hydroxymethyltransferasemitochondrial (mSHMT), glycine decarboxylase-mitochondrial (mGDC), glycerate kinase-chloroplastic (cpGLYK), glutamine synthetase-chloroplastic (cpGS2), glutamate synthase (cpGOGAT), serine:glyoxylate aminotransferase-peroxisomal (pSGT), and hydroxypyruvate reductase-1 and − 2 (HPR-1 & -2). The corresponding transcript ids were identi ed and extracted from the plants ensembl database 22 . This is done since few genes were not functionally annotated. Expression pro les (in RPKM -reads per kilobase of transcript per million mapped reads) including statistical signi cance and log fold change details for the genes of interest were extracted from the transcriptome analysis (Supplemental File_S1) and studied for its biological signi cance. Based on the results obtained, the expression pro les for transcript ids annotated with chloroplastic pyruvate dehydrogenase complex gene (pdc) were also studied from the transcriptome dataset and results are tabulated and given in Supplemental File_S1. In addition, expression levels for Rubisco small subunit (rbcS) transcripts were also compared between the three tissues for all the three genotypes. Since the expression values of the rbcS transcripts are also signi cantly downregulated in developing grains (Supplemental File_S1), when compared to leaves, ratio for expression level between leaf and developing grain' in each genotype has been worked out (excel sheet 'Ratio' in Supplemental File_S1). For those transcripts with expression values signi cantly higher in leaves are greater than one. The rbcS transcript with highest expression in both leaf and developing grain is identi ed, and its ratio is used as the threshold ratio to identify the set of photorespiratory genes that are signi cantly downregulated, and simultaneously above the threshold ratio (cells highlighted in green, in excel sheet 'Ratio' in Supplemental File_S1).
Result And Discussion
The eight genes of photorespiratory enzymes viz., phosphoglycolate phosphatase-chloroplastic (cpPGLP), glycolate oxidase-peroxisomal (pGOX), glutamate:glyoxylate aminotransferase-peroxisomal (pGGT), serine hydroxymethyltransferase-mitochondrial (mSHMT), glycine decarboxylase-mitochondrial (mGDC), glycerate kinase-chloroplastic (cpGLYK), serine:glyoxylate aminotransferase-peroxisomal (pSGT), and hydroxypyruvate reductase (HPR); two genes encoding for glutamine synthetasechloroplastic (cpGS2), glutamate synthase (cpGOGAT), were studied and found to be signi cantly downregulated in the developing grains (ca. 3-5 days post-anthesis) than the leaves, in all the three rice genotypes (Supplemental File S1). Although it can be argued that the downregulated expression pattern in developing grains is an expected one when compared to leaves, to identify the biological signi cance, expression pattern for rbcS gene transcript was also studied (Supplemental File_S1, 'ratio' worksheet). Those genes for which the expression pattern ratio between leaf and developing grains are greater than the ratio of rbcS gene, they were identi ed to play proportionately equal or higher role as in the leaves. However, downregulation of photorespiratory genes might lead to cell toxicity due to the accumulation of 2-PG and glyoxylate, notably when the plant is under abiotic stress 9,10 . Conversion of these two metabolites into non-toxic compounds is primarily important to overcome the cellular toxicity, as well as to sustain the availability of ADP and NADP for accepting light energy 23 . Diversion of the 2-PG to bypass the photorespiratory process is reported to improve the plant biomass 6-8 . Alternatively, chloroplastic pyruvate dehydrogenase complex (plPDC) is reported to detoxify glyoxylate, producing CO 2 in chloroplast 12 , potential for a natural photorespiratory bypass to enrich the CO 2 for rubisco carboxylation process. In addition to glyoxylate, glycolate also acts as a substrate for plPDC, and CO 2 production from these metabolites are competitively inhibited in the presence of pyruvate 12 .
So, to understand the expression pattern at transcriptional level, we compared the expression levels of plPdc (Supplemental File_S1) and found that the transcript levels of plPdc were signi cantly higher in the developing grains as compared to the leaves, in all the three genotypes. It gives an insight on the possible use of plPDC to establish a photorespiratory bypass (Fig. 1). This would potentially aid in developing an e cient photorespiratory bypass, in planta, through enhanced plPdc gene expression levels targeting for higher biomass or yield. Whether the upregulated plPdc driverts the photorespiratory process or viceversa, is unknown yet. Plants accomplishing C 2 photosynthesis have evolved for the preferential downregulation of mGDC (glycine decarboxylase, mitochondrial) or anatomical rearrangements (with more chloroplasts at the periphery) in mesophyll cells either reduce CO 2 release or provides high resistance to CO 2 e ux 7,24 . This is commonly called 'C 2 shuttle' and helps increase the plant productivity with high biomass or yield. With initiation of photorespiration through oxygenation reaction of Rubisco, conversion of the toxic metabolites 2-PG (through glycolate) and glyoxylate in chloroplast itself through plPDC to release CO2 will enrich the carbon ux for Rubisco's carboxylation process would simulate a natural photorespiratory bypass. Present study gives an insight for the probable existence of certain group of plants that have evolved to recapture the CO 2 released in the process of converting glycolate / glyoxylate to Acetyl-CoA through plPDC in chloroplast itself, possibly having the shortest C 2 shuttle that also help enhance the plant productivity. Expression levels of Acetyl-CoA carboxylase (ACCase), the key enzyme that channelizes the carbon ux for fatty acid (FA) biosynthesis, is insigni cant between leaf and grain tissues studied and suggesting for the expression of plPDC is not associated with FA biosynthesis. Alternatively, these Acetyl-CoA pools formed through the action of plPDC might possibly utilized for Nterminal acetylation through the action of plastidic N-terminal acetyltransferases (plNATs). This is in line with the reports suggesting ca. 30% of the plastid proteins are subjected to the action of NATs, especially the chlorophyll binding proteins and other enzymes of photosynthetic apparatus [25][26][27][28][29] .
Overall, our results show that, the signi cant downregulation of photorespiratory genes in the developing grains of rice as compared with leaves exhibit biological signi cance; with the ratio (leaf to developing grain tissues) for photorespiratory genes being greater than rbcS gene (Supplemental File_S1). The signi cant upregulation of the chloroplastic pdc gene speci cally in the developing grains, might convert glyoxylate / glycolate, to CO 2 in chloroplasts for carbon xation (Fig. 1), thereby preventing carbon loss 12,30 . This nding provides an insight for possible development of an in planta photorespiratory bypass in the leaves of C 3 plants to envision for higher biomass or yield. Figure 1
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Domain: Engineering Environmental Science Biology
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Meuffelsia, a New Genus of Long-Legged Flies from South Africa, with a Key to Afrotropical Peloropeodine and Allied Genera (Diptera: Dolichopodidae)
ABSTRACT The genus Meuffelsia Grichanov, gen. n. is described from South Africa to accommodate two new species, M. erasmusorum Grichanov. sp. n. and M. manningi Grichanov, sp. n. The new genus has been placed in the subfamily Peloropeodinae, and is considered close to the genera Micromorphus and Peloropeodes. Brief information on the habitat of M. erasmusorum is provided; in general, representatives of the new genus seem to prefer mesic riparian environments. A key to Afrotropical peloropeodine and allied genera of long-legged flies is compiled, and characters of the new genus are discussed.
INTRODUCTION
The 'World catalog of Dolichopodidae' (Yang et al. 2006) lists 15 genera in the subfamily Peloropeodinae, which is still poorly defined (Grichanov 2000). Several unrevised genera of uncertain taxonomic position included in the subfamily blur its border with Sympycninae. There are two relatively well-defined and distinctive genera (Peloropeodes Wheeler, 1890 andMicromorphus Mik, 1878) that are distributed worldwide, including the Afrotropics. These genera differ from other members of the subfamily, first of all, in having sessile hypopygia.
A male and a female of a peculiar peloropeodine species have been collected using a Malaise trap at the Sanyati Nature Farm near Loewsburg, KwaZulu-Natal, South Africa. A study of published descriptions (Parent 1938;Robinson & Vockeroth 1981;Meuffels & Grootaert 1987, 1997a, b, 2004Bickel 1992Bickel , 1999Bickel , 2004Runyon & Hurley 2003;Negrobov 2003;Grootaert 2006, etc.) has been unsuccessful in seeking an appropriate genus for this species. Subsequent treatment of collections from the Natal Musem led to discovery of two more specimens of the same species, and of a series consisting of one male accompanied by three females that represent another close species. Both species belong to a new genus that shares features of Micromorphus and Peloropeodes, but their inclusion in either of the latter genera would diffuse the generic concepts of both taxa. This paper offers a description of a new genus along with a key to all Afrotropical peloropeodine and sympycnine genera, with three species groups of Sympycnus and two genera previously included into the subfamily Peloropeodinae.
MATERIAL AND METHODS
The left and right lateral views of the hypopygium, or male genital capsule, are illustrated for the new species. In describing the hypopygium, 'dorsal' and 'ventral' refer to the morphological position prior to rotation and flexion. Thus, in figures showing a lateral view of the hypopygium, the top of the page is morphologically ventral, while the bottom is dorsal. Morphological terminology follows Robinson and Vockeroth (1981), Stuckenberg (1999), and Grichanov (2007).
All studied material is housed in the collection of the Natal Museum, Pietermaritzburg, South Africa (NMSA). TAXONOMY Subfamily Peloropeodinae Robinson, 1970 Genus Meuffelsia Grichanov, gen. n. Etymology: The genus is named for the famous Dutch researcher of the Dolichopodidae, Henk Meuffels. Gender feminine. Type species: Meuffelsia erasmusorum Grichanov, sp. n. Diagnosis: The generic diagnosis is based on the two very close included species, and lists features considered to be of generic importance.
Length less than 2.0 mm; body dark, with dark setae; dorsal part of postcranium slightly concave; face without setae, relatively broad, slightly narrowed downward; pedicel globular; postpedicel small, subtriangular; stylus dorsoapical; labella with 6 pseudotracheae; posterior part of mesonotum distinctly flattened and slightly depressed; acrostichals biserial; 6 dorsocentrals; scutellum with 2 strong bristles and 2 minute adjacent lateral hairs; fore and mid coxae with anterior and apical cilia; hind coxa with Grichanov, sp. n., general habitus of the holotype in alcohol before dissection. 1 seta at middle; legs simple, with simple setae; mid and hind femora with strong anterior subapical seta; hind tarsus simple; wing nearly as long as body, relatively broad; m-cu short; segment 7 small, with tergum broad and sternum reduced; segment 8 large; hypopygial foramen left lateral; hypopygium with rounded-ovate cercus; hypandrium long and thick, asymmetrical, fused at base to epandrium; ventral surface of epandrium bare; surstyli asymmetrical, with left dorsal arm shorter or longer than right one, both broad, bearing a few short setae, and ventral arms of surstyli subequal in length, thin, directed ventrad, bearing a few short setae at apex; oviscapt with tergum 9+10 split medially into two arcuate narrow hemitergites, each bearing 4 short black acanthophorites; female cercus short, widened distad; anal plate broad, wider than long. Grichanov, sp. n. Figs 1-6 Etymology: The species is named in honour of Thea and Cobus Erasmus, on whose property the types were collected. Description: Colour (dry paratypes): Frons and face weakly shining bronze, brownish pollinose. Palpus and proboscis blackish brown; thorax bluish black; legs with coxae, femora and tarsi from apex of basitarsus mainly light brown; other podomeres mainly reddish yellow; abdomen black with brown sternites. Male. Head: Frons and face with black ground colour. One strong but short postvertical seta positioned far from postocular setal row; postocular setae all dark; 1 or 2 upper and 1 lower setae slightly longer than the others. Face under antennae 2× as wide as height of postpedicel. Antenna black, slightly longer than head height. Scape small, vase-like, with projecting inner angle forming distal denticle; pedicel larger, globular, with ring of short setulae and elongate apicodorsal seta; postpedicel subtriangular, as long as high at base, densely haired; stylus inserted dorsally at 2/3 from postpedicel base, 2× as long as main segments of antenna combined, shortly pubescent, with short thick segment 1 and filiform segment 2. Ratio of lengths of scape to pedicel to postpedicel to stylomere 1 to stylomere 2, 3:3:4:1:20. Palpus and proboscis yellow-brown, palpus subtriangular, with dark pubescence and dark short bristle. Thorax: With dark bristles, mesonotum blackish brown, pleura dark brown. One long and 1 short intra-alar, 1 humeral, 1 supra-alar, 2 notopleurals, 1 sutural, 1 presutural; 6 (2+4) dorsocentrals, with first slightly shorter and last slightly longer than others; 5th seta not shifted towards lateral margin; 6 or 7 pairs of biserial acrostichals extending to well-developed posterior mesonotal flattening. Upper propleuron without setae; proepisternum with 1 strong seta and 1 short hair above fore coxa. Legs: Mainly dirty yellow; mid and hind coxae, hind femur and apical segments of all tarsi distinctly brownish. Fore and mid coxae with dark anterior and apical cilia; hind coxa with 1 black bristle at middle. Fore leg without extraordinary setae; fore femur with just 1 or 2 short posteroventrals at apex; fore tibia simple, without strong setae; fore tarsus simple, with ordinary setulae. Length ratio of fore femur to tibia to tarsus (segments from first to fifth), 19:23:10:4:3:3:4. Mid femur simple, bearing 1 strong anterior subapical bristle and 1 fine posteroventral seta; mid tibia simple, with 1 very strong anterodorsal and 1 fine posterodorsal setae at basal third and 3 apical setae; no ventral setae; mid tarsus simple; segments 1-4 each with circlet of short apical spinules. Length ratio of mid femur to tibia to tarsus (segments from first to fifth), 30:31:14:5:4:3:4. Hind femur with 1 strong anterior subapical seta; hind tibia simple, with 1 strong anterior at basal 1/3, and 3 short posterodorsal, 2 or 3 very short ventral, 3 simple short apical setae, with only anterior apical seta somewhat longer, without strong dorsal subapical seta; hind tarsus simple; hind basitarsus with 1 short basoventral and 1 or 2 short apicoventral setae, with very small basal tooth posteriorly directed towards apex of hind tibia. Length ratio of hind femur to tibia to tarsus (segments from first to fifth), 35:35:15:14:6:4:5. Habitat: The type specimens from Sanyati Nature Farm were collected in the primarily indigenous riverine bush (Fig. 7) As reported by Dr R. M. Miller, the collecting site of the other two paratypes was most probably along the small river that runs down the valley with indigenous forest. Grichanov,sp. n. Figs 8,9 Etymology: The species is named after J. Manning, who collected the type series. Description: Male. Similar to M. erasmusorum sp. n. in all respects except as noted.
Epandrium globular, asymmetrical, distinctly (1.5×) longer than high, as wide as high; hypopygium with rounded-ovate densely haired cercus; hypandrium mostly concealed, asymmetrical, slightly shifted towards left side, deeply bifurcated from its midlength (ventral view), with broad, apically pointed lobes almost equal in length and shape; aedeagus expanded at apex, with adjacent long thin process; ventral surface of epandrium bare; distal epandrial lobes short and broad, bare, without projections; surstyli
9
Comparison: The two described species can hardly be distinguished by external morphology. Nevertheless, wing vein m-cu is located slightly closer to the wing base in M. erasmusorum than in M. manningi; therefore, wing vein ratios differ somewhat in both males and females of the two species. The species are easily distinguished by hypopygium morphology. The hypandrium is deeply bifurcated, with almost equal lobes in M. manningi, but with only a large triangular lobe on the right side of the main arm in M. erasmusorum. The surstyli are rather distinct in their shape and setation pattern, and their ventral arm in M. manningi is peculiar in bearing several long and strong pedunculate setae on the basal half. The latter species also has a somewhat more elongated epandrium and smaller cercus than M. erasmusorum. It is also worth noting that the left dorsal arm is shorter than the right one in M. erasmusorum, and vice versa in M. manningi.
Key to Afrotropical peloropeodine and allied genera and species groups Regarding Afrotropical Peloropeodinae and Sympycninae, the 'World catalog of Dolichopodidae' includes 11 genera, some of which should be excluded from the list (Grichanov 1998(Grichanov , 2008a. This key does not reflect phylogenetic relations of included taxa, being compiled for practical use only. Species groups in the genus Sympycnus follow Grichanov (2008b). 11 Two rather than one postverticals; strong ventral subapical seta on hind tibia; wing veins R 4+5 and M 1+2 slightly diverging rather than parallel; strongly oblique crossvein m-cu forming acute (ca 60°) angle with CuA 1 ; mid femur with ventral bristles in basal part; male wing costa with long and thick stigma beyond R 1 ; epandrial foramen mostly middorsal . The following character states places Meuffelsia in the Peloropeodinae (see Yang et al. 2006): Occiput convex backward. Vertex nearly flat. Upper occiput slightly concave, verticals located nearly at level of lateral ocellus; 1 short postvertical. Postocular bristles onerowed. Eyes with tiny hairs. Male eyes separated on face; male face distinctly narrowing downward. Mesonotum with flat mid-posterior slope. Propleuron sparsely haired, with 1 bristle on lower portion. Mid and hind femora with 1 anterior preapical bristle. Hind coxa with 1 outer bristle at middle. Anal cell present; anal vein present as a short fold. Male abdominal tergite 7 short triangular, without hairs and bristles; sternite 7 reduced. Male genitalia relatively large and mostly exposed; surstylus well developed; epandrial lateral lobe distinct; hypandrium long.
The following character states are common to Meuffelsia, Afrotropical Micromorphus and Peloropeodes (after Grichanov 2000): Antenna slightly longer than head height. Scape small, bare, vase-like, with slightly projecting inner apex. Stylus dorsal. Palpus and proboscis short, haired; palpus with one black seta. Pseudotracheae symmetrically sclerotised. One strong vertical seta, half as long as head height; 2 strong ocellar setae of the same length with additional pair of short hairs posteriorly. Postocular setae in one row. Mesonotum distinctly flattened in front of scutellum. Mesonotum with 1 humeral, 1 posthumeral, 2 notopleural, 1 presutural, 1 sutural, 2 intra-alar and 1 supra-alar setae, 1 strong propleural seta. Scutellum with 2 strong setae, with or without very short lateral hairs. Fore tibia without major setae. Hind coxa with 1 black external seta. Mid and hind femora with 1 anterior subapical seta. Mid tarsomeres 1-4 with short apicoventral spinules. Wing costa with 2 strong bristles before humeral crossvein; upper seta longer than lower. Anal lobe long and narrow; anal vein fold-like; alula undeveloped; anal angle obtuse or absent.
Meuffelsia shares with Micromorphus such characters as simple legs, no strong dorsal seta on antennal pedicel, and distal part of CuA 1 being 4-5 times longer than m-cu. Nevertheless, Micromorphus shows striking differences to Meuffelsia: no acrostichals and basal denticle on hind basitarsus; dorsal and ventral subapical setae on hind tibia, 1 strong and 1 hair-like intra-alar setae; setose epandrial lobes at base and at apex of ventral epandrial surface; and symmetrical surstyli. Meuffelsia shares with examined Afrotropical and European species of Peloropeodes such characters as: presence of two rows of acrostichals; well developed segment 7 in males; and usually asymmetrical hypopygium. Nevertheless, Peloropeodes differs in many morphological characters: antennal pedicel with strong dorsal seta; postpedicel usually much larger than pedicel; male mid coxa with apical spine of glued cilia; hind tibia with 1 stronger subapical dorsal seta, slightly longer than diameter of tibia; various podomeres, especially on fore leg, often modified, bearing long setae or cilia; and fore tarsomere 5 always modified, with enlarged asymmetrical claws.
The combination of character states is distinctive to Meuffelsia but any of them may also occur in other Peloropeodinae. However, the postabdomen morphology is very remarkable for the genus, and the unusual male surstylus shape has not yet been described in other genera of the subfamily.\===
Domain: Environmental Science Biology. The above document has
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Callus Induction and Micropropagation of Lilium candidum L. Using Stem Bulbils and Confirmation of Genetic Stability via SSR-PCR
Natural populations of Lilium candidum L. are remarkably affected by biotic and abiotic factors therefore there is a requirement to develop effective micropropagation protocol to provide mass production, multiplication and conservation of these plants. For this reason, this study was aimed to develop an efficient micropropagation method for multiple shoot production via somatic embryogenesis induced from L. candidum stem bulbils and also to determine the genetic stability of in vitro grown plants using SSR markers. The obtained results of this study are the first comprehensive reports including an investigation of genetic fidelity on somatic embryogenesis of L. candidum. After surface sterilization of bulbils, the calculated regeneration percentage of them was 89.5% and the callus induction was achieved using leaf segments of in vitro grown bulbils. The well formed somatic embryos were obtained from smooth whitish-yellow colored calli and these somatic embryos produced well formed healthy L. candidum seedlings with adventitious roots. All rooted seedlings were easily adapted to greenhouse conditions and the genetic stability of in vitro grown seedlings were determined by using SSR-PCR technique and it was calculated as 100%. ARTICLE HISTORY Received: June 15, 2020 Revised: September 22, 2020 Accepted: December 03, 2020
INTRODUCTION
Lilium candidum being a member of Liliaceae family is a perennial herbaceous medicinal and aromatic plant [1]. Because of its attractive white flowers, aromatic and medicinal components, L. candidum has widely been cultivated in many countries such as in USA, Italy, Netherlands, Spain, Germany, France and Turkey [2]. L. candidum is a species adapted to the Mediterranean climate. The other Lilium species, which are spread in our country are distributed in areas under the influence of the Black Sea climate which is cooler and more rainy climate. Their natural populations are remarkably affected by biotic and abiotic factors such as anthropological pressure, diseases, pathogen attacks, carbon fuel pollution, dramatic climate changes, therefore there is a requirement to develop effective micropropagation protocol to provide mass production, multiplication and conservation of these plants [3].
Plant tissue culture techniques providing very useful approach for rapid propagation of plant species help to preserve especially the economically important and endangered species. them in an aseptic condition in liquid or on semi-solid nutrient medium [4,5]. Although the earliest studies on plant tissue culture date back to the early twentieth centuries, the main studies improved completely from 1970s onwards, as technological improvements began to be increased and theoretical limitations started to be overcome by expanding interest in usage of biotechnological techniques [6]. Until today many plant tissue culture techniques have been developed for improvement and breeding of different group plant species such as conifers [7], dicots [4] and monocots [8].
Somatic embryogenesis being one of the most important micropropagation tools has been applied throughout different types of in vitro systems for plant mass production. This tool serves also many advantages for in vitro propagation of true-to-type clones, rapid regeneration of genetic transformed and somatic hybridized plants and induction and selection of mutant types. Additionally, somatic embryogenesis plays an important role in key studies on totipotency and understanding of principle pathways of morphogenesis. Because of all these possible advantages of somatic embryogenesis, it has been tempting studies on investigation of in vitro conditions for somatic embryo induction of different plant species [9] and an excessive number of procedures for effective in vitro regeneration based on somatic embryogenesis have been developed for many economically important plant species [10].
The continuity of genetic stability during in vitro growing and subculturing periods is very important for clonal propagation of especially medicinal, aromatic, rare and endangered plant species [4]. It is important to maintain genetic stability in micropropagated cultures. Molecular markers are more stable and highly reproducible compared to the various morphological, cytological and protein markers used to detect variation in tissue cultivated plants. A molecular marker such as SSR, ISSR, RAPD, AFLP etc. is beneficially used in tissue culture studies to test the genetic stability of in vitro regenerates [11]. In all prokaryotes and eukaryotes, SSRs expressing sequences between 1 and 6 nucleotides on DNA are one of the preferred markers in genetic diversity studies due to their high mutation rate and consequently high polymorphism rate. They are an excellent source of polymorphism for eukaryotic genomes [12].
Although there are many published papers for method development on somatic embryogenesis of Lilium spp., [13][14][15][16][17][18][19][20], none of them reported genetic stability investigation after in vitro propagated L. candidum natural populations using SSR molecular marker techniques. The current study was carried out to improve efficient micropropagation method for multiple shoot propagation via somatic embryogenesis induced from L. candidum stem bulbils and also to determine the genetic stability of in vitro grown plants using SSR markers. The obtained results of this study are the first comprehensive reports including investigation of genetic fidelity on somatic embryogenesis of L. candidum.
Plant Materials
Plant samples belonging to natural populations of L. candidum L. were collected from Nif Mountain (İzmir, Turkey). The legal authorization letter for sample collection was obtained from Republic of Turkey Ministry of Agriculture and Forestry, document number 36178555-604.01.01/488551 and all collected samples were taxonomically identified by Dr. Hasan Yildirim and Dr. Ademi Pirhan.
In vitro Culture Establishment
The stem bulbils of L. candidum ( Figure 1A-B) obtained from natural populations were sterilized according to surface sterilization protocol of Özüdoğru et al. [4]. After surface sterilization, the bulbils were transferred to semi solid Murashige and Skoog (MS) [21].
Acclimatization to Greenhouse Conditions
Multiple rooted shoot clusters derived from in vitro grown somatic embryos of L. candidum ( Figure 2A) were acclimatized under greenhouse conditions by transferring them into 100 mL plastic pots ( Figure 2B) including nitrogen-rich peat and to gradually decrease the relative humidity of peat, the pots were closed with transparent pots and a hole was drilled on top of the transparent pots every day [4]. The plastic pots were irrigated with tap water for seven days, the transparent pots were removed after seven days and the plastic pots were transferred to greenhouse conditions ( Figure 2C).
Data Collection and Statistical Analysis
In vitro regeneration of L. candidum bulbils, callus induction and somatic embryogenesis data were calculated as percentage values. All data were collected after four weeks incubation at standard culture conditions and all treatments were repeated at least three times. The statistical analysis of the non-parametric data was performed by means of the test for homogeneity rates, and the differences obtained by treatments were chosen using nonparametric statistical test [24]. Separate data were exposed to ANOVA, monitored by the least significant difference test at P≤0.05 to compare means. Data were analyzed by SPSS package program (IBM, version 21).
Determination of Genetic Stability
DNA isolation was performed from the mother plant, callus derived from in vitro regenerated bulbils, developing somatic embryogenesis, and acclimated seedlings (callus, somatic embryogenesis and acclimated seedlings were obtained after the third subculture) in order to confirm whether there was a variation. All samples were stored at -20 °C. SSR markers we previously determined as polymorphic were used in the naturally grown L. candidum genotypes.
DNA Isolation
DNA isolation was performed in all the above mentioned examples by modifying the method of Lodhi et al., [25]. After isolation, DNA samples were run on a 1% agarose gel to determine their purity and were visualized with a gel imaging system (MS, Major Science).
SSR Analysis
For SSR analysis, SSR primers to be used in the study were determined by referring to Du et al. [26]. Of these SSR primers, 12 pairs of them were identified as polymorphic in naturally grown L. candidum populations in Turkey ( Table 1).
The total volume of PCR amplification reaction used in the study was 20 µL. Each PCR reaction consisted of 1Taq PCR buffer, 0.2 mM dNTPs, 2 mM MgCl2, 0.4 µM forward primer, 0.4 µM reverse primer, 0.2 units Go Taq Polymerase (Promega Go taq-M8295), 100 ng DNA and dH2O. PCR amplification; The initial denaturation step was carried out at 94 °C for 5 minutes followed by 35 cycles of 1 minutes denaturation at 94 °C, 30 second annealing at (48-55 °C) and 1 minutes extension at 72 °C with a final extension at 72 °C for 7 minutes. The PCR products obtained were run at a consistent voltage of 4.5 V/cm on a 4 % agarose (1.2% Biomax Basic Agarose and 2.8% Delta Micropor Agarose) gel in 1TBE buffer for 3 hours by electrophoresis and bands were visualized by EtBr (Sigma-Aldrich®). Fragment size was determined using 100 bp ladder (ABM Cat: G016).
Initation of L. candidum in vitro Cultures
After surface sterilization, in vitro culture initiation was provided by transferring L. candidum bulbils to MS initation medium described above, and obtained clean material percentage was calculated as 65.5%. The regeneration percentage of clean bulbils was calculated as 89.5% after four weeks incubation on OM regeneration medium described above. We used ~ 29 stem bulbis for each experiment, the clean material obtained showed regeneration of ~ 89.5%.
Induction of Calli and Somatic Embryogenesis
For each experiment, approximately 30 leaf fragments were used as the explant source for callus induction, The callus induction was achieved by using leaf fragments of in vitro regenerated L. candidum bulbils on MS callus induction medium described above and after two weeks incubation, the smooth whitish-yellow colored calli were successfully formed from widening and hardening leaf segments. Callus development was observed in each explant. The average callus induction rate per explant was calculated as 100%. After three weeks incubation, all cells of well formed calli produced healthy somatic embryos by transferring to OM somatic embryo producing medium described above. All of somatic embryos produced well formed healthy L. candidum seedlings and all seedlings have proper adventitious roots to adapt greenhouse conditions.
Determination of Genetic Stability
Micropropagation and callus culture experiments were carried out during the study to determine whether there were any somaclonal variations in the plants by the effect of nutrient media and growth regulators. For this purpose in the current study SSR markers were used and scanned for whether there was a genetic variation between the materials we produced by using micropropagation. The materials obtained from callus regeneration, somatic embryogenesis and acclimated plants were checked with SSR markers and the produced band profiles of these materials were found to be the same as the mother plant. The 12 pairs of primers produced 24 scorable bands (average: 2 bands/pairs of primer). As an example, SSR-PCR amplification of primer LS-ZJU 11 in DNAs of L. candidum mother plant, in vitro regenerated bulbils, in vitro grown somatic embryos and acclimatized plantles were visualized on agarose gel mixture is shown (Figure 3).
The plant tissue culture technologies having wide application area such as single in vitro cells, tissues and organs production, calli and suspensions in big-mass scale production, has become an important tool for plant biotechnology [27]. Plant tissue culture generally defining all procedures of in vitro cultivation, growth and maintenance of plant materials, has been developed and used for basic studies on cell differentiation, growth, division and fusion, plant physiology and biochemistry experiments, metabolic and genetic engineering, gene transformation, conservation of plant biodiversity [28]. In the current study, indirect somatic embryogenesis were achieved by using calli induced from in vitro grown L. candidum bulbils.
Although callus induction was achieved by using MS medium, the somatic embryogenesis were obtained by using OM medium, both of media were supplemented with NAA. One of the main differences between the two media is that OM contains a different nitrogen salt [The OM medium contains a different NO3 -[Ca(NO3), 2.54 mM] as nitrogen source and it also contains lower concentrations of other nitrate salts than the MS medium (NH4NO3, 5.15 mM; KNO3, 6.09 mM)]. There have been many studies on potential benefits of different nitrogen sources such as NH4 + and NO3and main purpose of these studies was to develop nutrient components of culture medium for different plant species. For example, the different concentrations of these forms of nitrogen in the nutrient media have produced very positive responses on shoot regeneration [40], plant recovery efficiency in ovule cultures [41] and somatic embryo development [42]. In the current study, because of previous studies [3,43] in the literature, at first the MS medium was tested for in vitro propagation of L. candidum. However, in subsequent studies, OM [22] medium containing different concentrations of NH4 + and NO3was reported to overcome possible growth problems after transfer to in vitro condition, accelerate the adaptation of the plant to the nutrient medium and provide better growth and it was obtained very positive results in comparison to MS [21] medium. (1,2), in vitro regenerated bulbils (3,4), in vitro grown somatic embryos (5,6), and acclimatized plantles (7,8) with SSR LS-ZJU11 [26] primers were visualized on agarose gel mixture (1.2% Biomax Basic Agarose and 2.8% Delta Micropor Agarose); M, 100 bp DNA ladder (ABM Cat: G016).
Plant cell and tissue culture have become a tool for the rapid reproduction of valuable species. Many plants can be produced with plant tissue culture by making continuous subculture under optimal conditions. However, in subculture studies performed by changing the growth environment (by accelerating or slowing down the growth), extending the changing intervals of the environment or increasing the number of subcultures have some risks. One of these risks is the emergence of somaclonal variations. Somaclonal variations are variations of genetic origin that occur between plants regenerated from somatic tissue originated callus, cell and protoplast cultures. These variations can be observed in plants as morphological, physiological and agricultural features. They are demonstrated by phenotypic, cytological and molecular level investigations [44]. Many reports have documented the assessment of genetic stability of micropropagated plants using SSR markers [12,45]. Liu and Yang [15] stated that 11 ISSR primers were used to determine the genetic stability of regenerated shoots in comparison to their mother plants. They reported that the genetic similarity between clonal samples and the mother plant was between 0.92 and 1.0. All 15 micropropagated materials and mother plants were grouped in a single master cluster with 92% similarity. They estimated the rate of somaclonal variation in plantlets to be 4.2%, emphasizing that direct shoot formation from explant regeneration indicates a safe method for the reproduction of "true-to-type" plants. Their results is acceptable for efficient mictopropagation, howewer, Asmita et al. [20] tested genetic stability using 10 SSR markers in twenty one in vitro regenerated plants. They produced a total of 273 bands from 10 SSR markers. The number of scorable bands for each primer ranged from 1 to 2. Among them, polymorphism information content was not recorded. Similar to our study, they stated that the banding profile of micropropagated plants is monomorphic and similar to the mother plant. Bi et al. [19] did not observe any polymorphism in embryo-like structure when analyzed with ISSR markers in five Lilium species and hybrids. Also, and no change in flow cytometry ploid level was observed. AFLP and ISSR markers have been used to detect genetic stability in direct shoot regenerants and ISSR markers showed no polymorphism but AFLP markers showed less than one percent [17]. Yadav et al. [16] used 6 RAPD markers and reported that there were no genetic variations in the regenerated micro bulbs. They stated that the results obtained through in vitro produced Lilium spp bulblets were clonally identical with mother plants. Varshney et al. [13] observed no change in progeny (randomly selected after four and 12 subcultures) with the RAPD marker.
CONCLUSION
In the current study, results of investigations based on SSR markers revealed that 12 pairs of primers produced amplified products with same monomorphic patterns of the mother plant, callus derived from in vitro regenerated bulbils, developing somatic embryogenesis, and acclimated material seedlings. It can be concluded that the results obtained by micropropagation protocol we employed here did not stimulate somaclonal variation in clones for these specific SSRs which were determined to be polymorphic markers in L. candidium genotype based on our previous studies.
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Domain: Environmental Science Biology
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An integrative taxonomic study of the needle nematode complex Longidorus goodeyi Hooper, 1961 (Nematoda: Longidoridae) with description of a new species.
Needle nematodes are polyphagous root-ectoparasites parasitizing a wide range of economically important plants not only by directly feeding on root cells, but also by transmitting nepoviruses. This study deciphers the diversity of the complex Longidorus goodeyi through integrative diagnosis method, based on a combination of morphological, morphometrical, multivariate analysis and molecular data. A new Longidorus species, Longidorus panderaltum n. sp. is described and illustrated from a population associated with the rhizosphere of asphodel (Asphodelus ramosus L.) in southern Spain. Morphologically, L. panderaltum n. sp. is characterized by having a moderately long female body (5.2-7.0 mm), lip region bluntly rounded and slightly offset by a depression with body contour, amphidial pouch with slightly asymmetrical lobes, odontostyle 80.5–101.0 µm long, tail short and conoid rounded. Longidorus panderaltum n. sp. is quite similar to L. goodeyi and L. onubensis in major morphometrics and morphology. However, differential morphology in the tail shape of first-stage juvenile, phylogeny and haplonet analyses indicate they are three distinct valid species. This study defines those three species as members of L. goodeyi complex group and reveals the taxonomical complexity of the genus Longidorus. This L. goodeyi complex group demonstrated that the biodiversity of Longidorus in this region is still not fully clarified.
Introduction
Needle nematodes of the genus Longidorus Micoletzky, 1922 are a globally important group of ectoparasitic nematodes and considered a major group of plant-pathogens. These nematodes use their needle stylet to feed on the apical root cells inducing galls in the tips and reducing the yield and quality of a range of horticultural or agricultural crops and some of them are recognised as vectors of important nepovirus (Taylor and Brown 1997;Decraemer and Robbins 2007;Palomares-Rius et al. 2017a). Parasitism by Longidorus spp. have a detrimental effect on root growth by inducing hypertrophied uninucleate cells, highly active metabolically, followed by hyperplasia with synchronized cell division (Palomares et al. 2017a). This genus constitutes a great complex group of around 180 valid species (Archidona-Yuste et al. 2019a;Cai et al. 2020a, b;Amrei et al. 2020) and species delimitation is critical from a phytopathological, ecological and biogeographical point of view.
The morphological convergence among Longidorus spp. and the existence of cryptic species complexes make the accurate identification of species considerably more difficult (De Luca et al. 2004;Gutiérrez-Gutiérrez et al. 2013;Archidona-Yuste et al. 2016b, 2019aCai et al. 2020a, b). In the family Longidoridae, morphological and morphometric studies in addition to molecular sequencing have been used simultaneously to group specimens into species, including multivariate analysis using morphometric characters in which a high number of measured individuals were analysed in order to find morphometric differences amongst them (Archidona-Yuste et al. 2016a;Cai et al. 2020b). Also, the utility of DNA barcoding and molecular species delimitation approaches in species discovery and the detection of cryptic lineages into the genus Longidorus have been demonstrated by numerous studies Gutiérrez-Gutiérrez et al. 2013;Palomares-Rius et al. 2017c;Archidona-Yuste et al. 2016b, 2019aLazarova et al. 2019;Amrei et al. 2020). Specifically, molecular methods using different fragments of nuclear ribosomal DNA (including 28S rRNA, 18S rRNA and ITS), mitochondrial DNA (cytochrome c oxidase subunit I, coxI and nicotinamide dehydrogenase subunit 4. nad4) gene sequences have been used to provide precise identification of species and elucidate the phylogenetic relationships within the genus Longidorus Neilson et al. 2004;Palomares-Rius et al. 2008;Gutiérrez-Gutiérrez et al. 2012;Kumari and Subbotin 2012;Subbotin et al. 2014;Archidona-Yuste et al. 2016a, b).
In recent years, several studies have demonstrated that the species diversity within the family Longidoridae remains as a major gap in the biodiversity of soil nematodes, particularly in the Iberian Peninsula which is considered a plausible speciation centre for this family (Coomans 1996;Archidona-Yuste et al. 2016a, b;Cai et al. 2020a, b). This suggests that the continued systematic sampling in unexplored environments in this area could lead to an increase in the overall species richness of this group of nematodes. Following this sampling strategy during the spring of 2019 in southern Spain, we observed a high density of a needle nematode morphologically resembling Longidorus goodeyi suggesting a wider geographical distribution for this nematode species or the occurrence of a new species complex within the genus Longidorus. This fact prompted us to undertake a detailed comparative morphological and molecular study with previous reported data including topotype specimens of this species. In addition, a detailed integrative approach was conducted in order to clarify the taxonomical status of the new nematode population detected where the preliminary results indicated that this population belongs to an unknown Longidorus species and therefore, the existence of a new species complex within this genus. Therefore, the objectives of this study were: (1) to discover the diversity of L. goodeyi complex through integrative taxonomy combining morphological analysis and a species delineation approach based on multivariate morphometric methods and nuclear haplonets tools; (2) to describe a new species of the genus Longidorus which belongs to the L. goodeyi complex group; (3) to characterise molecularly the sampled Longidorus sp. population using the D2-D3 expansion segments of the 28S rRNA gene, ITS1 and partial 18S rRNA gene; and (4) to study the phylogenetic relationships of the identified Longidorus species with available sequenced species.
Material and methods
Nematode population sampling, extraction and morphological identification Specimens from the population of the unidentified Longidorus species were collected during the spring season of 2019 in a natural pasture of asphodel (Asphodelus ramosus L.) with a stony soil at 1,800 m elevation in La Pandera Mountain, Valdepeñas de Jaén, Jaén province, in Andalusia, southern Spain (Table 1). Soil samples were collected using a shovel, randomly selecting four to five cores, and considering the upper 5-50 cm depth of soil. Nematodes were extracted from a 500-cm 3 sub-sample of soil by centrifugal flotation and a modification of Cobb´s decanting and sieving methods (Coolen 1979;Flegg 1967).
Specimens for study using light microscopy (LM) and morphometric studies were killed and fixed in an aqueous solution of 4% formaldehyde + 1% glycerol, dehydrated using alcohol-saturated chamber and processed to pure glycerine using Seinhorst's method (Seinhorst 1966) as modified by De Grisse (1969). Specimens were examined using a Zeiss III compound microscope with differential interference contrast at magnifications up to 1,000x. Photomicrographs of nematodes were taken by a Nikon DM100 (Nikon, Barcelona, Spain). All measurements were expressed in micrometres (µm). For line drawings of the new species, light micrographs were imported to CorelDraw version X7 and redrawn. All other abbreviations used are as defined in Jairajpuri and Ahmad (1992).
Topotype specimens of L. goodeyi from Rothamsted, UK and a population from Yorkshire, UK were also used for morphological, morphometric and molecular analyses after verifying that their morphology was congruent with that of the original description.
Nematode molecular identification
To avoid mistakes in the case of mixed needle populations in the same sample, three to four live nematodes from each population were temporarily mounted in a drop of 1M NaCl containing glass beads to ensure specimens were not damaged. All necessary morphological and morphometric data by taking pictures and measurements using the above camera-equipped microscope were recorded. This was followed by DNA extraction from single female individuals and polymerase chain reaction (PCR) --assays were performed as described by Castillo et al. (2003). The D2-D3 expansion segments of 28S rRNA was amplified using the D2A (5'-ACAAGTAC C G T G A G G G A A A G T T G -3 ' ) and D3B (5' -TCGGAAGGAACCAGCTACTA-3') primers (De Ley et al. 1999). The ITS1 region was amplified using forward primer 18S (5´TTGATTACGTCCCTGCCCTTT-3´) (Vrain et al. 1992) and reverse primer rDNA1 (5´-ACGAGCCGAGTGATCCACCG-3´) (Cherry et al. 1997). The portion of 18S rRNA was amplified using primers 988F (5´-CTC AAA GAT TAA GCC ATG C-3´), 1912R (5´-TTT ACGGTC AGA ACT AGG G-30), 1813F (5´-CTG CGT GAG AGGTGA AAT-3´) and 2646R (50-GCT ACC TTG TTA CGA CTT TT-3´) (Holterman et al. 2006). Finally, the portion of the coxI gene was amplified as described by Lazarova et al. (2006) u s i n g t h e p r i m e r s C O I F ( 5 ′ -G A T T TTTTGGKCATCCWGARG-3′) and COIR XIPHR2 PCR cycle conditions for ribosomal genes were: one cycle of 94°C for 15 min, followed by 35 cycles of 94°C for 30 s, annealing temperature of 55°C for 45 s, 72°C for 3 min, and finally 72°C for 10 min. The cycle for mtDNA was as described by He et al. (2005): 95°C for 10 min, five cycles at 94°C for 30 s, 45°C for 40 s, and 72°C for 1 min, and a further 35 cycles at 94°C for 30 s, 37°C for 30 s, and 72°C for 1 min, followed by an extension at 72°C for 10 min. PCR products were purified after amplification using ExoSAP-IT (Affimetrix, USB products), and used for direct sequencing in both directions using the primers referred above. The resulting products were purified and run on a DNA multicapillary sequencer (Model 3130XL genetic analyser; Applied Biosystems, Foster City, CA, USA), using the BigDye Terminator Sequencing Kit v.3.1 (Applied Biosystems, Foster City, CA, USA), at the Stab Vida sequencing facilities (Caparica, Portugal). The newly obtained sequences were submitted to the GenBank database under accession numbers indicated on Table 1 and the phylogenetic trees.
Recognition of putative species within Longidorus goodeyi complex and species delimitation approach
This species group was identified from previous largescale taxonomic and phylogenetic studies in the genus Longidorus (Archidona-Yuste et al. 2016b;2019a;Cai et al. 2020a, b). From the analyses of phylogenetic relationships analyses, a well-supported clade that included the Iberian Peninsula species was identified (clade I; Archidona-Yuste et al. 2019a). Morphological comparison showed that several of the diagnosis characters defining the genus Longidorus (Chen et al. 1997;Loof and Chen 1999;Peneva et al. 2013) were characteristic of the group as a whole, highlighting a hemispherical convex-conoid tail shape. We named the group the L. goodeyi complex, after the oldest described species within the group, and used the main diagnostic features characterizing this species to ascertain morphologically closely related species (viz. L. goodeyi and L. onubensis Archidona-Yuste et al. 2016b). Additional morphological traits were then recognized as diagnostic characters of this nematode complex such as overall nematode size and shape, odontostyle length, location of dorsal and ventrosublateral gland nuclei on the terminal pharyngeal bulb or the lip region shape amongst others (Chen et al. 1997;Loof and Chen 1999;Peneva et al. 2013). The new population of Longidorus sp. detected in this study was also included in this group given the close relationship morphologically with L. goodeyi as outlined above. An iterative analysis of morphometric and molecular data using two independent strategies of species delimitation was utilised to asses described and undescribed specimens and to determine species boundaries within this newly-defined species complex.
Species delineation using morphometry was conducted with principal component analysis (PCA) in order to estimate the degree of association among species within the L. goodeyi-complex (Legendre and Legendre 2012). PCA was based upon the following morphological characters: L (body length), the ratios a, c, c', d, d', V, odontostyle and odontophore length, lip region width and hyaline region length (Table 2, Archidona-Yuste et al. 2016a, b;Jairajpuri and Ahmad 1992). Prior to the statistical analysis, diagnostic characters were tested for collinearity (Zuur et al. 2010). We used the collinearity test based on the values of the variance inflation factor (VIF) method that iteratively excludes numeric covariates showing VIF values > 10 as suggested by Montgomery and Peck (1992). PCA was performed by a decomposition of the data matrix amongst populations using the principal function implemented in the package 'psych' (Revelle 2019). Orthogonal varimax raw rotation was used to estimate the factor loadings. Only factors with sum of squares (SS) loadings > 1 were extracted. Finally, a minimum spanning tree (MST) based on the Euclidean distance was superimposed on the scatter plot of the L. goodeyispecimens complex against the PCA axes. MST was performed using the ComputeMST function implemented in the package 'emstreeR' (Quadros 2019). All statistical analyses were performed using the R v. 3.5.1 freeware (R Core Team 2019).
Species delineation based on molecular data was performed using nuclear haplonet tools in order to determine species boundaries and to clarify putative molecular species within L. goodeyi complex. Haplotype network was constructed to each of the two separated datasets, i.e. the nuclear 28S region and the ITS1 region. Alignments were converted to the NEXUS format using DnaSP V.6 (Rozas et al. 2017); and TCS networks (Clement et al. 2002) were applied in the program PopART V.1.7 ( [URL]). In this case, no heterozygous individuals were found in nuclear 28S and ITS1 sequences, so haploweb was not suitable for analysis and individuals were simply classified as haplogroups.
Phylogenetic analyses D2-D3 expansion segments of 28S rRNA, ITS1, partial 18S rRNA and ITS1 rRNA sequences of the unidentified Longidorus species, L. onubensis and L. goodeyi populations were obtained in this study. These sequences and other sequences from species of Longidorus spp. from GenBank were used for phylogenetic analyses. Outgroup taxa for each dataset were chosen following previously published studies (He et al. 2005;Holterman et al. 2006;Gutiérrez-Gutiérrez et al. 2013;Archidona-Yuste et al. 2019a;Cai et al. 2020a, b;Radivojevic et al. 2020). Multiple sequence alignments of the different genes were made using the FFT-NS-2 algorithm of MAFFT V.7.450 (Katoh et al. 2017). Sequence alignments were visualised using BioEdit (Hall 1999) and edited by Gblocks ver. 0.91b (Castresana 2000) in Castresana Laboratory server ( [URL]_ server.html) using options for a less stringent selection (minimum number of sequences for a conserved or a flanking position: 50% of the number of sequences + 1; maximum number of contiguous non-conserved positions: 8; minimum length of a block: 5; allowed gap positions: with half). Phylogenetic analyses of the sequence datasets were based on Bayesian inference (BI) using MrBayes 3.2.7a (Ronquist et al. 2012). The bestfit model of DNA evolution was obtained using JModelTest V.2.1.7 (Darriba et al. 2012) with the Akaike Information Criterion (AIC). The best-fit model, the base frequency, the proportion of invariable sites, and the gamma distribution shape parameters and substitution rates in the AIC were then given to MrBayes for the phylogenetic analyses. Unlinked general timereversible model with invariable sites and a gammashaped distribution (GTR + I + G) for the D2-D3 expansion segments of 28S rRNA and the partial 18S rRNA and a transitional model with a gamma-shaped distribution (TIM2 + G) for the ITS1 region. These BI analyses were run separately per dataset using four chains for 2 × 10 6 generations for each molecular marker. The Markov a Based on 20 female specimens of Longidorus panderaltum n. sp. from a population sample, 20 female specimens of Longidorus goodeyi from topotype population sample, and 8 female specimens of Longidorus onubensis from paratype population sample. All populations were molecularly identified. The odontophore length and d ratio were excluded by the multicollinearity test and then, they were not included in the multivariate analysis for the Longidorus goodeyi complex. Values of morphometric variables 1 to 2 components (eigenvector > 0.41) are underlined. b Morphological and diagnostic characters according to Jairajpuri and Ahmad (Jairajpuri and Ahmad 1992) with some inclusions. a = body length/maximum body width; c = body length/tail length; c' = tail length/body width at anus; d' = body diameter at guiding ring/body diameter at lip region; Odt = odontostyle length; V = (distance from anterior end to vulva/body length) x 100.
Chains were sampled at intervals of 100 generations. Two runs were conducted for each analysis. After discarding burn-in samples and evaluating convergence, the remaining samples were retained for further analyses. The topologies were used to generate a 50% majority-rule consensus tree. Posterior probabilities (PP) are given on appropriate clades. Trees from all analyses were visualised using FigTree software V.1.4.4 ( [URL] and descriptions
Species delimitation was carried out using two independent methods based on morphometric (multivariate analysis) and molecular data using ribosomal sequences (haplonet). Multivariate morphometric and haplonet methods were performed on the studied populations to verify species identifications. The integration of this procedure with the analysis of nematode morphology allowed us to verify Longidorus panderaltum n. sp. as valid new species within the L. goodeyi complex. Additionally, we maintained a consensus approach for the different species delimitation methods, including concordant results in phylogenetic trees inferred from nuclear markers and/or different morphological or morphometric characteristics.
Multivariate morphometric analyses of Longidorus goodeyi complex
In the principal component analysis (PCA), the first two components (sum of squares (SS) loadings > 1) accounted for 73.20% of the total variance in the morphometric characters of the L. goodeyi-complex (Table 2). Table 2 includes the SS loadings for the three extracted factors, which were a linear combination of all characters in the analysis. The eigenvectors for each character were used to interpret the biological meaning of the factors. First, principal component 1 (PC1) was mainly dominated by nematode body length and the a ratio with a high positive weight (eigenvector = 0.418 and 0.449), relating this component with the overall nematode size and shape. PC2 was mainly dominated by high positive weight for the lip region width and the c' ratio (eigenvectors = 0.510 and 0.523, respectively) as well as similar but negative weight for the c ratio (eigenvector = -0.535) ( Table 2). This component was therefore related with the lip and tail shape. Overall, these results suggest that all of the extracted PCs were related to the overall size and shape of nematode populations. The results of the PCA were represented graphically in Cartesian plots in which populations of the L. goodeyi-species complex were projected on the plane of the x-and y-axes, respectively, as pairwise combination of PC1 and 2 (Fig. 1). In the graphic representation of the L. goodeyi complex, the specimens of L. goodeyi and L. panderaltum n. sp. were projected showing a clustered distribution pattern owing to their low morphometric variation within population (Tables 3 and 4). However, the specimens of L. onubensis were projected showing an expanded distribution owing to the wide morphometric variation detected in this species (Archidona-Yuste et al. 2016b) (Fig. 1). We observed that all species were clearly separated amongst them, being this spatial distribution dominated by the two extracted principal components (PC1 and 2, 73.20% of the total of variance) ( Table 2 and Fig. 1). The spatial separation dominated by PC1 grouped species according to the nematode body length and the maximum body width as derived by the ratio a (Table 2). Thus, L. panderaltum n. sp. specimens having shorter and wider nematode body were located at the left side, and on the opposite side was L. onubensis, which is characterized by longer and narrower nematode body (Fig. 1). However, specimens of L. goodeyi were located in the middle part of the plane and clearly grouped among the specimens of L. panderaltum n. sp. and L. onubensis, having an intermediate nematode body length and maximum body width between these two species (Fig. 1). Likewise, the spatial separation dominated by PC2 grouped species according to the lip region width and female tail length as derived by the ratios c and c' ( Table 2). In this case, L. goodeyi specimens having a longer tail and wider lip region were located on the top side (above y = 0), clearly separating from the specimens of L. panderaltum n. sp. and L. onubensis which showed similar values for these diagnostic characters ( Fig. 1; Table 3; Archidona-Yuste et al. 2016b). A minimum spanning tree (MST) superimposed on the plot of the first two principal components showed that the morphometric variation of the L. goodeyi-specimens complex located L. goodeyi as link connecting for L. panderaltum n. sp. and L. onubensis, indicating a wider morphological separation between these last two species (Fig. 1). These results support the denomination of this species complex using L. goodeyi not only because it is the oldest described species but also for its central position in the morphometric variation of this species complex.
Therefore, the 28S rRNA and ITS1 haplonets clearly resolved L. panderaltum n. sp., L. goodeyi and L. onubensis as separate and genetically isolated lineages. Besides, no heterozygous individuals were found in nuclear 28S and ITS sequences, so haploweb was not suitable for analysis and individuals were simply classified as haplogroups.
Difficulties to obtain coxI sequences from L. goodeyi prevent to carry out this analysis for mitochondrial genes.
Guiding ring single, located approximately ca. 2 times the lip region width from anterior end. Odontostyle moderately long, 1.5 times as long as odontophore, straight or slightly arcuate. Odontophore weakly developed, with rather weak swollen base. Pharynx short, 495.5 ± 52.7 (430.0-651.0) µm, extending to the Male Not found.
Juveniles Four developmental juvenile stages were detected based on body, odontostyle and replacement odontostyle length, and tail shape (Figs. 4 and 5). Habitus more or less an open C-shape. Morphologically similar to female, except for their size and sexual characteristics (Table 3). The 1st-stage juveniles were characterized by the replacement odontostyle inserted into odontophore base with a tail convex-conoid shape with a short subdigitate terminus (Figs. 3 and 4). J1-J4 tail length 28.2, 30.0, 33.8, and 30.8 µm long, respectively.
Type habitat and locality
Rhizosphere of Asphodel (Asphodelus ramosus L.) at 1,858 m elevation in La Pandera Mountain, Valdepeñas de Jaén, Jaén province, in Andalusia, southern Spain
Molecular characterization and phylogenetic relationships of Longidorus panderaltum n. sp.within the genus Longidorus The amplification of D2-D3 segments of 28S rRNA, ITS1, 18S rRNA, and partial coxI regions yielded single fragments of ca 900 bp, 1100 bp and 1800 bp, and 500 bp, respectively, based on gel electrophoresis.
Phylogenetic relationships among Longidorus species inferred from analyses of D2-D3 expansion segments of 28S rRNA, partial 18S rRNA, and ITS1 gene sequences using BI are given in Figs. 7, 8 and 9, respectively. Since scarce similarity was detected for ITS1 sequences from L. panderaltum n. sp. (MT271721-MT271726) and L. goodeyi (MT271727-MT271732) with those deposited in GenBank, only closer species were included in the analyses of this region.
For the ITS1 region sequences, the 50% majority rule consensus BI tree of a multiple sequence alignment containing 23 sequences and 964 characters is showed in Fig. 8. Longidorus panderaltum n. sp. (MT271721-MT271726) clustered with L. goodeyi (MT271727-MT271732) in a high supported clade (PP = 1.00), and forming a well-supported major clade (PP = 0.99) with L. vinearum (KT308892-KT308893), L. magnus Lamberti, Bleve-Zacheo and Arias, 1982 (HM921340) and L. lusitanicus (KT308891), whereas L. onubensis (MT271733- Fig. 8 Phylogenetic relationships of Longidorus panderaltum n. sp. and Longidorus goodeyi with closer species within the genus Longidorus. Bayesian 50% majority rule consensus trees as inferred from ITS1 rRNA sequences alignments under the TIM2 + G model. Posterior probabilities more than 70% are given for appropriate clades. Newly obtained sequences in this study are in bold. Fig. 9 Phylogenetic relationships of Longidorus panderaltum n. sp. and Longidorus goodeyi within the genus Longidorus. Bayesian 50% majority rule consensus trees as inferred from 18S rRNA sequences alignments under the GTR + I + G model. Posterior probabilities more than 70% are given for appropriate clades. Newly obtained sequences in this study are in bold. MT271735, KT308882-KT308883) clustered separately in a high supported clade (PP = 1.00). Finally, for partial 18S rRNA gene sequences, the 50% majority rule consensus BI tree was based on a multiple sequence alignment containing 89 sequences and 1681 characters. Longidorus panderaltum n. sp. (MT271715-MT271716) clustered with L. goodeyi (MT271717-MT271720) in a well-supported clade (PP = 1.00). However, L. onubensis (KT308897) did not cluster with other species of L. goodeyi complex (Fig. 9), but clustered inside a major highly supported clade (PP = 1.00) including other species from the Iberian Peninsula.
Discussion
The primary objective of this study was to unravel the diversity of the L. goodeyi complex by using integrative approaches including morphology, morphometry and molecular analyses. Our results confirmed that the needle nematode L. goodeyi it is composed by a complex group of three distinct species (L. goodeyi, L. onubensis and L. panderaltum n. sp.) that can be separated using integrative approaches. Consequently, we have described a new species of the genus Longidorus that can be separated from L. goodeyi complex by a combination of morphological, allometrical, and molecular analyses. Our results demonstrated that the use of integrative taxonomy may help to distinguish very similar species and to unravel the biodiversity in this complex group of plant-parasitic nematodes. These analyses supported the separation of L. panderaltum n. sp. from other species in the L. goodeyi complex and reinforced the importance of these analyses to decipher species boundaries that are essential for agronomic management, ecological analyses and implications in food security. Our results suggested that other populations of L. goodeyi reported from central Spain (Arias 1977;Arias et al. 1985), from The Netherlands that differed from the type description in some morphological characters (Seinhorst and Van Hoof 1981), or those from France, that were smaller than that of paratypes (Dalmasso 1969), need to be confirmed by integrative taxonomy in order to clarify their identity. The application of multivariate analysis may help to differentiate these closely related species. In particular, detailed morphology of J1 tail shape of these L. goodeyi populations may also help to clarify the identification of these records. In Longidoridae, J1 individuals can be identified by the position of the replacement odontostyle, which lies mostly within the odontophore, with the anterior tip near the base of the functional odontostyle, and have practical significance when distinguishing closely related species (Hunt 1993;Robbins et al 1996).
Phylogenetic inferences based on the D2-D3 expansion domains of 28S, ITS1 and 18S rRNA genes suggest that L. panderaltum n. sp. and L. goodeyi are closely related species (Figs. 7, 8 and 9). Results of all analyses on the three species of L. goodeyi complex were consistent and clearly separated them by phylogenetic and species delimitation methods. In all cases these species clustered in a major clade comprising the majority of Longidorus species reported in the Iberian Peninsula with a characteristic tail (hemispherical convex-conoid tail shape) as reported in previous studies (Gutiérrez-Gutiérrez et al. 2013;Archidona-Yuste et al. 2016b;2019a;Cai et al. 2020a, b). Sequences of nuclear ribosomal RNA genes, particularly D2-D3 expansion segments of the 28S rRNA gene and ITS1 region, have proven to be a powerful tool for providing accurate species identification of Longidoridae (Palomares-Rius et al. 2017b;Archidona-Yuste et al. 2016b;2019a;Cai et al. 2020a, b). By contrast, the low nucleotide variability found in partial 18S rRNA makes it difficult to identify individuals to the species level as previously described in other studies (Gutiérrez-Gutiérrez et al. 2013;Archidona-Yuste et al. 2016b;a).
The 28S rRNA and ITS1 haplonet analyses of L. panderaltum n. sp. showed it as a unique species, and clearly different from L. goodeyi and L. onubensis. No heterozygous individuals were found in those three species.
The description of L. panderaltum n. sp. suggests that the biodiversity of needle nematodes in Southern Europe is still not completely deciphered and requires further research. Interestingly, the phylogenetic relationships among Iberian Peninsula species could provide insight into the speciation of some of these species specifically to the Iberian Peninsula, additionally of other main centres of origin in other parts of the world, as suggested by Coomans (1985). However, this hypothesis regarding the evolutionary patterns in the genus Longidorus must be analysed using biogeographical models and a higher number of sequences from other Longidorus spp. given the increasing diversity of this genus in the samplings at the Iberian Peninsula (Archidona-Yuste et al. 2016b; a). These results enlarge the diversity of Longidorus in Spain and agree with previous data obtained for the phylogeny and biogeography of the genus Longidorus in the Mediterranean Basin (Navas et al. 1990;Navas et al. 1993;Gutiérrez-Gutiérrez et al. 2011;Gutiérrez-Gutiérrez et al. 2013;Archidona-Yuste et al. 2016b;2019a;Cai et al. 2020a, b).
In summary, the present study extends the biodiversity of the genus Longidorus by integrating morphological, morphometrical and molecular characterizations and elucidates phylogenetic relationships with other Longidorus spp. of the new species described. The molecular markers obtained could be used for precise and unequivocal diagnosis of this species, which may help for effective and appropriate phytopathological or ecological studies.
Acknowledgements This research was financially supported by grant 201740E042, "Análisis de diversidad molecular, barcoding, y relaciones filogenéticas de nematodos fitoparásitos en cultivos mediterráneos" from Spanish National Research Council (CSIC), and by the Humboldt Research Fellowship for Postdoctoral Researchers awarded for the corresponding author. Authors thank G. León Ropero and J. Martín Barbarroja (IAS-CSIC) for the excellent technical assistance. The first author acknowledges the China Scholarship Council (CSC) for financial support. The fifth author acknowledges Spanish Ministry of Economy and Competitiveness for the "Ramon y Cajal" Fellowship RYC-2017-22228. The corresponding author is a recipient of Humboldt Research Fellowship for Postdoctoral Researchers at Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany.
Funding Information Open Access funding provided by Projekt DEAL.
Compliance with ethical standards
Conflict of interest All authors certify that 1) they do not have any actual or potential conflict of interest, 2) the study described is original and has not been published previously, and is not under consideration for publication elsewhere, 3) all prevailing local, national and international regulations and conventions, and normal scientific ethical practices, have been respected. We also certify that all authors have reviewed the manuscript and approved the final version of manuscript before submission.
Research involving human participants and/or animals No specific permits were required for the described fieldwork studies. Permission for sampling the forests was granted by the landowner. The sites are not protected in any way.
Informed consent All the authors certify that the work carried out in this research followed the principles of ethical and professional conduct have been followed. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [URL]/4.0/.
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Domain: Environmental Science Biology
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Comparative Transcriptomics Analysis of Phytohormone-Related Genes and Alternative Splicing Events Related to Witches ’ Broom in Paulownia
Paulownia is a native fast-growing tree in China that has been introduced into many countries. However, it is often infected by Paulownia witches’ broom (PaWB) disease, which can lead to large declines in yield. PaWB is caused by a phytoplasma that is an obligate biotrophic plant pathogen. Until now, the molecular mechanisms of interactions between the host plants and the phytoplasma have not been clear. In previous studies, it was reported that PaWB-infected Paulownia exhibited healthy morphology after being treated with methyl methane sulfonate (MMS) at the concentration of 20 mg·L−1 (for Paulownia tomentosa (PT) and Paulownia fortunei (PF) or 15 mg·L−1 (for P. tomentosa × P. fortunei) MMS. In this study, the whole transcriptome expression profile of PaWB-infected Paulownia was studied using high-throughput sequencing technology. In total, 74 significantly differentially expressed genes were detected among three species of healthy, PaWB-infected Paulownia, and the Paulownia treated with MMS. We identified and analyzed genes related to the roles of phytohormones and alternative splicing events involved in regulating plant growth. In response to phytoplasma infection, the concentrations of the plants’ phytohormones were altered, leading to morphology transformation. This research will provide valuable information to detect the molecular mechanisms involved in the Paulownia response to phytoplasma infection.
Introduction
Paulownia, a native species in China, has been cultivated for more than 2000 years. The trees are fast-growing deciduous hardwood, and have been introduced into other continents, including the Americas, Australia, Europe, and Africa [1]. Paulownia has high, commercial value and the wood is used for furniture, artificial boards, and musical instruments [2]. However, the widespread occurrence of Paulownia witches' broom (PaWB) disease has led to large economic losses.
PaWB is caused by a phytoplasma, which is an obligate biotrophic pathogen [3]. More than 1000 plant species have been impacted by the phytoplasma all over the world, including jujube, mulberry, sweet potato, and Paulownia [4][5][6][7]. Phytoplasmas are cell wall-less prokaryotes and are highly diverse in genome structure and content. They belong to the class Mollicutes, and primarily inhabit nutrient-rich phloem sieve elements in plants and are transmitted by phloem-feeding Hemiptera insects such as Cicadellidae (leadhoppers), Psylla (psyllids), and Fulgoridae (planthhoppers) [8,9]. The bacteria invasive inhabitants of both plants and insects and require these hosts for dissemination in nature. Phytoplasma was first discovered using an electron microscope in 1967 and named a mycoplasma-like organism [10]. The phytoplasma can invade plants and insects [11]. Subsequently, phytoplasma-infected plants showed yellow symptoms, witches' broom, virescence in flowers, and phyllody [12]. However, in phytoplasma-infected sap-feeding insects, the phytoplasmas live for 10 days or longer, indicating that the phytoplasma is beneficial to the insect hosts [13]. The first full-scale phytoplasma classification was on the base of Polymerase Chain Reaction (PCR) amplified 16S rDNA Restriction Fragment Length Polymorphism (RFLP) analysis. All known phytoplasmas were classified into 19 groups and over 40 subgroups [8]. The phytoplasmas produce effectors that are encoded in mobile genetic elements, including 46 of the 56 effector genes in the aster yellows witches'-broom (AY-WB) phytoplasma genome. Until recently, only five complete phytoplasma genomes had been assembled, and nine effective draft phytoplasma genome sequences were also available [14,15]. Phytoplasma genomes are generally between 530 kb and 1200 kb in length. The Australian Grapevine Yellows (AUSGY) genome is longer than the AY-WB and onion yellows M (OY-M) genomes, and the Apple Proliferation phytoplasma (AP) genome is the shortest of the known complete phytoplasma genomes, with lots of important metabolically functional genes missing, some of which code for proteins involved in ATP synthases, oxidative phosphorylation, and amino acid biosynthesis [16][17][18]. Thus, phytoplasmas have to acquire their nutrients from the host plants.
The PaWB phytoplasma falls into the category of the Aster Yellow Phytoplasmas (Candidatus Phytoplasma asteris), which genomes were between 600 kb and 1000 kb in length [16]. PaWB-infected Paulownia have small internode lengths, leaf chlorosis, phyllody, witches' broom, and dieback of branches. Recently, it was reported that in Jujube (Ziziphus jujube) and Paulownia, miRNAs and their corresponding target genes which were involved in witches' broom were identified [19]. In Paulownia, proteins encoded by genes of folate and fatty acid synthesis, phytohormone signal transduction, carbohydrate metabolism, secondary metabolism, photosynthesis, and ribosomes have been reported to be related to PaWB disease [3,20,21].
Our research team discovered that PaWB disease could be cured or the morphology changes could be decreased by increasing the DNA methylation levels in infected seedlings. PaWB-infected Paulownia can be recovered through the MMS treatment at appropriate concentrations [22]. A previous study showed that PaWB-infected Paulownia tomentosa × Paulownia fortunei exhibited relatively healthy morphology after treatment with 15 mg•L −1 MMS, although there were still phytoplasmas present, which were detected by nested PCR. However, when the MMS concentration was 20 mg•L −1 , the morphology of P. tomentosa and P. fortunei infected by PaWB returned to normal [23].
The expression patterns of the genes which encode proteins associated with alternative splicing and plant hormones were researched in our former study. The parent relationships among the three Paulownia species (P.tomentosa, P. fortunei, and P. tomentosa × P. fortunei) used in this study are also discussed. The molecular mechanisms in these three species have been reported separately in previous studies [3,20,24]. Moreover, we analyzed the gene expression across all three species to identify genes related to PaWB. Our study provides a comprehensive genomic resource for investigating genes related to PaWB by studying the changes among healthy and infected seedlings, and infected seedlings treated with MMS using Illumina sequencing technology.
Plant Materials
All tissue-cultured seedlings were from the Institute of Paulownia, Henan Agricultural University, Zhengzhou, Henan Province, China. The tissue-cultured seedlings of healthy P. tomentosa (PT), P. fortunei (PF), and P. tomentosa × P. fortunei (PTF), and PaWB-infected P. tomentosa (PTI), P. fortunei (PFI), and P. tomentosa × P. fortunei (PTFI) were used in this study. The seedlings were cultured on 1/2 MS in triangular flasks. After 30 days, terminal buds from PFI and PTI seedlings were transferred into 1/2 MS and 0 mg•L −1 MMS (controls) or 20 mg•L −1 MMS (PFI-20 and PTI-20) was added. The PTFI terminal buds were transferred into 1/2 MS and 0 mg•L −1 MMS (control) or 15 mg•L −1 MMS (PTFI-15) was added. All the seedlings were cultured in a controlled chamber in the dark for five days at 20 • C, followed by 25 ± 2 • C with a photon flux intensity of 130 µmol•m −2 •s −1 . After 30 days, 1.5 cm terminal buds were collected from each seedling group, and the same numbers were then mixed separately to obtain pooled samples for each group. Following this, the specimens were frozen in liquid nitrogen, before being stored at −80 • C.
Total RNAs Extraction and cDNA Library Construction
Total RNAs were extracted from the terminal buds of PT, PF, PTF, PTI, PFI, PTFI, PTI-20, PFI-20, and PTFI-15 samples with TRIzol reagent (Invitrogen, Carlsbad, CA, USA). RNA was then purified by an RNeasy Mini Elute Clean Kit (Qiagen, Dusseldorf, Germany) based on the manufacturer's protocols. The total RNA was treated with DNaseI (TaKaRa, Dalian, China) to avoid genomic DNA contamination. The eukaryotic mRNA was enriched by oligo (dT) magnetic beads. Then, the mRNA was broken into small fragments which were used as templates to synthesize the first-strand of cDNA by SuperScript II reverse transcriptase (Life Technologies, Carlsbad, CA, USA). DNA polymerase I and RNase H were used to synthesize the second-strand cDNA. Then, the double-stranded cDNAs were cleaned and washed for end reparation using EB butter. A single adenosine (A) nuclotide was connected with adapters to the 3' ends. Next, PCR was amplified using the suitable fragments as templates. Following this, the resultant cDNA libraries were qualified using an Agilent 2100 Bioanalyzer (Agilent Technologies, Inc., Santa Clara, CA, USA) and the quality was assessed using an ABI StepOnePlus Real-Time PCR System (ABI, New York, NY, USA). Next, cDNA libraries were sequenced using the Illumina HiSeqTM 2000 platform (Illumina, San Diego, CA, USA) based on the manufacturer's protocol.
Obtaining Clean Reads, and Transcript Annotation
Primary sequencing data (raw reads) produced by Illumina sequencing were filtered to obtain high-quality clean reads by getting rid of reads containing poly-N or adapter sequences low quality reads using SolexaQA's DynamicTrim.pl( [URL] clean reads were mapped to the P. fortunei reference genome by SOAPaliner/SOAP2 [25,26]. In addition, the clean reads that mapped to the reference genome were searched against the National Center for Biotechnology Information (NCBI) nonredundant protein sequence database (Nr) ( [URL]/), the gene ontology (GO) with Blast2GO program [27], and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases ( [URL] BLAST.
All the sequencing data generated in this study is available from the SRA-Archive ( [URL]) of NCBI under the study accession SRP067302, SRP028911, SRP068599, and SRP057771.
Differentially Expressed Genes among the Nine Libraries
Differentially expressed genes (DEGs) among the nine libraries were confirmed by the Audic strict algorithm [28]. The p-value threshold in multi-hypothesis analysis was identified using the false discovery rate (FDR) value [29]. Gene expressions were considered to be significantly different when the FDR was ≤0.001 and the absolute value of the log2Ratiol was ≥1. The gene expression level was calculated using the FPKM method [30]. The DEGs were then annotated in the KEGG database.
Quantitative Real-time PCR (qRT-PCR) Analysis of DEGs
Total RNA was extracted from the nine samples using TRIzol (Sangon, Shanghai, China). The iScript™ cDNA Synthesis Kit (Bio-Rad, Hercules, CA, USA) was used to synthesize first-strand cDNA according to the manufacturer's protocol. The specific primers were designed with Beacon Designer, version 7.7 (Premier Biosoft International, Palo Alto, CA, USA). The PCR amplification primers were designed as previously reported [31]. The qRT-PCR experiment was carried out by the Taq SYBR ® Green qPCR Premix kit (Yugong Biolabs, Lianyungang, China) and run on the CFX96™ Real-Time System (Bio-Rad, Hercules, CA, USA). Analysis conditions were as follows: 94 °C for 3 min, followed by 40 cycles of 94 °C for 15 s and 60 °C for 1 min. The results were analyzed by the 2 −ΔΔCt method [32]. The 18S rRNA was chosen as the reference gene.
Transcriptome Sequencing
We obtained a total of 637,352,656 raw reads and 605,317,226 clean reads from the nine libraries (PT, PTI, PTI-20, PF, PFI, PFI-20, PTF, PTFI, PTFI-15) using Illumina (paired-end) sequencing technology. The proportion of the clean reads which mapped to the reference genome is shown in Table S1. Among the nine libraries, the percentage of adapter reads was smallest (0.00%) in the PF, PFI, and PFI-20 libraries and highest (1.55%) in the PTF library. The highest and lowest percentages of low quality reads were 8.41% and 2.21% in the PFI-20 and PTI libraries, respectively (Table 1). The highest percentage of clean reads and the lowest percentage that mapped to the reference genome were from the PFI library and PTI-20 library, respectively.
Quantitative Real-time PCR (qRT-PCR) Analysis of DEGs
Total RNA was extracted from the nine samples using TRIzol (Sangon, Shanghai, China). The iScript™ cDNA Synthesis Kit (Bio-Rad, Hercules, CA, USA) was used to synthesize first-strand cDNA according to the manufacturer's protocol. The specific primers were designed with Beacon Designer, version 7.7 (Premier Biosoft International, Palo Alto, CA, USA). The PCR amplification primers were designed as previously reported [31]. The qRT-PCR experiment was carried out by the Taq SYBR ® Green qPCR Premix kit (Yugong Biolabs, Lianyungang, China) and run on the CFX96™ Real-Time System (Bio-Rad, Hercules, CA, USA). Analysis conditions were as follows: 94 • C for 3 min, followed by 40 cycles of 94 • C for 15 s and 60 • C for 1 min. The results were analyzed by the 2 −∆∆Ct method [32]. The 18S rRNA was chosen as the reference gene.
Transcriptome Sequencing
We obtained a total of 637,352,656 raw reads and 605,317,226 clean reads from the nine libraries (PT, PTI, PTI-20, PF, PFI, PFI-20, PTF, PTFI, PTFI-15) using Illumina (paired-end) sequencing technology. The proportion of the clean reads which mapped to the reference genome is shown in Table S1. Among the nine libraries, the percentage of adapter reads was smallest (0.00%) in the PF, PFI, and PFI-20 libraries and highest (1.55%) in the PTF library. The highest and lowest percentages of low quality reads were 8.41% and 2.21% in the PFI-20 and PTI libraries, respectively (Table 1). The highest percentage of clean reads and the lowest percentage that mapped to the reference genome were from the PFI library and PTI-20 library, respectively.
Analysis of the Common DEGs
The 74 common DEGs were annotated using the KEGG database. Among them, 40 genes were assigned to 12 KEGG pathways (Figure S1). DEGs related to the "biosynthesis of other secondary metabolites" formed the largest group (25%), followed by the "metabolism of terpenoids and polyketides" and "amino acid metabolism" (15%). In addition, the relative expression of 74 DEGs lg(FPKM) is shown in Figure 2.
Some of the DEGs encoded proteins associated with the fatty acid synthesis pathway, such as acyl-ACP thioesterase, 3-ketoacyl-CoA synthase, and arginine decarboxylase-like. In addition, several DEGs encoded proteins related to phytohormone signal transduction involving jasmonic (JA) and gibberellins (GA), including gibberellin receptor GID1, and transcription factors MYC2 and PIF3. Meanwhile, the DEGs which encoded proteins including flavonoid 3 -monooxygenase (HCT) and flavonoid 6-hydroxylase (CYP71D9) were related to flavonoid biosynthesis.
Alternative Splicing Analysis between Healthy and PaWB-Infected Plants
Alternative splicing (AS) is a crucial regulator in photosynthesis and the defense reaction and plays crucial roles in regulating functional differences and genetic expression [33]. AS can increase the complexity of transcriptomes and produces a number of different transcripts from pre-mRNA [34,35]. In this study, we classified the AS events into four categories based on the RNA sequencing data: alternative 3 splice site, exon skipping, intron retention, and alternative 5 splice site. The common AS genes among the three PaWB-infected libraries (PFI, PTI, and PTFI) are shown in Figure 3. In total, 8529 common AS genes resulting in 11,152 transcripts were detected in the three libraries (Table S2). Intron retention was the most common of the four AS categories, which is consistent with previous studies [36]. Moreover, we found that 13 of the DEGs participated in AS events, showing that AS events may be related to the Paulownia tolerance response to environmental stresses.
Alternative Splicing Analysis between Healthy and PaWB-Infected Plants
Alternative splicing (AS) is a crucial regulator in photosynthesis and the defense reaction and plays crucial roles in regulating functional differences and genetic expression [33]. AS can increase the complexity of transcriptomes and produces a number of different transcripts from pre-mRNA [34,35]. In this study, we classified the AS events into four categories based on the RNA sequencing data: alternative 3′ splice site, exon skipping, intron retention, and alternative 5′ splice site. The common AS genes among the three PaWB-infected libraries (PFI, PTI, and PTFI) are shown in Figure 3. In total, 8529 common AS genes resulting in 11,152 transcripts were detected in the three libraries (Table S2). Intron retention was the most common of the four AS categories, which is consistent with previous studies [36]. Moreover, we found that 13 of the DEGs participated in AS events, showing that AS events may be related to the Paulownia tolerance response to environmental stresses.
Verification of Candidate Genes by qRT-PCR
The seven DEGs for qRT-PCR assays were selected from the 74 DEGs to verify the accuracy of the transcriptome data and the relative expression and FPKM are shown in Figure 4 and Table 2. The results revealed that the five genes expressions from the seven DEGs were down-regulated in PT vs. PTI, PF vs. PFI, and PTF vs. PTFI, and up-regulated in PTI vs. PTI-20, PFI vs. PFI-20, and PTFI vs. PTFU-15. Additionally, two DEGs were up-regulated in PT vs. PTI, PF vs. PFI, and PTF vs. PTFI, and down-regulated in PTI vs. PTI-20, PFI vs. PFI-20, and PTFI vs. PTFI-15. Some non-DEGs such as genes related to peroxidase 31 (Per31), carotenoid cleavage dioxygenase 4 (CarCD), and polyphenol oxidase (PolO) et al., have been confirmed through qRT-PCR in previous studies [3,20,24]. These genes expression levels were similar to the expected expressions through transcriptome analysis, indicating that this transcriptome data was enough to be used to assess transcriptome variations involved in the morphological variations in PaWB.
Discussion
Paulownia yield is adversely affected by PaWB, the most severe disease in Paulownia cultivating regions of China. However, the molecular mechanisms of Paulownia are not clear. In this study, genes related to PaWB were identified by transcriptome sequencing, which enabled the absolute measurement of gene expression and generated useful data with great accuracy. Variations in gene expression levels between healthy and infected plants will promote detecting the molecular mechanisms of the infected Paulownia. The transcriptome of a hybrid species P. tomentosa × P. fortunei with its maternal P. tomentosa and paternal P. fortunei transcriptomes was compared. The results showed that the common genes between P. tomentosa × P. fortunei and P. fortunei were more than the common genes between P. tomentosa × P. fortunei and P. tomentosa. Moreover, the percentage of genes in the P. tomentosa genome that mapped to the P. fortunei reference genome was far lower than the percentages of genes in the P. fortunei and P. tomentosa × P. fortunei genomes that were mapped. Previous studies have indicated that the genetic relationships of hybridized plants are usually closer to the female parent in normal situations. Furthermore, most cells derived from the hybridization in Angiospermae were of unilinear descent and resulted from the female parent. Hence, the female parent deeply influences the hybridization [37]. The results are not in keeping with previous studies for the following possible reasons. In the hybrid, most of the genes derived from P. fortunei (paternal plant) mapped to the reference genome and were not lost, whereas fewer genes derived from P. tomentosa (maternal plant) mapped to the reference genome, indicating that more P. tomentosa may have been lost. Because the reference genome is derived from P. fortunei, the distant relationships between the P. fortunei and P. tomentosa may account for the smaller number of P. tomentosa genes that were mapped. Hence, further research is required to verify this result.
DEGs Related to Phytohormones
Phytohormones play significant roles in regulating the growth and development of plants [38]. Plant morphology can be altered by changing phytohormone concentrations and by environmental factors. Phytohormones such as salicylic acid, brassinosteroids, auxins, GAs, and JAs play pivotal roles in biomass production and regulate plant growth processes [39]. In this study, several genes that encode proteins associated with phytohormones were identified, including Myelocytomatosis protein (MYC2), transcription factors related to GA synthesis, and GA receptor gibberellin insensitive dwarf (GID1), which served as a soluble protein localized to both the cytoplasm and nucleus that promoted elongation, leaf expansion, and germination in plants.
JA is a small molecule phytohormone that regulates growth in most plants, including adaptation to stresses and defense against pathogen invasion [40,41]. The JA receptor COI1 is an F-box protein and is part of the Skp1/Cullin/F-box E3 ubiquitin ligase complex (SCF). The JA ZIM-domain (JAZ) is a substrate of SCF and the major molecular link between MYC2 and SCF in the JA-signaling complex as the active form of JA. MYC2 is the positive regulation factor mediated by JA in regulating the inhibition of stem elongation and tolerance of plants to oxidative damage. In previous studies, the MYC2 which was related to plant-pathogen interactions was identified [42]. When the JA concentration is high, JAZs are degraded, leading to MYC2 being released to promote the JA regulation of plant growth. In this study, the FPKM of genes encoding MYC2 in healthy Paulownia was higher than in PaWB-infected Paulownia, implying that changes in MYC2 content influenced the regulation of JA. Hence, alterations in JA content may have led to reduced resistance to stresses in the PaWB-infected Paulownia.
GA is a crucial plant hormone that can promote stem growth and induce germination. DELLA proteins are nuclear transcriptional regulators that are critical negative regulators in the GA-signaling cascade [43]. Phytochrome-interacting factors (PIF), a key transcriptional activator in GA signal transduction, is released when the degradation of DELLA occurs after DELLA and GID1 combine. Then, downstream target genes are expressed to promote the elongation of hypocotyls. The interactions between DELLA and GID1 are an important step in GA-signal transduction, which can promote the production of plant axillary buds. In this study, the FPKM of genes encoding GID1 was signally higher in PaWB-infected plants than in healthy Paulownia among the three species, indicating that the infected plants produced a number of GID1s in response to phytoplasma invasion. In addition, complete or partial functional loss in GID1 will influence GA signaling and alterations in GA balance may lead to changes in the numbers of axillary buds produced.
In addition, GA pathways may participate in JA signal transduction to regulate the stress responses and development of plants [44]. A previous study indicated that DELLA, which plays a crucial role in the GA signal pathway, and MYC2, compete for binding to JAZ, leading to MYC2 being released. Thereby, downstream gene expression of MYC2 which related to JA-responsive was promoted by DELLA in response to JA. In this study, genes ending the DELLA protein were found in the six comparisons, indicating that the GA and JA may together regulate Paulownia growth.
In a previous study, some genes that encoded the protein related to flavonoid biosynthesis were detected. The flavonoids were the products of the low molecular weight of secondary metabolism. It participated in various stages of growth and development in plant and resisted phytoplasma invasion [45]. Fan et al. [3] discovered several genes which were related to flavonoid biosynthesis through transcriptome analysis. The flavonoid compounds were primarily accumulated in infected-PaWB Paulownia compared with healthy seedlings in order to respond to the disease. They speculated that the leaf color altering was caused by expression level variations of key genes related to flavonoid biosynthesis. In addition, Xia Ye et al. [46] discovered the 20 genes related to flavonoid biosynthesis in Ziziphus jujube Mill through iTRAQ proteomics and RNA-seq transcriptomics analysis. Meanwhile, the 3-ketoacyl-CoA synthase that was related to fatty acid synthesis was discovered in the DEGs of this study. Fatty acids play an important role in cell membrane composition. Additionally, several genes related to the fatty acid biosynthesis were discovered. The expression of four genes was in keeping with 3-ketoacyl-CoA [24]. The above results suggest that the obtained data were reliable.
Relationship between AS and the Paulownia Plants Responses to PaWB
AS, a pivotal regulatory mechanism, participates in many physiological processes, controls plant development, and increases the complexity of proteomes and transcriptomes [34]. Moreover, AS plays crucial roles in defense responses and photosynthesis in plants. Among the 74 common DEGs, 13 involved in amino acid metabolism, casein kinase, and plant hormone signal transduction underwent AS events. To identify the relationship between AS and PaWB, the genes involved in AS events in the PaWB-infected Paulownia were studied. PIF3 is the foundation PIF member and plays a crucial role in mediating early steps in light-induced chloroplast development by regulating photoresponsive nuclear genes. Leaf color is related to chloroplast development, the size and number of chloroplasts, and the production of photosynthetic pigments. Fan et al. [42] also found the genes which encode PIF3 involved in light signal transduction. PIF3 takes effect in the response of Paulownia in abiotic stresses and is involved in biosynthesis and signaling pathways of hormones including brassinosteroids, GA, and abscisic acid [47]. Previous researches revealed that the over-expression of PIF3 could improve the tolerance of plants to abiotic stresses. Gene encoding PIF3 was up-regulated in healthy vs. infected Paulownia seedlings and down-regulated in infected vs. infected seedlings treated with MMS in this study. The up-regulated PTF3 in PaWB-infected Paulownia has an impact on chloroplast development, leading to altered leaf color compared with healthy seedlings. Furthermore, the results indicated that healthy seedlings may have a great capacity to respond to abiotic stresses compared with infected Paulownia.
The gene encoding F-box also participated in AS events. As part of the SCF complex subunit, the F-box plays a significant role in phytohormone signal transduction and regulating the development and growth in plants. JA can regulate the growth in most plants, including their ability to adapt to stresses and their defense against pathogen invasion. In addition, SCFCOI1 can mediate core JA-dependent responses. The SCF complex can degrade JAZ, leading to MYC2 release, so the genes that respond to JA are expressed [48]. In conclusion, the F-box protein may participate in regulating Paulownia growth to improve its capacity to adapt to stresses. This speculation needs to be verified by further research because of the lack of reliable data. The plant-specific Neutrophil-activating protein (NAP) transcription factor participates in the regulation of plant growth in plants. The NAP subfamily can respond to cold, salt, and drought stresses. In earlier studies, the FPKM of genes encoding NAP was higher in PaWB-infected seedlings than in healthy plants. We speculate that the infected Paulownia could produce plenty of NAPs in order to survive.
Together, our results reveal the significant roles of AS in regulating growth in Paulownia plants and provide new insights to study transcriptome complexity in Paulownia.
Conclusions
In this study, comparative transcriptomics analysis for PT, PTI, PTI-20, PF, PFI, PFI-20, PTF, PTFI, and PTFI-15 was performed. To further study the molecular mechanisms of the PaWB-infected Paulownia, the genes related to PaWB were identified. Results showed that the common genes between P. tomentosa and P. tomentosa × P. fortunei were less than the genes between the P. fortunei and P. tomentosa × P. fortunei. Such phenomena may be attributed to the fact that the reference genome was derived from P. fortunei and the relationships between P. fortunei and P. tomentosa were distant according to the sequence results. Meanwhile, we found several genes in 74 DEGs, which encode proteins including MYC2, transcription factors involved in GA synthesis, and GA receptor GID1. These results will help us to understand the interactions between the plants and phytoplasmas and make a difference for future studies of Paulownia responses to phytoplasma infections.
Author Contributions: Y. D. analyzed the data, wrote the manuscript, and reviewed drafts of the paper; H. Z. performed experiments, analyzed data, and wrote the manuscript; G. F. conceived and designed the experiments and supervised the study; X. Z.analyzed the data and provided funding; Z. W. analyzed the data, and prepared figures and/or tables; Y. C. contributed reagents/materials/analysis tools.
Figure 1 .
Figure 1. The DEGs were selected from the P. tomentosa, P. fortunei, and P. tomentosa × P. fortunei. The CT, CF, and CTF were the common genes with opposite co-regulation patterns in PF vs. PFI and PFI vs. PFI-20, PT vs. PTI and PTI vs. PTI-20, and PTF vs. PTFI and PTFI vs. PTFI-15, respectively.
Figure 1 .
Figure 1. The DEGs were selected from the P. tomentosa, P. fortunei, and P. tomentosa × P. fortunei. The CT, CF, and CTF were the common genes with opposite co-regulation patterns in PF vs. PFI and PFI vs. PFI-20, PT vs. PTI and PTI vs. PTI-20, and PTF vs. PTFI and PTFI vs. PTFI-15, respectively.
Figure 2 .
Figure 2. The lg(FPKM) of DEGs in nine libraries. The deepest red among all the colors represents the maximum value of lg(FPKM) and the deepest blue represents the minimum value of lg(FPKM).
Figure 2 .
Figure 2. The lg(FPKM) of DEGs in nine libraries. The deepest red among all the colors represents the maximum value of lg(FPKM) and the deepest blue represents the minimum value of lg(FPKM).
Figure 3 .
Figure 3. The common genes of AS occurred in PTI, PFI, and PTFI.(A) The common genes of Alternative 3′ splice in the three species are shown.(B) The common genes of alternative 5′ splice in the three species are shown.(C) The common genes of intron retention in the three species are shown.(D) The common genes of exon skipping in the three species are shown. The figures represent the number of genes.
Figure 3 .
Figure 3. The common genes of AS occurred in PTI, PFI, and PTFI.(A) The common genes of Alternative 3 splice in the three species are shown.(B) The common genes of alternative 5 splice in the three species are shown.(C) The common genes of intron retention in the three species are shown.(D) The common genes of exon skipping in the three species are shown. The figures represent the number of genes.
Figure 4 .
Figure 4. The qRT-PCR analysis of P. tomentosa, P. fortunei, and P. tomentosa × P. fortunei selective DEGs. The RNA-Seq represents the lg(FPKM).(A) relative expression of phylloplanin; (B) relative expression of Pollen Ole e 1 allergen and extensin family protein; (C) relative expression of lipid transfer protein 2; (D) relative expression of Art v 3 allergen precursor; (E) relative expression of prolyl 4-hydroxylaseprolyl; (F) relative expression of RING finger and CHY zinc finger domain-containing protein 1; (G) relative expression of hydroperoxide dehydratase.
Figure 4 .
Figure 4. The qRT-PCR analysis of P. tomentosa, P. fortunei, and P. tomentosa × P. fortunei selective DEGs. The RNA-Seq represents the lg(FPKM).(A) relative expression of phylloplanin; (B) relative expression of Pollen Ole e 1 allergen and extensin family protein; (C) relative expression of lipid transfer protein 2; (D) relative expression of Art v 3 allergen precursor; (E) relative expression of prolyl 4-hydroxylaseprolyl; (F) relative expression of RING finger and CHY zinc finger domain-containing protein 1; (G) relative expression of hydroperoxide dehydratase.
4. 1 .
Differences among the P. tomentosa × P. fortunei and Parental Responses to Phytoplasma
Table 1 .
The percentage of adapter reads and low-quality reads occupied raw reads.
Table 2 .
The relative expression and FPKM of genes for qRT-PCR assays.
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Domain: Environmental Science Biology
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Population biology of the ‘ uçá ’-crab , Ucides cordatus ( Linnaeus , 1763 ) ( Brachyura : Ucididae ) , in mangroves of the Joanes River , Bahia State , Brazil
This study evaluated the population structure, relative growth and morphological maturity size in Ucides cordatus in the Joanes River, Bahia State (Brazil). Crabs were sampled in a fragmented and human-altered mangrove ecosystem in a Brazilian Environmental Protected Area (EPA). A total of 431 crabs were sampled (265 males and 166 females) between September 2011 and August 2012. The following measurements were taken using precision callipers: carapace width (CW) and length (CL), the length of the propodus of the major cheliped (PL, males only), and the width of the 5th somite of the abdomen (AW, females only). Specimens were also weighed to a precision of 0.5 g on a scale to obtain the total wet weight (WW). The relative growth of this species was studied by using biometric relationships to estimate the morphological maturity size in both sexes (PLxCW in males and AWxCW in females). The overall sex ratio was 1.6:1, with a predominance of males (p < 0.05). The population structure of ‘uçá’-crabs was composed of two normal curves (juveniles and adults) in both sexes. A relative growth analysis using the CLxCW relationship revealed a negative allometric growth in adult males whereas adult females exhibited an isometric growth, with an inverse pattern occurring in juveniles. In males, the PLxCW relationship indicated a change in allometric growth at 39.4 mm CW (isometric growth in juveniles changing to positive allometry growth in adults). In females, the AWxCW relationship indicates that morphological maturity occurs at a delay (44.5 mm CW) and that growth changes between phases (positive allometry to isometry). Isometric growth was found using the WWxCW relationship, also regardless of sex. A literature review combined with results previously published about this species in northeast Brazilian region suggests that U. cordatus had a seasonal reproduction during six months (from December to May), with a fattening season in the following six months (June to November). Joanes River is an environmental protected area, but had a high anthropic pressure by closed condos and marinas. Despite the high anthropic pressure on this EPA, the population structure and reproduction of U. cordatus did not appear to be affected. (EEBM) Universidade Católica do Salvador (UCSAL), Campus Pituaçu, Núcleo Integrado de Estudos em Zoologia (NIEZ). Av. Prof. Pinto de Aguiar, 2589, Pituaçu. 41740-090 Salvador, Bahia, Brazil. E-mail(BJBN) Universidade Federal da Bahia (UFBA), Campus Ondina, Laboratório do Programa de Monitoramento, Avaliação e Reabilitação de Ecossistemas Naturais e Artificiais do Estado da Bahia (MARENBA). Rua Barão de Geremoabo, s/no, Ondina. 40170-115 Salvador, Bahia, Brazil. E-mail(MAAP) Universidade Estadual Paulista (UNESP), Campus Experimental do Litoral Paulista (CLP), Laboratório de Biologia de Crustáceos, Grupo de Pesquisa em Biologia de Crustáceos (CRUSTA). Praça Infante D. Henrique, s/no, Parque Bitaru. 11330-900 São Vicente, São Paulo, Brazil. E-mail: pinheiro@ clp.unesp.br. * Corresponding author
Introduction
Mangroves are important natural ecosystems that promote shoreline stability (Moura and Querino, 2010), retain contaminants in sediments and vegetation (Bernini et al., 2006;Pinheiro et al., 2012), serve as a nursery for several animal species (Adams et al., 2006;Tse et al., 2008), and are indicators of regional/global climatic change (Lima and Oliveira, 2011), in addition to other functions. Nevertheless, Brazilian mangrove ecosystems have been negatively influenced by many anthropic changes, primarily urban expansion (Sobrinho and Andrade, 2009), vegetal suppression and loss of endemic animals (Moreau et al., 2010).
Crustaceans are important faunal components in the mangrove ecosystem (Nicolau and Oshiro, 2007). Among crustaceans, the 'uçá'-crab -Ucides cordatus (Linnaeus, 1763) -is part of the family Ucididae according Števčić (2005) and Ng et al. (2008). This species occupies a particularly important position because of its continuous geographic distribution (Amapá to Santa Catarina in Brazil, according to Castro et al., 2008). This ucidid crab lives exclusively in tropical and subtropical mangrove ecosystems, where it builds burrows in the sediment (Hattori and Pinheiro, 2003) and uses the leaves and propagules of mangrove trees as its source of food (Christofoletti et al., 2013). This crustacean is a central component of the benthic mangrove macrofauna (Alves and Nishida, 2004) and a vital fishery resource to artisanal fishermen (Rodrigues et al., 2000). Ucides cordatus was previously classified as overexploited or threatened by exploitation (Ibama, 2004), but its conservation category was recently reviewed and changed to a lower threatened status (NT -near threatened, according MMA, 2014).
Relative growth studies provide the opportunity to estimate the size at morphological maturity for brachyuran crustaceans, making it possible to better understand their adaptive strategies, such as the differential growth of chelipeds and abdomens between the sexes (Masunari and Swiech-Ayoub, 2003;Lima and Oshiro, 2006;Castiglioni and Coelho, 2011). Biometric evaluations can also be extremely valuable tools with which to detect possible anthropic impacts in the environment (Araújo et al., 2012). Other population parameters (e.g., structural composition, longevity, growth/ mortality rates, sex ratio, etc.) can also be quantified and used to explain possible deviations (Wunderlich and Pinheiro, 2013). The size at sexual maturity, for example, is an important population parameter in fishery management and is commonly used by Brazilian lawmakers to promote the sustainable use of 'uçá'-crabs (i.e., by mandating a minimum capture size) (Ibama, 2003a;2003b) and its sustainable management (Ibama, 2011). The size at sexual maturity for U. cordatus has been defined by fishery regulations as 60 mm CW, with little variation among Brazilian regions (Dalabona and Silva, 2005) despite the known effect of latitude on size at sexual maturity (Leite et al., 2009;Castiglioni et al., 2011;Castiglioni and Coelho, 2011). Nevertheless, there are not studies about anthropic changes and their effects on maturity size of U. cordatus.
The reproductive season of pleocyemate crustaceans is determined by the months during which females are ovigerous and/or possess mature gonads (Alves, 1975;Sastry, 1983). According to this definition, Pinheiro and Fransozo (2002) report that 'uçá'-crabs have a seasonal reproductive pattern, as reproduction only occurs during a few months of the year. The non-reproductive months are considered to be the 'fattening' season (Souto, 2008).
Population structure, biometry and size at morphologic maturity of U. cordatus were examined for the first time in the mangroves of the Joanes River estuary, in South Bahia (Brazil), constituting a pioneer study in this Brazilian Environmental Protected Area (EPA).
Study area
The Joanes River is an important estuary in the state of Bahia (Fig. 1) and provides water to many municipalities in the north littoral of the state. This area is an important tourist destination, but it suffers from the urban expansion of the metropolitan region of Salvador, the capital of this Brazilian state (Reis-Filho et al., 2010). This area is part of the Environmental Protected Area (EPA) of Joanes-Ipitanga (12°53'32"S 38°24'09"W) and consists of mangrove fragments surrounded by marinas and closed condominiums. According to Köppen's climate classification (Alvares et al., 2013), this Brazilian region is a tropical zone without a dry season (Af ). April and May are the wettest months, and there is an average annual precipitation > 1,600 mm and an average temperature of 25.4 °C (Barboza et al., 2006). Five mangrove areas were demarcated in Joanes River (Fig. 1). These areas were fragmented and altered by human action (e.g., closed condos and marinas), occurring inside of the Joanes-Ipitanga EPA and randomly selected to better representation of the population analysis. The size and arboreal composition was determined for each area by polygon delimitation of a satellite photo obtained from Google Earth TM . The size and arboreal composition for each area is as follows: Area 1 (52,656 m 2 ) and Area 2 (31,342 m 2 ) both contained a fringe of Rhizophora mangle around an interior of Laguncularia racemosa, Area 3 (30,435 m 2 ) contained only R. mangle, Area 4 (56,886 m 2 ) contained a fringe of L. racemosa surrounding an interior of Avicennia schaueriana, and Area 5 (21,994 m 2 ) predominately contained A. schaueriana. These five mangrove areas contained a total area of 193,313 m 2 and had a mangrove canopy height between 4 and 5 m.
Sampling procedures
In each of five study areas, Ucides cordatus were captured monthly by local crab-catchers for one year (September 2011 to August 2012), except for two months (December and January) during which artisanal fisherman were unavailable because of a regional holiday. Crabs were captured by hand (a capture method called 'braceamento' by Pinheiro andFiscarelli, 2001 andVasques et al., 2011), with an effort of one fisherman per hour. Each crab was sexed according to Pinheiro and Fiscarelli (2001), those with ovigerous condition were registered, and specimens were submitted to biometry during field collection using a vernier caliper (0.01 mm) and a portable precision scale (0.5 g). The following morphometric variables were registered: carapace width (CW) and length (CL); propodus length of the greater cheliped (PL, males only); abdominal width of the 5 th somite (AW, females only); and wet weight (WW). After these data have been obtained, crabs were released in the same mangrove area from which they were captured, avoiding some effect on population or the environment (Cetrel, 2011).
Data categorization and statistical analysis
Crabs were categorized into three groups (males, females without eggs, and ovigerous females). The absolute abundance (number of individuals) in each category was established for each month, except for December and January due to sampling difficulties. Registries were obtained monthly during the study and clustered according two biological periods (reproductive: January to May; and non-reproductive: June to December). These biological periods were following results previously reported by Araújo and Calado (2008) to Alagoas state, but valid to all northeast Brazilian region (see review by Ibama, 2011). The sex ratio was also calculated for each biological period, and the sexual proportion was compared using the chisquare test (Sokal and Rohlf, 2003).
A Shapiro-Wilk (SW) normality test was applied to each biometric variable by sex. If normality was confirmed, the means were compared using Student's t-test; otherwise, a Kruskal-Wallis test was used (Sokal and Rohlf, 2003). Next, the population structure was determined for each sex using the frequency distribution of size classes (CW) to build histograms. Data were subjected to the Bhattacharya method using the NormSep routine in FiSAT software (Gayanilo et al., 1996) to determine the normality of the data components (mean ± standard deviation) that were compared between sexes.
Relative growth in U. cordatus was evaluated based on four biometric relationships, CLxCW, PLxCW, AWxCW, and WWxCW, where CW was the independent variable. Biometric relationships were fitted using power functions (y = ax b ), where a corresponded to the y-axis intercept, and b was the relative growth constant (isometry, b = 1; negative allometry, b < 1; and positive allometry, b > 1; according to Huxley, 1950 andHartnoll, 1982). The same equation was used in the WWxCW relationship, but in this case WW was a cubic variable and b was a function of 3 (e.g., isometry, b = 3; negative allometry, b < 3; and positive allometry, b > 3). Male (PLxCW) and female (AWxCW) relationships were analyzed using the "segmented" package in R, V. 2.5.0 (Ihaka and Gentleman, 1996). This mathematical method is similar to that applied by Legendre and Legendre (1998) and estimates the body size (CW) when a growth change has occurred during ontogeny. According to these authors, body size is related to the timing of the onset of morphological maturity in each sex. This relationship was statistically confirmed by using b-values between phase lines (juvenile and adult) with the same biometric relationship. The equations obtained for these relationships were classified by t-tests and compared with the relative growth equations previously obtained by Pinheiro and Hattori (2006) and with the WWxCW relationship previously obtained by Pinheiro and Fiscarelli (2009). All statistical procedures were evaluated with a significance level of 5%.
Except for PL (measured only in males) and AW (only in female crabs), the sizes of the other biometric variables were higher in males than in females (P < 0.05) (Tab.2). This finding was verified for both measures of U. cordatus (CW and CL) and total wet weight (WW) (Tab.3). A comparison among the size (i.e., CW) distribution histograms reveals similar amplitude of variation (25 mm) between sexes, but a greater maximum size in males (70 mm) than in females (55 mm). Males and females have average sizes from 55 to 60 mm and 45 to 50 mm CW, respectively (Fig. 2). Ovigerous females were found during only three months (March to May), and the greatest frequency of ovigerous females was found in May (Tab.1; Fig. 3).
The biometric relationships of PLxCW (males), AWxCW (females) and CLxCW (both sexes) could be represented by two separate equations (juvenile and adults, P < 0.05); the same was not true for the WWxCW relationship (P > 0.05) (see Tab. 3). The CLxCW relationship of juvenile males indicated isometric growth (b = 0.94; P > 0.05), which changed to a negative allometry in adults (b = 0.86; P < 0.05) (Tab.3). The intercept between these equations occurred at 53.7 mm CW. An inverse growth pattern occurred in females, where the b value changed from 0.91 (P < 0.05) to 1.09 (P > 0.05), with an intercept at 58.0 mm CW (Tab. III). The PLxCW relationship revealed an isometric growth in juvenile males (b = 1.12;P > 0.05) and allometric positive growth in adults (b = 1.39;P < 0.05), with an intercept at 39.4 mm CW (i.e., the size at the onset of morphological maturity in males) (Tab.3; Fig. 4). In females, a statistically significant morphological change was revealed by the AWxCW relationship (positive allometry in juveniles and isometry in adults), and both equations had intercepts at 44.5 mm CW (i.e., the size at onset of morphological maturity in females) (Tab.3; Fig. 5). The WWxCW relationship suggests isometric growth in both sexes, represented by only one equation for each sex (Tab.3).(Linnaeus, 1763). Results of a regression of each biometric variable, including carapace (CW, width; CC, length), the length of the propodus of the major cheliped (PL, for males), the width of the 5 th somite of the abdomen (AW, for females) and the wet weight (WW), with sex and developmental phase (juvenile and adult) and its growth allometric level (AL), represented by: 0 (isometry), + (allometric positive), and -(allometric negative). AF represents adult females, AM represents adult males, JF represents juvenile females, JM represents juvenile males, n is the number of individuals, TF is the total number of females, TM is the total number of males, and R 2 is the coefficient of determination.
Discussion
The sex ratio indicates that there is an overall predominance of male Ucides cordatus, with an increase in the proportion of this gender during the reproductive period and a decrease during the fattening (non-reproductive period). Castro et al. (2008) reports a dramatically different U. cordatus sex ratio (4.9:1) and suggests that sex ratio may vary with the severity of overfishing, the biological season (e.g., molting or reproduction) and/or food availability and abundance. Therefore, sex ratio data must always be carefully evaluated; the number of U. cordatus males (i.e., four times higher than that of females) certainly cannot be explained by differential fishery activity because only U. cordatus males are fishing targets in northern and northeast Brazilian regions (Capistrano and Lopes, 2012). Indeed, a review of the overall sex ratio of 'uçá'-crabs reveals discordant patterns, including: 1) a predominance of males reported by Wunderlich and Pinheiro (2013) in Iguape (São Paulo), 2) a predominance of females reported by Góes et al. (2010) at Vitória Bay (Espírito Santo), and 3) a gender balance reported by Araújo and Calado (2008) in the Estuarine Lagoon Complex of Mundaú-Manguaba at Maceió (Alagoas). Wunderlich and Pinheiro (2013) conduct studies in a pristine and underexplored mangrove area finding a sex ratio dependence of the arboreal composition and tidal flooding level. Therefore, it is strongly recommended the use of sex ratio compared with phytosociological data of the studied mangrove area to avoid a misinterpretation.
The population structure reveals that both amplitude and normal components (modes) are similar between the sexes. These findings can be explained by the slower U. cordatus growth constant of the Von Bertalanffy model (k), which ranges from 0.26 (in females) to 0.28 (in males) in Southeast Brazil (Pinheiro et al., 2005) and 0.25 (in females) to 0.17 (in males) in Northeast Brazil (Diele and Koch, 2010). The mean sizes (CW) of this species in Iguape (São Paulo) were 51.8 mm (males) and 47.0 mm (females), which were very close to the values obtained in this study (i.e., 55.4 and 44.5 mm, respectively). However, a comparison of the biometric data of this species does not show a national or regional morphometric pattern (Moura and Coelho, 2004;Lima and Oshiro, 2006). It is possible to speculate that U. cordatus can be a resilient mangrove species, because there was not an evident effect in his population structure, despite the human impacts confirmed in Joanes River estuary (e.g., real state pressure, sewage spills and waste) (Sousa, 2014).
Studies on the relative growth of crustaceans (Hartnoll, 1974;1978;1982) indicate some patterns using biometric relationships, with ontogenetic changes and relevant reproductive information based on secondary sexual characters. In brachyurans, the CLxCW relationship generally indicates an isometric growth without an expressive growth rate difference over ontogeny (Pinheiro and Fransozo, 1993). However, Pinheiro and Hattori (2006) reported a reduction of the CW growth rate in U. cordatus males during puberal molt (juvenile to adult phase); an opposite pattern was reported for females. Therefore, these authors verified that CLxCW relationship could be successfully used to indicate the CW morphologic maturity for this species that were similar between sexes (males: 59.1 mm; and females: 58.2 mm). These values were nearest to those obtained in this study (53.7 and 58.0 mm, respectively). It is possible that these puberty size greatly influences the life cycle of U. cordatus and possibly coincides with an increase of the gill chamber volume, an adaptation for air storage in the semi-terrestrial crabs (Greenaway, 1984a;1984b). This size difference can also be used to promote sex recognition, a fact previously suggested for this species by Pinheiro and Fiscarelli (2001).
Chela size in males can be used to reveal the maturity size. As in other crabs the chela size in adult males of U. cordatus is a secondary sexual character associated with the agonistic interaction (competition) among male individuals, and other reproductive behaviour related to courtship, handling of females, and other functions (Pinheiro and Fransozo, 1999;Nordhaus et al., 2009;Castiglioni and Coelho, 2011;Sampaio et al., 2011). The PLxCW relationship indicates that the chela size of males grows more quickly than its body (CW) after the puberal molt, verified from 39.4 CW mm in this study. The puberty size that was reported in this study was very similar to the size range (37.3 and 38.0 mm CW) registered by Castiglioni and Coelho (2011) to this same species in pristine mangroves of Tamandaré (Pernambuco, Brazil). These sizes are smaller than those indicated in a review by Ibama (2011), with a size at maturity for U. cordatus males ranging between 44 to 61 CW mm. These results confirm maturity sizes generally larger at lower latitudes, confirming the review presented by Ibama (2011).
It is necessary to note that mangroves at Joanes River are located in an Environmental Protected Area (Joanes-Ipitanga EPA). Nevertheless, many anthropic pressures were detected in this place, represented by diffuse pollution sources (e.g., organic wastewater, chemical residues from marinas, etc.) that can threaten the mangrove ecosystem. Considering this reduced environmental quality, these pollution sources could affect the population biology and reproduction of the mangrove biota in these areas (Andrade et al., 2007). Although an evident higher degree of pollution in the mangrove ecosystem of the Joanes River estuary than in the pristine mangroves studied by Castiglioni and Coelho (2011), a similar maturity size was registered among these areas. This finding suggests that U. cordatus is highly resilient in mangrove areas with different degrees of environmental quality.
Abdominal morphometry in brachyuran females changes over the life cycle, and most changes are detected between developmental phases (juvenile and adult) (Hartnoll, 1974;1982;Pinheiro and Fransozo, 1998). In portunid crabs, a clear overlap between these phases is evident, but this observation is not confirmed for all brachyuran families. Ucides cordatus females follow this latter biometric pattern, in which the abdominal width (AW) grows in positive allometry with body size (CW) in the juvenile phase and becomes isometric in the adult phase to maximize egg protection and incubation (Maciel and Alves, 2009;Mendonça and Pereira, 2009;Oliveira et al., 2013). In the Joanes-Ipitanga EPA, female morphological puberty (44.5 mm CW) was within the range of the U. cordatus female maturity sizes reported in Brazil (46.7 ± 5.7 mm CW) (Ibama, 2011).
In Tab. 4, the percent of each developmental phase (juvenile or adult) is presented as a function of sex and size at maturity size for each biometric relationship (i.e., carapace, chelae and abdomen) and compared to the minimum capture size defined by Brazilian law (Ibama, 2003a;2003b). A less conservative (but reliable) estimate of size at maturity may be obtained by using the sizes of the chelae (males) and the abdomen (females) instead of the sizes specified by Brazilian laws or by carapace relationships (CLxCW).
The relationship of WWxCW in U. cordatus indicates that there is an isometric growth rate for both sexes. This finding is in agreement with male data obtained by Pinheiro and Fiscarelli (2009) at Iguape (São Paulo, Brazil), but it contrasts with the negative allometry detected for females by these authors. It is possible that this slight weight reduction in females could be explained by differential degrees of repletion and gonadal maturation, as suggested by Araújo and Calado (2008) for crabs from the Mundaú-Manguaba Estuarine Lagoon Complex (Alagoas, Brazil). This regional difference illustrates the intrinsic relationship between biological processes in this species (e.g., growth and reproduction) and environmental factors, sex, and gonadal maturation (Pinheiro and Fiscarelli, 2009). The life cycle of U. cordatus is dramatically influenced by changes in temperature, rainfall and tidal amplitude; these factors determine the initiation (and time modulation) of some reproductive events (e.g., nuptial molt, mating, gonadal maturation and spawning) (Sant'Anna et al., 2014) and other biological events, such as the U. cordatus fattening period during the winter months.
The reproductive period in brachyuran crabs can be defined as the months with highest number of adult females (CW > maturity size), considering those with mature gonads and/or eggs (Castiglioni et al., 2011;Castilho-Westphal et al., 2013). In the present study, ovigerous females were only found during three months (March to May), due to absence of two sampling months (December and January), which corresponds to the minimum U. cordatus spawning period (ranging from 3 to 5 months) that was reported in a recent review (Ibama, 2011). However, Pinheiro and Fransozo (2002) suggest that the reproductive season in decapod crustaceans needs to be evaluated more extensively by categorizing reproduction as continuous, seasonal or seasonal-continuous. A more complete analysis of the reproductive biology of U. cordatus in Brazil revealed this species' seasonal reproduction, with a complete absence of females with mature gonads and/or ovigerous females during some months of year (Pinheiro and Fiscarelli, 2001;Ibama, 2011). According to Castiglioni et al. (2013), this species showed a higher breeding activity in the spring and summer months in the state of Pernambuco (Brazil), where a large number of mature individuals could be found. Therefore, the reproductive period of U. cordatus varies with geographic latitude (Pinheiro and Fiscarelli, 2001;Sampaio et al., 2011). A review on this subject in Brazil (Ibama, 2011) reveals that five months (December to April) represent the maximum length of the reproductive season, based on ovigerous females in the northern region, and that a reproductive delay occurs in the northeast region (generally from January to May). Nevertheless, in some regions, there is a shorter reproductive season of three months, as reported by Oliveira et al. (2013) for crabs from Ariquindá and Mamucabas (Pernambuco, Brazil). In addition, Souto (2007Souto ( , 2008) ) identified an eightmonth (March to October) fattening season (i.e., the period without ovigerous females) in Acupe (Bahia, Brazil).
Despite the high habitat fragmentation and anthropic pressures in the mangrove ecosystem of the Joanes River, the population parameters and reproduction of U. cordatus were similar to those of more preserved mangrove ecosystems. This finding suggests that U. cordatus is a mangrove species with great resilience to poor environmental quality, supported by the fact that in the Joanes estuary there was a large number of ovigerous females and juveniles.
Conclusions
The estimated size (i.e., carapace width, CW) at morphological maturity for Ucides cordatus in the Joanes River mangrove ecosystem was 39.4 mm (for males) and 44.5 mm (for females). These sizes corresponded to 82.3% of all males and 52.0% of all females. These estimates are less conservative than the maturity sizes specified by Brazilian law for this fishery resource (which represent 30.6% and 3.6% of all males and females, respectively).
The mangrove ecosystem of the Joanes estuary is reduced, fragmented and subject to many anthropic pressures. Nevertheless, this study does not indicate a reduction in the population or reproductive parameters of U. cordatus; instead, these parameters are similar to those of more preserved mangrove ecosystems. This finding indicates that this crustacean is a resilient species with respect to environmental quality.
Figure 1 .
Figure 1. Estuary of the Joanes River, Lauro de Freitas municipality (Bahia State, Brazil), indicating the five mangrove areas where 'uçá'crabs (Ucides cordatus) was captured monthly from September 2011 to August 2012. This satellite photo was modified based on Image © 2013 Digital Globe and Image © 2013 Terra Metrics, available at Google Earth TM 2013.
Figure 2 .
Figure 2. Ucides cordatus (Linnaeus, 1763). The absolute abundance of each sex by size class (CW) captured in the mangroves of the Joanes River, Lauro de Freitas Municipality (Bahia State, Brazil) between September 2011 and August 2012. Normal curves are shown by dashed lines (i and ii) and represent the normal components in each sex (mean ± standard deviation), and the continuous curve represents their average. N is the number of individuals and CW is carapace width.
Figure 3 .
Figure 3. Ucides cordatus(Linnaeus, 1763). The relative frequencies of females (ovigerous and non-ovigerous) captured monthly in the mangroves of the Joanes River, Lauro de Freitas Municipality(Bahia State, Brazil) between September 2011 and August 2012. In December and January, the crab-catchers were not available.
Figure 4 .
Figure 4. Ucides cordatus (Linnaeus, 1763). Scatter plot of the CLxCW relationship, based on male crabs captured monthly in the mangroves of the Joanes River, Lauro de Freitas Municipality (BA, Brazil) between September 2011 and August 2012. The arrow indicates the size at morphological maturity, PL is the propodus of the major cheliped, CW is the carapace width, N is the number of individuals, and R 2 is the coefficient of determination.
Figure 5 .
Figure 5. Ucides cordatus (Linnaeus, 1763). Scatter plot of the AWxCW relationship, based on female crabs captured monthly in the mangroves of the Joanes River, Lauro de Freitas Municipality (Bahia State, Brazil) between September 2011 and August 2012. The arrow indicates the size at morphological maturity, AW is the width of the 5 th abdominal somite, CW is the carapace width, N is the number of individuals, and R 2 is the coefficient of determination.
Table 1 .
Ucides cordatus(Linnaeus, 1763). The absolute abundance (number of individuals) by sex and population groups in the mangrove areas of the Joanes River, Lauro de Freitas municipality(Bahia State, Brazil), captured monthly between September 2011 and August 2012, with respect to the biological period (reproductive: December to May; non-reproductive: June to November). NO indicates females without eggs, OV indicates ovigerous females, TF indicates total number of females, TG indicates total number of crabs (males + females), and OF% indicates the percentage of ovigerous females of all total females.
* In these months, crab-catchers were not available to assist in field expeditions.
Table 2 .
Ucides cordatus(Linnaeus, 1763). Biometric variables, including the cephalothorax (CW, width; CC, length), the length of the propodus of the major cheliped (PL, for males only), the width of the 5 th somite of the abdomen (AW, for females only) and the wet weight (WW), based on the individuals captured monthly in mangroves of the Joanes River, Lauro de Freitas Municipality(Bahia State, Brazil)between September 2011 and August 2012. An n indicates the number of specimens measured and weighed, Min is the minimum value, Max is the maximum value, x is the mean, and s is the standard deviation.* Means of the same biometric variable followed by a distinct letter differed statistically (P < 0.05)
Table 4 .
Ucides cordatus(Linnaeus, 1763). Number of individuals (percent within parentheses) in each developmental phase (juvenile and adult) and maturity size estimated by the biometric relationships in comparison with minimum capture size by Brazilian laws. AW represents the abdominal width of 5 th somite, CW is the carapace width, and PL is the propodus length of major chela.
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Domain: Environmental Science Biology
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Morphological, cultural, pathogenic and molecular variability amongst Indian mustard isolates of Alternaria brassicae in Uttarakhand
Alternaria blight (Alternaria brassicae) causes severe foliar damage to Indian mustard in Uttarakhand. Ten (10) isolates of A. brassicae were collected from different hosts and characterized for cultural, morphological, pathogenic and molecular variations. A. brassicae colonies varied in their cultural behaviour ranging from cottony, flurry to feathery, with smooth to rough margins. Colour of colonies ranged between white, off white to light brown. Colony growth varied from slow, medium to fast with fast growth in isolate KM and least in JD on the potato dextrose agar (PDA) medium. Significant morphological variations in conidia length, conidia width, and number of horizontal septa were observed in the isolates. Average conidial size ranged from 105 to 135 × 10 to 20 μm. Isolates exhibited variations in disease index, number and size of the lesions. The dendrogram analysis, based on molecular (random amplification of polymorphic DNA, RAPD) basis revealed two groups at 15% similarity coefficient. Group I was composed of seven isolates namely, VR, DV, P7, LM. P10, KR and ND with 18% similarity (82% dissimilarity) while group II was composed of only three isolates namely, JD, KA and AS with only 24% similarity (76% dissimilarity).
INTRODUCTION
Alternaria brassicae (Berk.) Sacc., is an important necrotrophic pathogen causing Alternaria blight disease in Indian mustard [Brassica juncea (L.) Czern and Coss.]. It is very difficult to manage the disease, due to no proven source of resistance reported till date in any of the hosts (Meena et al., 2010b). The yield loss due to this pathogen is up to 47% in the entire mustard growing area (Meena et al., 2010a). One of the significant aspects of biology of an organism is the morphological and physiological characters of an individual within a species, which are not fixed. This holds true with fungi also, although it is not frequent in asexually produced individuals of the progeny. Variability studies are important to document the changes occurring in populations and individuals as variability in morphological and physiological traits indicate the existence of different pathotypes. Alternaria blight severity on oilseed Brassicas differ season to season, region to region and also individual crop to crop in India . This might be due to the existence of variability among geographically similar isolates of A. brassicae. The variability is a well known phenomenon in genus Alternaria and may be noticed as changes in spore shape and size, growth and sporulation, pathogenicity, etc. Diversity appears even in single spore isolates.
Many reports on the existence of variability among different Alternaria species from different hosts have been reported by earlier workers (Pryor and Gilbertson, *Corresponding author. E-mail2002; Pryor and Michailides, 2002;Quayyum et al., 2005;Kumar et al., 2008) as also within A. brassicae species (Kaur et al., 2007). Recently, Meena et al. (2012) studied the aggressiveness, diversity and distribution of A. brassicae isolates infecting oilseed Brassica in India. Variation in pathogen populations can generally be detected with methods like morphological, cultural, pathogenic and molecular specificity. DNA markers have become a powerful tool to study taxonomy and molecular genetics of a variety of organisms. The Random Amplified Polymorphic DNA (RAPD) allows quick assessment of genetic variability, and has been used to study inter-and intra-specific variability amongst the isolates of several fungal species. Reports are available who studied the genetic variation within and between Alternaria species by random amplified polymorphic DNA (RAPD) molecular marker Tewari, 1995, 1998;Pryor and Michailides, 2002;Kumar et al., 2008). Since the crop and disease are of paramount importance to the Uttarakhand state and no studies on pathogenic and genetic variability have been conducted. Keeping this in mind, the present investigation focused on morphological, cultural, pathogenic and molecular variability of ten mustard isolates of A. brassicae in Uttarakhand.
Collection of A. brassicae isolates
Plant material infected with A. brassicae was sampled randomly from different cultivars of B. juncea grown in the field of Crop Research Centre of G. B. Pant University of Agriculture and Technology, Pantnagar Uttarakhand, India. The isolates of A. brassicae were collected and designated as BJABI stands for Brassica juncea Alternaria brassicae isolates (Table 1). These selected infected spots were washed 3 to 4 times in sterilized distilled water and then surface sterilized by dipping in 4% NaOCl solution for 1 min, followed by washing with sterilized water 3 to 4 times. Surface sterilized leaf spot pieces were then aseptically transferred into 9 cm Petri dishes containing potato dextrose agar (PDA) and incubated at 25±2°C for seven days. Thereafter, growing mycelia from margin of apparently distinct colonies of the leaf spot pieces on the medium were aseptically transferred into another Petri plate containing PDA medium, where it was grown for 15 days at 23±2°C in the BOD incubator. On the basis of their conidiophore and conidial morphology as described by Simmons (2007), the pathogen was identified as A. brassicae (Berk.) Sacc. and purified by single spore isolation method. The isolated fungal pathogen cultures were maintained on PDA slants at 4°C.
Morphological variability of different isolates of A. brassicae
Ocular micrometer was calibrated and by use of micrometry , morphological variability among the 10 isolates of A. brassicae was studied in 2010 to 2011. Total of thirty conidia from each slide were examined at 40X magnification of light microscope and measured using ocular and stage micrometer. The average was used to calculate the conidial length, width and number of horizontal septa.
Cultural characteristics of different isolates of A. brassicae
The culture character was recorded on day 10 of inoculation of all isolates of A. brassicae. Characters like colony color, appearance, growth, shape, margin, sporulation and zonation were recorded by direct observation of culture-grown Petri plate on PDA which was incubated in B. O. D. incubator at 25°C temperature and 100% relative humidity.
Pathogenic variability of different isolates of A. brassicae
In order to confirm the identification of the disease and its causal agent, the pathogenicity test was conducted under polyhouse conditions in pot experiments using B. juncea cultivar Divya. Seedlings were raised in pots filled with sterilized soil. Spores from the colony were scraped in autoclaved distilled water and spore suspension of 2 × 10 3 spores/ml concentration was prepared (Giri et al., 2013). Such spore suspension of pathogenic inoculum of the isolates (AS, KA, LM, ND, P7, P10, and VR) was sprayed on 3rd/4th true leaves of 30 days old plant of B. juncea cultivar Divya by drop plus agarose artificial inoculation method (Giri et al., 2013). Three quantitative characters namely, disease index, average number of spot/10 cm 2 and average spot size (cm) were recorded on leaves at different time intervals after pathogen inoculation.
Molecular variability of different isolates of A. brassicae
Molecular variability among ten single spore cultures of A. brassicae was analysed by RAPD molecular marker. Genomic DNA of ten single spore cultures of A. brassicae was isolated separately by using standard cetyl trimethyl ammonium bromide (CTAB) extraction method of Doyle and Doyle (1990). Molecular variability among A. brassicae isolates were studied by using twenty six RAPD primers from Life Tech Company (Table 2). The polymerase chain reaction (PCR) master mix was prepared with 1X Taq polymerase buffer, 1.8 mM MgCl 2 , 0.4 mM dNTPs, 0.4 pM primers and 1.5 U of Taq polymerase. Thereafter, 20 µl of master mix was added with 5 µl (50 ng) DNA in PCR tubes. Forty PCR amplification cycles were carried out in PCR machine [Eppendorf, Germany; model: Mastercycler (R) family] by denaturation at 94°C for 1 min, annealing at 37°C for 1 min and extension at 72°C for 1 min. Each PCR amplification reaction was preceded by an initial denaturation at 94°C for 4 min followed by final extension at 72°C for 10 min. The amplified products were separated by electrophoresis in 1.5% (w/v) agarose (Genei, Bangalore) gel with 1X TBE buffer, stained with ethidium bromide (0.5 μg/ml) at 90 V for 3.0 to 3.5 h and photographed using gel documentation system (Alpha Innotech, USA Alpha Innotech, USA; model: AlphaImager TM 3400). The sizes of the amplification product were estimated using 100 bp to 3.0 kb ladder (Ф × 174 DNA/ BsuRI (Hae III), Fermentas. All the reactions were repeated in at least two independent experiments. All the amplified bands were scored as present or absent for each DNA sample and further, the RAPD reaction results were analyzed using software Gene Profiler. In order to analyze the relatedness among the species, a dendrogram based on unweighted pair group method with arithmetic average (UPGMA) and Nei and Li genetic distance matrix (Nei and Li, 1979) value was obtained.
Cultural characteristic of A. brassicae isolates
Isolates of A. brassicae showed variable cultural characteristics like colony color varied from white, off white to light brown, appearance of the colony from cottony, flurry to feathery, colony growth varied from slow, medium to fast, colony margin from wavy, smooth to rough ( Figure 2). Based on these characteristics, all A. brassicae isolates could be grouped into three colony types. Group 1 isolates (DV and P7) produced white colonies with a fluffy appearance.
The colony was circular in shape with smooth margins. Group 2 isolates (KA, LM, ND, P10 and VR) produced off white colonies with a cottony and feathery appearance. The colony was circular in shape with all types of margins. Group 3 isolates (AS, JD and LR) produced light brown colonies with cottony appearance. The colony was circular in shape with wavy and rough margins (Table 4). Such kind of variability among the different A. brassicae isolates were also reported by Vishwanath (1999) and Meena et al. (2012).
Pathogen aggressiveness of A. brassicae isolates
Different isolates of A. brassicae showed variable response on host B. juncea cultivar Divya. Variation in the disease index (Figure 4a), average number of spots/10 cm 2 (Figure 4b) and average size of spot (cm) (Figure 4c) on same host depending on aggressiveness of isolates revealed that the variability exist among A. brassicae isolates. ND was found to be the most aggressive whereas AS was found to be the least aggressive isolate. A similar study was conducted by Michereff et al. (2003) who studied 38 isolates of Alternaria brassicicola and estimates variability based on disease development and pathogen physiology and found that A. brassicicola isolates were highly variable. In another study, Kaur et al. (2007) reported the pathogenic variability among A. brassicae isolates considering only percent disease severity.
Molecular variability of A. brassicae isolates
Analysis by 26 RAPD primers revealed a high level of genetic variability among ten isolates of A. brassicae of different cultivars of B. juncea. Amplification of DNA of all the A. brassicae isolates produced 1014 scorable and reproducible RAPD markers. On an average, 39 bands were produced. The dendrogram prepared by using the similarity coefficients ( Figure 5) clustered the ten representative isolates into two major groups that is, Groups I and II at only 15% similarity coefficient (85% dissimilarity). Group I was composed of seven isolates namely, VR, DV, P7, LM. P10, KR and ND with 18% similarity (82% dissimilarity) while group II was composed of only three isolates namely, JD, KA and AS with only 24% similarity (76% dissimilarity).
Seven isolates of the group I were sub-clustered into two minor clusters, of which one was composed of six isolates namely, DV, P7, LM. P10, KR and ND with 24% similarity (76% dissimilarity) while another was composed of remaining one isolates VR 18% similarity (82% dissimilarity). Likewise, group II was sub-clustered into two minor clusters, of which one was composed of two isolates namely, KA and AS with 27% similarity (73% dissimilarity) while another was composed of remaining one isolates JD with 23% similarity (87% dissimilarity). The present results indicated high genetic divergence among the 10 isolates of A. brassicae. Polymorphism within an Alternaria species by RAPD molecular marker has been described by many workers Tewari, 1995, 1998;Kumar et al., 2008). Sharma and Tewari (1995) observed polymorphism among A. brassicae isolates from different geographical regions of the world. However, in 1998 they found low intra-regional variation among Indian and Canadian isolates of A. brassicae with 75% similarity among them. Although, the genus Alternaria is known as an imperfect fungus, it shows genetic variability within a species and this variability might be due to the existence of mutation, somatic hybridization, heterokaryosis, uniform host selection, extensive dispersal or of a cryptic sexual stage. High degree of genetic variability was observed among only ten isolates of A. brassicae from different B. juncea cultivars growing in Pantnagar region of Uttarakhand. This could be the probable possible reason behind extreme and different disease reaction of germplasm at Pantnagar from observations at most of other locations. In order to provide a better picture of the pathogenic as well as genetic divergence among A. brassicae populations of India, there is need to conduct similar holistic investigation among higher number of A. brassicae isolates which could be helpful to generate resistant material against Alternaria blight in oilseed Brassicas.
Conclusion
The variation in cultural, morphological, pathogenic and molecular characters of isolates observed indicated the existence of different strains of pathogen. Similar characters have formed the basis for defining the existence of different strains among the species of fungi imperfection.
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Domain: Environmental Science Biology
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Lecania cuprea and Micarea pycnidiophora (lichenized Ascomycota) new to Poland
Two lichenized fungi, Lecania cuprea and Micarea pycnidiophora, are reported for the first time from Poland. Lecania cuprea is also recorded as a new lichen species to the Western Beskidy Mts and the Pieniny Mts and M. pycnidiophora is new to the Carpathians. Illustrated descriptions, taxonomic notes, habitat requirements and known distributions for both species are provided.
Introduction
Recent lichenological exploration by the author in the Polish Western Carpathians has significantly extended our knowledge of lichen diversity in this mountainous area. Many species unrecorded to date in Poland in this region have already been reported (e.g. [1][2][3][4][5][6][7][8][9][10]), but data on several others have yet to be published; details of two such species, Lecania cuprea and Micarea pycnidiophora, collected in 2004 and 2014 are presented here. M. pycnidiophora, previously known only in Central Europe from České Švýcarsko (= the Bohemian Switzerland) in western part of the Czech Republic, is also reported for the Carpathians.
Material and methods
Specimens were determined by standard microscopy and simple spot test reactions. Hand-made apothecial and thallus sections mounted in water were used to measure all anatomical characters. In the case of Lecania cuprea, KOH was used to study its apothecial pigmentation.
Specimens have been deposited in the Herbarium of Gorce National Park (GPN). Nomenclature and synonyms of the presented taxa follow MycoBank ( [URL] are mapped (Fig. 1) according to the Polish Atpol grid square system [11] modified for lichens by Cieśliński and Fałtynowicz [12]. Brief descriptions of diagnostic characters are based on Polish collections.
Notes
Lecania cuprea markedly differs in phenotypic characters from the type species for the genus, L. cyrtella (Ach.)Th. Fr. It has no thalline margin, and its ascospores are narrowly fusiform and 3(-5)-septate while L. cyrtella has a distinct amphithecium in young development stage of ascocarps and 1-septate, narrowly ellipsoid ascospores. These characters and a distinct proper margin, which is usually darker colored than the disc (see Fig. 2a), as well as entirely ±stout excipular hyphae make this species similar to representatives of the genera Bacidia De Not. or Bilimbia De Not. However, phylogenetic relationships of L. cuprea have not been confirmed to date.
According to Fletcher et al. [14], the lumina of excipular fungal cells widen towards the outer edge of the proper margin. However, in Polish collections apices of the hyphae are not distinctly wider but the whole excipulum is made of ±stout hyphae up to 2-3 µm wide.
Lecania cuprea is a calciphilous species usually growing on under-hangs or vertical rock surfaces in woodlands, sometimes over-growing epilithic mosses, but its most recent Lithuanian collection was on a siliceous boulder influenced by a basic stream water [24].
The Polish records are the first for the Western Beskidy Mts (including Czech and Slovak part of the flysch Carpathians) and the calcareous Pieniny Mts (including Slovak part).
Notes
In its morphology and anatomy, M. pycnidiophora most closely resembles M. stipitata Coppins & P. James and M. neostipitata Coppins & May, by forming stipitate, pale pycnidia and immarginate apothecia. Main differences between these three species are in their chemistry: M. pycnidiophora produces gyrophoric acid with C+ red reaction of pycnidia, thallus and apothecia, while M. stipitata has no lichen secondary compounds and M. neostipitata, to date found only in North America, produces fumarprotocetraric and lobaric acids and contains K+ violet Sedifolia-grey pigment in its apothecial and pycnidial sections [32]. Differences in dimensions of their conidia are also emphasized, but they are not so distinct in M. pycnidiophora and M. neostipitata; 3.8-6 × 1-1.5 µm vs. 3.8-4.8× 1-1.5 µm [32].
Distribution and habitat requirements
Micarea pycnidiophora is a corticolous species very rarely reported from both sides of the northern Atlantic. It has been recorded in Britain, France and the Canary Islands [31], Belgium and Luxembourg [16,33], NW Spain [34] and south-eastern USA [32]. Recently it has also been discovered in the Czech Republic [35] and the Leningrad region in Russia [36], as well as in the Himalayas [37]. The records presented here are new to Poland and the Carpathians. Except for Russia, where M. pycnidiophora was collected for the first time on conifers (Picea), it is found on old deciduous trees (Fagus, Acer, Alnus, Quercus) or shrubs (e.g. Ilex, Rhododendron) [34,38].
The British population of M. pycnidiophora is said to be the richest in the World [39], but concentrated occurrences of this species on the European continent have also been found in the Ardennes Forest, in Belgium and Luxembourg [16]. These occurrences and the North American range of M. pycnidiophora could suggest that this species belongs to a subatlantic element in the lichen biota. In the light of this, both known Central European populations of this lichen in the Czech Republic and Poland (and indeed the Himalayas) appear to be isolated. Palice et al. [35] suggest that this species may represent a relic of formerly much richer epiphytic lichen biota as it was found in association with other lichen peculiarities. In the Polish Carpathians, M. pycnidiophora was associated with several old-growth forest lichens such as Loxospora elatina, Ochrolechia androgyna, Thelotrema lepadinum and Usnea spp., growing within one of the best preserved spruce-fir-beech forest in the Western Carpathians.
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Domain: Environmental Science Biology
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Risk Assessment Development of a risk assessment framework to predict invasive species establishment for multiple taxonomic groups and vectors of introduction
A thorough assessment of aquatic nonindigenous species’ risk facilitates successful monitoring and prevention activities. However, speciesand vector-specific information is often limited and difficult to synthesize across a single risk framework. To address this need, we developed an assessment framework capable of estimating the potential for introduction, establishment, and impact by aquatic nonindigenous species from diverse spatial origins and taxonomic classification, in novel environments. Our model builds on previous approaches, while taking on a new perspective for evaluation across species, vectors and stages to overcome the limitations imposed by single species and single vector assessments. We applied this globally-relevant framework to the Laurentian Great Lakes to determine its ability to evaluate risk across multiple taxa and vectors. This case study included 67 aquatic species, identified as “watchlist species” in NOAA’s Great Lakes Aquatic Nonindigenous Species Information System (GLANSIS). Vectors included shipping, hitchhiking/fouling, unauthorized intentional release, escape from recreational or commercial culture, and natural dispersal. We identified potential invaders from every continent but Africa and Antarctica. Of the 67 species, more than a fifth (21%) had a high potential for introduction and greater than 60% had a moderate potential for introduction. Shipping (72%) was the most common potential vector of introduction, followed by unauthorized intentional release (25%), hitchhiking/fouling (21%), dispersal (19%), stocking/planting/escape from recreational culture (13%), and escape from commercial culture. The ability to assess a variety of aquatic nonindigenous species from an array of potential vectors using a consistent methodology is essential for comparing likelihoods of introduction, establishment, and impact. The straightforward design of this framework will allow its application and modification according to policy priorities by natural resource managers. The ability to use a variety of information sources facilitates completion of assessments despite the paucity of data that often plagues aquatic nonindigenous species management.
Introduction
Nonindigenous species have the potential for both ecological and socio-economic impacts, and can be very costly or impossible to eradicate after establishment (Hobbs andHumphries 1995, Simberloff 2003). To prioritize management efforts, risk assessments can be used to evaluate vectors of introduction, species life history traits, habitat suitability, historical patterns of invasion, and impacts realized in other invaded regions (Keller 2009;Kulhanek et al. 2011;Gordon et al. 2012).
The use of risk assessment to address environmental threats began with a focus on environmental contaminants in the 1980s (e.g., Hayes 1997;Landis et al. 2013). Limited resources led to ranking management priorities according to risk levels (Burgman et al. 1999). By the early 2000s, risk assessment was implemented to aid decision-making in the fields of biological invasion and conservation biology, particularly to ensure regional biosecurity (e.g., Andersen et al. 2004).
Biological invasion risk assessment continues to be a young field, with a variety of approaches, scope, content, and required elements (reviewed in Dahlstrom et al. 2011;Verbrugge et al. 2012). There remains a paucity of consistency, consensus, and uniformity among approaches, particularly in addressing the following: 1) whether to include multiple taxa and vectors (versus a single taxon or vector); 2) where to set the assessment endpoint (i.e., introduction, establishment, or impact); 3) what impact types to consider; 4) whether to use a semi-quantitative, quantitative, or qualitative approach; and 5) how to deal with data gaps and other uncertainty.
Multiple taxa and vectors
Researchers have taken a variety of approaches when considering what to include in risk assessments. For example, they may consider a single species' risk to a given area (Therriault and Herborg 2008), a number of species within a particular taxonomic group (e.g., plants and fishes in Daehler and Carino 1999;Kolar and Lodge 2002;Copp et al. 2005); or a number of different taxa within a particular vector (e.g., Gollasch and Leppäkoski 2007;Leung and Dudgeon 2008). Species-specific assessments often compare species' life history and physiological traits to the climate and other environmental conditions of the recipient location (e.g., Kolar and Lodge 2002;Clarke et al. 2004;Gollasch 2006;Bomford et al. 2010). Such assessments can include detailed information about species distributions, reproductive characteristics, physiological constraints, and environmental preferences (UNEP/MAP-RAC/SPA 2008). Vectorspecific assessments often take a broader approach including vector strength to predict introduction potential and climate matching to predict establishment potential. This focus on single-taxon (Mendoza et al. 2009) or single-vector (US Army Corps of Engineers 2014) may lead to assessments that provide an incomplete picture of the full invasion risk.
Assessment endpoint
Biological invasion risk assessments can also have different endpoints, with species' introduction commonly chosen (Andersen et al. 2004); they are less consistent in their treatment of establishment (colonization and spread) and consequence (impact). However, given the estimated number of introductions that do not result in establishment (García-Berthou et al. 2005), an understanding of establishment potential remains important. Impact is necessary to give a full description of risk (which includes both the probability of an event occurring and the severity of the consequences). Determining impact is also a key element to species' management, as knowledge of small versus large effects allows better prioritization of management efforts (Parker et al. 1999).
Impact types
Biological invasion risk assessments often consider only environmental impacts (e.g., Ruiz et al. 1999) and ignore impacts to other core values, such as economic, social, human health, and cultural impacts (reviewed in Verbrugge et al. 2012). However, given that risk assessments often occur in a sociopolitical context, including these additional core values will ensure the consequences to all stakeholders are fully accounted for.
Semi-quantitative, quantitative, or qualitative approach
Invasion risk may be evaluated quantitatively (with numerical probabilities or descriptors), qualitatively (with categorical descriptors), semi-quantitatively (by representing quantitative data with categorical descriptors), or using rule sets or decision trees with arbitrary risk thresholds (in which a single criterion determines the outcome) (Hayes 1997;Keller et al. 2007b). Issues of objectivity and consistency in professional opinions can arise in qualitative assessments (Burgman et al. 1999, but see use of structured expert judgment in Wittmann et al. 2014). As such, quantitative approaches are often favored despite their sensitivity to weighting schemes (e.g., Pheloung et al. 1999) and dependence on complete data sets, which rarely occur (Campbell 2009).
Data gaps
While there are many forms of uncertainty, within the field of biological invasion risk assessment, gaps in knowledge present the greatest challengesparticularly in the understanding of species' impacts. Yet given the vital role of risk assessments in management, decisions must be made despite extensive knowledge gaps. Options include incorporating expert judgment, applying the precautionary approach and assuming an impact, or applying the hindsight approach and assuming no impact. While the devastating effects of nonindigenous species do not support this last approach, it is often applied (Davidson and Hewitt 2013).
We propose an assessment framework for aquatic nonindigenous species (ANS) that addresses several of the limitations discussed above. In particular, we aim to develop a semi-quantitative framework that facilitates comparison of multiple taxa and vectors, considers the full invasion process from introduction to impact, accounts for the breadth of possible impacts, and gauges uncertainty for each assessment. Such a framework will provide information needed to develop comprehensive policies that are not limited to isolated groups of organisms or vectors of introduction.
Methods
Using a semi-quantitative approach, we first developed a comprehensive framework for assessing aquatic species' invasion risk. This framework built upon previous approaches, while taking on a new perspective for evaluation across species and vectors and stages. We chose to structure this framework to consider introduction, establishment, and impact (hereafter, "assessment components") separately as interacting stages in the invasion process. Details of each of these components are described below (also see Appendices S1-S3 in Supplementary material).
Potential for introduction
The introduction assessment criteria and relative levels of introduction likelihood within each vector were chosen based on Kelly (2007), modified from Holeck et al. (2004) and the United States Geological Survey's (USGS) Nonindigenous Aquatic Species (NAS) database (United States Geological Survey 2011). Assessment criteria and relative levels of introduction likelihood within each vector were based upon the results of a literature review and expert opinion.
The potential for introduction assessment took into account a "proximity" proxy for each pathway using a suite of 12 paired questions (2 per vector). The first question in a pair considered potential pathways for introduction, assigning a score from 0 to 100-usually 100 for being in a particular pathway and 0 for not-while the second question evaluated the likelihood of a species to enter the Great Lakes through that pathway, using a multiplicative factor from 0 to 1. If a question could not be answered based on available data, an "unknown" option was available. A score sheet was kept for tallying the results for each species. Overall probability for introduction per vector (High, Moderate, Low) is determined by the adjusted point score for the species in that vector. Thresholds for introduction probability were set such that species in the closest proximity to the Great Lakes (relative to the pathway of introduction) would be evaluated as having High probability, those at intermediate distances would be evaluated as having Moderate probability, and those either not in the pathway or at the furthest distance would be evaluated as having Low probability. Unlikely represents a score of 0, Low a score of 1-39, Moderate a score of 40-79 and High a score of 80-100.
Potential for establishment
The establishment assessment component included variables that aid or detract from a species' establishment success and spread potential, as relevant to the body of water for assessment. In particular, we considered criteria within four broad categories deemed important in invasion biology (e.g., Williamson and Fitter 1996;Kolar and Lodge 2001;Lockwood et al. 2005;Hayes and Barry 2008;Kulhanek et al. 2011): 1) invasive biological/ecological attributes, 2) environmental compatibility, 3) propagule pressure (inoculum size, frequency), and 4) history of invasion and spread. We modified criteria used in the UK Non-Native Organism Risk Assessment scheme (Baker et al. 2007) for Great Lakes region-specific variables. Additional questions were considered resulting from a review of invasion literature for additional empirically-supported factors, e.g., overwintering (Magnuson et al. 1985), fecundity (Drake and Lodge 2006;Keller et al. 2007a), propagule pressure (e.g., Colautti et al. 2006), and climate change (Rahel and Olden 2008).
Overall species' establishment potential was determined by its total point score (up to 9 points for each of 18 questions). Answers to 3 of the 18 questions could lead to an overall percentage reduction in a species' score (absence of species critical in life cycle; prevention of establishment by herbivory, predation or parasitism of enemy present in Great Lakes; and control measures). Such adjustments are warranted when a variable would counter or prevent the species' establishment. Species can score a High establishment potential if at least three-quarters of the questions were scored as 9s or a Moderate establishment potential if more than half of the questions were scored as 6s (or were evenly split with equivalent numbers of 3s and 9s); otherwise the species is ranked as having a Low establishment potential. Low represents a score of 1-50, Moderate a score of 51-99 and High a score of >100.
Potential for impact
For the impact assessment component, we considered not only environmental and socio-economic impacts (including human health), but also potential beneficial effects, often omitted from biological invasion risk assessments. The inclusion of potential benefits recognizes that nonindigenous species may both be intentionally introduced for desired outcomes (e.g., biological control, recreation, economic gain) or accidentally introduced but result in a perceived benefit over time (e.g., aesthetic, ecological) (Schlaepfer et al. 2011). Thus, this approach is intended to allow managers and policy makers to weigh the contributions of nonindigenous species against potential harms.
We modeled this assessment component after an existing framework used to assess the realized consequences of established nonindigenous species in the Great Lakes (Sturtevant et al. 2014). However, instead of considering location-specific impacts, we accounted for impacts species may have had in any nonnative region. This approach has had great predictive power in previous applications (e.g., Ricciardi 2003). Scores for each of the 6 questions (0, 1, or 6) were summed per impact category (36 point maximum) and converted to an overall impact. If ≥1 or ≥2 questions were scored unknown, with low (1) or no (0) total impact sum, respectively, impact was scored Unknown; if 0 or ≤1 questions were scored unknown, with low (1) or no (0) total impact sum, respectively, impact was scored Low; if total impact sum ranged from 2-5, impact was scored Moderate; if total impact sum was ≥5, impact was scored High.
Application to Great Lakes Watchlist species
We tested our Great Lakes Aquatic Nonindigenous Species Risk Assessment (GLANSRA) framework in the Great Lakes region, selecting species based on NOAA's GLANSIS watchlist criteria ( [URL] addition to the previously determined GLANSIS criteria, for the risk assessment we additionally set the criterion that species must meet at least three of the following conditions: 1) a vector currently exists that could move the species into the Great Lakes, 2) the species is likely to tolerate/survive transport (including in resting stages) in the identified vector, 3) the species has a probability of being introduced multiple times or in large numbers, 4) the species is likely to be able to successfully reproduce in the Great Lakes, and 5) the species has been known to invade other areas; or the species was identified in one or more peer-reviewed scientific publications as having high probability for survival, establishment, and/or spread if introduced to the Great Lakes. While we relied principally on current climate conditions, particularly concerning species' ability to overwinter, to determine inclusion in the assessment, we included several species for which predicted increases in water temperature have led to explicit remarks concerning their future invasion probability.
After species selection, we determined if the GLANSRA framework could assess the full range of taxa under variable levels of information availability by completing the introduction, establishment, and impact assessment components for each species. The assessments were completed using an exhaustive literature review that included online species registries, aquatic invasive species databases, major bibliographic databases, peer-reviewed literature, published state and federal agency reports, reliable Internet sources, librarian services, expert consultation, and best professional judgment.
We compared species' scores for introduction, establishment, and impact to determine trends in predicted invasiveness. In particular, we considered taxonomic groups, geographic origins, vectors, establishment, and impacts in the Great Lakes, and areas of limited data availability.
Results
The GLANSRA framework yielded three separate semi-quantitative, question-driven assessment components for a species' potential introduction (6 pairs of questions), establishment (18 questions), and impact (6 questions for each of 3 broad categories). The final structure of each assessment component, based on the considerations described above, was as follows.
Potential for introduction
Vectors in the introduction assessment component included canals and waterways (1: dispersal), trade of live organisms (2: stocking/planting/escape from recreational culture; 3: unauthorized release; 4: escape from commercial culture), activities of recreational and resource users (5: hitchhiking/fouling), and commercial shipping (6: transoceanic shipping).
We also chose to include a "proximity" estimator for each vector using a suite of 12 paired questions (2 per vector; see Supplementary material Appendix S1). The first question in a pair considered potential means for introduction, assigning a score from 0 to 100, with 100 representing the maximum potential for being in a particular vector. The second question evaluated the likelihood of a species to enter the Great Lakes via that vector, using a multiplicative factor from 0 to 1. The product of these two questions was used to determine the final, adjusted quantitative introduction score for each vector. These quantitative scores were then used to assign a categorical If a question could not be answered from available data, a score of "unknown" was entered. The number of unknowns provided an estimate of assessment confidence. Assessment confidence levels were assigned based on the total number of questions that could not be evaluated (see Appendix S1).
Potential for establishment
In the establishment assessment component, contributing variables from a total of 18 questions were broadly grouped into four categories: invasive biological/ecological attributes, environmental compatibility, propagule pressure, and history of invasion and spread (Table 1; Appendix S2). While important to successful establishment and spread, initially proposed questions concerning genetic diversity of potential source population, genetic and phenotypic variation, and likelihood of introduction during time of year appropriate for establishment, were deemed unlikely to be able to answer a priori for most species and thus removed from this assessment component. Overall species' establishment potential was determined by its total point score. Three questions included an adjustment factor that led to an overall reduction in a species' score. Such adjustments are warranted when a variable would counter or prevent the species' establishment. The categorical probability of establishment for each vector (High, Moderate, Low) was determined by the quantitative score. Assessment confidence levels were assigned based on the total number of questions that could not be evaluated (see Appendix S2).
Potential for impact
The impact assessment component was divided into sets of six questions within three potential impact categories: environmental impact, socio-economic impact, and beneficial effect (Table 2; Appendix S3). Scores for each criterion were summed for each species' potential impact category and converted to a categorical impact ranking using the framework's scoring table, accounting for the level of uncertainty as before (i.e.number of unknowns). This system was based on that created for assessing the realized impacts of species already established in the Great Lakes (Sturtevant et al. 2014).
Application to Great Lakes Watchlist species
We applied the GLANSRA framework to the 67 Great Lakes watchlist species (scores for 5 of 67 species are shown in Table 3, as example). More than three-quarters of these species were either fishes or crustaceans, with the remaining species represented by annelids, rotifers, bryozoans, platyhelminths, mollusks, and plants. These species were native to five continents (Asia, Australia, Europe, North and South America), with the majority coming from Europe (69%), followed by Asia.
Introduction potential
Of the 67 species we assessed for the Great Lakes, than a fifth (21%) had a high potential for introduction and greater than 60% had a moderate potential for introduction (Figure 1). The species with a high potential for introduction originated from each of the included geographic regions, including all of the species from South America. Shipping (72%) was the most common potential vector of introduction, followed by unauthorized intentional release (25%), hitchhiking/fouling (21%), dispersal (19%), stocking/planting/escape from recreational culture (13%), and escape from commercial culture (6%).
Fishes were present in all vectors, while plants were present in all except for the shipping vector. Nine fishes (33%) and seven plants (88%) were present in multiple vectors. For instance, the Ide (Leuciscus leuciscus (Linnaeus, 1758)) had a high potential of introduction through both unauthorized intentional release and stocking/planting/escape from recreational culture, water hyacinth (Eichhornia crassipes (Mart.)Solms) was present in all except the shipping vector, and water lettuce (Pistia stratiotes Linnaeus) had a high potential for introduction in four vectors (dispersal, hitchhiking/fouling, unauthorized intentional release, and stocking/planting/escape from recreational culture). The bulk of crustaceans (83%) were assessed as having a high or moderate potential to be introduced via shipping.
Establishment potential
Most of the species we assessed had a moderate (72%) or high (24%) potential for establishment in the Laurentian Great Lakes, with two-thirds of these species originating in Europe (Figure 2). Annelids, mollusks, fishes, and crustaceans had the highest establishment potentials. There were unknown establishment questions for species in every taxonomic group, with environmental impact also having a significant percentage of questions answered "unknown" ) or no (0) total impact sum, respectively, impact was scored Unknown; if 0 or ≤1 questions were scored unknown, with low (1) or no (0) total impact sum, respectively, impact was scored Low; if total impact sum ranged from 2-5, impact was scored Moderate; if total impact sum was ≥5, impact was scored High.
(Figure 3). The question on size and frequency of inoculation events was the least answerable question (61% unknown) of the establishment assessment component, especially for crustaceans and fishes, followed by fecundity (28% unknown). In contrast, overwintering, climate, and critical species questions in the establishment assessment component were answerable for all assessed taxa. When comparing establishment potential with introduction potential, high introduction species are most likely to have a moderate potential for establishment. The majority of all species fell into the categories of moderate introduction and either moderate (39%) or high (29%) establishment potentials.
Impact potential
Of the species that we could rank for environmental impact (i.e.not "Unknown"), more than half (59%) had a high potential environmental impact (Figure 4). Fishes and plants comprised 75% of the high potential environmental impact species, while fishes and crustaceans comprised 86% of the moderate environmental impact species. Plants were ranked as either moderate or high environmental and socio-economic impact species. Competitive effects and trophic alteration had the most potential for impact. More than a third (39%) of species-including mollusks, crustaceans, platyhelminths, rotifers, and fishes-could not be assessed for overall potential environmental impact due to lack of impact data. Furthermore, we could not assess more than a third of all species in each environmental impact category, with the exception of Question E2 (competition), due to the lack of impact data. Across all levels of potential impact, the majority of species with potential environmental impact to the Laurentian Great Lakes, including unknowns, originated from Europe. Species originating from South America were assessed to have high potential environmental impact, and cosmopolitan species had either a low or unknown potential environmental impact. Most species with a high likelihood of introduction also had a high or moderate potential environmental impact, which suggests that nonindigenous species likely to be introduced may also have environmental impact.
Socio-economic impact and beneficial effect could be assessed for all but four and one species, respectively. Most species (70%) were assessed as having low socio-economic impact, particularly all crustaceans and two-thirds of the fishes (Figure 4). High or moderate socio-economic impacts were limited to fishes, bryozoans, mollusks, and plants (Figure 4). The greatest number of species with high potential socio-economic impact (n=5) originated from Asia. All species that originated from North America or with a cosmopolitan distribution had a low potential socio-economic impact. The greatest socio-economic effects were likely from species impacting recreation and infrastructure.
Most species with a high potential for introduction had either a low (n = 7) or high (n = 6) potential socio-economic effect. More than half (51%) of the assessed species had a low potential beneficial effect. Less than a fifth (16%) of the species had high potential benefits, with the majority of these originating in Europe or with commercial or recreational benefits. There were some low benefit species in every vector with a high potential for introduction except unauthorized intentional release. Two-thirds of species with a low potential benefit had a moderate potential for introduction. The species most likely to establish were also most likely to have a high environmental impact, a moderate socioeconomic impact, and a low beneficial effect.
Framework development
While improving the ability of biological invasion risk assessments to capture multiple taxa and vectors, the final framework complements and builds on several existing frameworks. For example, the Great Lakes and Mississippi River Interbasin Study (GLMRIS) risk assessment, which is a working framework applied to assess the risk of species moving between the Mississippi River and Great Lakes, also examines the potential for movement (introduction), establishment, and impact at the species level (US Army Corps of Engineers 2012). However, limitations of this framework include the qualitative nature of the probabilities and the absence of live trade-related vectors. Snyder et al. (2014) perform a risk assessment for species from the Ponto-Caspian region with quantitative data, but only perform the assessment for a single taxon: fish. The variables that contribute to the assessment are specific to a single taxon (e.g., % mature length at age 2, egg diameter) and as such, the framework could not be applied across a variety of taxa. A risk assessment by Howeth et al. (2016) is similar; although providing a thorough analysis of risk, the framework is limited to freshwater fish in live trade. Despite this, their study is relevant to this framework in that it supports the use of climate similarity and fecundity in predicting risk. So while many frameworks exist to assess various taxa and vectors, this GLANSRA framework was successful in allowing the assessment of multiple taxa and vectors. Other strengths of the tool include assessment of the full suite of positive and negative impacts to account for multiple stakeholder values in light of potential consequences, as well as pan-invasion stages (introduction, establishment, consequence) to gauge risk more fully.
Testing framework
We found a near global distribution representing five continents from which potential Great Lakes invaders had a high potential of introduction. This is similar to the source distribution for nonindigenous species currently established in the Great Lakes (NOAA 2012). All except 3 of the 67 species we assessed were determined to have at least some potential for introduction to the Great Lakes. This suggests that rigorous analysis of each species supported our pre-screening criteria.
The majority (82%) of species had a high or moderate potential for introduction, with the number of species likely to be introduced via shipping exceeding the sum of those species with a high likelihood to be introduced by intentional release, hitchhiking or fouling, and dispersal. The highest likelihood of introduction from European (Ponto-Caspian) species and via the shipping vector is potentially biased by the literature and history of invasion. The shipping bias notably excluded plants, which are least likely to survive the conditions of a ballast water environment unless as seeds. Furthermore, it is interesting to consider that our assessment predicted mollusks, annelids, rotifers, and bryozoans to only be introduced via shipping, despite International Maritime Organization regulations (IMO 2004). However, most of the species assessed for shipping lacked a high potential for introduction, suggesting that current precautionary practices and regulations may have at least decreased the influence of this vector (although it remains a source of potential introductions; Grigorovich et al. 2003).
Despite the overall shipping bias, species with a high potential for introduction-including those of a particular taxonomic group (e.g., fishes, plants)were fairly evenly distributed among vectors, with the exception of the more strictly-controlled commercial vector. Moreover, we found that all assessed taxonomic groups had members with either a high or moderate potential for introduction. This suggests that managers need to go beyond single vector-or taxon-based assessments when developing their prevention and monitoring strategies.
The majority (96%) of the species we assessed, including those from each taxonomic group and continent of origin, were determined to have either a high or moderate potential for establishment in the event they become introduced. These species come, in large part, from regions similar to the Laurentian Great Lakes and have histories of invasions elsewhere. Furthermore, while we found that species with a high potential for introduction often had a moderate potential for establishment, most of the assessed species had a moderate potential for introduction and either a high or moderate potential for establishment. Managers should therefore not only be interested in preventing the introduction of species with the highest potential, but also craft strategies that address species with an intermediate likelihood of introduction as they may be as or even more likely to become established if introduced.
The predictive power of the framework would have been better understood using species from the watchlist that have (or have not) since established, and/or resulted in impacts in the Great Lakes. However, the watchlist is new (species' assessments completed 2014-2015) and the invasion rate in the Great Lakes has declined, such that no watchlist species have become established since the completion of this analysis. Grass carp Ctenopharyngodon idella has been found several times in the Great Lakes, and will likely be moved to the established list soon, becoming the first of the watchlist species to establish.
Uncertainty
Like most risk assessments, this framework was faced with the challenge of addressing uncertainty, both in its development and its application to the Great Lakes watchlist. Most risk assessment frameworks incorporate uncertainty in some form, with a large variety in methodology (Dahlstrom et al. 2011). While comparing the treatment of uncertainty for all invasive species risk assessments is beyond the scope of this paper, it is worth highlighting several general approaches. Many frameworks attach a qualitative assessment of uncertainty (e.g., low, medium, high) to the assessment score (e.g., Baker et al. 2007; the establishment component of this framework, US Army Corps of Engineers 2014). Other frameworks include "unknown" as a potential assessment category, so that a question with incomplete information does not get scored but rather assigned as "unknown" (as seen in the introduction component of this framework). This framework uses a more complicated version of this approach for the impact component, in that the assessment score is mitigated by the number of unknowns to produce a categorical descriptor of unknown, low, medium or high.
In applying the framework to the Great Lakes watchlist, most of the uncertainty was epistemic in nature and associated with the impact component. We were able to determine introduction potential for all but 3% of the species-vector questions, with unknowns (i.e.lack of sufficient information) distributed fairly evenly across species and vectors. In assessing the establishment component, questions related to reproductive ecology (e.g., fecundity, propagule pressure) of potential invaders had the highest uncertainty in terms of number of unknowns. The need for further research in these areas is particularly acute, as these factors have been found to have high predictive power for invasiveness (Eschtruth and Battles 2009). In contrast, we found climate matching, overwintering ability, and species interdependence information to be readily accessible. The environmental impact component had the greatest amount of uncertainty overall, with a sizeable proportion of species across most taxonomic groups (except plants, annelids, and bryozoans) having insufficient information to adequately support assessment of environmental impacts, particularly with regard to competition. Notably, environmental impacts were better documented for these watchlist species (39% unknown) than for established Great Lakes invaders (49% unknown).
While primary literature remains the preferred choice, grey literature and expert judgment are supported alternatives used in many risk assessments (Dahlstrom et al. 2012). We were able to address some of the literature-based knowledge gaps and still make decisions using expert judgment and a precautionary approach.
Conclusion
Our framework addresses key considerations in biological invasions risk assessment, including holistic treatment of invasion stage, taxonomic groups, and impact types (sensu Kumschick and Richardson 2013). This assessment may be customized for other regions and serve as a model for designing terrestrial frameworks that consider invasion across its multiple stages, taxa, and impact categories. Our cross-taxon and -vector tool is furthermore able to incorporate information from multiple sources to elucidate vectors of introduction, evaluate establishment potential, and predict potential impacts. It will also allow managers to make more informed decisions about which vectors to monitor and allocate resources accordingly. Managers will be able to set thresholds with respect to their tolerance of risk concerning the likelihood of species establishment and impact. Finally, this framework is adaptable and easily amendable globally, and as more information about species or vectors becomes available.
Figure 1 .
Figure 1. Number of species in each introduction score category, by taxonomic group. The maximum score over all vectors was used, as this represents the greatest potential for introduction. Unlikely represents a score of 0, Low a score of 1-39, Moderate a score of 40-79 and High a score of 80-100.
Figure 2 .
Figure 2. Number of species in each establishment score category, by taxonomic group. Low represents a score of 1-50, Moderate a score of 51-99 and High a score of >100.
Figure 4 .
Figure 4. Number of species in each environmental(Env) and socio-economic (Soc) impact score category, by taxonomic group. If ≥1 or ≥2 questions were scored with low (1) or no (0) total impact sum, respectively, impact was scored Unknown; if 0 or ≤1 questions were scored unknown, with low (1) or no (0) total impact sum, respectively, impact was scored Low; if total impact sum ranged from 2-5, impact was scored Moderate; if total impact sum was ≥5, impact was scored High.
Table 1 .
Establishment criteria assessed, by category.
Table 2 .
Impact criteria assessed, by category.
probability of introduction. These categories (High, Moderate, Low) were vector-specific and chosen based on a combination of equal intervals and expert judgment (see Supplementary material Appendix S1).
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Domain: Environmental Science Biology
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Four species of Caligus Müller, 1785 (Copepoda, Siphonostomatoida, Caligidae) parasitic on marine fishes of Taiwan
Four species of Caligus, with two new species, are reported from five species of marine fishes of Taiwan. They are: Caligus arii Bassett‐Smith, 1898 on the body surface of Trichiurus lepturus Linnaeus, Caligus dasyaticus Rangnekar, 1957 on the body surface of Dasyatis navarrae (Steindachner), Caligus dactylus n. sp. on the gill filaments of Dactyloptera peterseni (Nyström), and Caligus lutjani n. sp. in the oral/gill cavities of Lutjanus argentimaculaltus (Forsskål) and Lutjanus bohar (Forsskål). Both C. arii and C. dasyaticus are reported for the first time from Taiwan. Caligus dactylus differs from its congeners by the possession of the following combination of features: large myxal process on the maxilliped; a pair of parallel tines on the sternal furca with truncate tip; simple elements (without accessory process) at the tip of leg 1 exopod; and an armature formula of I‐0; I,III on leg 4. Caligus lutjani is distinguished by carrying a two‐segmented abdomen; a pair of diverged tines on the sternal furca with acute tip; an accessory process on the middle two of the terminal four elements on the exopod of leg 1; and a bipectinate, spiniform process on the exopod of leg 4 at the insertion of each of the five outer spines.
Introduction
Although 34 species of sea lice belonging to Caligus Mü ller, 1785 have been reported from Taiwan (Ho and Lin 2004), continuous examination of marine fishes landed at various fishing ports on Taiwan has yielded 21 more species of unrecorded congeners. In this paper we shall report four species, two of them new to science. The two known species, Caligus arii Bassett-Smith, 1898 and Caligus dasyaticus Rangnekar, 1957, are rare. We have found so far only one female specimen each in 10 years of examination of fishes of Taiwan for parasitic copepods.
Materials and methods
Fishes caught and landed at fishing ports on Taiwan were purchased and transferred in an icebox to the National Chiayi University, where fishes were examined under a dissection microscope for the copepod parasites. The copepod parasites removed from the fish hosts were preserved in 70% ethanol. They were later cleared in 85% lactic acid for 1-2 h before dissection in a drop of lactic acid on a wooden slide (Humes and Gooding 1964). The removed body parts and appendages were examined under the compound microscope with a series of magnifications up to 15006. All drawings were made with the aid of a camera lucida and measurements, in millimeters unless mentioned otherwise, were taken after soaking the specimens in lactic acid. In cases where there were more than 10 specimens in the collection, measurements were taken from 10 randomly selected specimens.
Descriptions
In the following descriptions, only those features showing sexual dimorphism are given in the cases when the male is available. Female Body ( Figure 1A) 5.82 long, excluding setae on caudal ramus. Cephalothoracic shield subcircular, 2.12 long and 2.02 wide, excluding lateral, hyaline membrane. Fourth pediger distinctly wider than long, 0.2260.54. Genital complex, 1.8661.18, bottle-shaped, with posterolateral corners slightly protruded posteriorly. Abdomen ( Figure 1A) with two unequal segments, proximal segment longer than wide 1.6660.42, and anal segment wider than long but measuring only 1466348 mm. Caudal ramus ( Figure 2E) attaching to posterolateral surface of anal segment, distinctly wider than long, 28664 mm; armed with three short and three long setae. Egg sac not seen. Antennule ( Figure 1B) two-segmented; proximal segment carrying on anterodistal surface 25 setose and two naked setae (on dorsal side), distal segment short, about 2.42 times as long as wide, with one subterminal seta on posterior margin and 11 setae plus two aesthetascs on distal margin. Antenna ( Figure 1C, with broken tip) three-segmented; proximal segment smallest, with spatula-like process on posteromedial corner; second segment subrectangular and unarmed; distal segment long, curved claw bearing two setae, one proximal and other one close to medial region. Postantennal process lacking, except two usual, setule-bearing papillae. Mandible ( Figure 1D) with four sections; bearing 12 teeth on medial margin of distal blade. Maxillule ( Figure 1C) comprising stout dentiform process and basal papilla with two short and one long setae. Maxilla ( Figure 1E) two-segmented; proximal segment (lacertus) unarmed; slender distal segment (brachium) carrying subterminal, hyaline membrane on outer edge, terminal calamus slightly longer than subterminal canna. Maxilliped ( Figure 1F) three-segmented; proximal segment (corpus) unarmed, twice as long as next two segments combined (subchela); middle (shaft) segment armed distally with a pinnate seta and in middle region small, blunt element; distal (claw) segment sharply pointed and slightly bent. Box of sternal furca ( Figure 1G) subsquare, carrying two tines with truncate tip.
Armature on rami of legs 1-4 as follows (Roman numerals indicating spines and Arabic numerals, setae):
Exopod
Endopod Leg 1 1-0; III,1,3 (Vestigial) Leg 2 I-1; I-1; II,I,5 0-1; 0-2; 6 Leg 3 I-1; I-1; III,5 0-1; 6 Leg 4 I-0; I-0; III (Absent) Leg 1 ( Figure 2A) protopod with long, plumose, outer seta and another small, plumose, inner seta in addition to a patch of spinules on ventral surface; vestigial endopod fringed with setules around distal region; first segment of exopod with row of setules on posterior edge and short spiniform seta at outer distal corner; outer three of four terminal elements on last segment of exopod with sharply pointed, slender tip and hyaline membrane on posterior margin, middle two elements with long accessory process and element four with a small, spiniform seta. Leg 2 ( Figure 2B) coxa small, with large plumose inner seta on posterior edge and setule-bearing papilla on ventral surface; basis with simple, outer seta and medial papilla bearing long setule; both outer and medial edges of protopod fringed with large marginal membrane; similar membrane on outer margin of elongated, proximal segment of exopod; basal segment of endopod with small distolateral membrane, but terminal two segments bearing rows of setules on outer surface. Leg 3 ( Figure 2C) protopod (apron) with short, outer and long, inner seta; large marginal membrane on outer edge and another marginal membrane on posterior edge of basis inner to velum; setule-bearing papilla on basis near both ends of this posterior membrane; and a patch of spinules on lateral edge of ventral surface of protopod. Leg 4 ( Figure 2D) protopod with small, plumose, outer seta; pectens on three exopodal segments at insertion of each of five outer spines. Leg 5 (inserted drawing in Figure 1A) represented by three small, plumose setae on a protuberance located on posterolateral margin of genital complex.
Remarks
his is the first record of C. arii from a host other than a marine catfish. However, we consider the carrier of our C. arii, a largehead hairtail, Trichiurus lepturus Linnaeus, to be a fortuitous host, because we have examined in the past 10 years no fewer than 1000 T. lepturus, and only one female C. arii has been recovered from them. Nevertheless, our specimen from Taiwan fits very well with the description of the syntype (reg. no. is 98.12.2.9, in the Natural History Museum, London) given by Pillai (1969). The characteristic features of the present species are: (1) two-segmented, long abdomen with tiny anal segment; (2) minute caudal ramus attaching subterminally to anal segment; (3) absence of postantennal process; (4) maxilliped with slender corpus; and (5) reduced proximal segment and spine on leg 3 exopod.
If Kirtisinghe's (1964) proposal to move Barnard's (1955) ''Caligus arii B-S.'' to Caligus dakari van Beneden, 1892 is correct, then, C. arii is so far known only from Ceylon (Bassett-Smith 1898) and India (Pillai 1963). This means that the present report is the first record of C. arii outside of the Indian Ocean.
Female
Body ( Figure 3A) 5.62 long, excluding setae on caudal ramus. Cephalothoracic shield slightly longer than wide, 2.9662.78, excluding lateral, hyaline membrane. Fourth pediger slightly wider than long, 0.4860.58. Genital complex like a wide-mouthed bottle, 1.1461.04. Abdomen, 0.9260.54, with wider anterior half; narrow posterior half with central furrow on posterodorsal part ( Figure 3A). Caudal ramus ( Figure 3H) slightly longer than wide, 0.1860.16, with narrow base and armed with usual three short and three long plumose setae. Egg sac not seen.
Antennule ( Figure 3B) two-segmented; proximal segment carrying on anterodistal surface 24 setose and three naked setae (two dorsal and one terminal), distal segment armed as in C. arii but relatively longer, about 3.7 times as long as wide. Antenna ( Figure 3C) three-segmented; first two segments small and unarmed; distal segment long claw with uneven medial margin, one small seta in basal region, and another one at about one-third length from base. Postantennal process ( Figure 3C) comprising a long, bent claw with two setule-bearing basal papillae and a bluntly pointed lobe with small subterminal protuberance. Mandible ( Figure 3D) as in C. arii. Maxillule ( Figure 3C) comprising long, slender dentiform process and tiny basal papilla with two short and one long setae. Maxilla ( Figure 3F) essentially as that in C. arii. Maxilliped ( Figure 3G) slender as in C. arii. Box of Armature on rami of legs 1-4 as follows (Roman numerals indicating spines and Arabic numerals, setae): Leg 1 ( Figure 4A) protopod protruded laterally and bearing papilla tipped with two setules, also carrying plumose, outer seta and another similar inner seta in addition to a
Exopod
Endopod Leg 1 1-0; III,I,3 (Vestigial) Leg 2 I-1; I-1; II,I,5 0-1; 0-2; 6 Leg 3 I-1; I-1; III,4 0-1; 6 Leg 4 I-0; III (Absent) large patch of spinules on ventral surface; vestigial endopod tipped with two tiny setae; first segment of exopod with row of setules on posterior edge and small hyaline membrane at base of short, spiniform, outer seta; middle two of four terminal elements on last segment of exopod bearing long accessory process; fourth element small as in C. arii; digitiform process on posterior surface close to pectens at base of outer three terminal elements. Leg 2 ( Figure 4B) essentially armed and decorated as in C. arii, except for papilla on ventral surface of coxa and basis carrying two tiny setules instead of single, long setule. Leg 3 ( Figure 4C) protopod (apron) with short, outer and long, inner seta; marginal membrane on posterior edge of basis except portion with velum; setule-bearing papilla on basis near both ends of inner marginal membrane; pecten on proximal segment of exopod close to insertion of outer spine. Leg 4 ( Figure 4D) slender; protopod with small, plumose, outer seta; tip of exopod with two short, outer and one long, inner spines; pecten on exopodal segments at insertion of proximal outer spine and distal inner spine. Leg 5 (inserted drawing in Figure 3A) represented by four small, plumose setae on a protuberance located on posterolateral margin of genital complex.
Remarks
Since this species has been reported to occur on the stingrays in India and Japan, it would be expected to be found on the stingray from Taiwan. While the specimen from Taiwan is unquestionably identical with those reported by Rangnekar (1957) from India and Shiino (1960) from Japan, it is noticeably different from that reported by Pillai (1968) from India. The main differences are found in Pillai's (1968) specimens with (1) unarmed; distal segment a claw strongly bent at tip and carrying two setae in basal region. Postantennal process ( Figure 5C) small spine with two papillae in basal region bearing two setules, another similar setule-bearing papilla nearby on cephalon. Mandible ( Figure 5D) as in C. arii. Maxillule ( Figure 5C) comprising short, bluntly pointed digitiform process and papilla bearing three unequal setae. Maxilla ( Figure 5E) generally constructed as in C. arii, except brachium (distal segment) being shorter than lacertus (proximal segment).
Maxilliped ( Figure 5F) three-segmented; proximal segment (corpus) robust, produced on basal, medial surface (myxal area) into large pointed tooth-like process with distal trough; middle segment (shaft) as long as terminal claw, bearing medial seta at its tip; in closing, claw tip inserted into trough on tip of myxal process. Box of sternal furca ( Figure 5G) subsquare, carrying two parallel tines with truncate tip.
Armature on rami of legs 1-4 as follows (Roman numerals indicating spines and Arabic numerals, setae): Leg 1 ( Figure 6A) protopod with plumose, outer seta and another small, plumose, inner seta in addition to a papilla bearing two setules; vestigial endopod small, tipped with two setules; first segment of exopod with row of setules on posterior edge and pecten near base of outer spiniform seta; middle two of terminal four elements with twisting rows of setules but no accessory process; pecten near base of two outer, terminal elements. Leg 2 ( Figure 6B) coxa small, with large plumose inner seta on posterior edge and small setulebearing papilla on ventral surface; basis with simple, outer seta and medial papilla bearing long setule; both outer and medial edges of protopod fringed with large marginal membrane; similar membrane on outer margin of elongated, proximal segment of exopod; outer spines on basal two segments of exopod long. Leg 3 ( Figure 6C) protopod (apron) with short, outer and long, inner seta; large marginal membrane on outer edge following setule-bearing papilla on basis near both ends of this posterior membrane; and rows of denticles on lateral edge, another patch of spinules and setule-bearing papilla on ventral surface of protopod. Leg 4 ( Figure 6D) protopod with small, plumose, outer seta; pectens on two exopodal segments at insertion of each of five outer spines. Leg 5 ( Figure 7A) represented by two papillae on posterolateral margin of genital complex, with one bearing one small, plumose setae and another bearing three similar setae.
Male
Body ( Figure 7B Antenna ( Figure 7C) three-segmented; proximal segment smallest and unarmed; middle segment largest, without armature except light corrugation on medial surface; terminal segment a sharp claw armed with two basal inner setae and a large, basal tooth. Leg 5 ( Figure 7D) constructed as in female but located differently from female, close to midway along lateral margin of genital complex. Leg 6 ( Figure 7D) represented by a lobe tipped with two plumose setae located on posterolateral corner of genital complex.
Etymology
The specific name dactylus is Greek (5 a finger or toe), and refers to the thumb-like myxal process on the corpus of the maxilliped.
Remarks
This new species is characteristic in having the following features in the female: (1) the length of abdomen is less than one-half that of genital complex; (2) the genital complex is wider than long; (3) the corpus of the maxilliped is equipped with a large myxal process; (4) tines on the sternal furca are parallel and truncate at tip; (5) the middle two of the terminal four elements on the exopod of leg 1 have no accessory process; and (6) the armature formula of leg 4 is I-0; I,III. Checking the known species of Caligus revealed that the above combination of six characters is shared with only one species, namely Caligus priacanthi Pillai, 1961. Caligus priacanthi is so far known only from India. Pillai (1961) reported the female only in his original description of the species, but description of the male was later provided by Prabha and Pillai (1986). Comparison with those two works showed that C. dactylus can not be identified with C. priacanthi. The differences seen in the female are the structures of the proximal segment on the antenna, calamus on the maxilla, tines on the sternal furca, outer spines on the proximal and middle segments of the exopod of leg 2; and in the male, the abdomen and the myxal process on the maxilliped. Besides, the hosts are different. The Indian C. priacanthi is a parasite of the moontail bullseye, Priacanthus hamrur (Forsskål). Antennule ( Figure 8B) two-segmented; both proximal and distal segments armed as in C. arii; distal segment long, about 3.73 times as long as wide. Antenna ( Figure 8C) threesegmented; proximal segment smallest, armed with bluntly pointed process on posteromedial corner; middle segment subrectangular and bearing small adhesion pad on ventral surface; distal segment a claw strongly bent at tip and carrying two setae in basal region. Postantennal process ( Figure 8C) a small, bent claw bearing two papillae in basal region, each tipped with four setules, another similar setule-bearing papilla nearby on cephalon. Mandible ( Figure 8D) as in C. arii. Maxillule ( Figure 8C) comprising short, bluntly pointed digitiform process and papilla bearing three unequal setae. Maxilla ( Figure 8E) generally constructed as in C. arii, except subterminal hyaline membrane on brachium (distal segment) becoming a spiniform process. Maxilliped ( Figure 8F) three-segmented and constructed as in C. arii, except having a simple (instead of pinnate), distal seta on shaft. Box of sternal furca ( Figure 8G) oblong, carrying two diverging tines with sharp tips.
Armature of rami on legs 1-4 as in C. dactylus. Leg 1 ( Figure 9A) protopod with plumose, outer seta and another plumose, inner seta in addition to a setule-bearing papilla on outer margin and a patch of spinules on ventral surface; vestigial endopod small, tipped with two setules; first segment of exopod with row of setules on posterior edge; middle two of terminal four elements with accessory process connected to spine proper by hyaline membrane. Leg 2 ( Figure 9B) constructed essentially as that in C. dactylus. Leg 3 ( Figure 9C) protopod (apron) with short, outer and long, inner, plumose seta; large marginal membrane on outer edge following serrated margin and another marginal membrane on posterior edge of basis inner to velum; setule-bearing papilla on basis near both ends of this posterior membrane. Leg 4 ( Figure 9D) protopod produced subterminally on outer margin before insertion of plumose, outer seta; exopod twosegmented; proximal segment with setule-bearing papilla on outer margin; short, bipectinate, spiniform process on both exopodal segments at insertion of each five outer spines. Leg 5 (inserted drawing in Figure 8A) represented by two papillae on posterolateral margin of genital complex, with one bearing one small, plumose setae and another with two similar setae.
Antenna ( Figure 10B) three-segmented; proximal segment unarmed; middle segment largest, with two unequal corrugated pads on medial surface; terminal segment produced into a small plate with short, sharp claw and two basal setae. Corpus of maxilliped ( Figure 10C) robust, with three (two sharp and one blunt) processes in myxal area; in closing, tip of claw inserting into trough in largest, middle process. Leg 5 ( Figure 10D) constructed as in female. Leg 6 not seen.
Etymology
The new species is named after its host-the fishes of the genus Lujanus.
Remarks
Caligus lutjani is characterized by a combination of the following four features: (1) the twosegmented abdomen is about one-half the length of the genital complex; (2) sternal furca has a pair of diverged, sharply pointed tines; (3) middle two of the terminal four elements on the exopod of leg 1 are armed with accessory process; and (4) leg 4 has a formula of I-0; I,III. This combination of morphological features is shared with only one of more than 250 congeners-Caligus novocaledonicus Kabata, 1968. However, close comparison with the latter showed that the specimens from Taiwan belong to a different species. The differences found in C. lutjanus are: (1) the lack of a conical projection at the base of the postantennal process; (2) presence of a bipectinate, spiniform process on the exopod of leg 4 near the insertion of each of the five outer spines (see Figure 9D); and (3) a long (versus broad) caudal ramus. The host of C. novocaledonicus is Lethrinus miniatus Forster, caught in New Caledonia. The parasite has not been reported again since the publication of its original description by Kabata (1968).
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Domain: Environmental Science Biology
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The influence of morphological variation on migration performance in a trans-hemispheric migratory songbird
Abstract For long-distance migratory songbirds, morphological traits such as longer wings and a smaller body size are predicted to increase migration efficiency. Due to previous limitations in our ability to track the long-distance journeys of small-bodied birds, the relationship between morphology and start-to-finish migration performance has never been fully tested in free-living songbirds. Using direct-tracking data obtained from light-level geolocators, we examined the effects of morphological factors (wing and body size) on spring and fall migration performance (flight speed, duration of stopovers, total stopovers taken) of a widely distributed, trans-hemispheric migratory songbird, the purple martin (Progne subis) (n = 120). We found that smaller-bodied birds spent fewer days at stopovers along fall migration, but larger-bodied birds spent fewer days at stopover and took fewer stopovers during spring migration. More of the variation in fall migration performance was explained by morphology, as compared to spring migration, possibly indicating a larger influence of environmental conditions on spring performance. Overall, our results partially support long-standing and previously untested predictions regarding the influence of intrinsic factors on migration performance. Future research should examine the influence of environmental variation on migration performance as well as additional morphological traits that may contribute to migration performance.
to wing length) is also predicted to increase migration efficiency, since relatively larger bodies experience greater body and parasite drag due to increased surface area in contact with the resisting airflow, that is, the resisting force of airflow against the bird during flight increases proportional to its body size [12]. Thus a smaller bodied bird experiences less resistance during flight, which allows for longer potential flight times and fewer stopovers to complete migration [12]. Migration performance is directly related to reproductive success in many bird species via benefits to birds from arriving at breeding sites early to claim the most productive breeding territories and or mates [13][14]. However, intrinsic factors that promote efficient migration (i.e. long wings, small body size) may have trade-offs with other important correlates of fitness, such as foraging efficiency or the ability to evade predators [15], resulting in within-species or within-sex or age-class variability in migration performance [16]. Wing length and body size can also be affected by nutritional status during moult and development, which could result in subsequent constraints on migration performance [17]. Whether within-species variation in wing length and body size is correlated with start-to-finish migration performance has yet to be fully tested in free-flying, longdistance migratory songbirds.
In this study, we used purple martins as a model system for examining long-distance migration performance, as they are a common, wide-ranging, longdistance migratory songbird that breeds throughout North America (Fig. 1) and migrates to South America during the winter [18]. Purple martins show extensive withinspecies variation in migration timing and distance, with individuals travelling between 10,000 and 22,000 km annually, depending on their breeding site [6,(Fraser et al. unpub. data)]. Within-species variation in morphological traits, such as wing length and body size, may exist within purple martins as a consequence of their broad latitudinal breeding distribution (26°N -53°N), where differences in climate and food quality and availability could result in different selection pressures on wing or body size [19]. Purple martins also have an aerial foraging strategy, potentially making them more sensitive to trade-offs between migration performance and foraging efficiency [8]. Their foraging behaviour and/ or migration range are similar to other aerial insectivores (e.g., cliff swallow (Petrochelidon pyrrhonota)) and other passerines (e.g., Connecticut warblers (Oporornis agilis)), thus factors affecting migration performance in purple martins may also be applicable to similar species.
Our objective was to test the hypothesis that intraspecific variation in wing and body morphology contributes to variation in migration performance in purple martins. Using data collect across the breeding range of purple martins we examined the influence of wing length and body size on migration performance. We inferred the size of the birds through tarsus length, as this measure has been shown to be a legitimate predictor of body size for both males and females of the same species [20][21]. We predicted, based on theoretical models [2,4,[7][8][9][10][11][12], that a bird with longer wings and a smaller body size would travel at a faster flight speed, stop for fewer days during migration, and take fewer stopovers to complete migration than a bird with shorter wings and a larger body size. Previous studies have examined how wing morphology affected arrival timing and energy expenditure during migration [15,22], and how wing morphology affected migration duration [23], but we present the first study to examine how intraspecific variation in wing and body morphology, across multiple sites and varying breeding latitudes, affect flight speed, total number and duration of stopovers using start-to-finish migration data. The results of our research fill a gap in our knowledge of migration behaviour and may have important implications for our understanding of the selective forces that shape the performance of longdistance migration in songbirds.
Material and Methods
This study was carried out in accordance with the recommendations of the Ornithological Council 'Guidelines to the Use of Wild Birds in Research' and was approved by University of Manitoba's Animal Care Committee (Animal Care Protocol Number: F14-009-1) and the York University Animal Care Committee (Animal Care Protocol Number: 2009-2W(R1)).
Geolocator Deployment-Between 2007 and 2013, we deployed 332 light-level geolocators (British Antarctic Survey, models MK10, MK12, MK14, MK20) at 10 different breeding colonies across the breeding range of purple martins (Fig. 1). Geolocators were attached to the back of the bird using a leg-loop backpack harness made of Teflon ribbon [24]. Purple martins were caught at their nest boxes using drop door or pole traps at their breeding colonies. For each individual, we measured wing length and tarsus length and identified their sex and age. Wing length was taken by measuring the length of the flattened wing chord. Tarsus length was measured using a standard protocol [25] and was used as an indicator of overall structural body size. Purple martins were identified as either second year (SY; i.e., sub-adults), or after second year (ASY; i.e., adults) birds, based on plumage colouration [25]. Birds were recaptured at the same breeding site in the year following deployment, resulting in individual migration tracks for 120 purple martins (36% of all geolocators deployed between 2007 and 2013). Return rates of birds were not lower for birds carrying geolocators as compared to birds that were banded only [26].
Data Analysis -We used the software package BASTrak (British Antarctic Survey) to analyze the data retrieved from the geolocators. Purple martins are diurnal migrants [27], so we used the latitude and longitude coordinates at midnight to determine the stationary location of nightly stopover locations along the migration route. Birds were considered stationary when latitude and longitude remained constant within 2 degrees between consecutive midnight locations. Using this criterion, we were able to determine when each bird arrived and departed from a stopover site, overwintering site, or breeding site.
Using the position data derived from geolocators, we calculated migration flight speed, the total number of stopover days, the total number of stopovers, and the total distance travelled for both fall migration and spring migration. We calculated the total distance travelled during migration by measuring the distance between stopover sites from start-to-finish during fall and spring migration. Migration speed (km*day -1 ) measured only the speed of the migrant when flying, and excluded stopover days. Speed was calculated by taking the total fall or spring migration distance over the number of days spent flying during the migration period of interest. We identified stopover days as all days spent at a particular stopover site. The total number of stopovers and the total number of stopover days for fall or spring migration were calculated by taking the sum of all stopovers and stopover days, respectively, from start-to-finish of fall or spring migration.
Statistical analyses -We first compared wing and tarsus length across age-sex classes and breeding latitudes, to document demographic patterns in morphology, using Student's t-tests and ANOVA. We then examined the influence of wing length and tarsus length on each component of migration performance: migration speed, total number of stopover days, and the total number of stopovers taken (the dependent variables) for both fall and spring migration using general linear models (GLMs). As purple martins breeding further north must travel further on migration compared to those breeding at more southern latitudes, we included breeding latitude as a covariate in addition to wing length and tarsus length. For stopovers, we also included migration distance as a covariate, as longer distances (independent of breeding latitude) could result in more stopovers. We report the model estimates (± standard error, SE) for the effects of wing and tarsus length on measures of migration performance, as well as the results for overall model fit. All tests were performed using R [28]. Sample size differed in some cases between models because migration data were not available for all locations or individuals, as in the small number of cases of battery failure prior to spring migration, or when birds were travelling during the equinoxes, during which time latitudes could not be determined.
Results
We tracked 120 purple martins (Table 1) . Across the 10 breeding colonies, tarsus length varied significantly (F 1, 116 = 270.9, r 2 = 0.7, P < 0.05), where tarsus length was positively associated with breeding latitude (i.e., birds from Florida had the shortest tarsi and birds with the longest tarsi were found in New Jersey); for every 1° increase in breeding latitude, tarsus length is predicted to increase by 0.22 mm. No significant differences in wing length were found between separate breeding colonies (F 1, 118 = 0.57, r 2 < 0.001, P = 0.45). Breeding latitude was also significantly correlated with the total distance travelled during fall migration (F 1, 118 = 149.4, r 2 = 0.56, P < 0.05) and spring migration (F 1, 109 = 218.9, r 2 = 0.67, P < 0.05). This collinear relationship prevents breeding latitude from being used in any models that have migration distance as a covariate. Two separate models were applied to the total duration of stopovers and total number of stopovers; one model using breeding latitude and one model using migration distance as a covariate. Furthermore, tarsus length was also positively correlated with both fall and spring migration distance (fall: F 1, 116 = 45.36, r 2 = 0.28, P < 0.05; spring: F 1, 108 = 75.39, r 2 = 0.41, P < 0.05), but wing length did not share any significant correlation with fall or spring migration distance (fall: F 1, 118 = 1.76, r 2 < 0.01, P = 0.19; spring: F 1, 109 = 0.23, r 2 < 0.01, P = 0.63). Using a linear model, we determined that tarsus length and wing length were not collinear (F 1, 116 = 1.09, r 2 < 0.01, P = 0.3).
Variation in fall migration
We nested tarsus length by breeding latitude or migration distance (for stopover models) to control for variation in tarsus length between different breeding latitudes. Wing length and tarsus length were poor predictors of fall migration speed ( Fig. 2A, B; P wing = 0.22, P tarsus = 0.2) with less than 1% of the variation explained by the model (Table 2; F 3, 113 = 1.4, r 2 = 0.01, P = 0.29).
In the model with breeding latitude as a covariate, 31% of the variation observed in the duration of fall stopovers was explained by wing length, tarsus length (nested within breeding latitude), and breeding latitude (Table 2; F 3, 112 = 18.34, r 2 = 0.31, P < 0.05) but the morphological factors were poor predictors of the duration of fall stopovers (P wing = 0.2, P tarsus = 0.8). The model with fall migration distance as the covariate explained a greater amount of variation in the duration of fall stopovers (Table 2; F 3, 112 = 22.15, r 2 = 0.36, P < 0.05). In this model, wing length was, again, not a significant predictor ( Fig. 3A; P = 0.07) but tarsus length, when nested in fall migration distance, was positively correlated with the duration of fall stopovers ( Fig. 3B; P < 0.05).
Using breeding latitude as a covariate, the model was a poor predictor of the total number of fall stopovers (Table 2; F 3, 55 = 0.75, r 2 < 0.01, P = 0.53); the morphological variables were not significant predictors of the total number of fall stopovers (P wing = 0.57, P tarsus = 0.27). Applying fall migration distance in the model explained more variation in the total number of fall stopovers than applying breeding latitude as a covariate but the model was still a poor predictor (Table 2; F 3, 55 = 1.24, r 2 < 0.01, P = 0.31) and the variables were also insignificant (Fig. 4A, B; P wing = 0.69, P tarsus = 0.08).
Variation in spring migration
Variation in spring migration speed could not be explained by wing length or tarsus length, after correcting for variation in tarsus length between different breeding latitudes (Table 2; F 3, 94 = 0.87, r 2 < 0.01, P = 0.46). The morphological factors were also not significant predictors of spring migration speed in the model (Fig. 2C, D; P wing = 0.27, P tarsus = 0.21).
The duration of spring stopovers could not be predicted by wing length or tarsus length, in the model with breeding latitude (Table 2; F 3, 96 = 1.49, r 2 = 0.02, P = 0.22), but tarsus length was a significant predictor of the duration of spring stopovers when we used spring migration distance as a covariate (P tarsus < 0.05). Tarsus length was negatively correlated with the duration of spring stopovers (Fig. 3D), a contrast to the positive correlation tarsus length showed with the duration of fall stopovers (Fig. 3B). Although the model with spring migration distance is significant, only 9% of the variation observed in the duration of spring stopovers could be explained by the morphological factors (Table 2; F 3, 94 = 4.07, r 2 = 0.09, P < 0.05).
Wing length and tarsus length were poor predictors of the total number of stopovers taken during spring migration when breeding latitude was applied as a covariate (Table 2; F 3, 53 = 0.8, r 2 < 0.01, P = 0.5; P wing = 0.42, P tarsus = 0.22) but tarsus length was a significant predictor when nested in spring migration distance (P tarsus < 0.05). Tarsus length was negatively correlated with the total number of spring stopovers (Fig. 4B). The model using spring migration distance as a covariate explained more variance in the total number of spring stopovers (Table 2; F 3, 51 = 3.47, r 2 = 0.12, P < 0.05) than the model with breeding latitude.
Discussion
We found that variation in body size correlated with en-route migration performance in a long-distance migratory songbird, whereby in fall, smaller-bodied birds (inferred through tarsus length) took fewer stopovers while in spring, larger-bodied birds complete migration with fewer individual stops and fewer total days spent at stopovers. Our fall migration results support predictions that energy expenditure during migration is modulated by variation in body morphology, subsequently influencing the number of stopover days needed for refueling during migration. We also found that wing length was not significantly different between different breeding colonies but larger-bodied birds were found at higher latitude colonies. The novelty of this result contrasts with previous studies that showed variation in wing length across different latitudes [29][30]. We speculate that wing length may be influenced by external factors, such as variation in climate [30], as well as ecological demands, such as selection for shorter wings to allow for greater aerial manoeuvrability at breeding sites with high predation rates [31].
The stopover models (total duration of stopovers and the total number of stopovers) required two models to control for variation in body size (one with breeding latitude and another with migration distance as a covariate). Comparing the two models revealed that greater variation in the total duration of stopovers and the total number of stopovers was explained in the model that included migration distance. The significance of migration distance in these models suggests the migration strategy adopted by larger-bodied birds differs from smaller-bodied birds, as a result of adaptation to longer migration distance.
Fall migration performance
Larger birds (as measured by tarsus length) had more stopover days during fall migration, which may be due to a higher requirement of energy to power flight or having to travel further on fall migration, resulting in an increased rate of fuel expenditure [4,32]. Body size (tarsus length) and wing length were poor predictors of migration flight speed and the total number of stopovers taken on fall migration. Our results do not support the theoretical models proposed by Pennycuick [32] that larger birds must fly faster than smaller birds to achieve the same distance travelled during migration. A previous study [15] of another neotropical, long-distance migratory songbird, the Swainson's thrush (Catharus ustulatus), found that individuals with more pointed wingtips and a lower wing loading arrived earlier during spring migration, which could be a result of faster flight speed, or fewer stopovers.
More pointed wingtips are often associated with longer wings [8], but our results revealed longer wings do not contribute to greater migration performance, contrary to our predictions.
Male purple martins had significantly longer wings than females but body size was similar between sex classes. However, our results do not suggest this confers any advantages to their migration performance, as we did not find longer wings to have any significant effect on fall or spring migration performance. We found no significant differences in morphology by age class, indicating that sub-adult (second year) and adult (ASY) birds could perform similarly during fall migration. This contrasts with previous studies, which show that younger birds have shorter wings and related poorer migration performance [16,31]. However, our younger age-class (SY) birds were completing their second fall and spring migration (as opposed to their inaugural migrations) and thus it is perhaps not surprising that their morphology during their second year was not significantly different than that of older adults. Examining other measures of wing morphology (in addition to wing length), such as aspect ratio and wing loading, which are predicted to have direct effects on migration performance [4,8], may provide insight on potential sex-or age-dependent differences in factors driving these traits.
Spring migration performance
In contrast to fall migration, larger bodied birds tended to spend fewer days at stopovers and took fewer stopovers to complete spring migration. It is possible that larger-bodied birds are better able to tolerate adverse weather during early spring at breeding sites. Martins, like other aerial insectivores, are susceptible to early-spring cold snaps, and thus natural selection could result in larger-bodied birds being favoured to arrive earlier at breeding sites [33]. During a cold spring, smaller-bodied birds might be at a thermoregulatory disadvantage [34], in contrast to fall migration when food is abundant, weather is generally more favourable, and small bodies are advantageous for flight efficiency [32]. Sex-differences in migration performance are usually most apparent in spring, when males initiate migration and arrive at breeding sites earlier than females [35]. However, our results suggest that within-species variation in morphology is not an important component of migration performance in spring, and sex-differences in wing size are unlikely to account for any observed protandry in purple martins. However, overall models for spring migration performance were weaker than for fall migration performance.
Selection pressures on morphology may differ between spring and fall migration in that extrinsic factors, such as environmental conditions, play a larger role in shaping spring migration performance. Prey availability and abundance may differ between spring and fall migration for many migratory songbirds [36], which has been found to affect migration performance [37] resulting in behavioural differences between the two migration periods.
It has yet to be tested whether spring phenology and available resources (i.e., variation in insect emergence and abundance as a result of temperature differences) at stopover sites affects spring migration performance in martins. An investigation into habitat quality of stopover sites during spring migration may reveal differences in physiological condition within-species that contributes to spring migration performance [38]. Endogenous timing factors likely also play a role, given strong selection for early arrival at breeding sites in spring. A previous study [6] found that purple martins did not depart for spring migration earlier or migrate at a faster rate in response to warmer temperatures and an earlier spring, suggesting endogenous schedules may not be very flexible to changing environmental conditions.
In conclusion, we tested long-standing predictions about the influence of morphological traits on migration performance in purple martins. However, we emphasize that because purple martins adopt a fly-and-forage strategy [39], which is uncommon among typical longdistance migratory songbirds, our results are more applicable to other species with a similar migration range and behaviour, such as other swallows. We found strong evidence that variation in body size contributes to overall migration performance, whereby smaller-bodied birds spent fewer days at stopovers during fall migration, but larger-bodied birds spent fewer days at stopovers and took fewer stopovers to complete spring migration. We found considerable variation in migration performance within purple martins, so it is important to examine what factors contribute to intraspecific variation, such as how optimization of migration performance may result in a trade-off with foraging ability and provisioning rates. We recommend examining other factors that may influence or limit migration performance, as wing-morphology characteristics in purple martins may contribute to or be under selection to support other life-history functions, such as terrestrial and aerial predator avoidance, or to enhance foraging or provisioning ability, in addition to migration performance.
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Domain: Environmental Science Biology
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Genetic structure analysis of Eufriesea violacea ( Hymenoptera , Apidae ) populations from southern Brazilian Atlantic rainforest remnants
Random amplified polymorphic DNA (RAPD) markers were used to analyze the genetic structure of Eufriesea violacea populations in three fragments (85.47, 832.58 and 2800 ha) of Atlantic rainforest located in the north of the Brazilian state of Paraná. A total of twelve primers produced 206 loci, of which 129 were polymorphic (95% criterion). The proportions of polymorphic loci in each population ranged from 57.28% to 59.2%, revealing very similar levels of genetic variability in the groups of bees from each fragment. Unbiased genetic distances between groups ranged from 0.0171 to 0.0284, the smallest genetic distance occurring between bees from the two larger fragments. These results suggest that the E. violacea populations from the three fragments have maintained themselves genetically similar to native populations of this species originally present in northern Paraná.
Euglossines are relatively long-tongued bees, commonly showing a bright metallic blue, green or bronze integument, and are found exclusively in Neotropical America (Dressler, 1982;Cameron, 2004). Male euglossines are not tied to a nest but leave it upon hatching (Dodson, 1966;Dressler, 1982) and both female and male euglossine bees often fly long distances between dispersed resources, making them especially important in crosspollination of widely scattered plant species in tropical forests (Janzen, 1971). However, some studies have shown that habitat fragmentation may adversely affect populations of such pollinators and evidence suggests that in isolated Brazilian rainforest fragments there has been a decline in the number of males in some euglossine species (Powell and Powell, 1987;Becker et al., 1991). Furthermore, despite being able to fly long distances in continuous forest, deforested areas only 100 m wide have created barriers to the movement of some euglossine species (Powell and Powell, 1987).
It is known that genetic variability can be substantially reduced in small and isolated populations through ge-netic drift and inbreeding, resulting in the loss of alleles and a decline in heterozygosity. Such reductions may result in decreased fitness and the eventual extinction of populations (Fergunson et al., 1995). Thus, it is now widely appreciated that understanding patterns of genetic variation is critically important for the conservation of threatened species (Allnut et al., 2003).
During the past thirty years, advances in molecular technology have greatly increased the number of DNA-based markers capable of revealing genetic variation in a wide range of species. Random amplified polymorphic DNA (RAPD) analysis, a simple and straightforward PCR-based technique (Williams et al., 1990), employs a class of markers increasingly used to evaluate genetic variability and structure in a variety of organisms (Kimberling et al., 1996;Almeida et al., 2001;Allnutt et al., 2003).
Despite the recognized importance of euglossine bees as Neotropical pollinating agents, little is yet known about the genetic variability and structure of populations of these bees inhabiting forest fragments.
A number of studies carried out in remnants of Atlantic rainforest in southeastern Brazil have revealed variation in the frequencies of males belonging to the euglossine bee species Eufriesea violacea (Blanchard) (Rebêlo and Garófalo, 1991;Garófalo et al., 1998;Jesus and Garófalo, 2000), a very seasonal species of bees mainly found in south and southeastern Brazilian Atlantic rainforest (Wittmann et al., 1989;Rebêlo and Garófalo, 1991;Peruquetti and Campos, 1997). Sofia and Suzuki (2004) have reported that a reduction in fragment size negatively affected the frequency of E. violacea males.
During the research presented in this paper we studied E. violacea in terms of its possible sensitivity to environmental disturbances, because changes in the relative abundance of individuals could be the result of species susceptibility to environmental stress. The aim of our study was to use RAPD markers to investigate the genetic variability and structure of Eufriesea violacea populations occurring in three southern Brazilian Atlantic rainforest remnants.
Samples were collected in three Brazilian Atlantic rainforest remnants (all public reserves) composed of subtropical semi-deciduous vegetation (Fernandes and Bezerra, 1990), which were located in the north of the southern Brazilian state of Paraná (Figure 1).
Godoy Forest State Park (GF fragment) lies within the Londrina municipal boundary (23°27' S, 51°15' W) about 15 km from the center of the city of Londrina and consists of a 580 ha area of very well-preserved native vegetation continuous with another native forest fragment consisting of about 2200 ha situated outside the protected area of the Park but making an effective total fragment area of about 2800 ha of forest.
Arthur Thomas Municipal Park (AT fragment) comprises an 85.47 ha area of secondary vegetation (produced by intense anthropogenic interference) in the urban center of Londrina 14.2 km from the GF fragment.
All three fragments show signs of past and present anthropogenic disturbance, with both the GF and SF fragments being surrounded by agricultural crops and the AT fragment being situated inside an urban park.
Bees were sampled in November and December of 2001 and 2002, between 10:00 a.m. and 1:00 p.m., when euglossine males are most active (Ackerman, 1983;Powell and Powell, 1987;Santos and Sofia 2002). Male euglossines were collected with an insect net when they were attracted to eucalyptol and vanillin chemical baits consisting of 5 cm diameter balls of absorbent paper saturated with one of the fragrances and placed at the edge of forest about 4 m apart and 1.5 m above the ground, the bait chemicals being replenished every hour to prevent volatility losses (Santos and Sofia 2002).
Forty-five males were collected, 15 from each forest fragment, placed in plastic tubes, transported alive to the laboratory and kept frozen (-20 °C) until needed for DNA extraction. The bees were identified by one of the authors (S. H. Sofia) and voucher specimens deposited at the Zoology Museum of Londrina State University (UEL, Londrina Paraná, Brazil).
Total genomic DNA was extracted from each bee by a modification of the method of Raeder and Broda (1985). Each bee was separately ground into a fine powder using liquid nitrogen and a mortar and pestle, the powder subsequently being homogenized in a microcentrifuge tube containing 700 mL of extraction buffer (1% sodium dodecyl sulfate, 200 mM Tris-HCl, 250 mM NaCl, 25mM EDTA, pH 8.0) and 5 mL of proteinase K (20 mg.mL -1 ) and then incubated for 2 h at 64 °C. After incubation genomic DNA was extracted with an equal volume of phenol, followed by phenol/chloroform/isoamyl alcohol (25:24:1, v:v:v) and then chloroform/isoamyl alcohol (24:1 v:v) which was then mixed and centrifuged at 10,000 g for 10 min. After centrifugation the DNA was precipitated from the supernatant with two volumes of ice-cold ethanol and 10% by volume of 3 M NaCl and pelleted at 13,000 g for 15 min, washed with 100 mL of 70% ethanol, dried at room temperature and re-suspended in 100 mL of TE buffer (10 mM Tris, 1 mM EDTA pH 8.0). The DNA concentration was determined in a 200 DyNA Quant fluorometer (Hoefer) using the dye Hoechst 33258, all DNA samples being diluted to a standard concentration of 5 ng mL -1 . All isolated DNA was either used immediately or stored at -20 °C.
The RAPD profiles were generated from total genomic DNA as described by Williams et al. (1990). Final reaction volumes were 15 mL and contained 15-25 ng of template DNA, 250 mM dNTP (Pharmacia), 0.3 mM of ten-nucleotide primer (Operon Technologies, Alameda, CA, USA), 4.0 mM MgCl 2 and 1 U of DNA polymerase (Biotools) in the reaction buffer supplied. The RAPD technique is sensitive to changes in reaction conditions (e.g.primer, MgCl 2 , dNTP concentrations etc), so exactly the same reaction conditions were used for all samples. From primer kits OPC, OPW, OPAM, a total of 40 different decamer primers were initially screened with a subset of E. violacea DNA samples for the production of clear RAPD profiles. Control reactions were run containing all components except genomic DNA and none of the 12 primers used yielded detectable amplified products. DNA amplifications were carried out at in a PTC-100 thermocycler (MJ Research Inc.) and the amplification protocol consisted of 4 min denaturation at 92 °C followed by 40 cycles of 40 s at 92 °C, 1.5 min at 40 °C, and 2 min at 72 °C with a final step of 5 min at 72 °C. Samples (15 mL) of the amplification products were separated by electrophoresis on 1.4% agarose gels using TBE buffer (0.89 mM Tris, 0.89 mM boric acid, 2 mM EDTA pH 8.3) diluted 1:20 (v:v), run at 3 V.cm -1 , stained with ethidium bromide and photographed under UV light using a Kodak T-Max 100 film.
The RAPD marker profiles were determined by direct comparison of the amplified electrophoretic DNA profiles from each individual. Samples of all individuals from each forest fragment were placed on the same agarose gel, so as to make an inter-population comparative analysis. Amplified DNA marker bands were scored in a binary manner as either present (1) or absent (0) and entered into a binary data matrix. Only RAPD bands that could be unequivocally scored were counted in the analysis. Each locus was treated as a two-allele system, with only one of the alleles per locus being amplifiable by the PCR. It was also assumed that marker alleles from different loci did not co-migrate to the same position on a gel, and that populations were under the Hardy-Weinberg equilibrium (Lynch and Milligan, 1994).
A pair-wise similarity matrix was constructed using the Jaccard (J) index (Sneath and Sokal, 1973). On the basis of the J-values of the samples the unweighted pair group method with averages (UPGMA) clustering method was adopted and the NTSYS-PC package (Rohlf, 1992) used to generate a similarity dendrogram.
The TFPGA 1.3 software (Miller, 1997) was used to calculate the following: genetic variability as estimated from the proportion of polymorphic loci (P) using the 95% criterion; average heterozygosity (He), which estimates the frequency of heterozygotes in a population and is also a measure of genetic variability; Nei's genetic distance and identity (Nei, 1978); and Fisher's exact test, applied to the differences in marker frequencies between pairs of populations (the standard error was calculated using 100 batches of 1,000 permutations per batch and 2000 de-memorization steps).
Genotypic diversity between populations was tested by applying the G-test to allele frequencies at the loci, using the population genetics package POPGENE 1.31 (Yeh et al., 1997). To get a clear overall picture of the G-test results we calculated the `proportion of significance', defined as the number of RAPD loci with significant (p = 0.05%) G-test differences between allele frequencies in different populations divided by the total number of polymorphic loci.
Twelve of the 40 primers screened produced clear RAPD patterns consisting of a total of 206 loci, 129 of which were polymorphic (95% criterion). A RAPD electrophoretic profile is shown in Figure 2, for one of the selected primers. The number of bands per primer varied from 11 to 25. No population-specific band was observed. Very similar proportions of polymorphic loci (P) were found in the three populations: 57.28% (GF), 58.74% (SF) and 59.22 (AT), with an overall mean of 58.41%. The estimated of average heterozygosity (He) for bees from the three areas were also very alike: 0.1924 (SF), 0.1959 (GF) and 0.1961 (AT), indicating similar levels of genetic variability. Research on male euglossines in the Amazon Forest suggests that forest fragments of small sizes (< 100 ha) could compromise the survival of some euglossine species, which have declined in numbers of individuals after forest fragmentation (Powell and Powell, 1987). Although the smallest rainforest fragment (AT) studied by us was only about 84 ha E. violacea males from this fragment showed a similar proportion of polymorphic loci to the other two rainforest fragments, suggesting that the size of the rainforest fragments did not affect the genetic variability of the E. violacea population living in them. Reinforcing this idea, the estimated average heterozygosity (He) indicated similar levels of genetic variability among the three populations.
Populations of Eufriesea violacea 481
Euglossine bees are considered strong fliers, able to fly considerable distances across lakes and forests (Janzen, 1971;Dressler, 1982). A study by Murren (2002) at Panamá has shown that Eulaema cingulata (Fabricius) from a mainland site visited bait traps on an island 500 m away. Also, a number of euglossine species have been observed moving between Brazilian rainforest remnants (Raw, 1989;Tonhasca et al., 2003), although other studies carried out in Brazil have shown that males from some euglossine bee species do not cross the intervening matrix between forest fragments (Powell and Powell, 1987;Peruquetti et al., 1999).
Our estimates of Nei's (1978) unbiased genetic distances ranged from 0.0171 to 0.0284 and genetic identity from 0.9720 to 0.9831 (Table 1), the highest genetic identity being between bees from the larger SF and GF fragments 60 Km apart. The highest genetic distance (0.284), and therefore the lowest genetic identity (0.9720), occurred between bees from the SF and AT fragments.
In our study, the values of Fisher's exact test between pairs of E. violacea populations from the three rainforest fragments (SG-GF =172.41;SF-AT = 222.54;GF-AT = 219.27)indicated the there were no significant differences between pairwise populations (p > 0.05) and hence no genetic differentiation among the three populations. Also, the analysis of genotypic diversity by the G-test showed little variation between groups of individuals from different fragments, with significance proportion values ranging from 11.38% (SF-AT) to 15.90% (GF-AT).
Since E. violacea is a very seasonal euglossine species with just one generation per year (Wittmann et al., 1989;Peruquetti and Campos, 1997) it is possible that the three populations of E. violacea studied have maintained themselves genetically similar to native populations originally present in the tropical rainforest before fragmentation took place, this conjecture being reinforced by the fact that the genetic distance lowest value occurred between bees from the GF and SF fragments (Table 1).
Since male euglossines are not tied to a nest but leave it upon hatching, the occurrence of a single panmitic E. violacea population among the three rainforest fragments studied could also be due to the fact that males are able to move between nearby fragments. Our results appear to support this hypothesis because the AT fragment population was more similar to the GF fragment population than to the SF population (Table 1), indicating that some bees could be migrating between the AT and GF fragments. Tonhasca et al. (2003) observed that male euglossine bees were able to move within and between forest patches and proposed that the long-established fragments surrounding the larger forest remnant of Atlantic rainforest were functionally connected with regard to euglossine bee dispersal. The E. violacea males from the three rainforest fragments showed a mean coefficient of similarity (calculated for all pair-wise comparisons) of 0.71, ranging from 0.91 between bees AT11 and AT15 (Figure 3), which exhibited very similar RAPD banding patterns, to about 0.62 for bees from more distantly-separated forest fragments (AT13 and SF4; GF13 and SF4). These results suggest only a moderate degree of genetic diversity among males of this species in the three forest remnants studied (Figure 3), and G-test analysis of the genotypic diversity showing little variation between groups of individuals from different fragments lends support to this idea.
Overall, our results suggest that in spite of habitat fragmentation resulting from intense anthropogenic interference to this Brazilian rainforest E. violacea populations are maintaining satisfactory levels of genetic variability. Such findings may influence proposed strategies for conservation of both these Neotropical pollinators and the Atlantic rainforest remnants of southern Brazil.
Figure 1 -
Figure 1 -Location of São Francisco State Park (SF) and Londrina district containing Godoy's Forest State Park and the Arthur Thomas Municipal Park. The sites were all in the north of the southern Brazilian state of Paraná.
Figure 2 -
Figure 2 -DNA polymorphism (investigated using primer OPC9) of 45 Eufriesea violacea males from three rainforest fragments. Column M shows the 100 bp molecular weight marker (Biotools); columns 1 to 15 show the results for the São Francisco Park samples (SF); columns 16 to 30 show the results for the Godoy Forest samples (GF); columns 31 to 45 show the results for the Arthur Thomas Park samples (AT), and C = control.
Table 1 -Figure 3 -
Figure 3 -Dendrogram constructed using the Jaccard coefficient and the unweighted pair group method with averages (UPGMA) method for Eufriesea violacea males from three forest remnants in northern Paraná, Brazil. SF = São Francisco State Park, GF = Godoy Forest, AT = Arthur Thomas Park.\===
Domain: Environmental Science Biology. The above document has 2 sentences that start with 'These results suggest',
2 sentences that start with 'violacea populations from the three',
2 sentences that start with 'violacea males from',
2 sentences that end with 'were polymorphic (95% criterion)',
2 sentences that end with 'levels of genetic variability',
2 paragraphs that end with 'southern Brazilian Atlantic rainforest remnants',
2 paragraphs that end with 'et al., 2003)'. It has approximately 2729 words, 98 sentences, and 34 paragraph(s).
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Optimization of 14 microsatellite loci in a Mediterranean demosponge subjected to population decimation, Ircinia fasciculata
The recovery potential of decimated populations of sponges will largely hinge on their populations’ size retrieval and their connectivity with conspecifics in unaffected locations. Here, we report on the development of microsatellite markers for estimation of the population connectivity and bottleneck and inbreeding signals in a Mediterranean sponge suffering from disease outbreaks, Ircinia fasciculata. From the 220,876 sequences obtained by genomic pyrosequencing, we isolated 14 polymorphic microsatellite loci and assessed the allelic variation of loci in 24 individuals from 2 populations in the Northwestern Mediterranean. The allele number per locus ranged from 3 to 11, observed heterozygosity from 0.68 to 0.73, and expected heterozygosity from 0.667 to 0.68. No significant linkage disequilibrium between pairs of loci was detected. The 14 markers developed here will be valuable tools for conservation strategies across the distributional range of this species allowing the detection of populations with large genetic diversity loss and high levels of inbreeding.
Introduction
Sponges play an ecologically important role given their abundance and diversity as well as their contribution to primary production and nitrification through complex symbioses in marine benthic communities (Webster 2007). In the Mediterranean, massive mortalities are drastically reducing population size and creating extensive gaps in the distribution of many species, including sponges. In particular, periodic episodes of massive die-offs have been reported for the genus Ircinia (Cebrián et al. 2011) and the environmental stress due to elevated seawater temperatures has been suggested to trigger the disease (Maldonado et al. 2010;Cebrián et al. 2011).
Molecular markers have been widely applied in conservation biology to evaluate the vulnerability of marine species but studies on population genetics using microsatellite markers are available for very few sponges (see Pérez-Portela et al. 2013). Here, we report on the optimization of microsatellite markers further estimation of genetic diversity indexes, populations' connectivity, and for detecting signals of recent bottlenecks in order to evaluate the degree of vulnerability of the sponge Ircinia fasciculata from the Mediterranean.
Methods
Sponge tissue was dissociated prior to DNA extraction and the bacterial symbionts removed by sequential centrifugations. Genomic DNA was extracted using DNeasy Tissue and Blood extraction kit (QIAGEN) to a final DNA concentration of 2 lg and distributed in two lanes of a plate. Pyrosequencing was performed on a Roche Life Science 454 GS-FLX System at the Scientific-Technical Services of the University of Barcelona. Sequences (220,876) were searched for perfect microsatellites (di-, tri-, tetra-and penta-nucleotides) with at least eight repeats and enough priming regions with Phobos ( [URL]/ cm/cm_phobos.htm). 40,109 sequences contained microsatellites, 61.7 % being dinucleotide, 3.4 % trinucleotide, 3.4 % tetranucleotide, and 0.7 % pentanucleotide. Thirtysix primer sets were designed with the software PRIMER 3 for 14 dinucleotide loci, 14 trinucleotide loci, 7 tetranucleotide loci, and 1 pentanucleotide locus.
Amplification success and polymorphism were tested in two populations of the NW Mediterranean (Costa Brava) collected in 2010-2012: Els Caials (42817 0 19 0 'N 3816 0 40 0 'E) and Blanes (41841 0 N 2848 0 E). Total DNA was extracted and amplified using the REDExtract-N-Amp Tissue PCR Kit (Sigma Aldrich). Forward primers were labeled with a fluorescent dye (Table 1). Samples were amplified on a PCR System 9700 (Applied Biosystems) with an initial 2 min denaturation step at 95°C; followed by 35-40 cycles of 95°C for 30 s, 52-70°C for 35 s (depending on each locus; Table 1) and 72°C for 15 s, followed by a 3 min final extension at 72°C. Amplification products were analyzed on an Applied Biosystems 3730xl Genetic Analyzer at the Scientific-Technical Services of the University of Barcelona. The length and allele scoring of PCR products was estimated relative to the internal size standard GeneScan 600LIZ using the software PEAKS-CANNER v1.0 (Applied Biosystems).
Results and discussion
Out of the 36 microsatellite loci attempted, a total of 14 polymorphic microsatellite were optimized, including a selection of different microsatellite types (see Table 1) because different microsatellite types are equally valid to assess genetic diversity and populations structure of marine invertebrates. No evidence of linkage disequilibrium was detected across all pairwise comparisons. Failed amplifications due to presence of null alleles were detected in 2 loci of the Caials population (Table 1). Five markers showed Hardy-Weinberg disequilibrium after Narum corrections but the overall populations were in HWE (Table 1). Heterozygosity deficit was observed in 5 loci (3 loci in the Caials population and two in the Blanes population; Table 1). The average gene diversity over 14 loci using the Tajima index was 0.684 ± 0.367 in Caials, and 0.668 ± 0.354 in Blanes. Using the genetic G-W index (Garza and Williamson 2001), it seems that all markers were indicating bottleneck events in both populations (Table 1), but more populations should be sequenced for further confirmation. The application of the microsatellite markers developed herein to additional I. fasciculata populations will allow to understand how the genetic diversity is distributed in this species, and to know its overall status, identifying hotspots of genetic diversity, populations affected by large genetic diversity loss and high levels of inbreeding. Information on population genetics of the species is crucial for the assessment of ecological threats and recovery potential following disease episodes and population decimation, and the developing of management strategies when necessary.
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Domain: Environmental Science Biology
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Labiobaetis Novikova & Kluge in Ethiopia (Ephemeroptera, Baetidae), with description of a new species
Material collected between 2017 and 2019 in Ethiopia in the Awash River catchment substantially increased our knowledge of Labiobaetis Novikova & Kluge in this country. Four species were previously reported based on ecological investigations of Ethiopian rivers: L. glaucus (Agnew, 1961), L. latus (Agnew, 1961), L. vinosus (Barnard, 1932) and L. bellus (Barnard, 1932). We have identified six different species using a combination of morphology and genetic distance (COI, Kimura 2-parameter). Two of them, L. alahmadii Gattolliat & Al Dhafer, 2018 and L. potamoticus Gattolliat & Al Dhafer, 2018 were previously assumed to be endemic to the Arabian Peninsula. The status of L. bellus is discussed and remains unresolved. One species is new to science; it is described and illustrated based on its nymphs. A key to the nymphs of all Ethiopian species is provided. The interspecific K2P distances in Ethiopia are between 17% and 23%, the intraspecific distances are usually between 0% and 1%. The total number of Labiobaetis species worldwide is augmented to 145. The Afrotropical species of Labiobaetis are discussed in comparison to the species of other realms.
Introduction
The family Baetidae has the highest species diversity among mayflies, comprising ca. 1,100 species in 114 genera (updated from Sartori and Brittain 2015;Jacobus et al. 2019;Cruz et al. 2020), which is approximately one third of all mayfly species worldwide. They have a cosmopolitan distribution except in New Zealand (Gattolliat and Nieto 2009). Investigations of the molecular phylogeny of the Order Ephemeroptera revealed the relatively primitive status of the family (Ogden and Whiting 2005;Ogden et al. 2009;Ogden et al. 2019).
Labiobaetis Novikova & Kluge, 1987, is one of the richest genera of mayflies with 144 previously described species (Barber-James et al. 2013; and citations therein). The distribution of Labiobaetis is nearly worldwide, except for the Neotropical realm, New Zealand and some remote islands. After a long period of controversy, Labiobaetis is nowadays widely accepted as a valid genus (Gattolliat 2001;Fujitani et al. 2003;Fujitani 2008;McCafferty et al. 2010;Kluge and Novikova 2011Kluge 2012;Webb 2013;Kubendran et al. 2014Kubendran et al. , 2015Shi and Tong 2014). The history and concept of the genus Labiobaetis were recently summarized in detail (Shi and Tong 2014;Kaltenbach and Gattolliat 2018). Kluge and Novikova (2016) established a new tribe Labiobaetini including the genera Labiobaetis and Pseudopannota Waltz & McCafferty, 1987, based on a unique combination of imaginal and nymphal characters.
Recently, integrative taxonomy was applied to collections from the highly diverse regions of Southeast Asia and New Guinea, where 65 species were described and named . This contribution will focus on the Afrotropical country of Ethiopia.
Taxonomic studies of Labiobaetis have a long history in the Afrotropical realm. First, several species were described from South Africa by , Crass (1947) and Agnew (1961) under the genus Baetis Leach, 1815. Thereafter, Kopelke (1980) named a few species from Central Africa under Baetis, based on adults only. Later, Gillies (1993Gillies ( , 1994 published new species from West and East Africa, still assigned to Baetis. Lugo-Ortiz and McCafferty (1997) made a revision of Labiobaetis in the Afrotropical region including Madagascar and subsequently, provided a revision of the widespread species L. glaucus (Agnew, 1961). Gattolliat (2001) described six new species in his comprehensive study of the genus Labiobaetis in Madagascar. Kluge and Novikova (2016) contributed to the fauna of Central Africa and defined the tribe Labiobaetini. Finally, Gattolliat et al. (2018) studied the species from Saudi Arabia, which is bordering the Palaearctic realm, and described two new species. Until now, Labiobaetis encompasses 25 species in the Afrotropical realm, including two species only known from Saudi Arabia (Barber-James et al. 2013;Gattolliat et al. 2018).
The examined material was collected between 2017 and 2019 during ecological studies of the Awash River (Englmaier et al. 2020;Kebede et al. 2020). The collection area encompassed the whole Awash River catchment, including its major affluents (Fig. 1). The Awash River is endorheic; it springs in the Ethiopian Highlands at an altitude of > 3000 m in the Chilimo Forest and flows into the arid Afar Depression, where it finally drains into the saline Lake Abbe at the Ethiopian-Djibouti border, at an altitude of ca. 250 m (Englmaier et al. 2020 and citations therein). The study area including the physical conditions at the sampling sites are described and illustrated in detail in Englmaier et al. (2020: fig. 1, table 1). Apart from the protected Chilimo Forest, the region is subject to extensive anthropogenic impact (intensive agriculture, overgrazing by livestock), resulting in the loss of natural vegetation (Englmaier et al. 2020 and citations therein). The eco-geographical features of Ethiopia, including altitude, geology, hydrology, rainfall, temperature, soil types and land cover, as well as its freshwater ecoregions, are described in Haile and Moog (2016). Ethiopia shares two ecoregions, mainly the Central Eastern Africa ecoregion, but also to a small extent the North Africa and Sahara Desert ecoregion in the northwestern part of the country (Barber-James and Gattolliat 2012). So far, the diversity of Labiobaetis in Ethiopia has only become known through an ecological study of the benthic fauna of mountain streams and rivers (Harrison and Hynes 1988). Four species were reported in this study: L. glaucus (Agnew, 1961), L. latus (Agnew, 1961), L. vinosus and L. bellus . The identity and status of L. bellus is unclear and will be discussed below. Here, we report three additional species from the Awash River catchment, one of which is described and illustrated as a new species, based on nymphs. The total number of Labiobaetis species worldwide is augmented to 145.
Materials and methods
All specimens were collected between 2017 and 2019 by Wolfram Graf (University of Natural Resources and Life Sciences, Austria) and Yonas Terefe (Ambo University, Ethiopia) and preserved in 70-96% ethanol. Table 1. Sequenced specimens.
Species
The dissection of nymphs was performed in Cellosolve (2-Ethoxyethanol) with subsequent mounting on slides with Euparal liquid, using an Olympus SZX7 stereomicroscope.
The DNA of part of the specimens was extracted using non-destructive methods allowing subsequent morphological analysis (see Vuataz et al. 2011 for details). We amplified a 658 bp fragment of the mitochondrial gene cytochrome oxidase subunit 1 (COI) using the primers LCO 1490 and HCO 2198 (Folmer et al. 1994; see Kaltenbach and Gattolliat 2020 for details). Sequencing was done with Sanger's method (Sanger et al. 1977). The genetic variability between specimens was estimated using Kimura-2-parameter distances (K2P, Kimura 1980), calculated with MEGA 7 (Kumar et al. 2016, [URL] GenBank accession numbers are given in Table 1, nomenclature of gene sequences follows Chakrabarty et al. (2013).
Drawings were made using an Olympus BX43 microscope. To facilitate the determination of the new species and the comparison of important structures with other species, we partly used a combination of dorsal and ventral aspects in one drawing (see fig. 1).
Photographs of nymphs were taken using a Canon EOS 6D camera and the Visionary Digital Passport imaging system ( [URL]) and processed with Adobe Photoshop Lightroom ( [URL]) and Helicon Focus version 5.3 ( [URL]). Photographs were subsequently enhanced with Adobe Photoshop Elements 13.
The distribution maps were generated with SimpleMappr ( [URL], Shorthouse 2010). The GPS coordinates of the sample locations are given in Table 2.
The terminology follows Hubbard (1995) and Kluge (2004). The description follows the form of other recent descriptions of Labiobaetis, as for example in .
List of Labiobaetis species from Ethiopia
Differential diagnosis. Nymph. Following combination of characters: A) colouration: abdomen dorsally brown, with light pattern as Gattolliat et al. 2018: figs 32, 33; B) scape without distolateral process; C) labial palp segment II with thumb-like protuberance; segment III slightly pentagonal; D) maxillary palp segment II with excavation at inner distolateral margin; E) fore femur rather broad, length ca. 3× maximum width; dorsal margin with ca. 18 curved, spine-like setae and many fine, simple setae, and basally some additional spine-like setae near margin; femoral patch reduced; F) fore tibia dorsally with a row of short, spatulate setae (Gattolliat et al. 2018: fig. 26 Biological aspects. The specimens were collected at altitudes between 480 m and 1600 m. Further characteristics of sampling sites are given in Englmaier et al. 2020: table 1. In Saudi Arabia, the species occurs in medium-size streams with stony substrates, preferably in relatively fast flowing water or even at the base of small waterfalls . Distribution. Ethiopia (Fig. 2a), Saudi Arabia ). Differential diagnosis. Nymph. Following combination of characters: A) colouration: abdomen dorsally uniform brown; B) scape with well-developed distolateral process; C) labial palp segment II with broad, thumb-like distomedial protuberance; segment III oblong; D) maxillary palp segment II with strong excavation at inner distolateral margin; E) fore femur rather slender, length 3.6× maximum width; dorsal margin with 18-27 curved, spine-like setae, and a partial row of spine-like setae near margin; femoral patch absent; F) hind protoptera well developed; G) seven pairs of gills; H) paraproct with 15-20 stout marginal spines. Description. Nymph . Body length 7.3-8.5 mm. Cerci: ca. 2/3 of body length. Paracercus: ca. 2/3 of cerci length. Antenna: approx. twice as long as head length.
Antenna ( Fig. 4g) with scape and pedicel subcylindrical, with well-developed distolateral process at scape. Labrum (Fig. 5a). Subrectangular, length 0.7× maximum width. Distal margin with medial emargination and small process. Dorsally with medium, fine, simple setae scattered over surface; submarginal arc of setae composed of one plus ca. 17 long, feathered setae. Ventrally with marginal row of setae composed of lateral and anterolateral long, feathered setae and medial long, bifid setae; ventral surface with ca. nine short, spine-like setae near lateral and anterolateral margin.
Right mandible (Fig. 5b, c). Incisor and kinetodontium fused. Incisor with four denticles; kinetodontium with three denticles, inner margin of innermost denticle with row of thin setae. Prostheca robust, apically denticulate. Margin between prostheca and mola slightly convex. Tuft of setae at apex of mola present.
Left mandible (Fig. 5d, e). Incisor and kinetodontium fused. Incisor with four denticles; kinetodontium with three denticles. Prostheca robust, apically with small denticles and comb-shaped structure. Margin between prostheca and mola slightly convex, with minute denticles towards subtriangular process. Subtriangular process long and slender, above level of area between prostheca and mola. Denticles of mola apically constricted. Tuft of setae at apex of mola absent.
Both mandibles with lateral margins almost straight. Basal half with fine, simple setae scattered over dorsal surface.
Maxilla (Fig. 5g, h). Galea-lacinia ventrally with two simple, apical setae under canines. Inner dorsal row of setae with three denti-setae, distal denti-seta tooth-like, middle and proximal denti-setae slender, bifid and pectinate. Medially with one pectinate, spine-like seta and six simple setae increasing in length distally. Maxillary palp slightly longer than length of galea-lacinia; 2-segmented; palp segment II 1.4× length of segment I; setae on maxillary palp fine, simple, scattered over surface of segments I and II; apex of last segment rounded, with strong excavation at inner distolateral margin.
Labium (Fig. 5i, j). Glossa basally broad, narrowing toward apex; shorter than paraglossa; inner margin with ca. seven spine-like setae, distalmost seta much longer than other setae; apex with one long, one medium and one short, robust seta; outer margin with 5-7 spine-like setae increasing in length distally; ventral surface with fine, simple, scattered setae. Paraglossa sub-rectangular, curved inward; apex rounded; with three rows of long, robust, distally pectinate setae in apical area and three or four medium, simple setae in anteromedial area; dorsally with row of five long, spine-like, simple setae near inner margin. Labial palp with segment I 0.7× length of segments II and III combined. Segment I ventrally with short, fine, simple setae. Segment II with broad thumb-like distomedial protuberance; distomedial protuberance 0.9× width of base of segment III; ventral surface with short, fine, simple setae; dorsally with two or three long, spine-like setae near outer margin. Segment III oblong; apex slightly pointed; length 1.2× width; ventrally covered with short, spine-like, simple setae and short, fine, simple setae.
Hind protoptera (Fig. 4h) well developed. Foreleg (Fig. 4a, b). Ratio of foreleg segments 1.1:1.0:0.4:0.1. Femur. Length 3.6× maximum width. Dorsal margin with 18-27 curved, spine-like setae and partial second row near margin in basal area; length of setae 0.14× maximum width of femur. Apex rounded, with pair of spine-like setae and some short, stout setae. Many stout, lanceolate setae scattered along ventral margin; femoral patch absent. Tibia. Dorsal margin with row of short, stout setae and fine simple setae, and row of short, stout setae near margin. Ventral margin with row of short, curved, spine-like setae, distally of patellotibial suture one longer, curved, spine-like seta, on apex some longer setae and tuft of fine, simple setae. Anterior surface scattered with stout, lanceolate setae. Patellotibial suture present on basal half area. Tarsus. Dorsal margin with row of short, stout setae and fine, simple setae. Ventral margin with row of curved, spine-like setae. Claw with one row of 10-13 denticles; distally pointed; with ca. five stripes; subapical setae absent.
Terga (Fig. 4c). Surface with irregular rows of U-shaped scale bases and scattered fine, simple setae. Posterior margin of tergum IV with triangular spines, ca. as long as wide.
Gills (Fig. 4d, e). Present on segments I-VII. Margin with small denticles intercalating fine simple setae. Tracheae extending from main trunk to inner and outer margins. Gill I ca. 2/3 length of segment II; gill IV as long as length of segments V and half VI combined; gill VII slightly longer than length of segment VIII.
Etymology. Referring to the strongly developed excavation at inner, distolateral margin of maxillary palp segment II.
Biological aspects. The specimens were collected at an altitude of 2400 m in relatively cold water (15.9 °C; see Englmaier et al. 2020: table 1). The sampling site lies in a protected area (S1, National Forest Priority Area), unlike all other sampling sites in this study (Englmaier et al. 2020).
Biological aspects. The specimens were collected at an altitude of 1600 m. Further characteristics of the sampling site are given in Englmaier et al. (2020). Harrison and Hynes (1988) reported the species at 1900 m in marginal vegetation.
Gattolliat et al. 2018: figs 1-15, 19
Differential diagnosis. Nymph. Following combination of characters: A) colouration: abdomen dorsally brown, with pattern as Gattolliat et al. 2018: fig. 19; B) scape without distolateral process; C) labial palp segment II with small, thumb-like protuberance; segment III slightly pentagonal; D) maxillary palp segment II without excavation at inner distolateral margin; E) fore femur rather broad, length ca. 3× maximum width; dorsal margin with ca. 8 curved, spine-like setae; femoral patch reduced; F) hind protoptera well developed; G) seven pairs of gills; H) paraproct with ca. 36 stout, marginal spines. Distribution. Ethiopia (Fig. 2b), Saudi Arabia ) and potentially Iran (Tahmasebi et al. 2020). Kopelke 1980 Kluge and Novikova (2016), there is no morphological difference between L. vinosus and L. tenuicrinitus. Kluge (2020) also indicates the synonymy of both species. However, no formal synonymy has been established so far. As we have not seen material of L. tenuicrinitus, we are not in a position to formally synonymise both species. Further, the genetic barcode (COI) of both species remains unknown. Differential diagnosis. Nymph. Following combination of characters: A) colouration: abdomen dorsally brown, with pattern as Kluge and Novikova 2016: fig. 113; B) scape without distolateral process; C) labial palp segment II with broad, thumb-like protuberance; segment III conical; D) maxillary palp segment II with excavation at inner distolateral margin; E) fore femur rather broad, length ca. 3× maximum width; dorsal margin with 8-18 curved, spine-like setae and basally a partial second row of setae; F) hind protoptera absent or minute; G) six pairs of gills.
Biological aspects. The specimens were collected at altitudes of 1260 m and 1600 m. Further characteristics of sampling sites are given in Englmaier et al (2020). Harrison and Hynes (1988) reported the species at 2500 m in marginal vegetation.
Assignment to Labiobaetis and affinities
For the assignment of the new species to Labiobaetis we refer to Kluge and Novikova (2014). Labiobaetis is characterized by a number of derived characters, some of which are not found in other taxa (Kluge and Novikova 2014): antennal scape sometimes with a distolateral process (Fig. 4g); maxillary palp two segmented with excavation at inner distolateral margin of segment II, excavation may be poorly developed or absent (Fig. 5g); labium with paraglossae widened and glossae diminished; labial palp segment II with distomedial protuberance (Fig. 5i). The concept of Labiobaetis is also based on additional characters, summarized and discussed in Gattolliat (2018, 2019). Labiobaetis excavatus sp. nov. is morphologically related to L. latus, sharing the distolateral process at scape, well-developed hind protoptera, seven pairs of gills, and the broad, distomedial protuberance at segment II of the labial palps. The main differences are the stronger distolateral excavation at the maxillary palp of L. excavatus sp. nov. (Fig. 5g, h; Lugo-Ortiz and McCafferty 1997: fig. 6), the number of spine-like setae at dorsal margin of femur (18-27 in L. excavatus sp. nov., plus a partial second row near margin; 13-18 in L. latus) and the presence or absence of setae at the apex of the left mola (present in L. latus, absent in L. excavatus sp. nov.). The strong distolateral excavation of the maxillary palp is very similar to L. punctatus Gattolliat, 2001, from Madagascar, which is also missing the setae at apex of the mola of the left mandible. However, the Malagasy species has no distolateral process at scape and differs by many other characters (Gattolliat 2001: figs 44-54).
Comparison to other realms and species groups
Remarkably, all Afrotropical species of Labiobaetis have a submarginal arc of feathered setae on the dorsal surface of the labrum (Gillies 1994;Lugo-Ortiz et al. 1999;Gattolliat 2001;Gattolliat et al. 2018, this study). In contrast, several additional types of these setae were described from all other regions. The majority of species occur in the Oriental realm and New Guinea. In New Guinea, simple setae were the predominant type, but also feathered setae, clavate setae with pectination, dendritic and lanceolate setae with and without pectination were described (Lugo-Ortiz et al. 1999;Kaltenbach and Gattolliat 2018). In Southeast Asia, simple, feathered and clavate setae are predominant and comparably frequent, but also lanceolate and dendritic setae were described (Müller-Liebenau 1984;Shi and Tong 2014;Gattolliat 2019, 2020;. The type of the dorsal, submarginal setae together with the shape of the distomedial protuberance of labial palp segment II and often combined with other characters are building the base for the morphological species groups defined in Southeast Asia and New Guinea Gattolliat 2018, 2019;. These morphological groups within Labiobaetis are primarily a working tool but some may be natural groups and could also serve as a basis for future studies on the generic delimitation and phylogeny of this genus. Afrotropical Labiobaetis are not only sharing the feathered type of dorsal, submarginal setae on the labrum, but also have mostly a broad thumb-like distomedial protuberance of labial palps segment II. A lot of the variation between the species is coming from different Table 3. Intraspecific (bold) and interspecific genetic distances of the sequenced specimens (COI; Kimura 2-parameter; %, mean, minimum-maximum).
combinations of characters like seven or six pairs of gills, presence or absence of hind protoptera and presence or absence of a distolateral process at scape. The reduction and secondary loss of these characters seems to be a general tendency in Labiobaetis (Kluge and Novikova 2014;Gattolliat 2018, 2019) and they are, therefore, less reliable characters to define morphological groups. There are a few species with a narrow distolateral protuberance at labial palps segment II (L. piscis Lugo-Ortiz & Mc-Cafferty, 1997;L. longicercus Gattolliat, 2001;L. potamoticus), which are at the same time sharing seven pairs of gills, the absence of a distolateral process at scape and, more important, the absence of setae at the apex of the mola of the left mandible. These species are probably forming a morphological group amongst the other Afrotropical species. However, this is out of the scope of this paper and further investigations on other Afrotropical regions are necessary to discuss possible relationships of Labiobaetis species in this realm. Based on the present knowledge, all Afrotropical species of Labiobaetis seem to be morphologically closely related to the Southeast Asian operosus and difficilis groups (Kaltenbach and Gattolliat 2019). Both groups are very close to each other; the only difference is the presence (operosus group) or absence (difficilis group) of hind protoptera, which is a rather unreliable group character (see above).
The distribution of the Labiobaetis species seems to be also different in the Afrotropical realm compared to Southeast Asia and New Guinea. Apart from Madagascar, where all Labiobaetis species are endemic to the island (Gattolliat 2001), some Afrotropical species have a wide or even very wide distribution, e.g. L. potamoticus (Saudi Arabia, Ethiopia, potentially Iran), L. latus (Ethiopia, Kenya, South Africa), L. vinosus (Ethiopia, DR Congo, Tanzania, Uganda, South Africa) and especially L. glaucus (Ethiopia, Iran (?), Saudi Arabia, Comoros, Kenya, Namibia, Zimbabwe, South Africa). On the contrary, most species in Southeast Asia and New Guinea are restricted to smaller regions or are endemic to one island. An exception is L. moriharai Müller-Liebenau, 1984, known from Malaysia, Vietnam and Borneo . The reason for this difference is probably due to the high number of islands in Southeast Asia, especially in Indonesia and the Philippines, and the extreme landscape structure in New Guinea, facilitating allopatric speciation and endemicity (Toussaint et al. 2013(Toussaint et al. , 2014Gattolliat 2018, 2019;. The huge African continent is in comparison geographically less structured, which is generally facilitating larger distribution areas of species.
Labiobaetis bellus
Since its description as a new species by , L. bellus was regularly reported from South Africa and other countries, mainly in ecological studies of rivers (e.g. Crass 1947;Harrison 1950;Kimmins 1960;Oliff and King 1964;Chutter 1970Chutter , 1971Harrison and Hynes 1988;Samways et al. 2011). However, apart from a rather sketchy drawing of the labial palp fig. 13k), there are no further drawings of the mouthparts in and his description of the nymph is not precise enough to differentiate it unambiguously from other spe-cies. Additionally, he mentioned that L. bellus and Cheleocloeon excisum "...approach each other very closely in the character of the mouth-parts of the nymphs." (Barnard 1932: 204). Later, already Kimmins (1960) was not sure about his determination of "Baetis ? bellus" from Uganda and proposed to solve the determination issues by studying nymphs rather than adults. Lugo-Ortiz and McCafferty (1997) did not mention L. bellus at all in their comprehensive study on Afrotropical Labiobaetis, contrary to L. vinosus, which described in the same paper. We may assume that these authors could not clarify the identity and the status of L. bellus. It remains unclear what Harrison and Hynes (1988) and other authors include in their concept of "L. bellus". Moreover, most of the reports of the species were anterior to the revision of the genus in the Afrotropics (Lugo-Ortiz and Mc-Cafferty 1997) and must be therefore considered as uncertain. Therefore, we refrain from further treatment of L. bellus before its species concept is clarified based on material from South Africa.
In comparison to L. excavatus sp. nov. with its broad distomedial protuberance at labial palp segment II similar to L. latus, the drawing of L. bellus in Barnard 1932: fig. 13k shows a more slender protuberance, more similar to L. piscis and L. potamoticus; Labiobaetis piscis and L. potamoticus may be easily confused with each other and L. potamoticus is abundant in the Awash River. In addition, L. bellus was reported from several places and different altitudes in the Awash River, contrary to L. excavatus sp. nov., which was found in the natural Chilimo Forest (2400 m) only, despite intensive sampling efforts along the Awash River. Further, L. excavatus sp. nov. is very similar to L. latus, which is reported additionally to L. bellus by Harrison and Hynes (1988). Therefore, we may assume that "L. bellus" sensu Harrison and Hynes (1988) has obvious differences to L. latus and thus to L. excavatus sp. nov. as well. As a conclusion, we assume that L. excavatus sp. nov. cannot be conspecific with L. bellus, the latter species being in the need of a taxonomic revision.
Two species, L. alahmadii and L. potamoticus, have intraspecific distances of up to 4%. In L. alahmadii, two specimens from Ethiopia have of genetic distance of 3%-4% to all other sequenced specimens from Ethiopia and Saudi Arabia. All other specimens have distances of 0%-1% between themselves, as well in Ethiopia as between Ethiopia and Saudi Arabia. Intraspecific distances of 4%-6% were also reported in some cases for Labiobaetis species in New Guinea, Indonesia, Borneo and the Philippines (Kaltenbach and Gattolliat , 2019, as well as in aquatic beetles in the Philippines (Komarek and Freitag 2020). Ball et al. (2005) also reported a case with 6% intraspecific distance in a mayfly in North America and intraspecific K2P distances of more than 3.5% are not uncommon within Plecoptera as well (Gill et al. 2015;Gattolliat et al. 2016). In L. potamoticus, the specimens from Ethiopia have distances of 0-1% between each other, and the higher distances of 3-4% are only between specimens from Ethiopia and Saudi Arabia, which can be explained by the greater geographic distance.
The COI sequence of L. latus from Ethiopia has a distance of 22% to another specimen from South Africa, reported in Gattolliat et al. (2018: table 1; GenBank MH070297, GBIF00465142), without any morphological difference between the two specimens. In the meantime, a second specimen from the same location in South Africa was sequenced and has the same barcode as the first specimen. Further, several COI barcodes with a distance of just 5-6% to the one from Ethiopia were obtained from specimens in South Africa as well, which may be explained by the geographic distance between Ethiopia and South Africa. There seem to be two different widespread mitochondrial lineages corresponding to the morphological concept of L. latus. This problem cannot be solved without additional investigations, including in particular nuclear genes, as it was recently done in the similar case of Baetis harrisoni (Pereira da Conceicoa et al. 2012. Different mitochondrial lineages with the same morphology were already reported several times in Labiobaetis Gattolliat 2018, 2019;. The number of sampled localities and different habitats in Ethiopia is still limited and there are regions without any collection activities so far (Fig. 2). However, the distribution of Labiobaetis species in Africa is often much more widespread than in other regions and suitable habitats are limited in this semiarid area. Therefore, we may expect a few, but not many more species to be discovered in Ethiopia with further collections.
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Domain: Environmental Science Biology
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THE COUNTERACTING EFFECT OF POTASSIUM CYANIDE IN SODIUM AZIDE-INHIBITED GERMINATION OF PAULOWNIA TOMENTOSA STEUD . SEEDS
The effect of some respiratory inhibitors on light-induced Paulownia tomentosa Steud. seed germination was studied. Millimolar solution of sodium azide was sufficient to completely prevent germination induced by a 5-min red light pulse. The inhibitory effect of azide was absent if seeds were rinsed before phytochrome activation by light. Sodium azide was effective only if present in the period of Pfr activity. The escape time from azide inhibition, compared to the escape from far-red light action, was delayed for about 24 hours. When azide was applied after phytochrome activation, its effect depended on how long it was present in the incubation medium. The removal of azide allowed full restoration of germination by another red light pulse and the far-red escape time did not differ from the escape of untreated, i.e., water-imbibed seeds. Potassium cyanide alone did not produce any effect in light-stimulated germination of these seeds. However, it counteracted the inhibitory effect of azide in light-stimulated germination, if applied simultaneously at a concentration three times higher. UDC 582.916.21 : 581.142.044
INTRODUCTION
The germination of empress tree (Paulownia tomentosa Steud.)seeds is phytochrome-controlled. Depending on seed maturation conditions, the light requirement for maximum germination varies from a very brief exposure to several hours of irradiation (in some cases up to 18 h) (Borthwick et al. 1964;Grubišić et. al. 1985). Prolonged imbibition in darkness or in heavy water leads to an increase in the light requirement for maximum germination (Grubišić andKonjević, 1986, 1990). In seeds with natural and induced long-term light requirements, two short (5-min) red light pulses, separated by a certain period of darkness, could substitute for this requirement. Moreover, the long light requirement can be reduced to a single 5-min red light pulse by the application of inorganic nitrates and nitrites (Grubišić and Konjević, 1990), substances with electron-accepting properties (Giba et al. 1994), or nitric oxide-releasing compounds (Giba et al. 1998).
Azide is a well known dormancy-breaking agent for a variety of seeds (Bewleyand Black, 1982). Seeds of pigweed (Taylorsonand Hendricks, 1973), apple (Dziewanowska et al. 1979), wild oat (Adkins et al. 1984), dormant rice (Cohnand Hughes, 1986) and oat (Côme et al. 1988) can be stimulated to germinate by NaN 3 . The same is true of some other respiratory inhibitors (cyanide, carbon monoxide, sulfide). On the other hand, azide and cyanide do not substitute for the light requirement for breaking dormancy in empress tree seeds (Grubišić, 1980). The opposing effect of these inhibitors in germination is not uncommon. Hendricks and Taylorson(1972) showed that azide inhibited, but cyanide stimulated germination of seeds of Lactuca sativa and Amaranthus albus.
In the study presented here, we investigated the effects of sodium azide on light-induced seed germination, focusing on the period of P fr activity. It is demonstrated that azide reversibly inhibits germination of P. tomentosa seeds. In addition, cyanide was found to counteract the inhibitory effect of azide in this species.
Plant material and seed manipulation
Seeds of empress tree (Paulownia tomentosa Steud.) were collected in the Botanical Garden of the University of Belgrade and stored at room temperature until use. Lots of 100 seeds were placed in 6-cm diameter Petri dishes, with 2 ml of distilled water or aqueous solution of the substance to be tested. Seeds were rinsed three times with 3 ml of distilled water before replacing the test solutions. The pH values of the solutions were recorded at the start of imbibition and before removal of the seeds. Diluted HCl and NaOH were used to adjust the pH of the test solutions to the appropriate pH value. Germination was performed at 25±1 o C, in darkness. Specific experimental protocols and irradiation regimes are described in the Figure Legends and Tables. Only for one subset of experiments (Table 1) was a batch of freshly harvested seeds used. These seeds could not be induced to germinate with a 5-min red light pulse, but required several hours of long red light irradiation.
Germination was scored seven days after the inductive red-light treatments or ten days after the start of imbibition. All experiments were repeated three times, with 3-5 replicates each. The data points represent means of pooled results; standard errors are not shown since they never exceeded 3%. . The pH value of the medium was recorded using a laboratory pH-meter (InoLab, pH Level 1, WTW, Weilheim, Germany).
Inhibition by azide
The effects of different concentrations of sodium azide and potassium cyanide on P. tomentosa seed germination are shown in Fig. 1. Azide, up to 10 -4 M, applied at the onset of imbibition, was ineffective in inhibiting red light-induced germination, while addition of 10 -3 M azide completely suppressed it. On the other hand, cyanide concentrations as high as 10 -2 M, applied in the same way, failed to affect germination. For further experiments sodium azide was used in the inhibitory concentration (10 -3 M) and applied at the beginning of each of the three germination phases, i.e. imbibition (3 days), the phase of P fr activity (3 days after the red light pulse), and the phase of radicle elongation (4 days). At the end of each phase, the seeds were washed out and the incubation medium replaced by distilled water. The inhibitory effect of azide Seeds were imbibed either in water or in increasing concentrations (x-axis) of test solutions for 3 days at 25 0 C, irradiated with 5-min red light, and left in darkness. Germination was scored 7 days after the red light treatment.˜-sodium azide; £potassium cyanide. Insert: Effect of sodium azide applied in different phases of P. tomentosa seed germination. Seeds were supplied with distilled water or 10 -3 M solution of sodium azide at different germination phases. Following these phases, the seeds were rinsed. In all treatments, seeds were irradiated 3 days after the onset of imbibition with 5-min red light. Germination was scored 7 days after the light treatment.£-distilled water; ¢-sodium azide; I-imbibition; II-P fr activity; III-radicle elongation; R-5-min red light.
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PDF created with FinePrint pdfFactory Pro trial version www.pdffactory.comwas evident only if it was administered during the period of P fr activity (insert in Fig. 1).
In addition to this, the escape from azide inhibition was followed. A comparison of azide-inhibition escape and escape from the inhibitory effect of far-red light is shown in Fig. 2. In both experiments escape was completed 72 h after the red light pulse. However, while the inhibition to 50% of red light-induced germination by 5min of far-red light was estimated to be at 31 h, the same effect of azide occurred about 20 h later. That was confirmed by probit analyses (insert in Fig. 2). Accordingly, there is an obvious 24-h shift of the escape from azide inhibition. If the seeds were supplied with 10 -3 M sodium azide after the inductive 5-min red light pulse, the percent of germination decreased with the duration of sodium azide treatment. Subsequent stimulation of seed germination by an additional red light pulse, after rinsing, revealed that the azide inhibitory effect in the P fr activity phase is reversible (Fig. 3).
In a similar experimental approach (seeds treated by azide, then rinsed), the far-red light escape time was rechecked and compared to the escape time for the far-red inhibitory effect in seeds imbibed only in water. Pretreatment with 10 -3 M azide did not affect the escape time (Fig. 4), indicating full restoration of the phytochrome pigment system.
A subsequent set of experiments was performed in an attempt to understand the azide inhibitory effect in light-induced germination. A batch of P. tomentosa seeds requiring long light irradiation for maximum germination was used. As was shown earlier (Grubišić and Konjević,1990), two red light pulses of 5-min separated by a 12 hours-long period of darkness can substitute for the continuous light requirement of these seeds. The application of a 10 -3 M concentration of azide inhibited germination if applied after the second red light pulse only (Table 1). Seeds were imbibed in water for 3 days in darkness at 25 0 C, irradiated with 5-min red light, and returned to darkness. After that, they were either irradiated with 5min far-red light at the indicated times (x-axis) and again returned to darkness (£); or transferred to 10 -3 M solution of sodium azide in darkness for 72 h (period of P fr activity), rinsed and irradiated with 5-min red light, and then irradiated with 5-min far-red light at the indicated time intervals (x-axis) and again returned to darkness (˜). Germination was scored 7 days after the last red light treatment.
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The pH dependence of azide inhibition
Empress tree seeds germinate in the incubation medium over a wide pH range (Turner et al. 1988). To evaluate the pH dependence of azide inhibition, seeds were treated with 10 -3 M sodium azide solution adjusted to different pH. The inhibitory effect of azide was assayed at an initial pH range of 2.9 to 10.1. The degree of inhibition due to azide was found to vary as the pH varied (Fig. 5). Complete inhibition was achieved at higher pH values of the incubation medium, i.e. no lightinduced seed germination occurred at pH of 4.6.
Effects of azide in the presence of cyanide
Potassium cyanide alone did not affect the germination of P. tomentosa seeds. In contrast, when seeds were treated with a mixture of azide and cyanide solution in a two-factorial experiment, germination was significantly promoted. Although cyanide did not influence light-stimulated germination of these seeds, it did revert the inhibitory effect of azide if applied simultaneously at three-fold higher concentration (Fig. 6).
Since it is known that cyanide raises the pH of the incubation medium, both the initial and final pH values at the highest concentrations of inhibitors were monitored. It turned out that the final pH values never fell below 6.4 (data not shown). Azide completely inhibited lightinduced germination of P. tomentosa seeds at any pH value above 4.6 (Fig. 5). Therefore, the counteracting effect of cyanide in azide-inhibited germination of seeds Table 1. Effect of sodium azide in germination of P. tomentosa seeds with a natural long-term light requirement. Seeds were imbibed for 3 days in darkness at 25 0 C in distilled water (£) or in 10 -3 M solution of sodium azide (¢), irradiated with 5-min red light, and returned to darkness. They then were rinsed and the solutions were replaced. After a corresponding interval of 12 h, seeds were irradiated with another 5-min red light pulse, rinsed again, and transferred to distilled water or sodium azide solution. Germination was scored 7 days after the first red light treatment. I-imbibition (3 days); II-phytochrome activity (3 days); III-radicle elongation (4 days); R-5-min red light. PDF created with FinePrint pdfFactory Pro trial version www.pdffactory.com of this species cannot be ascribed to cyanide-changed pH values of the incubation medium.
DISCUSSION
In light-induced seeds of P. tomentosa, the presence of 10 -3 M sodium azide in the incubation medium inhibited germination. Potassium cyanide did not affect this process. In the presence of azide, an inductive 5-min red light pulse was completely ineffective. After rinsing and replacement of azide solution by distilled water, seeds still did not germinate and required an additional red light pulse for germination instead (Fig. 3). A direct azide interaction with the phytochrome molecule is questionable. Azide seemed to affect one of the (early) steps in the phytochrome transduction chain. This assumption is supported by the fact that escape from azide inhibition existed, the time needed for 50% escape being about 20 h longer than that for escape from far-red light inhibition (Fig. 2).
It was anticipated earlier that pH control of the incubation medium, when utilizing dormancy-breaking chemicals that are weak acids or bases, might improve their effectiveness and reproducibility. It has been shown in dehulled red rice seeds that the dormancy-breaking activity of azide, cyanide, and hydroxylamine are pHdependent. In each case, activity was observed at pH values that favor formation of the uncharged form of azide (pK a =4.7), cyanide, (pK a =9.3), and some other compounds (Cohnand Hughes, 1986).
Azide did not substitute for light in breaking dormancy of P. tomentosa seeds. However, the inhibitory effect of azide in light-induced germination of seeds of this species was pH-dependent and occurred when the initial pH value of the incubation medium was 4.6 (Fig. 5). In addition, the final pH value of the azide solution differed from the initial one, varying from 4.1 to 7.3 (data not shown). Otherwise, under such experimental conditions the molecules of the inhibitor would have been overwhelmingly ionized (deprotonated). Taking into account these findings, we speculate that N 3 represents the inhibitory form of the azide molecule in light-induced germination of empress tree seeds.
In seeds with a long-light requirement induced to germinate by two pulses of red light separated by a period of darkness, azide prevented the effect of the second pulse only (Table 1). It was previously shown that the need for two red light pulses in these seeds can be modified by addition of nitrates (Grubišić and Konjević, 1990) or different NO-releasing compounds (Giba et al. 1998), or by upward and downward temperature shifts (Grubišić and Konjević, 1992). All of these treatments make germination possible under suboptimal light conditions, i.e. after induction by one red light pulse.
It is surprising that cyanide, ineffective by itself in P. tomentosa seed germination, can overcome the inhibition of azide if applied simultaneously. The counteracting cyanide effect was noticed only when both inhibitors were present in the incubation medium in higher concentrations, i.e., above the millimolar range. The finding that the effect of one factor (cyanide) was evident only in the presence of inhibitory concentrations of another one (azide) implies that there is an interaction between the two factors studied.
It has been suggested that the apparent resistance of germination to cyanide is an experimental artefact due to extreme cyanide volatility at the usual pH used in germination experiments (Yu et al. 1981). In light-induced germination of P. tomentosa seeds, the relief of azide inhibition by cyanide was not a result of altered pH in the incubation medium. Thus, the possibility that the "cyanide effect" might not be cyanide-specific was ruled out.
Data of the kind presented here on the effects of coincidental application of azide and cyanide have not been reported so far in studies of seed germination. However, a similar approach has been applied in experiments with isolated animal cells and tissues. For instance, the vasorelaxant effect of azide was partially reversed or prevented by an excess of free cyanide (Smithand Wilcox, 1994). In addition, the cyanide effect appeared to be competitive and reversible, although the same concentrations of cyanide alone remained without effect (Kruszyna et al. 1982(Kruszyna et al. , 1985)). Thus, azide-inhibited germination of P. tomentosa seeds may turn out to be an appropriate tool for further studies of phytochrome-controlled germination.
Fig. 1 .
Fig. 1. Effect of sodium azide and potassium cyanide on light-induced germination of P. tomentosa seeds. Seeds were imbibed either in water or in increasing concentrations (x-axis) of test solutions for 3 days at 25 0 C, irradiated with 5-min red light, and left in darkness. Germination was scored 7 days after the red light treatment.˜-sodium azide; £potassium cyanide. Insert: Effect of sodium azide applied in different phases of P. tomentosa seed germination. Seeds were supplied with distilled water or 10 -3 M solution of sodium azide at different germination phases. Following these phases, the seeds were rinsed. In all treatments, seeds were irradiated 3 days after the onset of imbibition with 5-min red light. Germination was scored 7 days after the light treatment.£-distilled water; ¢-sodium azide; I-imbibition; II-P fr activity; III-radicle elongation; R-5-min red light. 31
Fig. 2 .
Fig. 2. Escape time for far-red light and inhibitory effect of sodium azide in lightinduced germination of P. tomentosa seeds. After imbibition in water for 3 days in darkness at 25 0 C, seeds were irradiated with 5-min red light. After that, they were either irradiated with 5-min far red light at the indicated time intervals and returned to darkness (£) or transferred after rinsing to 10 -3 M sodium azide solution and returned to darkness (˜). Insert: Probit analysis of escape time from azide inhibition and far-red light inhibitory action.
Fig. 4 .
Fig. 4. Escape time from far-red light inhibitory action in water-imbibed and sodium azide-treated seeds of empress tree. Seeds were imbibed in water for 3 days in darkness at 25 0 C, irradiated with 5-min red light, and returned to darkness. After that, they were either irradiated with 5min far-red light at the indicated times (x-axis) and again returned to darkness (£); or transferred to 10 -3 M solution of sodium azide in darkness for 72 h (period of P fr activity), rinsed and irradiated with 5-min red light, and then irradiated with 5-min far-red light at the indicated time intervals (x-axis) and again returned to darkness (˜). Germination was scored 7 days after the last red light treatment.
Fig. 5 .
Fig.5. The pH dependance of azide inhibition. Seeds were imbibed for 3 days in darkness at 25 0 C in distilled water (£) or in 10 -3 M sodium azide solution (˜) with initial pH values in the range of 2.76-10.28. After that, they were irradiated with a 5-min red light pulse and returned to darkness. Germination was scored 7 days after the light treatment.
Fig.6. Effect of sodium azide and potassium cyanide on light-induced germination of P. tomentosa seeds. Seeds were incubated in a mixture of different concentrations of sodium azide and potassium cyanide (10 -2 M-10 -6 M) for 3 days in darkness at 25 0 C, irradiated with 5-min red light, and returned to darkness. Germination was scored 7 days after the light treatment.
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Domain: Environmental Science Biology
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Molecular Identication of Sarcocystis Halieti in Birds of Prey From Spain
Background: Members of the genus Sarcocystis are protozoan parasites characterized by a prey-predator two-host life cycle. Sarcocysts are formed in muscles or CNS of the intermediate host (IH), while sporocysts develop in the small intestine of the denitive host (DH). Various birds of prey were conrmed to be DH for Sarcocystis spp. By contrast, only two species, S. wobeseri and S. falcatula were identied in the muscles of birds of prey. The latter species is pathogenic and can cause encephalitis in various birds. The aim of the present study was to identify Sarcocystis species in the muscles of birds of prey from Spain. Methods: In the period between 2019 and 2020, muscle tissues of 59 birds collected from Spain were examined for the presence of Sarcocystis spp. Sarcocysts in fresh squashed samples were morphologically characterised under a light microscope (LM). Sarcocystis species were identied by means of 28S rRNA and ITS1 sequence analysis. Results: With the help of methylene blue-staining microscopic sarcocysts were detected in 3/59 (5.1%) birds of prey from Spain. Under LM, one type of sarcocysts was observed. Sarcocysts were thread-like (1050–2160 × 130–158 μm), had a thin (0.7–1.4 μm) and smooth cyst wall. Septa divided the cysts into compartments lled with banana-shaped (5.9 × 1.7 μm) bradyzoites. On the basis of DNA sequence results, S. halieti was identied in the western marsh harrier (Circus aeruginosus) and the black kite (Milvus migrans) for the rst time. Sarcocysts of S. halieti detected in the black kite and the western marsh harrier were shorter and wider as compared to those observed in the great cormorant (Phalacrocorax carbo) and the herring gull (Larus argentatus). Hence, S. halieti might infect birds belonging to three different orders, Suliformes, Charadriiformes
bald eagle (Haliaeetus leucocephalus) [7]. Likewise, an undescribed Sarcocystis species causing encephalitis has been detected in an immature northern goshawk (Accipiter gentilis atricapillus) from Minnesota [4]. Recently, S. wobeseri was identi ed in pectoral and cardiac muscles of the white-tailed sea eagle (Haliaeetus albicilla) [10]. Thus, sarcocysts of two Sarcocystis species, S. falcatula and S. wobeseri, were recorded in the tissues of birds of prey [7,10]. Three morphological types of sarcocysts were detected in the Eurasian buzzard (Buteo buteo) and the long-eared owl (Asio otus), and the third type of sarcocyst distinguished in the owl was recognised as S. otus [11]. However, this species is considered to be invalid [1].
The present paper describes a molecular identi cation of S. halieti in the muscles of birds of prey from Spain.
Methods
In the period between 2019 and 2020, leg muscles of 59 birds of prey (Accipitriformes, Falconiformes and Strigiformes) from Navarra (Spain) were examined for Sarcocystis ( Table 1). The analysed samples come from the birds admitted to the Wildlife Recovery Centre of Ilundain (Navarra). The samples were taken by the Centre's veterinary staff, during the routine diagnostic protocol of the cause of death of the specimens that enter the Centre dead or die there. This Centre belongs to the Government of Navarra and is managed by public company GAN-NIK. Muscle samples were kept frozen (-20ºC) until a morphological detection of sarcocysts. The prevalence and infection intensity of Sarcocystis were evaluated in methylene-blue stained muscle samples as previously described [12]. Genomic DNA was isolated from individual sarcocysts using the GeneJET Genomic DNA Puri cation Kit (Thermo Fisher Scienti c Baltics, Vilnius, Lithuania). Partial 28S rDNA was ampli ed with the help of the KL-P1F/KL-P2R primer pair [13] and the complete ITS1 (internal transcribed spacer 1) region was ampli ed using the SU1F/5.8SR2 primer pair [14]. The PCRs were conducted as described previously [15].
Visualisation, puri cation, and sequencing of PCR products were carried out using the previously described protocol [16]. The sequences obtained in this study were compared with those of various Sarcocystis spp. using the nucleotide BLAST program (megablast option) [17]. The multiple alignment was conducted using the MUSCLE algorithm loaded in MEGA7 software [18]. Selection of a nucleotide substitution model and phylogenetic analysis under Bayesian inference were carried out using TOPALi v2.5 [19].
In leg muscles of one of the black kites (Milvus migrans) sarcocysts were detected in methylene-blue stained muscle samples; however, they were not observed in fresh-squashed samples. Therefore, the muscle sample of this bird was digested with pepsin according to the modi ed protocol of Dubey et al. [1]. Five grams of leg muscles were cut into small pieces and suspended in 15 ml of saline solution (0.9%). The entire substance was homogenized in a commercial blender at top speed for 2 min with breaks. The homogenate was transferred into a 150 ml ask and 15 ml of digestion solution was added to it (pepsin, 0.26 g; NaCl 0.5 g; water up to 15 ml and 37% HCl to pH 1.1). The entire substance was incubated at 37°C for 2 hours and the suspension was used for DNA extraction. Genomic DNA was extracted as described above. External PCR primers were SU1F/5.8SR2 [14], meanwhile internal primers GsShalF1 (5′-GATAATTGACTTTACGCGCCATTAC-3′) and GsShalR1 (5′ GTGCACATCCATATATGCTCATTCT-3′) were designed in the present study. The rst run of a nested PCR assay was conducted as described by [15]. The second run of a nested PCR was carried out in the nal 12.5 µl volume consisting of 6.3 µl of DreamTaq PCR Master Mix, 0.5 µM of each primer, 1 µl from the rst run of PCR, and nuclease-free water. similarity to Sarcocystis sp. from the Cooper's hawk (Accipiter cooperii) (KY348755), and 92.3-92.5% similarity to S. columbae from the herring gull (MN450338-MN450339) and from the woodpigeon (Columba palumbus) (GU253885, HM125052). In ITS1 phylogenetic tree, the obtained sequences of Sarcocystis from the black kites and the western marsh harrier were placed in one cluster together with S. halieti and Sarcocystis sp. from the Chilean skuas (Fig. 1). It should be noted that the sequence of Sarcocystis from the black kite (MmEs1) formed a sister branch to other S. halieti sequences. The 1488 bp 28S rRNA sequence of Sarcocystis from the black kite (MmEs1) differed in 1-2 SNP from those of S. halieti (JQ733512, MF946610, MH130210) and in 7 SNP from those of S. columbae (HM125053), while 1508 bp sequence of Sarcocystis from the western marsh harrier (CaEs1) demonstrated 99.3-100% identity with S. halieti. Thus, on the basis of a molecular examination, S. halieti was identi ed in two black kites and a single western marsh harrier.
Discussion
The present study revealed new IH record for S. halieti. This Sarcocystis species was identi ed in the black kite and the western marsh harrier for the rst time. Thus far, S. halieti have not been observed in the muscles of birds of prey. Previously, S. halieti was detected in the great cormorant [20] and the herring gull [21]. The results of the present study extend the knowledge of S. halieti speci city for the IH and indicate that this species could form sarcocysts in the birds belonging to at least three different orders, Accipitriformes (present study), Charadriiformes [21] and Suliformes [20]. More avian Sarcocystis species, S. calchasi, S. columbae, S. falcatula, S. wobeseri can form sarcocysts in IH belonging to different orders [1,10,21,22]. The development of molecular research and expansion of the diversity of the examined host species leads to the detection of the known Sarcocystis species in different bird orders [22]. Such investigations are particularly important in terms of pathogenic species. It should be noted that highly pathogenic Sarcocystis species, such as, S. neurona, S. canis, S. felis, S. calchasi, S. falcatula are multihost speci c [1]. Up to date, it is not known whether S. halieti is pathogenic. Therefore, extensive histopathological studies of this species are recommended.
Sarcocysts of S. halieti detected in muscles of birds of prey seemingly differed morphologically from those previously described in other IH. For comparative purposes, sarcocysts of S. halieti from the great cormorant were very long, up to 6.5 × 0.1 mm [20], whereas sarcocysts from the herring gull were from 3960 µm to 7930 µm in length and from 43 µm to 128 µm in width [21]. Sarcocysts identi ed from the black kites and the western marsh harrier were shorter and wider (1050-2160 × 130-158 µm). Different shapes of S. halieti sarcocysts may be associated with a diverse type of the anatomical structure of a host. The distribution of muscle forces of accipitrids, falconids and strigiforms tend to possess greater proportions of distally inserted digital exor musculature (53-64%, on average) [23].
Due to a lack of a comprehensive microscopical examination it is di cult to compare morphologically the sarcocysts of S. halieti identi ed in the present work with those observed in other birds of prey. Two types of sarcocysts have been reported in the bald eagles from the USA [24]. The rst type of sarcocyst was microscopic, had a thin cyst wall with spines and contained bradyzoites 5 × 1 µm in size. Type II microscopic sarcocysts were immature and had a 2 µm thick striated cyst wall [24]. These sarcocysts were not similar to those observed in our study. By contrast, type II sarcocysts detected in the Eurasian buzzard [11] measured 694-1850 × 42-235 µm, had a seemingly smooth cyst wall and resembled S. halieti. Also, histologically thin walled (0.5 µm) sarcocysts having a smooth surface with no visible protrusions were found in the cardiac muscle of the white-tailed sea eagle [25]. The length of the sarcocysts was not determined, however, the diameter of the largest cross-sectioned cyst was 40 µm.
Subsequently, S. wobeseri was identi ed in the muscles of the white-tailed sea eagle from the UK [10]. Based on the current knowledge, sarcocsyts of S. halieti and S. wobeseri are morphologically indistinguishable [21]. Lastly, thin-walled (≤ 1 µm) and thick-walled (2-4 µm) sarcocysts were detected in the muscles of raptors from the south-eastern USA, however, no detailed microscopical examination was performed [8]. The most recent studies on Sarcocystis from birds of prey focused on diagnosis of this apicomplexan genus using muscle digestion and subsequent nested PCR [9]
Conclusions
In the present study, S. halieti was identi ed in the black kite and the western marsh harrier from Navarra (Spain) by means of 28S rDNA and ITS1 sequence analysis. This is a third Sarcocystis species detected in the muscles of birds of prey. Studies on Sarcocystis spp. from birds of prey are fragmentary. Therefore, further complex morphological, histopathological and molecular methods should be employed to provide a comprehensive description of Sarcocystis found in birds of prey. Declarations Conceptualization, P. P., A. B., S.Š and D. B.; formal analysis, P. P. and S.Š.; investigation, E. J. N., A. B., P. P. and D. B.; resources D. V. and I. O.; writing-original draft preparation, P. P., A. B., E. J. N., S.Š. and D. B.; writingreview and editing, P. P., A. B., E. J. N., S.Š. and D. B.; visualization P. P. and E. J. N.; supervision, P. P. and D. B.; project administration, P. P. and S.Š.; funding acquisition, A. B. P. P. and S.Š. All authors have read and agreed to the published version of the manuscript.
Funding
Not applicable.
Availability of data and materials
Data supporting the conclusions of this article are included within the article. The 28S rRNA and ITS1 sequences generated in the present study were submitted to the GenBank database under accession numbers MW926916-MW926917 and MW929599-MW929601, respectively.
Ethics approval and consent to participate
For this type of formal study consent is not required.
Consent for publication
Not applicable.
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Domain: Environmental Science Biology
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The CLAVATA receptor FASCIATED EAR2 responds to different CLE peptides by signaling through different downstream effectors
Meristems contain groups of indeterminate stem cells that are critical for organ initiation throughout plant development. The shoot apical meristem (SAM) maintains itself and initiates all shoot organs, such as leaves, floral organs and axillary branch meristems. Development and balanced proliferation of the SAM is regulated by a feedback loop between CLAVATA (CLV) and WUSCHEL (WUS) signaling. CLV signaling is initiated by secretion of the CLV3 peptide ligand, which is perceived directly or indirectly by a number of Leucine-Rich-Repeat (LRR) receptor-like kinases, including CLV1 and BARELY ANY MERISTEM (BAM) 1-3, and RECEPTOR-LIKE PROTEIN KINASE 2 (RPK2), as well as the receptor-like protein CLV2 in a complex with the CORYNE (CRN) pseudokinase. However, CLV2, and its maize ortholog FASCIATED EAR2 (FEA2) appear to function in signaling by several related CLV3/EMBRYO-SURROUNDING REGION (CLE) peptide ligands, including CLV3. Nevertheless, it remains unknown how CLV2 or FEA2 transmit specific signals from distinct CLE peptides. Here we show that FEA2 is involved in signaling from at least 2 distinct CLE peptides, ZmCLE7, a maize CLV3 ortholog, and ZmFON2-LIKE CLE PROTEIN1 (ZmFCP1), a newly identified CLE peptide functioning in SAM regulation. Signaling from these 2 different CLE peptides appears to be transmitted through 2 different candidate downstream effectors, COMPACT PLANT2 (CT2), the alpha subunit of the maize heterotrimeric G protein, and maize CRN. Our data provide a novel framework to understand how diverse signaling peptides can activate different downstream pathways through common receptor proteins.
Abstract.
Meristems contain groups of indeterminate stem cells that are critical for organ initiation throughout plant development. The shoot apical meristem (SAM) maintains itself and initiates all shoot organs, such as leaves, floral organs and axillary branch meristems. Development and balanced proliferation of the SAM is regulated by a feedback loop between CLAVATA (CLV) and WUSCHEL (WUS) signaling. CLV signaling is initiated by secretion of the CLV3 peptide ligand, which is perceived directly or indirectly by a number of Leucine-Rich-Repeat (LRR) receptor-like kinases, including CLV1 and BARELY ANY MERISTEM (BAM) 1-3, and RECEPTOR-LIKE PROTEIN KINASE 2 (RPK2), as well as the receptor-like protein CLV2 in a complex with the CORYNE (CRN) pseudokinase. However, CLV2, and its maize ortholog FASCIATED EAR2 (FEA2) appear to function in signaling by several related CLV3/EMBRYO-SURROUNDING REGION (CLE) peptide ligands, including CLV3.
Nevertheless, it remains unknown how CLV2 or FEA2 transmit specific signals from distinct CLE peptides. Here we show that FEA2 is involved in signaling from at least 2 distinct CLE peptides, ZmCLE7, a maize CLV3 ortholog, and ZmFON2-LIKE CLE PROTEIN1 (ZmFCP1), a newly identified CLE peptide functioning in SAM regulation. Signaling from these 2 different CLE peptides appears to be transmitted through 2 different candidate downstream effectors, COMPACT PLANT2 (CT2), the alpha subunit of the maize heterotrimeric G protein, and maize CRN. Our data provide a novel framework to understand how diverse signaling peptides can activate different downstream pathways through common receptor proteins.
Introduction.
Stem cell proliferation and differentiation throughout plant life is regulated by a feedback loop between the homeodomain transcription factor WUS and CLV ligand-receptor signaling (Mayer et al. 1998;Brand et al. 2000;Schoof et al. 2000;Yadav et al. 2011;Daum et al. 2014). The secretion of the diffusible glycopeptide CLV3 from the central zone (CZ) stem cells of the SAM is believed to initiate signaling through LRR receptors (Fletcher et al. 1999;Rojo et al. 2002;Kondo et al. 2006;Ohyama et al. 2009;Nimchuk et al. 2011b), which transmit the signal to restrict the expression of WUS in the organizing center (OC) cells. To balance this system, WUS non-cell-autonomously promotes stem cell fate by activation of CLV3 expression (Yadav et al. 2011;Daum et al. 2014). CLV3 is thought to be perceived by multiple receptor kinase and receptor like proteins, including the CLV1 LRR receptor-like kinase (Clark et al. 1993;Clark et al. 1997;Brand et al. 2000;Ogawa et al. 2008) and the related BAM receptors (DeYoung et al. 2006;Deyoung and Clark 2008;Nimchuk et al. 2015;Shinohara and Matsubayashi 2015), or by a heterodimer of the receptor like protein CLV2 and the transmembrane pseudokinase CRN (Kayes and Clark 1998;Jeong et al. 1999;Miwa et al. 2008;Muller et al. 2008;Bleckmann et al. 2010;Zhu et al. 2010;Nimchuk et al. 2011a), or by the receptor-like kinase RPK2 (Mizuno et al. 2007;Nodine et al. 2007;Kinoshita et al. 2010). The relationship between CLV1 and CLV2 is not clear-CLV1 can form homodimers, or higher order complexes with CLV2/CRN, to signal co-operatively in the SAM (Guo et al. 2010;Somssich et al. 2015), but it seems that CLV2/CRN is not essential for CLV3 perception or for CLV1 signaling (Muller et al. 2008;Nimchuk et al. 2011b;Nimchuk 2017). In contrast to CLV1, CLV2 does not bind CLV3 peptide directly (Shinohara and Matsubayashi 2015), and its expression is not restricted to the SAM, suggesting that it might function as a co-receptor in additional pathways beyond CLV3 signaling. Indeed, CLV2 appears to be involved in signaling by several CLE peptides (Fiers et al. 2005;Meng and Feldman 2010;Hazak et al. 2017) and in biotic interactions (Replogle et al. 2011;Hanemian et al. 2016), suggesting it plays diverse functions in plant development and immunity (Pan et al. 2016). The multiple roles of CLV2 promote the question of how it confers signal specificity. Two candidate downstream effectors of CLV2 have been identified. One is the transmembrane pseudokinase CRN, discovered in Arabidopsis, and the second is COMPACT PLANT2 (CT2), the heterotrimeric G protein alpha subunit, discovered in maize (Bommert et al. 2013a). However, since CRN and CT2 were identified in different species, their molecular and genetic interactions remain unknown.
How specificity is achieved is a common question in signal transduction pathways. Recently, we identified a distinct CLV receptor, FASCIATED EAR3 (FEA3) in maize and Arabidopsis, and found that FEA3 controls responses to the maize FCP1 (ZmFCP1) CLE peptide ). Here, we show that the maize CLV2 ortholog FEA2 also participates in ZmFCP1 signaling, in addition to controlling responses to the maize CLV3 ortholog, ZmCLE7 . To ask how specificity from these different CLE peptide inputs is achieved, we first isolated mutant alleles of the maize CRN gene. (Miwa et al. 2008;Muller et al. 2008;Bleckmann et al. 2010;Zhu et al. 2010;Nimchuk et al. 2011a), we found that fea2 was epistatic to Zmcrn in control of meristem size, but Zmcrn;ct2 double mutants showed an additive enhanced phenotype, suggesting they act in parallel pathways, despite the fact that FEA2 binds both ZmCRN and CT2 in co-immunoprecipitation (co-IP) experiments. Strikingly, ct2 and Zmcrn mutants were resistant to different CLE peptides, ZmCLE7 and ZmFCP1, respectively, but fea2 was resistant to both, suggesting that FEA2 controls responses to different CLE peptides by acting through different downstream effectors.
Results
Both fea3 and fea2 mutants are resistant to the ZmFCP1 peptide We recently described a new CLE signaling pathway in maize, in which ZmFCP1 peptide signals through FEA3 to restrict ZmWUS1 expression from below its organizing center expression domain . To test this model, we used a 2-component transactivation system (Wu et al. 2013;Je et al. 2016) to drive ZmFCP1 expression in developing primordia, below the ZmWUS1 domain Nardmann and Werr 2006). As previously described, this expression reduced meristem size of wild type SAMs ), however we found that meristem size was only partially rescued when ZmFCP1 expression was transactivated in a fea3 mutant background ( Figure 1A and B), suggesting that ZmFCP1 signals through additional receptors. We therefore conducted peptide response assays using fea2 mutants, and found that they were also insensitive to ZmFCP1 peptide treatment, as well as to ZmCLE7, the maize CLV3 ortholog ( Figure 1C) ). Interestingly, fea2;fea3 double mutants restored the size of ZmFCP1 treated meristems to control levels, suggesting that ZmFCP1 signaling is transmitted predominantly through both LhG4 driver led to a strong reduction in vegetative SAM size as compared to a non-transgenic control, but this effect was only partially rescued in a fea3 mutant background; SAM diameter was quantified (B). In CLE peptide treatments, fea2 mutants were resistant to ZmFCP1, as well as to ZmCLE7 (C), and fea3;fea2 double mutants showed additive resistance to ZmFCP1, restoring SAM size to normal (D). Scale bars; 100 µm in A. n = 20 (B, C) and 30 (D) plants for each genotype. Data in B, C and D are shown as means ± s.d. and shown as absolute value as well as % with each untreated control set to 100%: ***P value < 0.0001, two-tailed, two-sample t test.
FEA2 and FEA3 ( Figure 1D). fea3 mutants are resistant only to ZmFCP1, and not to ZmCLE7 ), so we next asked how FEA2 might transmit signals from different CLE peptides.
Zmcrn mutants are fasciated this Mu insertion line 3 times to the standard B73 inbred line, and dissected homozygous mutant or normal sib samples for meristem analysis. The maize crn (Zmcrn) mutants had larger vegetative shoot meristems (130 ± 4.1 µm, compared to 109 ± 4.6 µm for normal sibs, P value <0.0001, two-tailed t test, Figure ask if it is also associated with this yield trait. We conducted a candidate gene association study using a maize association panel of 368 diverse inbred lines Liu et al. 2015). We found that three SNPs in the 3'UTR region of CRN showed significant association with KRN in multiple environments, below the threshold P-value < 0.001 (Figure supplement 4 and ZmCRN and FEA2 function in a common pathway In Arabidopsis, CRN is thought to signal downstream of CLV2 and correspondingly the double mutants show an epistatic interaction (Muller et al. 2008). To ask if this relationship was conserved in maize, we measured the SAM size in a segregating double mutant population. As expected, both Zmcrn and fea2 vegetative meristems were larger than normal (166.3 ± 8.3 µm, or 176.1 ± 9.8 µm respectively, compared to 139.7 ± 4.8 µm for normal sibs, P value <0.0001, two-tailed t test, Figure 3A and B), and the Zmcrn; fea2 double mutants (177.2 ± 13.3 µm) were similar to the fea2 single mutants (Figure 3A and B). We also characterized ear inflorescence meristems and found that fea2 had stronger fasciated ears than those of Zmcrn, but the double mutants resembled fea2 single mutants ( Figure 3C) 18.6 µm, P value <0.0001, two-tailed t test, Figure 3D and E), suggesting an additive interaction. Zmcrn; ct2 double mutant ear inflorescences also showed additive enhancement in fasciation, compared to each single mutant (Figure 3F), confirming the additive interaction between ct2 and Zmcrn. In summary, double mutant analyses and quantification of meristem sizes indicated that ZmCRN functions in the same pathway as FEA2 and, as previously reported, CT2 also functions in the same pathway as fea2 (Bommert et al. 2013a), but CT2 and ZmCRN themselves function in different pathways. This result is most easily explained by the hypothesis that FEA2 functions in two different pathways, one with CT2 and a second with ZmCRN.
FEA2 interacts physically with CT2 and with ZmCRN
To test the two-pathway hypothesis, we tested protein-protein interactions using co-IP assays. We used an internal YFP fusion of CT2 that we found to be biologically active was able to pull down FEA2-Myc, but not CT2-YFP, even when FEA2-YFP was also co-expressed ( Figure 4B). We confirmed that CT2-YFP was properly expressed, because it could pull down FEA2-Myc (Figure 4C), as previously demonstrated in in vivo co-IPs (Bommert et al. 2013a segregating populations were grown in the presence of different peptides, and shoots fixed and cleared for SAM measurements after 12 days. We found that ct2 mutants were partially resistant to ZmCLE7, but not to ZmFCP1 peptide ( Figure 5A and B), suggesting that CT2 functions specifically in signaling by ZmCLE7, the maize CLV3 ortholog. In contrast, we found that Zmcrn mutants were partially resistant to ZmFCP1, but not to ZmCLE7 (Figure 5C and D), suggesting that ZmCRN functions specifically in a Embryos of each genotype were cultured with control, scrambled peptide (sCLV3) or with ZmFCP1 or ZmCLE7. Wild type SAM growth (double-headed arrows) was strongly inhibited by all peptides except sCLV3, and ct2 growth was insensitive only to ZmCLE7 peptide (A), whereas Zmcrn was insensitive only to ZmFCP1 peptide (C); SAM diameter was quantified (B, D). In treatments with both ZmFCP1 and ZmCLE7, only fea2 showed resistance, but Zmcrn or ct2 did not. Scale bars: 100 µm in A, C and E. N = 25 (C) plants for each genotype. Data in B, D and F are shown as means ± s.d.: ***P value < 0.0001, two-tailed, two-sample t test, NS, not significant ZmFCP1 signaling pathway. To confirm these results, we treated each mutant with both ZmCLE7 and ZmFCP1 together. We found that only fea2, but not ct2 or Zmcrn mutants, showed resistance to the double peptide treatment (Figure 5E and F). Together, these results suggest that FEA2 functions in both ZmCLE7 and ZmFCP1 signaling pathways, but CT2 and ZmCRN function specifically in ZmCLE7 or in ZmFCP1 signaling, respectively.
In summary, through identification of maize crn mutants, we were able to show that signaling through FEA2 by two different CLE peptides is differentiated using different candidate downstream signaling components; with the ZmCLE7 signal passing through CT2 and the ZmFCP1 signal passing through ZmCRN (Figure 6). 2008; Guo et al. 2010;Meng and Feldman 2010;Je et al. 2016;Hazak et al. 2017). So how is the information conferred by these different signals kept separate during transmission through a common receptor?
Discussion
To address this question and further decipher the FEA2 signaling pathway, we isolated mutants in the maize CRN ortholog, ZmCRN, by reverse genetics and by cloning a newly identified fasciated ear mutant fea*148. ZmCRN was predicted to encode a membrane localize pseudokinase, like CRN in Arabidopsis (Nimchuk et al. 2011a), and characterization of the mutants indicated that ZmCRN similarly functions as a negative regulator of stem cell proliferation. We found that fea2 was epistatic to Zmcrn, and FEA2 and ZmCRN interacted physically, suggesting that ZmCRN is a signaling component in the FEA2 pathway. Natural variation in the CLV-WUS pathway underlies yield improvements in different crop species including tomato, maize and mustard (Bommert et al. 2013b;Fan et al. 2014;Xu et al. 2015;Je et al. 2016), and FEA2 is a quantitative trait locus (QTL) for kernel row number (KRN) (Bommert et al. 2013b). In this study, we used a maize association panel of 368 diverse inbred lines to show that ZmCRN also has significant association with KRN under multiple environments Liu et al. 2015), suggesting that ZmCRN contributes to quantitative variation in this trait. Therefore, ZmCRN could be manipulated for maize yield enhancement.
Previously, we identified the alpha subunit of the heterotrimeric G protein, CT2, as an additional interactor of FEA2. fea2 is epistatic to ct2 in meristem regulation, similar to its genetic interaction with (Nimchuk 2017), and that CLV2/CRN can function with other CLE ligand-receptor complexes (Hazak et al. 2017). However, in Arabidopsis CRN is required for CLV2 trafficking to the plasmamembrane (Bleckmann et al. 2010). Our results suggest that the maize CLV2 ortholog FEA2 still functions (with CT2) in a crn mutant, so is presumably on the plasmamembrane even in the absence of ZmCRN.
How then can a single receptor recognize different signals and transmit them differentially?
The most obvious answer depends on the idea that FEA2 and CLV2 are co-receptors that function with LRR RLKs, which binds CLE peptides directly (Figure 6). This idea is supported by the finding that CLV1 binds CLV3 with high affinity, but CLV2 is unable to bind CLE peptides (Shinohara and Matsubayashi 2015), and that CLV2/CRN can function with different CLE ligand-receptor complexes (Hazak et al. 2017). There are conflicting results surrounding the interaction between CLV2 and CLV1; some experiments detect their physical interaction, but many of them use over-expression and are prone to false positive results, and in double mutant combinations clv2 and clv1 mutants act additively (Kayes and Clark 1998;Muller et al. 2008). This genetic result suggests they act separately, and the same is true for the orthologs FEA2 and TD1 in maize (Bommert et al. 2005). A possible explanation for these conflicting findings is that CLV2 may act with multiple CLE receptor RLKs. This model is supported by the observation that CLV1 homologs, the BAMs, function redundantly with CLV1, so multiple LRR RLKs do indeed function in meristem size control. Why then are clv2 phenotypes weaker than clv1? Maybe clv2, like clv1, has functional homologs. The use of CRISPR to make simultaneous multiplex gene knockouts should help solve these mysteries.
Despite not knowing the details of the CLE-receptor interactions, our data show that FEA2 can specifically transmit different peptide signals through two distinct downstream components that most likely converge on the regulation of ZmWUS expression to regulate stem cell proliferation in meristem development (Figure 6). This suggests a new working model for meristem size regulation, in which ligand binding can be transmitted by a common co-receptor working with different RLKs coupled to distinct signaling proteins. Our model differs from most well-studied ligand-receptor signaling pathways, in which the signaling pathways usually converge (Couto and Zipfel 2016 Nam and Li 2002;Chinchilla et al. 2007;Lu et al. 2010;Wang 2012;Sun et al. 2013).
Our study also highlights another source of variation in meristem receptor signaling by highlighting the role of an additional CLE peptide, ZmFCP1. The role of FCP1 in meristem maintenance has been characterized in both maize and rice (Suzaki et al. 2008;Je et al. 2016).
In summary, multiple receptor signaling pathways appear to be required to for the perception of different CLE peptide signals to fine tune meristem development. This complex system of multiple peptides, receptors and downstream components presumably confers robustness on the meristem structure, as well as providing flexibility to control meristem development according to different physiological or developmental cues. For example, meristem size responds to stress and developmental transitions, such as floral induction, and different signaling pathways may confer such responsiveness. Our results help explain how meristem size regulation is orchestrated by multiple CLE peptides and receptors, as observed in many species including Arabidopsis, rice, maize and tomato (Ito et al. 2006;Strabala et al. 2006;Suzaki et al. 2009;Nimchuk et al. 2015;Xu et al. 2015). They also support the idea that meristem signaling components are highly conserved between diverse plant species, and a major challenge is to understand how differential regulation of these common components leads to diversity in meristem organization and size across diverse plant taxa.
Plant growth and map based cloning.
Maize plants were grown in the field or in the greenhouse. To measure meristem size, segregating siblings were genotyped and shoot apices of 7-day-old plants ( Figure 2B) or 21-day-old plants ( Figure 3A and D) were dissected, cleared and measured as described previously (Taguchi-Shiobara et al. 2001). All measurements included at least 10 samples of each genotype, and two or three independent biological replicates, and mean values ± s.d. were presented, with significance calculated using two-tailed, two-sample t tests, and significant differences reported as P values.
Imaging.
Scanning electron microscopy was performed on fresh tissues of maize using a Hitachi S-3500N SEM, as described (Taguchi-Shiobara et al. 2001). For confocal microscopy, tobacco infiltrated tissues were dissected and images were taken with a Zeiss LSM 710 microscope, using 561nm laser excitation and 580-675nm emission for detection ZmCRN-mCherry. Subsequently for plasmolysis, leaf tissues were incubated for 30 min with 800mM mannitol and imaged.
Double mutant analysis.
Double mutants were constructed by crossing mutants introgressed into B73, followed by selfing or backcrossing to the F1. All plants were subsequently genotyped (primers are listed in S2 Table).
Protein expression and co-IP assays.
CT2-YFP, ZmCRN-mCherry, or FEA2-Myc expression constructs were infiltrated into 4-week-old
Nicotiana benthamiana leaves together with a P19 plasmids to suppress posttranscriptional silencing (Mohammadzadeh et al. 2016). The protein extraction and membrane fraction enrichment were described in Bommert et al., 2013. Briefly, the infiltrated leaves were harvested 3-d post infiltration. The leaf tissues were ground in liquid nitrogen to a fine powder then suspended in twice the volume of protein extraction buffer containing 150 mM NaCl, 50 mM Tris-HCl pH 7.6, 5% glycerol, and EDTA-free Protease inhibitor cocktail (Roche). After filtration through Miracloth, and centrifugation at 4,000g for 10 min at 4 o C, the extract was centrifuged at 100,000g for 1h at 4 o C to enrich the microsomal membrane fraction.
The resulting pellet was resuspended in the extraction buffer supplemented with 1% Triton X-100.
Lysates were cleared by centrifugation at 100,000g for 30 min at 4 o C to remove non-solubilized material.
ZmCRN-mCherry was immunoprecipitated uisng RFP-Trap (Chromotek) in membrane solubilization buffer for 40 min followed by washing 3 times with 1 ml of the same buffer. The IP'd proteins were eluted with 50 µl 1xSDS loading buffer at 95 o C, followed by standard SDS-PAGE electrophoresis and western blotting. CRN-mCherry was detected using an anti-RFP antibody (Rockland, 600-401-379), FEA2-Myc was detected using an anti-Myc antibody (Millipore, 05-724), and CT2-YFP was detected using an anti-GFP antibody (Roche, 11814460001). The co-IP experiment between ZmCRN-mCherry and FEA3-Myc was performed by the same protocol.
Peptide assays.
Maize embryos segregating for each mutant were dissected at 10 days after pollination, when the SAM was exposed, and cultured on gel media (Bommert et al. 2013a) containing scrambled peptide (30µM; Genscript) or ZmFCP1 peptide or ZmCLE7 peptide or a mixture of ZmCLE7 and ZmFCP1 peptides . After 12 days, the tissues were harvested for genotyping and the embryos were fixed in FAA (10%, formalin, 5% acetic acid, 45% ethanol) and cleared in methyl salicylate, and SAMs measured by microscopy, as described . Experiments used at least 10 embryos per genotype, and were replicated in triplicate.
Two-components transactivation assay.
The two-component transactivation assay was performed as described , and the lines were backcrossed into the fea3 mutant background. To measure meristem size, segregating siblings were genotyped and shoot apical meristems of 14-day-old plants ( Figure 1A) were dissected, cleared and measured as described previously (Taguchi-Shiobara et al. 2001).
Association analysis of the ZmCRN locus.
The candidate gene association analysis of ZmCRN with the kernel row number (KRN) trait was conducted in a maize association panel with 368 diverse inbred lines (Liu et al. 2015). The association between ZmCRN and KRN was established by a mixed linear model corrected by population structure, with P-value < 0.001 as threshold Li et al. 2013).
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Domain: Environmental Science Biology
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Enhancement of the expression of defense genes in tomato against Ralstonia solanacearum by N-octanoyl-L-homoserine lactone
Many Gram-negative bacteria use N-acyl-homoserine lactone (AHLs) as quorum sensing (QS) signalling molecules to monitor their population density and to regulate gene expression in a density dependent manner. Recently, it has been shown that AHLs are detected by the plants and they trigger plant defense responses. In this study, N-octanoyl-L-homoserine lactone (C8-HSL) has been used as resistant inducer against bacterial wilt disease of tomato caused by Ralstonia solanacearum. The present investigation focused on the role of defense related enzymes (phenylalanine ammonia lyase, peroxidase, polyphenol oxidase and lipoxygenase) in imparting resistance in tomato against R. solanacearum. Activities of these defense enzymes, increased in C8-HSL treated tomato plants, which were challenged with R. solanacearum. The transcripts accumulation studies for these enzymes were carried out using semi-quantitative reverse transcription PCR, with maximum mRNA accumulation in resistant cultivar upon treatment with C8-HSL. Quantitative real time-PCR (qRT-PCR) confirmed the maximum induction of all these four genes in C8-HSL treated plants. However, the expression of defense genes was higher in C8-HSL treated resistant cultivar than that of susceptible cultivar. Therefore, the results support the view that C8-HSL molecule enhances disease protection against R. solanacearum infection in tomato through the activation of defense genes.
INTRODUCTION
Many bacteria use small signalling molecule to communicate with each other and to co-ordinate their growth activities, this process is commonly referred to as quorum sensing (QS). The most common signalling molecules in Gram negative bacteria are N-acyl-Lhomoserine-lactones (AHLs). The AHLs are composed of a conserved homoserine lactone moiety and a fatty acid side chain that can vary in length (4 to 18 carbon atoms) (Decho et al., 2011). To date, approximately 50 types of AHL signalling molecules are found in bacteria (Jin et al., 2012). Recent reports indicate that bacteria commonly associated with plants are capable of producing a variety of AHLs (Cha et al., 1998). However, only little is known about the molecular ways of plants reacting with these bacterial QS signals. Bacteria like Xanthomonas oryzae pv.oryzae, Ralstonia solanacearum, Pseudomonas syringae and Dickeya didantii cause disease on plants. In recent years, evidence has accumulated that AHL molecules are able to function as priming agents. AHL molecules induce resistance against a broad spectrum of plant pathogens in different plant species (Schikora et al., 2016). Schenk and Schikora (2015) showed that AHL primed plants, upon a challenge with pathogens accumulate callose and phenolic compounds. Similar to AHL molecules produced by bacteria, commercially available pure AHL molecules also induce priming. The transport of AHLs within plants has been studied initially in Barley and Arabidopsis (Gotz et al., 2007;von Rad et al., 2008) by using radioactive labelled AHLs. Mathesius et al. (2003) observed that the legume plant, Medicago truncatula are able to respond to nanomolar concentrations of synthetic and purified AHLs and these compounds elicit major changes in protein expression. These changes suggest that bacterial QS signalling molecules might regulate the functions of these proteins, which include roles in defense responses of host plants, primary metabolism, plant hormonal response, transcriptional gene regulation, protein processing and activities of the cytoskeleton (Mathesius et al., 2003). Schuhegger et al. (2006) showed that treatment of roots with synthetic N-hexanoyl-L-homoserine lactone (AHLs (C6-HSL)) enhanced the expression of salicylic acid and ethylene dependent defense genes in tomato against the fungal leaf pathogen, Alternaria alternata. A transcriptomic approach by von Rad et al. (2008) in Arabidopsis showed gene expression changes for several hundred genes in shoots and roots in response to 10 µM concentration of C6-HSL. However, if plant can detect low concentration of AHLs, they might be able to respond before pathogen concentration build up (Teplitski et al., 2010). The long chain AHL, oxo-C14-HSL, activates resistance towards different obligate bio-trophic pathogens such as Golovinomyces orontii and Blumeria graminis in Arabidopsis and barley, respectively (Schenk et al., 2014). All these studies demonstrate that AHLs can induce resistance in plant by activating the defense mechanism.
Tomato (Solanum lycopersicum Mill.) is one of the important vegetable grown and consumed worldwide. Tomato is prone to a number of bacterial diseases among which bacterial wilt caused by R. solanacearum (Smith) is a very destructive harmful disease resulting in complete loss of the crop (Vanitha and Umesha, 2008;Prakasha et al., 2016). Control of bacterial wilt has been difficult due to the high variability of the pathogen, high ability to survive in diverse environments and its extremely wide host range. Using chemicals to control plant diseases is hazardous to the environment and living beings, so using biological control can overcome this problem. Schuhegger et al. (2006) results suggest that AHL molecule play an important role in the biocontrol activity of Serratia liquefaciens and other rhizobacteria in tomato, act as mediators of communication between prokaryotes and eukaryotes. AHLs may therefore be considered as potential candidates for a new group of general elicitors of plant defense as they induce expression of typical defense related proteins resulting in increased resistance against pathogen (Venturi and Fuqua, 2013).
Plants possess a range of active defense responses that contribute to resistance against a variety of pathogens. They respond to bacterial pathogen attack by activating various defense responses that are associated with the accumulation of several factors like defense related enzymes and inhibitors that serve to prevent pathogen infection. The interaction between the pathogen and host plant induces some changes in cell metabolism, primarily in the enzyme activities, including that of phenylalanine ammonia lyase (PAL), peroxidase (POX), polyphenol oxidase (PPO), lipoxygenase (LOX), superoxide dismutase and β-1,3-glucanase (Kavitha and Umesha, 2008). These enzymes play a crucial role with respect to the degree of host resistance, by increasing anti microbial activity, bio synthetic processes related to wall development such as phenol, lignification, polymerization of hydroxyproline-rich glycoproteins, regulation of cell wall elongation and wound healing (Belkhadir et al., 2004).
The aim of this study was to investigate whether C8-HSL induce resistance in tomato and also its efficacy in controlling bacterial wilt disease through differential expression of defense genes (PAL, POX, PPO and LOX). The changes in the gene expression patterns were also studied using specific primers for these enzymes. Defense genes were assessed to determine possible relationships between the activation of these enzymes and the protection of plants following treatment with C8-HSL and its mRNA accumulation was measured by quantitative real time-PCR upon challenge inoculation with the pathogen.
Plant material and growth conditions
Seeds of tomato cultivar, resistant (R) (cv. Golden) and susceptible (S) (cv. Rasi) to bacterial wilt were procured from private seed agencies in Mysore, Karnataka, India. From earlier studies in our laboratory, these two cultivars of tomato were selected on the basis of their response to bacterial wilt disease caused by R. solanacearum inoculation (Vanitha and Umesha, 2008). All seed samples were surface sterilized with 3% (v/v) sodium hypochlorite solution for 5 min and washed with distilled water three times.
Treatment with C8-HSL and challenge with R. solanacearum
Four-week-old plants grown in sterilized soil were treated with 10 µM C8-HSL (Cayman, USA) (Schuhegger et al., 2006). A100 x stock in dimethyl sulfoxide (DMSO) was diluted in 5 ml sterile distilled water and pipetted on the soil to avoid contact with shoots and leaves. Control plants received equal amounts of DMSO in water.
The wilt causing R. solanacearum (strain: RS-lpxC-DOB-2) inoculum was prepared by growing bacteria on 2,3,5triphenyltetrazolium chloride (TZC) agar medium for 48 h at 30°C (Kumar et al., 2016). The bacterial cells were collected in sterile distilled water and pelleted by centrifugation at 12,000 rpm for 10 min. The pellet was resuspended in distilled water and bacterial concentration was adjusted to 1 x 10 8 cfu/ml at absorbance 610 nm using UV-visible spectrophotometer (Hitachi U-2000, Japan) according to Ran et al. (2005).15 ml of bacterial suspension was poured onto the soil near the roots of tomato plants.
The four-week-old tomato plants inoculated and uninoculated were harvested at 0, 3, 6, 9, up to 72 h post inoculation (hpi) and stored at -80°C for subsequent analysis.
Enzyme studies
Phenylalanine ammonia lyase (PAL) activity was performed according to Lisker et al. (1983). One gram of tomato seedling, fresh mass was homogenized to fine paste in a pre-chilled mortar with 25 mM Tris-HCl buffer (pH 8.8). The homogenate was centrifuged at 10,000 rpm for 12 min at 4°C and the supernatant was directly used as enzyme source. The enzyme activity was determined by measuring the production of trans-cinnamic acid from L-phenylalanine using spectrophotometer (Hitachi U-2000, Japan). The reaction mixture contained 1 ml enzyme extract, 0.5 ml 50 mM L-phenylalanine and 0.4 ml 25 mM Tris-HCl buffer (pH 8.8). After incubation for 2 h at 40°C, the activity was stopped by the addition of 60 µl 5 M HCl and the absorbance was read at 290 nm against the same volume of reaction mixture without Lphenylalanine which served as blank.
Peroxidase (POX) assay was carried out as described by Hammerschmidt et al. (1982). One gram of fresh mass of plants was homogenized in 1 ml of 10 mM phosphate buffer (pH 6.0) and centrifuged at 10,000 rpm for 12 min at 4°C and the supernatant served as enzyme source. The reaction mixture consisted of 0.25% (v/v) guaiacol in 10 mM potassium phosphate buffer (pH 6.0) containing 10 mM H2O2. Addition of 0.1 ml of crude enzyme extract initiated the reaction and absorbance at 470 nm was measured for 1 min.
Polyphenol oxidase (PPO) activity was determined according to Mayer et al. (1966). The reaction mixture consisted of 1.5 ml of 0.1 M sodium phosphate buffer (pH 6.5) and 0.2 ml of the enzyme extract. The reaction was started with the addition of 0.2 ml of 10 mM catechol. The increase in absorbance was measured at 420 nm for 1 min.
Lipoxygenase (LOX) activity was estimated according to Borthakur et al. (1987). The activity was determined spectrophotometrically by monitoring the appearance of conjugated diene hydroperoxide, absorbing at 234 nm. The reaction mixture contained 2.7 mL of 0.2 M sodium phosphate buffer (pH 6.5), 0.3 ml of 10 mM linoleic acid in Tween 20 and 0.05 ml of the enzyme extract. Protein contents of the extracts were determined according to standard procedure of Bradford (1976) using BSA (Sigma, USA) as standard.
Analysis of defense genes by semi-quantitative reverse transcription PCR
The total RNA from four-week-old plants of resistant and susceptible tomato cultivars based on enzyme assays (PAL, POX, PPO and LOX) were extracted for analysis. RNA isolation was done using RNeasy Plant Mini Kit (QIAGEN, Germany) according to the manufacturer's instruction.
The complementary DNA was synthesised using 2 µg of RNA and first strand cDNA synthesis kit (Thermo Scientific, India). The reverse transcribed RNA was used as PCR template with gene specific primers for all the four genes (PAL, POX, PPO and LOX).18S rRNA gene primer specific to tomato was used as a constitutive control in all gene expression studies (Chandrashekar and Umesha, 2014). All the primer sequences were reconfirmed by BLAST analysis. The primers used are shown in Table 1. Semiquantitative RT-PCR assay conditions were, initial 3 min denaturation at 94°C, followed by 94°C for 30 s, 60°C for 40 s, and 72°C for 1 min and a final extension for 10 min at 72°C. The number of cycles was 35.
Analysis of defense gene expression by quantitative real time PCR
Each qPCR reaction (20 µl) consisted of 1 x SYBR Green (Thermo Scientific, India) PCR master mix, 3 pmol of each primer and 20 ng each of cDNA by using StepOnePlus™ Real Time PCR machine (Applied Biosystems, USA).qPCR steps were: denaturation at 95°C for 10 min, 40 cycles of 15 s at 95°C, 60 s at 60°C. For calculating the fold change in expression of genes in plants, the transcripts in both the control and treated were normalized to 18S rRNA and the difference in the 18S rRNA normalized cycle threshold value (∆∆CT) was used to obtain fold change (Livak and Schmittgen, 2001), with standard error being calculated from three replicated derived from each independent experiment.
Statistical analysis
All enzyme assay experiments were carried out in triplicates. Further, the experimental results were subjected to Duncan's multiple range tests at a significance level of P < 0.05. All statistical tests were performed using SPSS software.
Enzyme studies
Plants have endogenous defence mechanisms that can be induced in response to attack by pathogens. Inducing the plants own defense mechanisms by prior application of AHL (biological inducer) is thought to be a novel plant protection strategy. The temporal changes of all four enzyme activities of 10 µM C8-HSL primed followed by challenge inoculated with the pathogen along with their respective uninoculated controls were assayed. The temporal pattern studies of enzymes were undertaken to estimate the PAL, POX, PPO and LOX highest activities at regular intervals from 0 to 72 h.
In the resistant tomato cultivar, the temporal pattern of PAL enzyme revealed the maximum activity at 9 hpi (140 units) (Figure 1a), while in susceptible tomato cultivar, PAL enzyme revealed the maximum activity at 18 hpi (85 units) (Figure 1b). In the resistant tomato cultivar, the temporal pattern of POX enzyme revealed the maximum activity at 6 hpi (65 units) (Figure 1c), while in susceptible tomato cultivar POX enzyme revealed the maximum activity at 15 hpi (28 units) (Figure 1d). In the resistant tomato cultivar, the temporal pattern of PPO enzyme revealed the maximum activity at 12 hpi (72 units) (Figure 1e), while in susceptible tomato cultivar, PPO enzyme revealed the maximum activity at 24 hpi (35 units) (Figure 1f). In the resistant tomato cultivar, the temporal pattern of LOX enzyme revealed the maximum activity at 6 hpi (40 units) (Figure 1g), while in susceptible tomato cultivar, LOX enzyme revealed the maximum activity at 12 hpi (23 units) (Figure 1h). Therefore, gene expression studies were concentrated on only that particular time interval.
Semi-quantitative reverse transcription PCR
The gene expression pattern was altered when the plants were treated with C8-HSL and challenged with R. solanacearum in both resistant and susceptible tomato cultivars. The PAL, POX, PPO and LOX gene expressions was higher in resistant cultivar when the plants were treated with C8-HSL. The respective controls also expressed the accumulation of gene expression but they were not significant. The housekeeping gene, 18S rRNA expression was found to be unaltered in both resistant and susceptible cultivar in all the treatments (Figure 2).
Gene expression analysis by quantitative real-time PCR
Both resistant and susceptible tomato cultivars were raised and the four-week-old plants were treated with C8-HSL and inoculated with R. solanacearum (concentration of 1 x 10 8 cfu/ml). The gene expression studies were carried out based on the temporal pattern studies of enzymes. Based on the temporal activity of PAL (Figure 1a and b) and followed by semi quantitative RT-PCR (Figure 2); the authors selected 9 and 18 hpi for resistant and susceptible tomato cultivars, respectively. Based on the temporal activity of POX (Figure 1c and d) and followed by semi quantitative RT-PCR (Figure 2); 6 and 15 hpi were selected for resistant and susceptible tomato cultivars, respectively. Based on the temporal activity of PPO (Figure 1e and f) and followed by semi quantitative RT-PCR (Figure 2); 12 and 24 hpi were selected for resistant and susceptible tomato cultivars, respectively. Based on the temporal activity of LOX (Figure 1g and h) and followed by semi quantitative RT-PCR (Figure 2); 6 and 12 hpi were selected for resistant and susceptible tomato cultivars, respectively. For qRT-PCR analysis, the authors have selected the time interval which showed highest activity in both temporal as well RT-PCR analysis for PAL, POX, PPO and LOX gene and the total RNA was isolated and converted into cDNA (Thermo Scientific, India) as per manufacturer's instructions.
In the resistant tomato cultivar, the relative gene expression of PAL was up-regulated to 20 fold upon 10 µM C8-HSL treatment as compared to the control. Whereas pathogen inoculated resistant tomato cultivar up-regulates PAL activity to 16 fold which was significantly increased to 30 fold upon C8-HSL treatment and challenge inoculation with pathogen (Figure 3a). In the case of susceptible tomato cultivar, the relative gene expression of PAL was up-regulated to 12 fold upon 10 µM C8-HSL treatment as compared to the control. Whereas pathogen inoculated susceptible tomato cultivar down-regulates PAL activity to 4 fold which was significantly increased to 20 fold upon C8-HSL treatment and challenge inoculation with pathogen (Figure 3b).
In the resistant tomato cultivar, the relative gene expression of POX was up-regulated to 10 fold upon 10 µM C8-HSL treatment as compared to the control. Whereas, pathogen inoculated resistant tomato cultivar up-regulates POX activity to 5 fold which was significantly increased to 20 fold upon C8-HSL treatment and challenge inoculation with pathogen (Figure 3c). In the case of susceptible tomato cultivar, the relative gene expression of POX was up-regulated to 7 fold upon 10 µM C8-HSL treatment as compared to the control. Whereas pathogen inoculated susceptible tomato cultivar down-regulates POX activity to 2 fold which was significantly increased to 12 fold upon C8-HSL treatment and challenge inoculation with pathogen (Figure 3d).
In the resistant tomato cultivar, the relative gene expression of PPO was up-regulated to 12 fold upon 10 µM C8-HSL treatment as compared to the control. Whereas, pathogen inoculated resistant tomato cultivar up-regulates PPO activity to 7 fold which was significantly increased to 25 fold upon C8-HSL treatment and challenge inoculation with pathogen (Figure 3e). In the case of susceptible tomato cultivar, the relative gene expression of PPO was up-regulated to 9 fold upon 10 µM C8-HSL treatment as compared to the control. Whereas, pathogen inoculated susceptible tomato cultivar down-regulates PPO activity to 1 fold which was significantly increased to 15 fold upon C8-HSL treatment and challenge inoculation with pathogen (Figure 3f).
In the resistant tomato cultivar, the relative gene expression of LOX was up-regulated to 5 fold upon 10 µM C8-HSL treatment as compared to the control. Whereas pathogen inoculated resistant tomato cultivar up-regulates LOX activity to 4.5 fold which was significantly increased to 12 fold upon C8-HSL treatment and challenge inoculation with pathogen (Figure 3g). In the case of susceptible tomato cultivar, the relative gene expression of LOX was up-regulated to 4 fold upon 10 µM C8-HSL treatment as compared to the control. Whereas pathogen inoculated susceptible tomato cultivar down-regulates LOX activity to 1.5 fold which was significantly increased to 9 fold upon C8-HSL treatment and challenge inoculation with pathogen (Figure 3h).
DISCUSSION
Resistance in plants is a highly regulated phenomenon depending on several signalling pathways, each activated by a set of different biotic and abiotic stimuli (Schuhegger et al., 2006). Recently, it has become evident that plants can sense and respond appropriately to bacterial AHLs. It is reasonable that cross-kingdom signalling exits between plants and bacteria because plants and bacteria cohabited the earth for millions of years during which they might have evolved complex signalling networks consisting of different signalling molecules (Jin et al., 2012). In addition, plants seem to be able to detect various AHLs at quite low concentration (Mthesius et al., 2003). Schuhegger et al. (2006) in tomato reported that C6-HSL were able to induce resistance to the fungal leaf pathogen, Alternaria alternata. Mathesuis et al. (2003) found that over 150 proteins of approximately 2000 resolved protein spots were significantly altered in their accumulation in M. truncatula roots after the treatment with low concentration of 3OC12-HSL and 3OC16-HSL. In addition, von Rad et al. (2008) showed that the contact of Arabidopsis roots with C6-HSL resulted in distinct transcriptional change in the roots. Miao et al. (2012) found significant changes in protein accumulation for approximately 6.5% proteins of the total resolved proteins on 2-DE gels after the interaction of Arabidopsis roots with 3OC8-HSL, indicating that the responses of plants to AHLs are quite extensive. However, it is becoming increasingly evident that AHLs plays a positive role in activation of defense gene expression and pathogen defense. These data suggested that AHL play an important role in plant bacterial communication and a possible role in pathogen defense, and the authors decided to analyze the effect of C8-HSL on tomato plant along with R. solanacearum challenge inoculation.
Early and elevated levels of expressions of various defense enzymes are important features of plant resistance to pathogens. This is the first report where the role of defense related enzymes such as PAL, POX, PPO and LOX during the C8-HSL mediated elicitation of resistance in tomato against R. solanacearum was studied. Expression of these defense related enzymes (PAL, POX, PPO and LOX) are known to play a major role in determining the host resistance against various phytopathogens. These enzymes are either directly or indirectly involved in hypersensitive reaction (HR) development (Rusterucci et al., 1999), biosynthesis of cell wall strengthening material (lignin and suberin) and anti-microbial compounds (phytoalexins, furanocoumarin, quinines and pterocarpan) (Daayf et al., 1997), as also signalling molecules (salicylic acid and jasmonic acid) (Creelman and Mullet, 1997;Hammerschmidt, 1999).
Early induction of PAL is more important because it is the first key regulatory enzyme in the phenyl propanoid pathway leading to the production of phytoalexins and phenolic substances (Wang et al., 2004). In this study, maximum PAL activity was 9 h after inoculation (hpi). PAL activity increased in C8-HSL treated tomato plants challenged with the R. solanacearum, while tomato plants inoculated with the R. solanacearum alone had lower PAL activity. The role of PAL in imparting resistance to tomato against bacterial canker disease has been discussed by Umesha (2006). In contrast with the results from the present study, the PAL activity in roots of pepper plants from a resistant cultivar was high than for a susceptible cultivar after inoculation with Phytophthora capsici (Zhang et al., 2013). Iqbal et al. (2005) showed that during the infection of F. solani f. sp.glycines on the roots of soybean plants from a resistant cultivar, the PAL enzyme was up-regulated, and this was not observed in the susceptible cultivar.
POX is a key enzyme in the biosynthesis of lignin, in addition to its antimicrobial activity (Torres et al., 2006). Increased activity of cell wall bound peroxidises has been elicited in different plants due to pathogen infection. In this study, POX activity up-regulated after inoculation and reached its maximum at 6 hpi. Similar to PAL activity, tomato plants inoculated with the R. solanacearum alone recorded lower POX activity than C8-HSL treated plants. Leite et al. (2014) reported that POX activity was higher in the plants of a resistance genotype of common bean in response to Sclerotinia sclerotiorum infection than for a susceptible genotype.
PPO catalyses the oxidation of phenolic compounds to highly toxic quinines which play an important role in plant disease resistance. In this study, the activity of PPO reached maximum at 12 hpi in tomato plants. The PPO activity in plants treated with C8-HSL alone did not reach the level of activity seen in the plants treated with C8-HSL and inoculated with the R. solanacearum. PPO also plays a critical role in tomato's disease resistance to Pseudomonas syringe pv.tomato (Thipyapong et al., 2004).
The lipoxygenese enzyme initiates a metabolic route leading to the synthesis of various antimicrobial compounds involved in plant defense. In this study, the LOX activity was maximum at 6 hpi in plants treated with Jayanna and Umesha 201 C8-HSL and challenged with R. solanacearum. Similar to the above mentioned enzymes, LOX activity were lower in other treatments. However, high LOX activity may constitute in plants resistance to pathogens but with an addition increase upon infection (Devi et al., 2000).
The plants treated with C8-HSL followed by pathogen inoculation accumulated increased amounts of defense enzymes (PAL, POX, PPO and LOX) when compared with untreated control. Similar results were reported when the activities of PAL, POX, PPO, LOX were increased in tomato plants pre-treated with Pseudomonas fluorescens and challenged with R. solanacearum (Vanitha and Umesha, 2011). The RT-PCR studies were carried out to investigate the gene expression pattern of defense related enzymes (PAL, POX, PPO and LOX). The genes were compared with the internal control being 18S rRNA. The 18S rRNA was expressed in both cultivars. The expression of the defense genes was higher in resistant cultivar.
qRT-PCR was performed to evaluate the mRNA accumulation of differentially expressed defense genes in both resistant cultivar and susceptible cultivar. In the resistant tomato cultivar, the relative gene expression of defense genes (PAL, POX, PPO and LOX) was upregulated upon C8-HSL treatment as compared to the control, and significantly increased upon R. solanacearum inoculation. Whereas, in susceptible cultivar, the defense genes (PAL, POX, PPO and LOX) were down-regulated upon R. solanacearum inoculation as compared to the control, and interestingly upregulated upon C8-HSL treatment. Thus, the results show that C8-HSL can induce significant defense gene (PAL, POX, PPO and LOX) in both the tomato cultivar (Figure 3). Our findings were in accordance with Lata et al. (2010) who have showed the relationship of PEGinduced dehydration stress in tolerant and sensitive millet, plants where transcripts showed a differential expression pattern in both cultivars at different time points of stress treatment as analyzed by qRT-PCR. Song et al. (2011) results showed the treatment with abscisic acid (ABA) on tomato against Alternaria solani, effectively reduced disease severity in tomato plants, as enzyme activities were maintained at higher levels in ABA pre-treated and A. solani challenged tomato plants. Tomato defense genes were rapidly and significantly upregulated by ABA treatment which is well correlated with the present study.
In conclusion, the results of the present study confirm that C8-HSL trigger the defense mechanism in tomato plants by activating the defense enzymes and protect itself from the pathogen attack. Thus, this investigation shows that C8-HSL can be considered as potential candidates for elicitors for tomato plants against wilt disease, as they induce expression of typical defense related enzymes resulting in increased resistance against R. solanacearum. To the best of the authors' knowledge, this is the first report on the interaction between the C8-
Figure 1 .
Figure 1. Temporal pattern study of PAL, POX, PPO and LOX activity in resistant (R) and susceptible (S) tomato cultivars. Four-week-old plants were treated with C8-HSL, followed by challenged with pathogen. Both treated and control plants were harvested at different hours after pathogen inoculation, and subjected to enzyme estimation. The data are expressed as the average of three independent experiments with three replicates each. Bars indicate standard errors.
Figure 3 .
Figure 3. Relative expression levels of PAL, POX, PPO and LOX genes in four-week-old plants of both R and S tomato cultivars upon C8-HSL (10 µM) treatment and challenged with the pathogen. Total RNA (2 µg) was used to synthesis the cDNA of which 20 ng of individual cDNA was used to check the fold change of the PAL, POX, PPO and LOX genes which were carried out in three replicates. The gene expression levels were measured by qRT-PCR and normalized to the constitutive 18S rRNA gene. Each bar represents the mean of three independent experiments with standard error. R: resistant (cv. Golden); S: susceptible (cv. Rasi).
Table 1 .
List of primers.
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Domain: Environmental Science Biology
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Genetic Variation of Growth Traits and Genotype-by-Environment Interactions in Clones of Catalpa bungei and Catalpa fargesii f. duclouxii
Clones of Catalpa bungei and Catalpa fargesii f. duclouxii were studied over several years in central China to explore genetic variation in growth traits and to identify clones of high wood yield and high stability. The genetic parameters for height, diameter at breast height (DBH), and stem volume of clones, were estimated. The effect of clone × year on the increment of stem volume in the two species was analyzed by genotype and genotype × environment (GGE) biplot methods. Significant differences in growth traits among clones and between species were found. The growth of C. bungei exceeded that of C. fargesii f. duclouxii after 4 years. Furthermore, from the 5th year, the repeatability and genetic variation coefficient (GCV) of the C. bungei clones were higher than those of the C. fargesii f. duclouxii clones in most cases. The phenotypic variation coefficient (PCV) of the C. fargesii f. duclouxii clones was significantly lower than that of the C. bungei clones. The repeatability of stem volume was intermediate or high in the two species. ANOVA revealed significant effects of the clone by year interaction in these two species. GGE biplot analysis revealed that wood yield and stability were largely independent in C. bungei; clones 22-03, 19-27, and 20-01 were the optimal clones in this species. In contrast, the optimal clones 63 and 128 of C. fargesii f. duclouxii combined the desired characteristics of high yield and high stability. In conclusion, our results indicated that the height and stem volume of C. bungei was under strong genetic control, whereas that of C. fargesii f. duclouxii was influenced by the environment more than by genetic effects. Genetic improvement by clone selection can be expected to be effective, as the repeatability of stem volume was high. Francis and Kannenberg’s method and GGE biplot analysis were used in combination to evaluate the clones. C. bungei clone 22-03 and C. fargesii f. duclouxii clones 63 and 128 were identified as the optimal clones, which exhibited both a high increment of stem volume and high stability.
Introduction
Manchurian catalpa (Catalpa bungei) and Catalpa fargesii f. duclouxii belong to the Catalpa genus of the Bignoniaceae family and are native to China. C. bungei is mainly distributed in the Yellow River and Yangtze River regions. C. fargesii f. duclouxii is distributed within the Yunnan-Guizhou plateau. They are recognized for their straight stems and high quality timber, which is of high density and has high bending strength and hardness. These characteristics make them valuable material for furniture production [1,2]. However, their natural germplasm resources are becoming scarce due to hercogamy and deforestation [3]. Thus, the selection of fast-growing varieties is urgently needed to alleviate the shortage of Catalpa wood.
Tree breeding is the application of genetic, reproductive biology and economics principles to the genetic improvement and management of forest trees. Significant genetic variations among families or clones suggest a strong foundation for genetic improvement of Catalpa trees [4,5]. Clonal forestry has become increasingly important for forestry development [6][7][8][9]. In breeding work, the heritability of a target trait refers to the degree of variation in a phenotypic trait in a population that is due to genetic variation among individuals in that population [10]. Because clones of a single individual have the same genotype, we cannot estimate heritability. However, repeatability can be estimated. Repeatability is a measure of the stability of a trait expressed in a fluctuating environment. The higher the heritability or repeatability is, the greater the genetic control of the trait and the lower the influences of environmental effects [11]. Furthermore, the genetic gain of a selected population can be estimated by heritability or repeatability. Genetic gain can be improved more rapidly with appropriate genetic testing and selection of clones than of families or provenances. However, phenotypic variation arises from variation in individual genetic background and environmental effects [12]. Clones can have stronger genotype-by-environment interactions (GEIs) than families or provenances as a result of their specific genotypes [13]. Environmental effects can be divided into site and year effects. Environmental factors such as temperature, rainfall, atmospheric conditions, soil conditions and biotic factors vary among different sites and among years within sites. For perennial species, year effects should be seriously considered.
The systematic study of GEIs can reduce risk in variety selection and improve production [14,15]. Previously, regression coefficients were frequently used to study GEIs and evaluate trait stability [16,17]. However, this method ignores genetic effects among species and clones. The genotype and genotype × environment (GGE) model overcomes this defect by considering the effects of both genotype and genotype × environment. To date, GGE has been widely used to evaluate the growth stability in crop yield [18][19][20]. However, as the majority of crops are therophytes or biennials, GEI studies of crops mostly focused on site effects. Trees are perennials, and a year represents one growth cycle of a tree. To enhance genetic improvement and maximize clone potential, it is important to analyze the stability of plant growth over years. In our study, clones of C. bungei and C. fargesii f. duclouxii were investigated, and several years of data on clone growth were collected (1) to estimate and compare genetic parameters of growth traits in clones and evaluate the variation in growth traits, (2) to evaluate the stability of clone stem volume across years, and (3) to identify clones with high and stable yield as optimal clones.
Site and Materials
Ramets of 32 clones of C. bungei and 20 clones of C. fargesii f. duclouxii were planted in Laodong Village of Henan Province (32. Table 1. A randomized block design was applied, with 2 ramets in each clone plot and 5 replications. The height and DBH (diameter at breast height) of the clones were measured at the end of each yearfrom 2009 to 2014. Information on the distribution of materials is provided in Figure 1. elevation is 145 m, and the soil of the experimental field is yellow brown loam and has high natural fertility.
Data Analysis
Variation and the genetic parameters (repeatability, clonal variance) were estimated of growth traits among clones for C. bungei and C. fargesii f. duclouxii were analyzed. ASReml-R 3.0 [21] and SAS 9.4 [22] software was used to perform ANOVA, F-tests and evaluation of genetic parameters.
Analysis at a Species Level for Each Year
A multifactor linear model was followed for each individual trait per year: where yijkl is the observed value of clone j in species i in block k; is the mean value of the population; is the fixed effect of species i = 1, 2; ( ) is the fixed effect of clone j within species i, j = 1, 2, …, 20 for C. fargesii f. duclouxii, j = 1, 2, …, 32 for C. bungei; is the fixed effect of block k = 1, …, 5; ( ) is the fixed effect of the interaction of species i and block k; and is the random error, NID (Normally and independently distributed) (0, ). The experimental field was in a region with a humid and subhumid continental monsoon climate. The mean annual temperature ranges from 14.4 • C to 15.7 • C, the mean annual precipitation ranges from 703.6 mm to 1173.4 mm, and the annual frost-free period is 220 days to 245 days. The elevation is 145 m, and the soil of the experimental field is yellow brown loam and has high natural fertility.
Data Analysis
Variation and the genetic parameters (repeatability, clonal variance) were estimated of growth traits among clones for C. bungei and C. fargesii f. duclouxii were analyzed. ASReml-R 3.0 [21] and SAS 9.4 [22] software was used to perform ANOVA, F-tests and evaluation of genetic parameters.
Analysis at a Species Level for Each Year
A multifactor linear model was followed for each individual trait per year: where y ijkl is the observed value of clone j in species i in block k; µ is the mean value of the population; S i is the fixed effect of species i = 1, 2; C(S) ij is the fixed effect of clone j within species i, j = 1, 2, . . . , 20 for C. fargesii f. duclouxii, j = 1, 2, . . . , 32 for C. bungei; B k is the fixed effect of block k = 1, . . . , 5; (SB) ik is the fixed effect of the interaction of species i and block k; and e ijkl is the random error, NID (Normally and independently distributed) (0, σ 2 e ).
Analysis at a Clonal Level for Each Individual Year
An ANOVA to evaluate the clone effect in each species and year was carried out using the following model: where y ijk is the observed value of clone i in block j; µ is the mean value of the population; C i is the random effect of clone i = 1, 2, . . . , 20 for C. fargesii f. duclouxii, i = 1, 2, . . . , 32 for C. bungei, NID(0, σ 2 C ); B j is the fixed effect of block j = 1, 2, . . . , 5; and e ijk is the random error, NID(0, σ 2 e ). The formula of repeatability within years was as follows: where R is repeatability; σ 2 C and σ 2 e are the estimates of between-clone and within-clone variance, respectively, as obtained from the analysis of variance; and B is the number of blocks.
The formula of phenotypic variation coefficient was as follows: where σ is the standard deviation of the phenotypic variation, and X is the trait mean. The formula of genetic variation coefficient is expressed as follows: where σ 2 C is the clonal variance, and X is the trait mean. The genetic variation was estimated by Equation (2).
Analysis at a Clonal Level across Years
A second multifactor linear model was followed for each trait across years: where y ijkl is the observed value of clone j in year i in block k; µ is the mean value of the population; Y i is the effect of year i, NID(0, σ 2 Y ); C j is the effect of clone j, NID(0, σ 2 C ); B k is the effect of block k; (YC) ij is the effect of the interaction of year i and clone j, NID(0, σ 2 YC ); (YB) ik is the effect of the interaction of year i and block k, NID(0, σ 2 B ); (CB) jk is the effect of the interaction of clone j and block k; e ijkl is the random error. B k was treated as a fixed effect, and Y i , C j , (YC) ij , (YB) ik and (CB) jk were random effects, NID(0, σ 2 e ). In this model, year and block were 6 and 5, respectively, and the number of clones for C. bungei and C. fargesii f. duclouxii were 32 and 20, respectively.
The formula of repeatability across years was as follows: where R is repeatability; σ 2 C is the clone variance; σ 2 BC is the interaction of block and clone variance; σ 2 YC is the interaction of year and clone variance; σ 2 e is the environmental variance; and B is the number of blocks. The parameters were estimated using Equation (1). N, Y and C were the number of individuals, years and clones; MS: mean square; F: F statistic To interpret genotype × environment, a GGE biplot model was used and performed by R software 3.5.1 [23]. GGE biplots were constructed from the first two principal components (PC1 and PC2) derived by subjecting the environment-centered increment of stem volume means to singular-value decomposition. In this study, the weather conditions of different years were considered the environmental effect. The equation was as follows: where Y ij is the mean stem volume increment of clone i in year j; Y j is the mean stem volume increment of all clones in year j; λ 1 and λ 2 are the singular value decomposition for PC1 and PC2. ξ i1 and ξ i2 are the eigenvector of PC1 and PC2, respectively, for genotype i. η j1 and η j2 are the eigenvector of PC1 and PC2, respectively, for year j. ε ij is the random error. The formula of genetic gain was as follows: where R is repeatability; S is the selection differential; and X is the population mean.
Growth Differences between the Two Species in Different Years
The ANOVA results showed that the height of C. fargesii f. duclouxii was significantly greater than that of C. bungei in 2009 (Table 2 and Figure 2). However, in the fourth year, the height of C. bungei was consistently higher than that of C. fargesii f. duclouxii (Figure 2a). Stem volume showed patterns similar to that of height ( Figure 2c). From 2009 to 2011, the DBH of C. fargesii f. duclouxii was significantly higher than that of C. bungei. However, after 2012, the DBH of C. bungei exceeded that of C. fargesii f. duclouxii. This latter difference is likely the result of a genetic effect: C. bungei adapted more readily to the environment, as it is native to the Yellow River region.
Repeatability of Height, DBH and Stem Volume in the Two Species
The variance analysis of clones growth traits in different years was performed (Tables S1 and S2) It showed that most traits in 2009-2014 of two species were significantly different at the 0.05 or 0.01 level among clones. And the repeatability of traits was eatimated. The repeatability of height was consistently higher in C. bungei than in C. fargesii f. duclouxii (Figure 3a). The range of DBH repeatability in C. fargesii f. duclouxii was 0.65 to 0.72, which indicated a strong genetic effect on DBH in these clones ( Figure 3b). The repeatability of DBH in C. bungei was stable from 2010 to 2014 ( Figure 3b). The trends of repeatability in stem volume were largely identical between the two species: repeatability increased sharply from 2009 to 2010 and then remained largely stable. The repeatability of most of the traits in 2009 was very low. These might reflect the unstable statement of the plantlet, which was still taking root.
Repeatability of Height, DBH and Stem Volume in the Two Species
The variance analysis of clones growth traits in different years was performed (Tables S1 and S2) It showed that most traits in 2009-2014 of two species were significantly different at the 0.05 or 0.01 level among clones. And the repeatability of traits was eatimated. The repeatability of height was consistently higher in C. bungei than in C. fargesii f. duclouxii (Figure 3a). The range of DBH repeatability in C. fargesii f. duclouxii was 0.65 to 0.72, which indicated a strong genetic effect on DBH in these clones ( Figure 3b). The repeatability of DBH in C. bungei was stable from 2010 to 2014 (Figure 3b). The trends of repeatability in stem volume were largely identical between the two species: repeatability increased sharply from 2009 to 2010 and then remained largely stable. The repeatability of most of the traits in 2009 was very low. These might reflect the unstable statement of the plantlet, which was still taking root.
Variation Coefficients of Height, DBH and Stem Volume in the Two Species
The phenotypic variation coefficient (PCV) indicates the total degree of variation. The PCV of height was only approximately 10% for both species. The PCV of height in C. fargesii f. duclouxii increased in 2013 and 2014, whereas that in C. bungei decreased in 2013 and 2014 ( Figure 4a). Similar patterns were observed for the PCVs of DBH and stem volume (Figure 4b,c). These results indicated that the environmental responses of the two species changed in 2012. In addition, the PCV of stem volume in C. bungei and C. fargesii f. duclouxii ranged from 27.95%-36.50% and 26.48%-40.95%, respectively. The average PCV of stem volume was over 30% in both species. This result suggested there was abundant genetic variation, representing a strong foundation for improvement in stem volume in the two species.
The genetic variation coefficient (GCV) indicates the degree of variation due to genetic effects. The patterns of GCV for all traits were similar to those of repeatability. The GCV of height in C. fargesii f. duclouxii decreased continuously from the third year while the PCV of height in this species continuously increased (Figure 4a). These findings implied the environmental effect became more significant with increasing year in C. fargesii f. duclouxii. The GCVs of DBH and stem volume in C. fargesii f. duclouxii were approximately stable, but the PCVs of these two parameters continuously increased (Figure 4b,c). These data further suggested that the environmental effect played a leading role in the growth variation of C. fargesii f. duclouxii. In contrast, for C. bungei, the PCVs of height, DBH and stem volume continuously decreased from 2012, whereas the GCVs of DBH and stem volume remained largely stable (Figure 4b,c). These results suggested that the growth of C. bungei was under stronger genetic control than was that of C. fargesii f. duclouxii and that C. bungei exhibited stronger environmental adaptation than did C. fargesii f. duclouxii.
Variation Coefficients of Height, DBH and Stem Volume in the Two Species
The phenotypic variation coefficient (PCV) indicates the total degree of variation. The PCV of height was only approximately 10% for both species. The PCV of height in C. fargesii f. duclouxii increased in 2013 and 2014, whereas that in C. bungei decreased in 2013 and 2014 (Figure 4a). Similar patterns were observed for the PCVs of DBH and stem volume (Figure 4b,c). These results indicated that the environmental responses of the two species changed in 2012. In addition, the PCV of stem volume in C. bungei and C. fargesii f. duclouxii ranged from 27.95%-36.50% and 26.48%-40.95%, respectively. The average PCV of stem volume was over 30% in both species. This result suggested there was abundant genetic variation, representing a strong foundation for improvement in stem volume in the two species.
The genetic variation coefficient (GCV) indicates the degree of variation due to genetic effects. The patterns of GCV for all traits were similar to those of repeatability. The GCV of height in C. fargesii f. duclouxii decreased continuously from the third year while the PCV of height in this species continuously increased (Figure 4a). These findings implied the environmental effect became more significant with increasing year in C. fargesii f. duclouxii. The GCVs of DBH and stem volume in C. fargesii f. duclouxii were approximately stable, but the PCVs of these two parameters continuously increased (Figure 4b,c). These data further suggested that the environmental effect played a leading role in the growth variation of C. fargesii f. duclouxii. In contrast, for C. bungei, the PCVs of height, DBH and stem volume continuously decreased from 2012, whereas the GCVs of DBH and stem volume remained largely stable (Figure 4b,c). These results suggested that the growth of C. bungei was under stronger genetic control than was that of C. fargesii f. duclouxii and that C. bungei exhibited stronger environmental adaptation than did C. fargesii f. duclouxii.
Analyses of Growth Traits in the Two Species
The ANOVA showed that the height, DBH and stem volume of the two species were significantly different at the 0.01 level among clones and blocks and that year × clone had a significant effect at the 0.01 level on all these traits except height in C. fargesii f. duclouxii, where the interaction effect was significant at the 0.05 level (Table 3). These findings indicated that (1) the clones of the two species showed significant variation, which indicated the selection of clones could be performed with high reliability, and (2) GEIs were significant in the two species. Thus, an assessment of the stability of clone growth was necessary.
The variance components analysis indicated ( Figure 5) that the DBH had the highest proportion of genetic variance among the three traits and that height had the smallest for C. fargesii f. duclouxii. This result implied that the variation due to genetic effects was greater for DBH than for height. The proportions of genetic variance in height, DBH and stem volume were higher in C. bungei than in C. fargesii f. duclouxii. In addition, the variation in the year×clone effect on the three traits was greater for C. bungei than for C. fargesii f. duclouxii, indicating that the GEI of C. bungei may be greater than that of C. fargesii f. duclouxii. The results of the broad-sense repeatability estimation showed that the height repeatability of C. fargesii f. duclouxii was only 0.223 (Figure 6), indicating a low degree of genetic control. The repeatability of stem volume for the two species was high, suggesting that genetic improvements in volume are possible.
Analyses of Growth Traits in the Two Species
The ANOVA showed that the height, DBH and stem volume of the two species were significantly different at the 0.01 level among clones and blocks and that year × clone had a significant effect at the 0.01 level on all these traits except height in C. fargesii f. duclouxii, where the interaction effect was significant at the 0.05 level (Table 3). These findings indicated that (1) the clones of the two species showed significant variation, which indicated the selection of clones could be performed with high reliability, and (2) GEIs were significant in the two species. Thus, an assessment of the stability of clone growth was necessary.
The variance components analysis indicated ( Figure 5) that the DBH had the highest proportion of genetic variance among the three traits and that height had the smallest for C. fargesii f. duclouxii. This result implied that the variation due to genetic effects was greater for DBH than for height. The proportions of genetic variance in height, DBH and stem volume were higher in C. bungei than in C. fargesii f. duclouxii. In addition, the variation in the year×clone effect on the three traits was greater for C. bungei than for C. fargesii f. duclouxii, indicating that the GEI of C. bungei may be greater than that of C. fargesii f. duclouxii. The results of the broad-sense repeatability estimation showed that the height repeatability of C. fargesii f. duclouxii was only 0.223 (Figure 6), indicating a low degree of genetic control. The repeatability of stem volume for the two species was high, suggesting that genetic improvements in volume are possible.
Analysis of Increment of Stem Volume in the Two Species
The C. bungei clone 22-03 had the maximum increment of stem volume (0.0319 m 3 ) among the C. bungei clones, with a value 164.28% higher than the minimum increment, exhibited by clone 7-01 ( Table 4). The variation coefficient of clone 16-04 (51.18%) was the smallest among all of the C. bungei clones. However, its mean increment of stem volume (0.0209 m 3 ) was lower than the population value (0.0222 m 3 ). This result suggested that for clone 16-04, the increment of stem volume was stable but was associated with a very low growth rate. For C. fargesii f. duclouxii (Table 5), the largest increment of stem volume (0.0248 m 3 ) was found in clone 63 and was 82.35% higher than the minimum increment (in clone 110). Clone 74 had the minimum variation coefficient (28.48%). However, its increment of stem volume (0.0137 m 3 ) was very low. We found that the mean increment of stem volume of C. bungei was 32.30% higher than that of C. fargesii f. duclouxii. In contrast, the mean variation coefficient of C. fargesii f. duclouxii (48.66%) was lower than that of C. bungei (65.57%). The multiple comparison tests of stem volume of clones was also performed (Tables S3 and S4). The results showed that the 22-03 had the highest stem volume in 2009 to 2014 for C. bungeii. And the 63 had the highest stem volume in 2010 to 2014 for C. fargesii f. duclouxii. It indicated that the two clones maybe the optimal clones, but their yield stability still need to be evaluated. According to Francis and Kannenberg's [24] method, the variation coefficient of increment of stem volume was used as the abscissa, the increment of stem volume was used as the ordinate, and their means were used as boundaries to create a scatterplot to define clone yield and stability. Four groups were established for each species (Figure 7): Group I had a high increment but low stability, Group II had a high increment and high stability, Group III had a low increment but high stability, and Group IV had a low increment and low stability. Accordingly, 22-03, 1-1, 20-06, 20-07, and 16-05 of C. bungei and 63, 128, 26, 48, 43, and 60 of C. fargesii f. duclouxii were selected as high-increment and high-stability clones.
Forests 2018, 9, x; doi: FOR PEER REVIEW www.mdpi.com/journal/forests According to Francis and Kannenberg's [24] method, the variation coefficient of increment of stem volume was used as the abscissa, the increment of stem volume was used as the ordinate, and their means were used as boundaries to create a scatterplot to define clone yield and stability. Four groups were established for each species (Figure 7): Group I had a high increment but low stability, Group II had a high increment and high stability, Group III had a low increment but high stability, and Group IV had a low increment and low stability. Accordingly, 22-03, 1-1, 20-06, 20-07, and 16-05 of C. bungei and 63, 128, 26, 48, 43, and 60 of C. fargesii f. duclouxii were selected as high-increment and high-stability clones.
Stability and Increment of Stem Volume of Clones Analyzed by GGE Biplots
It was of interest to identify those genotypes for which a significant GEI was found, as these represent genotypes that adapted to the environment. A GGE biplot model was used to identify the clone that performed best in each year. All vertex clones were connected form a polygon; then, starting from the origin, vertical lines to the sides of the polygons were drawn, and the polygons were divided into multiple sectors. Each sector contained some clones and years or only clones. The vertex clone in each sector represented the highest-yielding clone in the years that fell within that particular sector. According to this rule, clone 1-1 was found to exhibit the highest increment of stem volume in 2014, and 22-03 had the highest increment of stem volume in 2010, 2011 and 2013. These data implied that 22-03 was an excellent clone with high increment of stem volume and stability. The sector containing Y2012 had two vertex clones, 19-01 and 22-08, indicating that these two clones had unique adaptability to the weather conditions in 2012. No year fell into the sector in which 7-01 and 22-10 were the vertex clones, indicating that these clones had the lowest increment of stem volume in all years tested (Figure 8a). In C. fargesii f. duclouxii, clone 63 was found to have the highest increment of stem volume in 2010-2012, and clones 110 and 15 were had the highest increments in 2013 and 2014, respectively.
Stability and Increment of Stem Volume of Clones Analyzed by GGE Biplots
It was of interest to identify those genotypes for which a significant GEI was found, as these represent genotypes that adapted to the environment. A GGE biplot model was used to identify the clone that performed best in each year. All vertex clones were connected form a polygon; then, starting from the origin, vertical lines to the sides of the polygons were drawn, and the polygons were divided into multiple sectors. Each sector contained some clones and years or only clones. The vertex clone in each sector represented the highest-yielding clone in the years that fell within that particular sector. According to this rule, clone 1-1 was found to exhibit the highest increment of stem volume in 2014, and 22-03 had the highest increment of stem volume in 2010, 2011 and 2013. These data implied that 22-03 was an excellent clone with high increment of stem volume and stability. The sector containing Y2012 had two vertex clones, 19-01 and 22-08, indicating that these two clones had unique adaptability to the weather conditions in 2012. No year fell into the sector in which 7-01 and 22-10 were the vertex clones, indicating that these clones had the lowest increment of stem volume in all years tested (Figure 8a). In There was no overall consistency between-clone yield and stability. To address this problem, the GGE biplot was used to predict an ideal variety. The center of the multiple concentric circles represented the ideal variety ( Figure 10). The closer to the smallest concentric circle, the better is the clone. The top 5 clones were 19-27, 20-01, 22-03, 20-06, and 22-01 for C. bungei ( Figure 10a) and 63, 128, 111, 26, and 48 for C. fargesii f. duclouxii (Figure 10b). There was no overall consistency between-clone yield and stability. To address this problem, the GGE biplot was used to predict an ideal variety. The center of the multiple concentric circles represented the ideal variety ( Figure 10). The closer to the smallest concentric circle, the better is the clone. The top 5 clones were 19-27, 20-01, 22-03, 20-06, and 22-01 for C. bungei ( Figure 10a) and 63, 128, 111, 26, and 48 for C. fargesii f. duclouxii (Figure 10b).
Identification of Optimal Clones
Using the Francis and Kannenberg method in combination with the GGE biplot, we identified 1 optimal clone for C. bungei and 2 optimal clones for C. fargesii f. duclouxii ( Table 6). The mean height of the optimal clones of C. bungei and C. fargesii f. duclouxii in the 6th year were 8.10 m and 7.39 m, respectively. The genetic gain, which was 3.81% and 0.57% for C. bungei and C. fargesii f. duclouxii, respectively, was low for this trait. The genetic gain of DBH was 14.32% and 11.13% for C. bungei and C. fargesii f. duclouxii, respectively, which was much higher than that for height. The genetic gain of stem volume can potentially reach 31.55% and 22.67% for C. bungei and C. fargesii f. duclouxii, respectively. Thus, stem volume has the potential for large genetic improvement via the selection of suitable clones.
Identification of Optimal Clones
Using the Francis and Kannenberg method in combination with the GGE biplot, we identified 1 optimal clone for C. bungei and 2 optimal clones for C. fargesii f. duclouxii ( Table 6). The mean height of the optimal clones of C. bungei and C. fargesii f. duclouxii in the 6th year were 8.10 m and 7.39 m, respectively. The genetic gain, which was 3.81% and 0.57% for C. bungei and C. fargesii f. duclouxii, respectively, was low for this trait. The genetic gain of DBH was 14.32% and 11.13% for C. bungei and C. fargesii f. duclouxii, respectively, which was much higher than that for height. The genetic gain of stem volume can potentially reach 31.55% and 22.67% for C. bungei and C. fargesii f. duclouxii, respectively. Thus, stem volume has the potential for large genetic improvement via the selection of suitable clones.
Genetic Variation of C. bungei and C. fargesii f. duclouxii Clones
This study aimed to evaluate the genetic parameters of growth traits in C. bungei and C. fargesii f. duclouxii in Henan Province in China and to explore the effect of genotype on growth patterns over years. The height and DBH of the clones were measured annually. The results showed that growth pattern and environmental adaptive ability differed between C. bungei and C. fargesii f. duclouxii. The growth of C. bungei exceeded that of C. fargesii f. duclouxii from the 4th year as represented by all traits. C. bungei showed stronger growth potential than C. fargesii f. duclouxii. As C. bungei is native to the Yellow River basin, it is understandable that C. bungei had a better response than C. fargesii f. duclouxii to the weather and soil conditions of the study area. Furthemore, C. fargesii f. duclouxii was distributing in environments with a much greater range of variation (Table 1) and it forced the species to be more plastic and thus exhibit potentially lower heritability values. Some reports also showed that fluctuations in the environment have major impact on the response of a population to environmental change and the potential for plasticity to evolve is facilitated after exposure to environmental fluctuations [25]. The mean repeatability of stem volume of C. bungei and C. fargesii f. duclouxii from 2010 to 2014 was high (0.72) and intermediate (0.58), respectively. A high repeatability estimate indicates that the selection of the trait in question would be effective and minimally influenced by environmental effects [11]. These findings suggest that stem volume in the two species can be improved by artificial selection.
In addition, the PCVs of growth traits in C. fargesii f. duclouxii were higher than those in C. bungei, whereas the GCVs of growth traits in C. fargesii f. duclouxii decreased or remained stable. The GCVs of height and stem volume were generally higher in C. bungei than in C. fargesii f. duclouxii. All of these findings provided further evidence to support that the influence of environment each year on C. fargesii f. duclouxii growth was strong, whereas the growth of C. bungei was more under genetic control than under environmental control. No consistent pattern in the genetic parameters of the 1-year-old trees was observed. The most likely reason for this finding was that the ramets were at the rooting stage in the first year. The unstable growth stage significantly limited the accuracy of genetic parameter estimation. Overall, our results indicated that there were significant differences in growth traits between species and among clones. These data provide a good foundation for genetic improvement.
Genotype Effect and Genotype and Environment Interaction
Plant growth is highly dependent on environmental conditions [26], and each species occupies a unique ecological niche in time and space; that is, it forms a unique, stable relationship with the environment [27]. For example, annual rainfall can affect the plant distribution [28,29], and effective temperature affects physiological functions [30,31]. The environment varies, even in the same place among years. Plants can perceive environmental changes and respond to them. Differences between species in their response to environmental fluctuations cause asynchronized growth series and within-species variability of responses also may impact the stabilizing effect of growth asynchrony [32]. In this study we already found that the two kinds of trees have different growth responses to the same environment. The genetic effect is the main cause of this phenomenon, the C. bungei native the test site, its genetic factors regulate the body to adapt to the special environment. So a good genotype is crucial for breeding. However, except genectic effect, GEI can't also be ignored. Revealing the mechanisms underlying genotype and environment interactions can greatly benefit forest breeding and selection. To do so, it is necessary to study the responses of clones to different environments and select clones with steady yields [15,33,34]. The GEI model can help tree breeders design effective breeding programs and select suitable genotypes for a given environment [4]. In trees, GEIs are widespread. Meier et al. [35] found that annual variation in the environment significantly impacted wood formation in Douglas fir (Pseudotsuga menziesii) clones. Studies of clones of white poplar [36], Michelia chapensis [37] and River red gum (Eucalyptus camaldulensis) [38] have also indicated significant GEI effects. In this study, we examined the GEIs of C. bungei and C. fargesii f. duclouxii clones. We found significant year and clone effects. A GGE biplot allows the visual interpretation of GEI [23,39,40]. We used GGE biplots to readily identify differences in the increment of stem volume and stability among clones and a GGE model to further analyze the GEI effect. According to the analyses, among C. bungei clones, clone 22-03 had the highest mean increment of stem volume and the highest values at 1, 2 and 4 years old. These results indicated that 22-03 was a high stability clone. In C. fargesii f. duclouxii, clones 63 and 128 had both high yield and high stability when we evaluated wood yield and stability independently.
Conclusions
Genetic variation is the precondition for genetic improvement. In this study, growth traits were significantly different between species and among clones. The C. bungei clones had greater growth potential than the C. fargesii f. duclouxii clones. Height, DBH and stem volume were all significantly larger in C. bungei than in C. fargesii f. duclouxii after 4 years of age. Moreover, the stem volume repeatability was intermediate or high in the two species, indicating that clone selection would be effective. The comparison of the genetic parameters between the two species showed that the growth of C. bungei was controlled more by genetic effects than environmental effects.
GEI is a very important factor for selecting breeding strategies. Our analysis indicates the two Catalpa species both have significant GEIs for increment of stem volume. Using GGE biplots, we found that wood yield and stability are largely independent in the C. bungei clones. However, clones 63 and 128 of C. fargesii f. duclouxii had both high wood yield and high stability. As each model has limitations, we combined Francis and Kannenberg's method with GGE biplot analysis to minimize error. C. bungei clones 22-03 and C. fargesii f. duclouxii clones 63 and 128, which adapted to the diverse climatic conditions in the experimental site and presented high yield, were identified as optimal clones. Supplementary Materials: The following are available online at [URL]1, Table S1. ANOVA of growth traits of clones for C. Bungei , Table S2. ANOVA of growth traits of clones for C. fargesii f. duclouxii, Table S3. Multiple comparison of stem volume of clones for C. Bungei, Table S4. Multiple comparison of stem volume of clones for C. fargesii f. duclouxii.
Author Contributions:
This study was carried out with collaboration among all authors. J. W. and W. M. conceived and designed the experiments; Y. X., N. W., W. Z. and Q. W. performed the experiments; G. Q. and N. L. carried out data correction; L. K. and Z. W. carried out manuscript revision; and Y. X. wrote the paper.
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Domain: Environmental Science Biology Agricultural and Food Sciences
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Effects of Polyphenols in Tea ( Camellia sinensis sp.) on the Modulation of Gut Microbiota in Human Trials and Animal Studies
: A diet high in polyphenols is associated with a diversified gut microbiome. Tea is the second most consumed beverage in the world, after water. The health benefits of tea might be attributed to the presence of polyphenol compounds such as flavonoids (e.g., catechins and epicatechins), theaflavins, and tannins. Although many studies have been conducted on tea, little is known of its effects on the trillions of gut microbiota. Hence, this review aimed to systematically study the effect of tea polyphenols on the stimulation or suppression of gut microbiota in humans and animals. It was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. Articles were retrieved from PubMed and Scopus databases, and data were extracted from 6 human trials and 15 animal studies. Overall, large variations were observed in terms of microbiota composition between humans and animals. A more consistent pattern of diversified microbiota was observed in animal studies. Tea alleviated the gut microbiota imbalance caused by high-fat diet-induced obesity, diabetes, and ultraviolet-induced damage. The overall changes in microbiota composition measured by beta diversity analysis showed that tea had shifted the microbiota from the pattern seen in animals that received tea-free intervention. In humans, a prebiotic-like effect was observed toward the gut microbiota, but these results appeared in lower-quality studies. The beta diversity in human microbiota remains intact despite tea intervention; supplementation with different teas affects different types of bacterial taxa in the gut. These studies suggest that tea polyphenols may have a prebiotic effect in disease-induced animals and in a limited number of human interventions. Further intervention is needed to identify the mechanisms of action underlying the effects of tea on gut microbiota.
Introduction
Studies on the relationship between gut microbiota and health have garnered much interest in recent years. The term "gut microbiota" is defined as the microbial ecosystem or community that resides within the human intestinal tract [1]. The gut ecosystem comprises microorganisms, mainly bacteria, and a small number of viruses, protozoa, and eukaryotic organisms such as fungi that are distributed throughout the gastrointestinal tract [2]. As stated by Nahoum et al., 2016, diversified microbiota are a crucial indicator of good health and well-being [3].
Gut microbiota play an important role in human health, and they are considered a "forgotten organ" and "super-organism" that maintains intestinal epithelium integrity [4][5][6]. The human gut contains an estimated 100 trillion microorganisms [7]; in addition, over 1000 different species of microbes colonize the human gut [8]. The dominant groups of bacteria phyla in the gut are Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria [5,9]. Fusobacteria, Cyanobacteria, and Verrucomicrobia phyla are usually less well-represented [10].
To the best of our knowledge, the effects of tea on gut microbiota were studied in cells (in vitro) and in mechanistic studies on animal models (in vivo) [41][42][43][44]. However, studies using cell lines or animal models to study gut microbiota have their own limitations. Casotta et al., 2020 showed that findings from animal models and cell cultures do not represent and are not translatable to humans [45]. The main limitation of in vivo studies is due to the host's tolerance of microbial infections, which varies greatly across different species [46]. In vitro colonic fermentation models are cheaper, are more reproducible, and can be conducted in a shorter time compared with in vivo studies [47]. Pham et al., 2018 showed several limitations of cell studies, including the absence of human or animal cells and low pH, which reduces microbial activity [47].
Furthermore, it remains a challenge to translate findings obtained from cells and animal models to humans [48]. The role of polyphenols in tea in modulating the human gut microbiota is not well understood. This highlights the need and importance of standardizing human studies, and better outcomes could be predicted. It is still too unclear to suggest an effective dose, choice of types (green, oolong, black, or dark tea) or forms (liquid, powder, or extract), and the duration of tea intake needed to increase the diversity of the gut microbiota in humans. Therefore, this systematic review aimed to contribute to the current updated evidence and knowledge on tea polyphenol stimulation or suppression of the diversity in gut bacteria population in humans and animals. The next aim was to determine the effective types of tea (green, black, oolong, or dark tea), dosage, tea forms (liquid, powder, or pure extract), and duration of intake to modulate the gut microbiota.
Search Strategy
Studies were selected using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Two search databases, namely PubMed and Scopus, were used to search for articles published between the years 2000 and 2020. The Boolean operator term AND was used to focus and narrow the search, while OR was used to expand the search by linking synonyms. The following key terms were applied during the search: (Tea) AND (Gut Microbiota OR Gut Microbiome OR Gut Microflora):
Study Selection
Two authors independently screened the articles and extracted the data. Jadad scoring was used to assess the risk of bias in human trials. The lowest possible score is 1, while the highest possible score is 5 (indicating the highest-quality human trials) [49]. Studies were qualified for eligibility according to pre-specified inclusion criteria. The inclusion criteria were: (1) English primary research paper published between 2000 and 2020; (2) papers on randomized control trials and in vivo studies; (3) studies with normal or overweight (BMI of 18.5-29.9) subjects, non-smokers and non-drinkers, free from medications or supplements; (4) subjects who have had a low-polyphenol diet before enrolling into intervention; (5) all subjects given Camellia sinensis tea and compared with placebo and/or no treatment; (6) study outcomes measuring gut microbiota diversity, including alpha diversity (richness, evenness, and relative abundance) and beta diversity (overall bacteria composition).
Results
A total of 5671 articles on human trials and in vivo animal studies were retrieved from the preliminary search using Scopus and PubMed. Duplicate articles (n = 2663) were removed and the remaining 3008 articles were screened for the relevant title and abstract. A total of 2963 nonrelevant articles were further excluded, and the remaining 45 articles were screened for full content. Twenty-four articles did not meet the inclusion criteria and were excluded. Among those, fifteen studies were excluded because they used multi-component tea supplements in the intervention. Eight studies focused on urinary metabolites of the gut microbiota rather than the composition of the commensal microorganism; thus, they were excluded. One randomized control trial described only intervention protocols and, hence, was excluded. A total of 6 human trials and 15 animal studies were included in the final qualitative review (n = 21); Figure 1 shows the PRIMA flow diagram. Three human studies showed a "high" risk of bias, as assessed by the Jadad score (Table 1). the highest possible score is 5 (indicating the highest-quality human trials) [49]. Studies were qualified for eligibility according to pre-specified inclusion criteria. The inclusion criteria were: (1) English primary research paper published between 2000 and 2020; (2) papers on randomized control trials and in vivo studies; (3) studies with normal or overweight (BMI of 18.5-29.9) subjects, non-smokers and non-drinkers, free from medications or supplements; (4) subjects who have had a low-polyphenol diet before enrolling into intervention; (5) all subjects given Camellia sinensis tea and compared with placebo and/or no treatment; (6) study outcomes measuring gut microbiota diversity, including alpha diversity (richness, evenness, and relative abundance) and beta diversity (overall bacteria composition).
Results
A total of 5671 articles on human trials and in vivo animal studies were retrieved from the preliminary search using Scopus and PubMed. Duplicate articles (n = 2663) were removed and the remaining 3008 articles were screened for the relevant title and abstract. A total of 2963 nonrelevant articles were further excluded, and the remaining 45 articles were screened for full content. Twenty-four articles did not meet the inclusion criteria and were excluded. Among those, fifteen studies were excluded because they used multi-component tea supplements in the intervention. Eight studies focused on urinary metabolites of the gut microbiota rather than the composition of the commensal microorganism; thus, they were excluded. One randomized control trial described only intervention protocols and, hence, was excluded. A total of 6 human trials and 15 animal studies were included in the final qualitative review (n = 21); Figure 1 shows the PRIMA flow diagram. Three human studies showed a "high" risk of bias, as assessed by the Jadad score (Table 1).
Green Tea and Gut Microbiota
Green tea is processed swiftly using fresh leaves to prevent fermentation [28]. Thus, the polyphenol content is higher in green tea compared with other types of tea [28]. Tables 2 and 3 summarize the findings on four human trials and five animal studies on the modulation effects of green tea and the gut microbiota.
No changes were observed in the gut microbiota from a high-quality clinical trial administering four decaffeinated green tea capsules daily containing 1315 ± 115.0 mg of catechins in post-menopausal women for one year [50]. Another trial observed the same results, except for the fact that overweight subjects showed a lower microbiota diversity compared with normal-body-weight subjects before the intervention [52]. Yuan et al., 2018 found that tea reversed the gut microbiota patterns seen in patients with colorectal cancer [53,[56][57][58][59]. However, it must be noted that Yuan et al., 2018 showed a low Jadad score for study quality [53]. The same study found increased levels of bacteria responsible for producing short-chain fatty acids (the main energy source of cells in gut lumen) after receiving 400 mL of green tea beverage per day (approximately two cups daily) for two weeks [53]. Jin et al., 2012 found an increase in probiotic Bifidobacteria when the subjects replaced their water with green tea liquids for ten days [55].
The effects of green tea in animal models were consistent. Mice were given different stressors to cause dysbiosis (imbalances) in their gut microbiota. Zhang et al., 2020 supplemented the diet of diabetic-induced mice with green tea for one month [60]. Diabetes had shifted all diversity measures of the microbiota, and incorporating tea in the diet lowered the indexes to levels almost similar to those in normal mice [60]. Wang et al., 2018 administered tea as drinking water along with a high-fat diet in human flora-associated mice for eight weeks [61]. Tea reversed all changes induced by obesity, hence increasing the overall microbial diversity [61]. Wang et al., 2016 supplemented green tea in a high-fat diet and showed an increased abundance of beneficial lactic acid bacteria (Lactobacillus sp.) [62]. Jung et al., 2017 exposed the mice to chronic ultraviolet rays, which subsequently changed the dominant phylum of microbiota [63]. Receiving tea extract for 10 weeks completely reversed the changes induced in the mice by the ultraviolet rays [63]. Seo et al., 2015 found a significant reduction in biomarkers of obesity and insulin resistance (ratio of Firmicutes to Bacteroidetes phyla and ratio of Bacteroidetes to Prevotella phyla) in the high-fat diet group after intubating tea extracts orally for eight weeks [64].
Oolong Tea and Gut Microbiota
Oolong tea is also known as "semi-fermented" or "partially oxidized" tea. Catechins in oolong tea are oxidized into theaflavins, thearubigins, and theabrownins during partial fermentation, hence producing a slightly darker color than green tea [71]. Oolong tea was supplemented in two murine studies (Table 3). Studies by Cheng et al. investigated the effects of oolong tea extracts in mice induced with human flora and given a highfat diet [41,43]. Tea increased gut microbiota diversity after four to eight weeks of tea supplementation [41,43].
Black Tea and Gut Microbiota
Black tea is a "fully fermented" tea and is characterized by a darker color and astringent taste due to a higher concentration of theaflavins, thearubigins, and theabrownins compared to other types of tea [71,72]. Polyphenol oxidase is a heat-labile enzyme present in black tea [72]. The activity of this enzyme is reduced by steam-heating during the fermentation of black tea, consequently reducing its antioxidant properties compared to green tea [72,73]. In this review, one human study demonstrated the effect of black tea on the gut microbiota (Table 3). Black tea infusion was given to hypocholesterolemic volunteers in a double-blind, randomized crossover feeding trial for six weeks [51]. However, no significant changes were observed in the gut microbiota [51].
Pu-erh Tea and Gut Microbiota
Pu-erh tea is a traditional Chinese tea. There are two types of Pu-erh tea, namely raw (unfermented) and ripe (after microbial fermentation) [74]. In this review, one human trial and four murine studies were done on Pu-erh tea (Tables 2 and 3). Huang et al., 2019 investigated the cholesterol-lowering activity of ripe Pu-erh tea in humans and animals [54]. In this study, male human subjects received 600 mL of tea infusion (approximately three cups) daily for four weeks, while the mice were provided with a daily dose of 450 mg of tea extracts per kg body weight in a high-fat diet for 26 weeks [54]. Hyper-cholesterolenriching bacterial genera were significantly reduced compared to high-fat diet numbers in human and animal studies [54]. Three murine studies demonstrated the effects of raw and ripe Pu-erh tea in restoring the altered gut microbiota caused by a high-fat diet. Lu et al., 2019 and Xia et al., 2019 showed that Pu-erh tea at a dose between 0.1 to 0.4 g of tea extracts for five to eight weeks effectively increased gut microbiota diversity [65,66]. Gao et al., 2017 found that ripe Pu-erh tea extract and Pu-erh tea polyphenol components increased gut microbiota diversity in the high-fat diet group [75].
Fuzhuan Tea and Gut Microbiota
Fuzhuan brick tea is a type of dark tea known as fungal fermented tea [76]. The polyphenol content in Fuzhuan tea is lower compared to green tea, due to the process of microbial fermentation occurring in dark tea production [77,78]. A series of reactions, including degradation, oxidation, condensation, structural modification, methylation, and glycosylation, are catalyzed by microbial exo-enzymes or occur as a result of microbial metabolism, leading to the development of dark tea quality [79][80][81]. Studies by Chen et al., 2018 and Foster et al., 2016 incorporated two different dosages of Fuzhuan tea extracts in mice receiving a high-fat diet (Table 3) [42,68]. Daily supplementation of Fuzhuan tea extracts at doses of between 200 to 400 mg for eight weeks was able to reverse the altered dominant phyla bacteria in the gut and also increase the levels of Lactobacillus and Bifidobacteriaceae [42,68].
Multiple Types of Tea and Gut Mmicrobiota
Two murine studies compared the modulating effects of multiple teas on the gut microbiota that were exposed to a high-fat diet (Table 3) [69,70]. Henning et al., 2017 showed that the supplementation of 0.5 g of decaffeinated green and black tea extract daily for four weeks increased the level of phylum Bacteroidetes while suppressing phyla Firmicutes and Actinobacteria [69]. The ratio of Firmicutes to Bacteroidetes was also reduced [69]. Liu et al., 2016 monitored the effects after feeding 100 mL of either green, oolong, or black tea liquid daily for 13 weeks, and they noted a reversed trend in the growth of bacteria, compared to those with only a high-fat diet [70].
Discussion
Gut microbiota are known for their large variations in terms of taxonomy and functionality [12]. Each individual has a unique gut microbiota profile that differs from another's [12]. Genetic and environmental factors directly influence gut microbiota composition [81]. In terms of genetics, the gut microbiota can be shaped according to birth gestational age, type of birth delivery, methods of milk-feeding, and weaning period [12]. The composition of gut microbiota also differs greatly due to many lifestyle-associated factors, including dietary choices, physical activity, body mass index (BMI), age, food additives and contaminants, and antibiotic consumption, which indirectly shape the gut microbiota composition [24,82].
This review showed that Camellia sinensis could modulate the gut microbiota. Overall, 3 human studies and 15 animal studies from a total of 21 included in the review showed a significant increase in diversity of the gut microbiota. Most animal studies were able to reverse the disrupted microbiota changes due to stressors such as diabetes, obesity, and ultraviolet ray damage. The beta diversity measured in murine studies showed an overall shift in the mice gut microbiota profile after tea supplementation. This indicates that the modulatory effects of tea were attributable to its ability in mediating specific imbalances in the gut. Three out of six human trials showed diversified microbiota as a result of incorporating tea [53][54][55]. An increase in the richness, evenness, and relative abundance of beneficial bacteria and a reduction in nonbeneficial bacteria were observed in the studies [53][54][55].
Green tea was the main type of tea used in this review. An average of two to five cups of green tea per day for 10 days and up to two weeks was associated with increased beneficial probiotic Bifidobacteria and their colon cancer-preventative properties in humans [53,55]. Colonic microbiota have the ability to metabolize tea polyphenols into short-chain fatty acids (SCFA) and phenolic acids, before being metabolized in the liver or being excreted [83]. A previous in vitro study showed that black tea prepared in bread had no impact on short-chain fatty acid (SCFA) production [84]. One portion of the bread containing 30% of polyphenols could be obtained from a cup of black tea [84].
In a low-quality human trial, green tea increased clusters of bacteria specializing in producing short-chain fatty acids (SCFA), namely Lachinospiraceae, Ruminococcaceae, Dorea, Roseburia, Feacalibacterium, Eubacterium, Blautia, and Coprococcus [53]. Short-chain fatty acids are a primary energy source for colonic epithelium cells, as they maintain intestinal homeostasis through anti-inflammatory actions [85,86]. With elevated fecal SCFA concentrations, SCFA-producing bacteria may promote reduced inflammation in the gut [85,86]. This might be important in the preventative steps against colorectal cancer, as inflammatory bowel disease patients showed reduced levels of dominant SCFAs-producing bacteria in several studies [87][88][89][90][91]. However, further study is needed to determine whether green tea could possibly modulate the gut microbiota in cancer patients.
Daily Pu-erh tea intakes of 600 mL (around three cups) for four weeks reduced the proliferation of hypercholesterol-enriching bacteria (Bacilli, Clostridia, Lactobacillus, Bacillus, Streptococcus, and Lactococcus) [54]. These bacteria are involved in bile acid metabolism, i.e., to generate bile salt hydrolase (BSH) enzymes that reduce cholesterol level [54]. Obese human subjects showed a higher Firmicutes/Bacteroidetes ratio after supplementation with polyphenols, and this has been proposed as a reason for weight loss [92,93]. A previous study showed that body weight and dramatic dietary patterns might affect the gut microbiota composition [94,95]. There was no substantial difference in bacterial composition after green tea supplementation in normal human subjects, and this could be due to their "optimum" state of energy balance [52]. However, more human trials are needed to confirm this.
Previous studies have shown that obese animals and humans have higher Firmicutes/Bacteroidetes ratios and higher Firmicutes compared with normal-weight individuals, proposing this ratio as a potential biomarker of obesity [96][97][98][99][100]. However, few studies have proved that a high-fat diet decreased both bacteria levels [67,68]. Tea supplementation increased the Firmicutes: Bacteroidetes ratio and Firmicutes compared with the high-fat group alone [67,68]. A recent human trial showed a higher Firmicutes:Bacteroidetes ratio and higher Firmicutes in normal-weight subjects after tea supplementation [53].
Meta-analyses failed to observe a clear correlation between the ratios of these two phyla and obesity, suggesting the complexity of how the gut microbiome modulates obesity [101]. Although the gut microbiota could contribute to the development of obesity, the evidence suggesting an association between obesity and alterations of the Firmicutes: Bacteroidetes ratio and Firmicutes is not convincing [82]. Thus, tea certainly has effects on the relative species abundance of the gut microbiota, although interpretations of the findings are still lacking [43].
In general, this review showed that low doses of tea might increase the gut microbiota diversity in a short period of time, compared with higher tea doses given for a longer period. A longer period of consumption with higher doses diminished the effects observed during a short period of supplementation. This suggests that the human gut microbiota are resilient toward longer and higher doses of tea supplementation. Human microbiota are stable upon reaching adulthood, and the composition of the gut microbiota remains relatively unaffected by acute perturbations, as its plasticity-like characteristics allow it to return rapidly to its initial composition [102,103]. This review showed a high variability in terms of different types of tea, food matrix, doses, and duration of tea supplementation. Each study used a different type of approach, i.e., richness, evenness, relative abundance, and β diversity.
Conclusions
Tea could increase alpha and beta diversities of the gut microbiota in animals, regardless of tea type, forms, dosage, and duration of intake. However, few effects were observed in humans due to a higher inter-variation in gut microorganisms between individuals. However, the exact mechanism of how tea affects trillions of microbiota in the gut is still poorly understood. More vigorous studies and trials on tea and gut microbiota are needed to understand the effects. While new evidence is needed, Camellia sinensis should be considered as a source of polyphenols in the diet. However, given the differences within and between human and animal studies, there is no specific dose and duration of tea that could be recommended for a healthy gut microbiota.
Conflicts of Interest:
The authors declare no conflict of interest.
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Domain: Environmental Science Biology Medicine
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Essential oils biological activity of the shrub Lippia alba (Verbenaceae)
Introduction: Lippia alba is an aromatic species belonging to the Verbenaceae family. Its essential oils have been used in different industries, because of its biological properties. Objective: Identify the perspectives of the biotechnological applications of Lippia alba essential oils. Methodology: A scoping review was conducted on the biological activity of Lippia alba essential oils registered until October, 2018 in EBSCO, Embase, Pubmed, Scopus, SciELO, and Lilacs databases. Results: Chemotypes I and III have been reported for different biological activities from the evaluations performed on microorganisms, fish, arthropods, small mammals, and cell lines; fundamentally associated with antibacterial, antifungal, cytotoxic, antioxidant, and sedative effects, among others. Records focused mainly on the health, fishing industry, and agrifood sectors. Conclusion: Studies on the effect of essential oil are promising, but do not reflect a continuity of the research toward prototypes or finished commercial products. Research groups must unify evaluation methodologies and include in all studies the relationship between phytochemical and biological activity for the meta-analyses to be possible. Likewise, they must join efforts through the National System for Agricultural Innovation (SNIA, for the term in Spanish) to generate finished products that impact upon society and facilitate progress in the country’s bio-economy.
Organic products are broadly used by the global population for essential healthcare (Plantlife International, 2004;Grand View Research, 2018). In 2017 the market of personal-care products reached $12.19-billion, and further growth is foreseen (Grand view research, 2018). Medicinal and Aromatic Plants (MAPs) are a natural source of chemical substances that provide diverse biological properties (Bouyahya, Guaouguaou, Dakka, & Bakri, 2017). That is why countries several countries (i.e. the United States, Brazil, India, Colombia, and Cuba) support scientific research that let the discussion of the biological properties of MAPs and support better uses focused on bioprospection (Tofiño-Rivera, Ortega-Cuadros, Melo- Ríos, & Mier-Giraldo, 2017).
Colombia has an outstanding floristic wealth, among which are included several promising MAPs of interest for science, such as Lippia alba (Mill.) N. E. Br, an aromatic species belonging to the Verbenaceae family, whose essential oils have been used in the cosmetic, food, and biomedical industries (Tofiño-Rivera et al., 2017;Linde, Colauto, Albertó, & Gazim, 2016) due to its biological properties. However, these properties are derived from the plant's phytochemical composition. The presence and concentration of secondary metabolites is influenced by agro-economic management applied to the production of biomass, the phenological cycle of the material harvested, edaphoclimatic conditions, extraction method of the essential oil (Olivero-Verbel, González-Cervera, Güette-Fernandez, Jaramillo-Colorado, Linde et al., 2016), and the evaluation technique employed in some cases (Ramírez & Castaño, 2009). This scoping review was conducted to identify the perspectives of the biotechnological application of essential oils (EO) of Lippia alba (Mill.) N. E. Br.
A scoping review (Armstrong, Hall, Doyle, & Waters, 2011) was carried out on the scientific literature that describes the biological activity of L. alba EO. Likewise, a search equation was elaborated by using the terms "Lippia alba and essential oil", which was used to access published articles deposited in EBSCO, Embase, Pubmed, Scopus, SciELO, and Lilacs, since the beginning of time for each database, until October 8 th , 2018. The registries recovered were filtered to eliminate duplicates using Zotero. Inclusion and exclusion criteria were applied. A select group of documents was obtained to which a detailed analysis was applied (Fig. 1). The variables of interest (year, country, author, title, institution, EO extraction technique, biological activity evaluated, evaluation method, object of study, and result obtained) were extracted and compiled in a database using Microsoft Excel 2016. IBM SPSS 23.0 was used to conduct the statistical analyses. Application of the research protocol was conducted independently by two researchers to guarantee reproducibility of the work, and the discrepancies observed were solved through a third-party concept.
Bibliometric analysis:
The search recovered 706 registries, from which 115 documents were analyzed (Fig. 1). It was identified that L. alba EO and their respective biological activity have been topics of interest in the scientific community since 1990. The publication of scientific articles has not been continuous. Countries with higher rate of published articles related with the subject are Brazil with 70 (60.5 %), Colombia with 29 (25.2 %), and India with 7 (6.2 %). The institutions that have led these investigations are Universidad Federal de Santa María with 18 (15.6 % publications), Universidad Industrial de Santander with 8 (6.9 % publications), Universidad de Cartagena with 6 (5.2 % publications), Universidade Regional do Cariri with 6 (5.2 % publications), and Universidade Federal da Bahía with 3 (2.6 % publications).
Chemotype I EO can be used to enhance aquaculture techniques because they maintain the freshness of fish stored in ice (Veeck et al., 2018), do not modify the organoleptic characteristics (Cunha et al., 2010;Toni et al., 2014;Hohlenwerger et al., 2017), or affect fish quality (Sena et al., 2016). However, further studies are needed, given that Veeck et al. (2018) reported that concentrations from 30-40 µl/L do not represent antimicrobial activity against mesophiles and psychrophiles, and Veit response to stress (Toni et al., 2015;Cárdenas et al., 2016;Batista et al., 2018); 2) prior sedation is not recommended with 200 µL/L during three minutes with L. alba E Oto reduce initial agitation, nor is treatment recommended with 30-40 µL/L EO for transport because it does not avoid oxidative stress in liver (Salbego et al., 2014); for Argyrosomus regius, 15 μL/L is not recommended to transport live fish, given that it does not inhibit stress (Cárdenas et al., 2016); 3) concentrations of 1 600 and 3 200 μL/L of chemotype III oils are effective against Anacanthorus spathulatus, Notozothecium janauachensis, and Mymarothecium boegeri, natural parasites of Colossoma macropomum; but these dosages are toxic to fish gills, therefore, there is a need to propose strategies to take advantage of the antihelmintic effect of these products without affecting the host organism, using cutting-edge biotechnological techniques, like nanotechnology (Soares et al., 2016). Chemotypes I and III seem to be the most common. Chemotype I has more reports on the biological effect; however, it must be considered that the cell and molecular characteristics of each microorganism or organism also interfere with the EO spectrum, given that on Tribolium castaneum and Sitophilus zeamais parasites, chemotype III was more effective than chemotype I (Peixoton et al., 2015a), but on Candida krusei and Aspergillus fumigatus, chemotype I was more effective (Mesa- Arango, et al., 2009). Additionally, chemotype I had anesthetic effect on Hypsiboas geographicus (Salbego et al., 2017a), but not on Neohelice granulata (Souza et al., 2018), and chemotype III was anti-inflammatory in the RAW 264.7 murine cell line (Sepúlveda-Arias et al., 2013), but this effect was not registered on Oreochromis niloticus (Rodrigues- Soares et al., 2018).
One of the critical variables associated with energizing an industry through L. alba EO corresponds to the phytochemical characterization of the plant material from which it is extracted, given that the availability of raw material with standardized conditions constitutes a critical point for the bio-industry (BIOinTropic, Universidad EAFIT, & Silo, 2018). We can highlight the lack of a comprehensive productive model for the agroindustrial use of L. alba because only specific studies are identified on a single management practice and for a specific area of the country (Zambrano, Buitrago, Durán, Sánchez, & Bonilla, 2013).
Regarding this work, it was found that 18.3 % of the articles did not relate the biological activity of the EO with its composition, given that no indication is provided of the chemotype or majority components. In this sense, under a general vision of the registries recovered, exploratory or preliminary works were observed without the continuity of the evaluation, according to the Colombian technical standards and norms by the WHO to advance in the formulation of bio-products (Decree 677, 1995;Decree 2510Decree , 2003WorWHO, 2004;ICONTEC, 2011), which is reflected in the lack of patents registered or pending, although research groups exist in Colombia that include L. alba as study model (Tofiño-Rivera et al., 2017). A vector and confluent effort as a country is needed here through strategies defined within the National Agricultural Innovation System (SNIA, for the term in Spanish) to encourage development based on bio-economy and, thus, take advantage of the diversity of L. alba chemotypes registered throughout the country -citral in Bolívar and Cesar, limonene in Arauca, and carvone in Cundinamarca, Tolima, Boyacá, Valle del Cauca, Santander, Antioquia, Quindío, and Cesar (Durán et al., 2007;Mesa-Arango et al., 2009;Olivero-Verbel, Guerrero-Castilla et al., 2010;Tofiño-Rivera et al., 2016)-, considering the specific uses referred by the scientific literature and which have been systematized in this document.
Unlike synthetic substances (Célis, 2018) and other natural standardized substances in which the concentration used over the same or different organisms will permit a meta-analysis for a finer verification of the biological effect of the substance in context (Valero et al., 2011), this possibility would not be rigorous in the case of the set of documents recovered in this research on the biological activity of L. alba EO because although many of the investigations refer to the chemotype or the majority component, the same concentration of EO, or the same chemotype, may have large variations in the concentrations of its majority or minority components. This variation is dependent on the equation that explains the phenotypic expression of an attribute F = G + A + GXA: chemotype -genetics G-, bioclimatic offer of the plantation -A-, and the genotype x environment interaction -GXA-, which are conditioners of the plant's physiological response over the secondary metabolism, upon the bioclimatic offer, agro-economic management, and harvest time (Palacio-López & Rodríguez-López, 2008). In addition to this heterogeneity exhibited by the raw material, the extraction method also affects the final composition of the EO, which hinders the possibility for a meta-analysis for the bioactivity of L. alba from a set of studies with high variability in the concentrations of the bioactive components of the EO (Durán et al., 2007;García-Perdomo, 2015;Barrientos, Reina, & Chacón, 2012;Delgado, Sánchez, & Bonilla, 2016). It would, then, be important to have a set of studies with detailed phyto-chemistry of the EO and which use the individual standardized bioactive components as controls (Ortega-Cuadros, Tofiño-Rivera, Merini, & Martinez-Pabón, 2018). Other exploratory revision works have registered the variable biological activity of the L. alba EO, specifically in the results of microbial control, dependent on the extraction technique used and the quality of biomass harvested. This work also highlighted high heterogeneity in assessment techniques of the biological activity of the L. alba EO used by the scientific community (Ortega-Cuadros & Tofiño-Rivera, 2019).
It is concluded that L. alba EO is a substance of broad spectrum of use, which is why industrial development is feasible based on this raw material. Nevertheless, it is necessary to establish within the SNIA research programs that prioritize the most consistent effects registered in this review, in accordance with national and global demands. Besides, progress is necessary on preliminary tests at pilot scale established by the Colombian norms to advance in the generation of commercial formulations. One of the first steps in this respect corresponds to the methodological harmonization, given that in some studies there was no appropriate control of the variables interfering on the biological quality of the L. alba EO, no detailed phyto-chemical analyses were presented, nor were adequate controls involved to facilitate a subsequent meta-analysis that guides the prioritizing of investment resources in the most consistent application lines. Another significant element is the generation of strategic alliances to elaborate protocols to standardize agro-economic management conditions of the plants in production zones, as well as articulation with the industry for scaling of the protocols formulated.
Ethical statement: authors declare that they all agree with this publication and made significant contributions; that there is no conflict of interest of any kind; and that we followed all pertinent ethical and legal procedures and requirements. All financial sources are fully and clearly stated in the acknowledgements section. A signed document has been filed in the journal archives.
ACKNOWLEDGMENTS
The authors thank the Library at Universidad de Antioquia, the Biblioteca Agropecuaria (BAC) and AGROSAVIA for funding the research "Development of production systems based on aromatic and medicinal species in agro-ecological associations with improved vegetable varieties (hot pepper, bean, and eggplant) for degraded soils of the Caribbean", "Development and validation of sustainable control strategies for parasitic diseases with the greatest productive impact on livestock systems in Colombia". Also, as the project: "Restoration of soils degraded by mining by using rhizo-remediation strategies based on the use of native aromatic species that promote the development of regional micro-economies", of the international mobility call -Argentina chapter, 2014 call by COLCIENCIAS. Finally, thanks also go to FONTAGRO, for its project, "Development of regional micro-economies in the production of essential oils harvested in mining soils", from the 2016 call. RESUMEN Actividad biológica de los aceites esenciales del arbusto Lippia alba (Verbenaceae). Introducción: Lippia alba es una especie aromática perteneciente a la familia Verbenaceae, cuyos aceites esenciales han sido empleados en diferentes industrias dada sus propiedades biológicas. Objetivo: identificar las perspectivas de aplicación biotecnológica de los aceites esenciales de Lippia alba. Métodos: se realizó una revisión sistemática exploratoria de la literatura sobre la actividad biológica de aceites esenciales de Lippia alba registrada hasta octubre 2018 en las bases de datos EBSCO, Embase, Pubmed, Scopus, SciELO, y Lilacs. Resultados: los quimiotipos I y III han sido reportados para diferentes actividades biológicas a partir de evaluaciones realizadas en microorganismos, peces, artrópodos, pequeños mamíferos, y líneas celulares; fundamentalmente asociadas con efectos antibacterial, antifúngico, citotóxico, antioxidante y sedante, entre otros. Los registros se enfocaron principalmente a los sectores salud, industria pesquera y agroalimentaria. Conclusión: los estudios sobre el efecto del aceite esencial son promisorios, pero no reflejan una continuidad de la investigación hacia prototipos o productos comerciales acabados. Los grupos de investigación deben unificar metodologías de evaluación e incluir en todos los estudios la relación entre fitoquímica-actividad biológica, para que los metaanálisis sean posibles. De igual manera, deben aunar esfuerzos por medio del Sistema Nacional de Innovación Agropecuaria (SNIA), para generar productos acabados que impacten en la sociedad y faciliten el avance de la bioeconomía del país.
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Domain: Environmental Science Biology Medicine
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Forskolin Modulates the Inhibitory Effect of C-Type Natriuretic Peptide on Hypoxia-Induced Atrial Dynamics and Hypoxia Inducible Factor 1 Alpha Activity
Our study investigated effects of C-type natriuretic peptide (CNP) on atrial dynamics and hypoxia inducible factor 1 alpha (HIF-1α) activity in perfused beating rat atria, under hypoxic conditions. Hypoxia significantly increased the levels of HIF-1α, concomitant with decreased trial dynamics. CNP (0.1 μmol/L) further decreased atrial dynamics under hypoxia and suppressed hypoxia-induced stimulation of HIF-1α expression. An adenylylcyclase (AC) activator, forskolin (0.1 μmol/L), significantly up-regulated atrial phosphodiesterase subtype 3A (PDE 3A) protein without affecting hypoxia-induced dynamics. In the presence of forskolin, the inhibitory effects of CNP on hypoxiainduced atrial dynamics and HIF-1α levels were significantly attenuated. Forskolin also prevented hypoxia-induced downregulation of PDE3A protein. These findings suggested that CNP inhibited atrial dynamics and HIF-1α activity in the isolated perfused beating rat atria under hypoxic conditions. Furthermore, both effects were modulated by the AC activator forskolin, through activation of CNP-PDE 3A signaling.
Introduction
Hypoxia is a common phenomenon in most cardiovascular diseases, including [URL] artery disease, heart failure, myocardial hypertrophy and pulmonary hypertension [1] [2] [3]. Hypoxia-inducible factor-1 (HIF-1) is a heterodimeric transcription factor that plays a major role in cellular adaptation to hypoxia [4]. It is composed of HIF-1α and HIF-1β subunits, and its activity is dependent on stability of the α-subunit [5] [6]. It was reported that cyclic adenosine monophosphate (cAMP)-dependent protein kinase (protein kinase A, PKA) phosphorylated Thr 63 and Ser 692 on HIF-1α in vitro, enhancing its transcriptional activity and increasing target gene expression of rat cardiomyocytes. PKA also stimulated binding of the coactivator p300 to HIF-1α, enhancing its transcriptional activity while counteracting inhibition by asparaginyl hydroxylation of the association of p300 with HIF-1α [7] [8]. Thus, cAMP promotes HIF-1 transcriptional activity and increases HIF-1α protein levels through PKA activation, exerting physiological and pathophysiological effects on the myocardium.
As an endocrine gland, the heart produces and secretes natriuretic peptides (NPs), such as atrial natriuretic peptide (ANP), brain natriuretic peptide (BNP) and C-type natriuretic peptide (CNP). Hypoxia potently stimulated cardiac ANP and BNP secretion [9] [10]. ANP and BNP conferred resistance in the ischemic heart to hypoxia and myocardial cell damage, resulting in cellular adaptation to hypoxia and cardioprotection, through activation of cyclic guanosine monophosphate (cGMP)-protein kinase G (PKG) signaling [11] [12]. Several studies demonstrated that ANP and BNP markedly down-regulated HIF-1α during renal ischemia/reperfusion (I/R) injury in mice [13] [14]. However, effect of CNP on HIF-1α regulation in the atrium is unclear. Our study, therefore, investigated effects of CNP on hypoxia-induced HIF-1α levels in isolated beating rat atria. We also evaluated effects of the adenylyl cyclase (AC) activator, forskolin, on regulation of hypoxia-induced HIF-1α levels by CNP.
Preparation of Perfused Beating Rat Atria
Sprague-Dawley (SD) rats of both sexes were used, with mean weights of 250 -300 g. Isolated perfused beating left atria were prepared as previously described [15] [16]. Soon after setting up each perfused atrium, transmural electrical field stimulation with a luminal electrode was started at 1.5 Hz (0.3 ms, 30 -40 V), and the atrium was perfused with HEPES buffer solution using a peristaltic pump (1 mL/min), allowing atrial pacing for measurement of changes in atrial pulse pressure. The perfused atrium was supplied with sufficient oxygen during the entire process. The HEPES buffer contained (in mmol/L) 118 NaCl, 4.7 KCl, 2.5 CaCl 2 , 1.2 MgCl 2 , 25 NaHCO 3 , 10 glucose, and 10 HEPES (pH 7.4 with NaOH), as well as 0.1% bovine serum albumin.
Hypoxic Atrial Model Preparation
The hypoxic atrial model was prepared as previously described [9]. Briefly, the atrial O 2 was replaced by N 2 gas and the normal HEPES buffer was replaced with N 2 -saturated HEPES buffer.
Intra-atrial pressure was recorded using a Physiograph (Power Lab 2/20) via a pressure transducer (Statham P23Db, Oxnard, CA, USA) and pulse pressure was calculated by the difference between systolic and diastolic pressures. Pulse pressures were expressed as cm H 2 O.
Changes in atrial pulse pressure (fold) = (value of pulse pressure − mean basal value of pulse pressure)/mean basal value of pulse pressure.
Experimental Protocol
Each atrium was perfused for 60 min to stabilize atrial dynamics and then the control cycle (12 min as an experimental cycle) was followed by infusion of hypoxic buffer for four cycles, monitoring changes in atrial dynamics. For western blot analysis, immediately after perfusion, the atrial tissue was frozen and stored at −80˚C until analyzed.
To investigate effects of CNP and forskolin on hypoxia-induced atrial dynamics, one cycle of hypoxia after the control was followed by three cycles of infused treatment agent plus hypoxia. The treatment agents used were CNP (0.1 µmol/L) and forskolin (0.1 µmol/L). In the control group, vehicle was introduced instead of treatment agent. Values obtained during the periods corresponding to control and experimental observations were compared.
Western Blot Analysis
Proteins derived from left atrial tissue were analyzed by western blotting. Atrial tissues were homogenized in radio-immunoprecipitation assay lysis buffer (Solarbio institute of Biotechnology, Shanghai, China), and protein concentrations were determined with a Bradford protein assay kit. Solubilized proteins were denatured in Lane Maker Loading buffer and proteins separated by 10% or 8% sodium dodecyl sulfate polyacrylamide gel electrophoresis. Protein bands were then transferred to polyvinylidene difluoride filter membranes (Beyotime Institute of Biotechnology, China). Each membrane was blocked with a 5% skim milk in phosphate buffer (PBST) solution at room temperature. After 2 h the membranes were incubated with the appropriate primary antibodies, overnight at 4˚C. The primary antibodies used were anti-phosphodiesterase subtype 3A (PDE3A, 1:1000, Abcam Shanghai, Shanghai, China) or anti-HIF-1α (1:1000, Abcam Shanghai), using, rabbit polyclonal β-actin (1:1000; Com Win Biotech, Beijing, China) as a loading control for all lanes. The membranes were then washed and incubated with secondary antibodies (1:2000) at room temperature for 2 h. After washing membranes thoroughly with PBST, stained bands were visualized by the ECL method (ECL Western Blot Kit, Com Win Biotech) and band densities quantified using Image J software (National Institutes of Health, Bethesda, MD, USA).
Statistical Analysis
The significance of differences among values was determined by one-way ANO-VA followed by Dunnett's multiple comparison test. An unpaired t-test was also applied. Statistical significance was defined as P < 0.05. All data were presented as means ± SEM.
Effect of CNP on Hypoxia-Induced Atrial Dynamics
As shown in Figure 1, hypoxia significantly decreased pulse pressure in isolated perfused beating rat atria (P < 0.05 vs. control, (a)). CNP also substantially decreased pulse pressure in the hypoxic atria (P < 0.05 vs. control, (b)), a net effect that was greater than that of hypoxia alone (P < 0.05 vs. hypoxia alone, (c)). These data indicated that CNP had a negative inotropic effect in the hypoxic atrium.
Effects of Forskolin on CNP-Induced Suppression of Hypoxic Atrial Pulse Pressure
To determined effects of an AC activator, forskolin, on regulation of hypoxiainduced atrial dynamics by CNP, a series of experiments were performed with this agent in the perfused beating rat atria. As shown in Figure 2, forskolin did not affect hypoxia-induced atrial pulse pressure (P < 0.05 vs. control; P > 0.05 vs. hypoxia). In contrast, the AC activator dramatically attenuated the inhibitory effects of CNP on hypoxia-induced pulse pressure (P < 0.05 vs. hypoxia; P < 0.05 vs. CNP; P < 0.05 vs. forskolin). These results suggested that forskolin reversed the inhibitory effects of CNP on atrial dynamics in the beating hypoxic atrium.
Effects of Forskolin and CNP on Atrial PDE3A Levels under Hypoxia
PDE 3A regulates intracellular cAMP levels, so we examined levels of this protein to investigate the mechanism of forskolin-mediated reversal of inhibition by CNP of hypoxia-induced atrial pulse pressure. Atrial PDE3A levels were determined by western blotting in hypoxic beating atria that had been treated with or without forskolin and/or CNP. Forskolin significantly up-regulated atrial PDE 3A protein levels under hypoxic conditions (P < 0.05 vs. control group; P < 0.05 vs. hypoxia group, Figure 3). There were no significant changes in PDE 3A levels with hypoxia alone or with hypoxia plus CNP. However, in the presence of forskolin, CNP dramatically suppressed levels of PDE 3A in hypoxic atria (P < 0.05 compared with all other groups, Figure 3). This indicated that CNP inhibited forskolin-induced PDE3A activity activation in hypoxic atria. . Effects of CNP (0.1 µmol/L) and forskolin (0.1 µmol/L) on hypoxiainduced atrial phosphodiesterase subtype 3A (PDE 3A) expression. Con, control; Hy, hypoxia; F, forskolin; C, CNP. Data were expressed as mean ± SEM, n = 5. *P < 0.05 vs. control group; #P < 0.05 vs. hypoxia group; ♦P < 0.05 vs. CNP group; &P < 0.05 vs. forskolin group.
Effects of CNP and Forskolin on HIF-1α Levels in Hypoxic Atria
We next investigated regulation by CNP of hypoxia-induced increases in atrial HIF-1α, as well as the impact of forskolin. As shown in Figure 4, hypoxia substantially increased atrial levels of HIF-1α (P < 0.05 vs. control group) and this effect was completely abolished by CNP (P < 0.05 vs. hypoxia group). In addition, forskolin clearly augmented the hypoxia-induced increase in HIF-1α levels in the atria (P < 0.05 vs. control group; P < 0.05 vs. hypoxia group). This effect was dramatically attenuated by CNP, though HIF-1α levels remained elevated, as compared with hypoxic atria with CNP alone (P < 0.05 vs. control group; P < 0.05 vs. hypoxia group; P < 0.05 vs. CNP group). These results suggested that, in hypoxic atria, CNP suppressed upregulation of HIF-1α and that this effect could be modulated by the AC activator forskolin.
Discussion
In our study, in isolated perfused beating rat atria under hypoxic conditions, CNP inhibited atrial dynamics and suppressed HIF-1α levels. This effect of CNP was modulated by the AC activator forskolin, through activation of CNP-PDE were identified in the heart [22] [23] [24]. PDE 3, a cGMP-inhibited PDE subtype, represents one of the major cAMP-degrading PDEs in the human heart [25]. PKA enhances HIF-1α transcriptional activity [7] [8] and PDE 3 inhibition leading to an elevating of intracellular cAMP levels [20], which subsequently, activates PKA. Thus, PDE 3 may be involved in regulation of HIF-1α activity. In our study, hypoxia significantly increased atrial HIF-1α protein levels and this effect was augmented by forskolin, which also increased PDE 3A levels. The ef- forskolin, through activation of CNP-PDE 3 signaling. Thus, PDE 3 is a potential regulatory target for modulating HIF-1α activity.
Conclusion
In conclusion, CNP inhibited atrial dynamics and HIF-1α activity in the isolated perfused beating rat atria under hypoxic conditions. These effects of CNP were modulated by the AC activator, forskolin, through increased CNP-PDE 3A signaling.
Figure 1 .
Figure 1. Effect of CNP (0.1 µmol/L) on hypoxia-induced pulse pressure (PP) in isolated perfused rat atria.(a) hypoxia-induced PP; (b) and (c) CNP modulated PP (data in (c) were derived from (a) and (b) control as well as the last cycle of the experimental period). Data were expressed as mean ± SEM, n = 6.*P < 0.05 vs. control; #P < 0.05 vs. hypoxia.
3A signaling. It is well known that CNP can bind to B-type natriuretic peptide receptors (NPR-B) and negatively affect cardiac myocyte function through activation of the guanylyl cyclase (GC)-cGMP-PKG signaling pathway[17] [18][19]. In addition, particulate GC (pGC) activation in atria by CNP led to increased pGC-cGMP-PDE 3 signaling and elevated cAMP levels[20]. In our study, hypoxia fect was, in contrast, blocked by CNP, without affecting PDE 3A levels. Nevertheless, the inhibitory effect of CNP on hypoxia-induced atrial HIF-1α protein expression was dramatically attenuated by forskolin. Under these conditions, there was concomitant suppression, by CNP, of the forskolin-induced increases in PDE 3A levels. These results indicated that the suppression by CNP of hypoxia-induced HIF-1α elevation in the perfused beating rat atria was modulated by
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Domain: Biology Medicine
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Fungal virulence depends on interactions between pathogen ploidy and genetic background in healthy and immunocompromised hosts
The yeast Candida albicans is a commensal of humans and opportunistic pathogen. Given that C. albicans transitions between benign commensal and harmful pathogen, studying the factors that contribute to virulence is often challenging and frequently depends on many contexts including host immune status and pathogen genetic background. However, ploidy has often been overlooked when studying C. albicans virulence. Given that polyploid C. albicans have been identified in clinical settings and that other fungal pathogens display extensive ploidy variation, assessing how ploidy impacts virulence has important clinical relevance. Here, we utilize the nematode C. elegans as a mini-host system to study how pathogen ploidy and genetic background impact fungal infection in healthy and immunocompromised host contexts. In addition to reducing overall survival of nematode hosts, Candida infection also results in negatively impacts host reproduction and allows us to survey lethal and non-lethal virulence phenotypes. While we do not detect any global differences in virulence between diploid and tetraploid C. albicans, there is a significant interaction between ploidy and C. albicans genetic background in both healthy and immunocompromised host contexts. Furthermore, in specific C. albicans genetic backgrounds tetraploids are more virulent than their diploid counterpart whereas other genetic backgrounds the diploid is more virulent. Importantly, these genetic background dependent ploidy patterns are consistent across lethal and non-lethal virulence phenotypes and host immune status. Introduction Virulence is measured by the reduction of host fitness resulting from a host-pathogen interaction. Therefore, virulence is not solely the property of the pathogen, but rather the product of the interaction between a host and its pathogen. While many biotic and abiotic factors contribute to virulence, the genotype-by-genotype interaction between hosts and pathogens is a primary determinant of whether a host gets infected and the resulting level of virulence. One important, yet understudied, element of an organism’s genotype is its ploidy level. For example, polyploidy of some host species is associated with elevated immune response. Furthermore, polyploidy and aneuploidy are well documented in fungal pathogens that infect plant and/or animal hosts. However there have only been a limited number of studies that investigate whether pathogen ploidy impacts virulence phenotypes and the few that have, often result in contradictory findings. One potential source of these contradictory results regarding ploidy is the genetic background or allelic composition of the pathogen. Allelic composition refers to not just the specific alleles present in a genome, but the amount of heterozygosity throughout the genome. Pathogen virulence depends on the pathogen’s specific allelic combination and phenotypic analysis of diverse clinical isolates within a pathogenic species clearly demonstrate that pathogen genetic background contributes to virulence expressivity. Ploidy intrinsically impacts allelic composition – haploids contain a single allelic set of genes, whereas diploids and polyploids can either be homozygous or heterozygous for any given locus, and dominance can mask recessive alleles. Allelic composition in polyploids is further complicated by multiple allelic ratios, in which one to four alleles may be present, depending on the mechanism and age of the polyploidization event. Thus, a major challenge in determining the specific role ploidy has on pathogen virulence is disentangling it from allelic composition. The opportunistic fungal pathogen Candida albicans, while typically a highly heterozygous diploid, shows tremendous ploidy variation, ranging from haploid to polyploid and is highly tolerant of aneuploidy. C. albicans is a commensal in the human gastrointestinal microbiota and various other niches. Despite its commensal existence, C. albicans causes a range of infection, including superficial mucosal infections to life threatening systemic infections. The severity of fungal infection is closely linked to the immune status of the host, with superficial mucosal infections occurring in healthy individuals and serious bloodstream infections almost exclusively in immunocompromised hosts, the latter of which has a mortality of up to 50%. While extensive allelic and ploidy variation including lossof-heterozygosity, aneuploidy and polyploidy are well-documented in laboratory and clinical C. albicans isolates, these large-scale genomic perturbations are often identified and studied in the context of antifungal drug resistance, and only limited studies have specifically investigated how C. albicans ploidy impacts virulence phenotypes. In this study we sought to identify how C. albicans ploidy impacts its virulence by using four diploid-tetraploid pairs, with each pair representing a distinct genetic background. We assessed virulence by monitoring four measures of host fitness using healthy and immunocompromised C. elegans hosts. While we find almost no overall relationship between ploidy and virulence, there are detectable differences among C. albicans genetic backgrounds and a clear interaction between C. albicans genetic background and its ploidy state on virulence phenotypes. We also observe these interactions in immunocompromised hosts. However, in some cases, the ploidy-specific pattern and/or the degree of virulence severity is different between healthy and immunocompromised hosts. Taken together, these results emphasize the importance of host genotype by pathogen genotype, in which genotype includes ploidy state, interactions on virulence phenotypes. Material and Methods Strains and media For this study, the C. albicans strains used are described in Table S1. C. elegans N2 Bristol (WT) and sek-1 mutant derivative were used to test host survival, fecundity and population growth in healthy and immunocompromised hosts, respectively. C. elegans populations were maintained at 20°C on lite nematode growth medium (NGM, US Biological) with E. coli OP50 as a food source and were transferred to a newly seeded E. coli plate every 34 days. For survival, fecundity and lineage growth assays, NGM was supplemented with 0.08 g/L uridine, 0.08 g/L histidine, and 0.04 g/L adenine to facilitate growth of auxotrophic C. albicans strains and 0.2 g/L streptomycin sulfate to inhibit E. coli overgrowth so C. albicans strains could proliferate. Survival and Fecundity Assays Seeding NGM plates and C. elegans egg preparation for survival and fecundity assays were performed as previously described. Briefly, C. albicans strains and E. coli OP50 were inoculated in 3 mL of YPD or 5 mL of LB, respectively, and incubated at 30°C for 1-2 days. C. albicans cultures were diluted to a final volume of 3.0 OD600 per mL and E. coli was pelleted and washed twice with 1 mL of ddH2O. The washed pellet was centrifuged for 60 sec at maximum and any excess liquid removed. The pellet was weighed and suspended with ddH2O to a final volume of 200 mg/mL. For uninfected treatments, 6.25 μL E. coli was mixed with 43.75 μL ddH2O. For C. albicans treatments, 1.25 μL C. albicans was mixed with 6.25 μL E. coli and brought to a final volume of 50 μL with ddH2O. All 50 μL was spotted onto the center of a 35mm supplemented-NGM Lite agar plate and incubated at room temperature overnight before addition of nematode eggs or transferred nematode. To synchronize host populations, C. elegans populations (~100 nematodes) were washed off NGM plates with M9 buffer and transferred to a 15 mL conical tube and pelleted by centrifugation for 2 min at 1200 rpm. The pellet was re-suspended in a 5.25% bleach solution, transferred to a microcentrifuge tube and inverted for 90-120 sec and subsequently centrifuged for 30 sec at 1500 rpm. The pellet was washed thrice with 1 mL M9 buffer and resuspended in 500 μL M9 buffer. ~100 eggs were added to uninfected or C. albicans treatment plates. 48 h later, a single L4 nematode (x10 per treatment) was randomly selected and transferred to an uninfected or C. albicans treatment plate and incubated at 20°C. Each founder nematode was transferred to new treatment plates every 24 h for seven consecutive days. For survival analysis, we documented whether the founder was alive, dead, or censored (i.e. crawled off the plate or were otherwise unaccounted). For fecundity analysis, any eggs laid for each 24-hour interval remained undisturbed on the plate and incubated at 20°C for an additional 24 h and the number of viable progeny produced per day was scored. Total brood size is the sum of viable progeny produced over seven days. Delayed reproduction is calculated by the number of progeny produced on Day 4 or later divided by the total progeny produced for each founder nematode. All experiments were performed in triplicate or more. Lineage Growth Assays Lineage growth assays were performed as previously described. In brief, a single L4 founder nematode (x6 per treatment) was randomly selected from a synchronized population and transferred to a 100mm treatment plate that contained a 300 μl seed of C. albicans and/or E. coli and incubated at 20°C for five days in which the founder produces F1 and F2 progeny. The entire population derived from the single founder were washed with M9 buffer and transferred to 15mL conical tubes and brought to a final volume of 10mL. Tubes were placed at 4°C for 1h to allow the nematodes to settle at the bottom. 20 μL samples were taken from each population and counted six independent times to determine the final population size for each founder nematode. All experiments were performed at least in duplicate. Statistical Analyses All statistical analyses were performed with GraphPad Prism. Survival curves were tested for significant differences using log-rank (Mantel Cox) tests. Lineage growth, total brood size and delayed reproduction data sets were tested for no
Introduction
Virulence is measured by the reduction of host fitness resulting from a host-pathogen interaction 1-3 . Therefore, virulence is not solely the property of the pathogen, but rather the product of the interaction between a host and its pathogen 4,5 . While many biotic and abiotic factors contribute to virulence 6,7 , the genotype-by-genotype interaction between hosts and pathogens is a primary determinant of whether a host gets infected and the resulting level of virulence 8,9 . One important, yet understudied, element of an organism's genotype is its ploidy level. For example, polyploidy of some host species is associated with elevated immune response 10,11 . Furthermore, polyploidy and aneuploidy are well documented in fungal pathogens that infect plant and/or animal hosts [12][13][14] . However there have only been a limited number of studies that investigate whether pathogen ploidy impacts virulence phenotypes and the few that have, often result in contradictory findings 12,[15][16][17][18] .
One potential source of these contradictory results regarding ploidy is the genetic background or allelic composition of the pathogen. Allelic composition refers to not just the specific alleles present in a genome, but the amount of heterozygosity throughout the genome.
Pathogen virulence depends on the pathogen's specific allelic combination 19 and phenotypic analysis of diverse clinical isolates within a pathogenic species clearly demonstrate that pathogen genetic background contributes to virulence expressivity 20,21 . Ploidy intrinsically impacts allelic composition -haploids contain a single allelic set of genes, whereas diploids and polyploids can either be homozygous or heterozygous for any given locus, and dominance can mask recessive alleles. Allelic composition in polyploids is further complicated by multiple allelic ratios, in which one to four alleles may be present, depending on the mechanism and age of the polyploidization event. Thus, a major challenge in determining the specific role ploidy has on pathogen virulence is disentangling it from allelic composition.
C. albicans causes a range of infection, including superficial mucosal infections to life
threatening systemic infections 35 . The severity of fungal infection is closely linked to the immune status of the host, with superficial mucosal infections occurring in healthy individuals and serious bloodstream infections almost exclusively in immunocompromised hosts, the latter of which has a mortality of up to 50% 35 . While extensive allelic and ploidy variation including lossof-heterozygosity, aneuploidy and polyploidy are well-documented in laboratory and clinical C. albicans isolates [20][21][22][23][24]36,37 , these large-scale genomic perturbations are often identified and studied in the context of antifungal drug resistance 21,30,31 , and only limited studies have specifically investigated how C. albicans ploidy impacts virulence phenotypes [15][16][17] .
In this study we sought to identify how C. albicans ploidy impacts its virulence by using four diploid-tetraploid pairs, with each pair representing a distinct genetic background. We assessed virulence by monitoring four measures of host fitness using healthy and immunocompromised C. elegans hosts 38 . While we find almost no overall relationship between ploidy and virulence, there are detectable differences among C. albicans genetic backgrounds and a clear interaction between C. albicans genetic background and its ploidy state on virulence phenotypes. We also observe these interactions in immunocompromised hosts. However, in some cases, the ploidy-specific pattern and/or the degree of virulence severity is different between healthy and immunocompromised hosts. Taken together, these results emphasize the importance of host genotype by pathogen genotype, in which genotype includes ploidy state, interactions on virulence phenotypes.
Strains and media
For this study, the C. albicans strains used are described in Table S1. C. elegans N2 Bristol (WT) and sek-1 mutant derivative 39 were used to test host survival, fecundity and population growth in healthy and immunocompromised hosts, respectively. C. elegans populations were maintained at 20°C on lite nematode growth medium (NGM, US Biological) with E. coli OP50 as a food source and were transferred to a newly seeded E. coli plate every 3-4 days. For survival, fecundity and lineage growth assays, NGM was supplemented with 0.08 g/L uridine, 0.08 g/L histidine, and 0.04 g/L adenine to facilitate growth of auxotrophic C. albicans strains and 0.2 g/L streptomycin sulfate to inhibit E. coli overgrowth so C. albicans strains could proliferate.
Survival and Fecundity Assays
Seeding NGM plates and C. elegans egg preparation for survival and fecundity assays were performed as previously described 38 . Briefly, C. albicans strains and E. coli OP50 were inoculated in 3 mL of YPD or 5 mL of LB, respectively, and incubated at 30°C for 1-2 days. C. albicans cultures were diluted to a final volume of 3.0 OD 600 per mL and E. coli was pelleted and washed twice with 1 mL of ddH 2 O. The washed pellet was centrifuged for 60 sec at maximum and any excess liquid removed. The pellet was weighed and suspended with ddH 2 O to a final volume of 200 mg/mL. For uninfected treatments, 6.25 µL E. coli was mixed with 43.75 µL ddH 2 O. For C. albicans treatments, 1.25 µL C. albicans was mixed with 6.25 µL E. coli and brought to a final volume of 50 µL with ddH 2 O. All 50 µL was spotted onto the center of a 35mm supplemented-NGM Lite agar plate and incubated at room temperature overnight before addition of nematode eggs or transferred nematode.
To synchronize host populations, C. elegans populations (~100 nematodes) were washed off NGM plates with M9 buffer and transferred to a 15 mL conical tube and pelleted by centrifugation for 2 min at 1200 rpm. The pellet was re-suspended in a 5.25% bleach solution, transferred to a microcentrifuge tube and inverted for 90-120 sec and subsequently centrifuged for 30 sec at 1500 rpm. The pellet was washed thrice with 1 mL M9 buffer and resuspended in 500 µL M9 buffer. ~100 eggs were added to uninfected or C. albicans treatment plates. 48 h later, a single L4 nematode (x10 per treatment) was randomly selected and transferred to an uninfected or C. albicans treatment plate and incubated at 20°C. Each founder nematode was transferred to new treatment plates every 24 h for seven consecutive days. For survival analysis, we documented whether the founder was alive, dead, or censored (i.e. crawled off the plate or were otherwise unaccounted). For fecundity analysis, any eggs laid for each 24-hour interval remained undisturbed on the plate and incubated at 20°C for an additional 24 h and the number of viable progeny produced per day was scored. Total brood size is the sum of viable progeny produced over seven days. Delayed reproduction is calculated by the number of progeny produced on Day 4 or later divided by the total progeny produced for each founder nematode. All experiments were performed in triplicate or more.
Lineage Growth Assays
Lineage growth assays were performed as previously described 38 . In brief, a single L4 founder nematode (x6 per treatment) was randomly selected from a synchronized population and transferred to a 100mm treatment plate that contained a 300 µl seed of C. albicans and/or E. coli and incubated at 20°C for five days in which the founder produces F1 and F2 progeny.
The entire population derived from the single founder were washed with M9 buffer and transferred to 15mL conical tubes and brought to a final volume of 10mL. Tubes were placed at 4°C for 1h to allow the nematodes to settle at the bottom. 20 µL samples were taken from each population and counted six independent times to determine the final population size for each founder nematode. All experiments were performed at least in duplicate.
Statistical Analyses
All statistical analyses were performed with GraphPad Prism. Survival curves were tested for significant differences using log-rank (Mantel Cox) tests. Lineage growth, total brood size and delayed reproduction data sets were tested for normality using the D'Agostino & Pearson omnibus normality test. For comparisons across genetic backgrounds, Kruskal-Wallis and Dunn's multiple comparison tests were performed. For pairwise comparisons between ploidy or host context, Mann Whitney tests were performed. Two-way ANOVAs were performed to test for interactions between C. albicans genetic background and ploidy or between C. albicans ploidy and host context.
Interactions between pathogen ploidy and genetic background impact virulence phenotypes in healthy hosts
First, we wanted to investigate whether C. albicans genetic background differentially impacted host fitness. To do this we infected C. elegans hosts with four genetically distinct strain backgrounds of C. albicans. Two of these genetic backgrounds are laboratory strains: a 'laboratory heterozygous,' which consists of the SC5314 reference strain and a 'laboratory homozygous,' a derivative of SC5314 in which the genome is completely homozygous. The other two genetic backgrounds are clinical strains: a 'bloodstream,' isolated from a candidemia infection of a male immunosuppressed patient and an 'oral/vaginal,' a pair of clinical strains isolated from a single immune-competent female patient with vulvovaginal candidiasis. For each C. albicans genetic background, we assessed four measures of host fitness: host survival (Fig. 1A), host lineage growth (Fig. 1B), host brood size (Fig. 1C), and host reproductive timing (Fig. 1D). For survival and lineage growth, all C. albicans genetic backgrounds were virulent, with significant differences observed between uninfected and infected treatments, and the only statistical difference between C. albicans genetic backgrounds was between the laboratory heterozygous and oral/vaginal strains for host survival. For brood size, only the laboratory homozygous genetic background was virulent and resulted in significantly smaller host brood sizes compared to the two clinical C. albicans backgrounds. The impact on host reproductive timing also depended on pathogen genetic background: the laboratory heterozygous and the clinical bloodstream strains were virulent (i.e. reduced amount of normal reproductive timing) whereas the other two C. albicans genetic backgrounds were not. The laboratory heterozygous strain impacted host reproductive timing significantly more than the three other pathogen genetic backgrounds. Together, these results suggest that while pathogen genetic background may not obviously contribute to host survival phenotypes, it may be important for other measures of fungal infection that are less lethal.
One factor that may dampen any differences in virulence among C. albicans genetic backgrounds is ploidy. For each genetic background, we used a diploid strain and a tetraploid strain that were related to each other (Table S1). The tetraploids for both the 'laboratory' strains were produced via mating in the laboratory. The 'bloodstream' pair consisted of a diploid strain isolated early in the infection and its corresponding tetraploid strain was isolated mid-to lateinfection following antifungal treatment. The 'oral/vaginal' pair consisted of a diploid strain isolated from the oral cavity and its corresponding tetraploid was isolated from the urogenital tract following antifungal treatment for vulvovaginal candidiasis. There have been limited and conflicted reports on the role of tetraploid in fungal virulence 15,16 . To assess how pathogen ploidy impacts virulence, we compared all the diploid strains to the tetraploid strains for host survival (Fig. 1E), lineage growth ( Fig. 1F), host brood size (Fig. 1G), and host reproductive timing ( Fig. 1H) in wildtype hosts. We did not observe a significant difference between these two ploidy states for any of the host fitness measures tested, suggesting that ploidy may not impact virulence phenotypes. However, when we account for C. albicans strain background, differences between pathogen ploidy emerge but depends on strain genetic background and a significant interaction between genetic background and ploidy is detected for host lineage growth ('interaction' p=0.0354, two-way ANOVA; Fig. S1B), brood size ('interaction' p<0.0001, two-way ANOVA; Fig. S1B), and reproductive timing ('interaction' p=0.0283, two-way ANOVA; Fig. S1B). While we cannot directly test for an interaction using host survival curves, we do detect differences between diploids and tetraploids for each C. albicans genetic background ( Fig. S1 and Table S2). Taken together, these results suggest that there is no global pattern in ploidy state and virulence, but ploidy in combination with genetic background does significantly contribute to C. albicans virulence phenotypes.
When we look at specific diploid-tetraploid pairs, representing different C. albicans genetic background, we see significant differences in virulence between the diploid and tetraploid for at least one fitness measure, for every C. albicans genetic background (Fig. 2). For two genetic backgrounds, the laboratory homozygous and clinical bloodstream strains, the diploid strain was more virulent than its tetraploid counterpart when differences between ploidy A) Survival curves for C. elegans populations that are either uninfected (exposed to just an E. coli food source, grey) or when infected with different C. albicans strains (indicated in legend). The number of worms analyzed (n) for each treatment is indicated in Table S1. Statistical significance was tested using pairwise comparisons of survival curves with Log-rank (Mantel-Cox) test. Astrisks denote statistical differences compared to the uninfected control (* indicates p <0.05, **** indicates p < 0.0001). C. albicans treatments that share letters are not significantly different, whereas treatments with differing letters are stastically different. B) Box and whiskers plot of host lineage growth which represents the total population size (representing the number of F1 and F2 progeny) produced within 7 days from a single founder C. elegans host infected with C. albicans. Boxes indicate the 25-75th quartiles with median indicated. Error bars are the normalized range of the data and circles indicate outliers. The mean and 95% CI of the uninfected control treatment are indicated by the grey dashed line and shaded grey box. Statistical significance was tested using one-way ANOVA. Astrisks denote statistical differences compared to the uninfected control (* indicates p <0.05, *** indicates p < 0.001). C. albicans treatments that share letters are not significantly different, whereas treatments with differing letters are stastically different, post-hoc Dunn's multiple comparison test. C) Total brood size and D) Average percentage of host progeny produced during days 1-3 of adulthood (normal reproductive timing) of C. elegans infected with C. albicans. Data and statistical analysis are the same as (B). E) % host survival on Day 7 for diploid (dip) and tetraploid (tet) C. albicans strains (colored symbols indicate specific C. albicans genetic background). Statistical significance was tested using Wilcoxon matched-pairs signed rank test and p values are indicated. states were detected. For the laboratory heterozygous and clinical oral/vaginal strains, the tetraploid strain was more virulent than its diploid counterpart. Furthermore, these genetic background specific ploidy patterns are generally consistent across host fitness measures. For example, the laboratory heterozygous and clinical oral/vaginal tetraploids are also more virulent than their diploid counterparts for host brood size (Fig 2C), and the clinical bloodstream diploid was more virulent than its tetraploid counterpart for lineage growth and delayed host reproduction ( Fig. 2B and D). We performed every pairwise comparison between treatments for host survival (Table S2), lineage growth (Table S3), brood size (Table S4), and reproductive timing (Table S5) and find significant differences in virulence between different C. albicans genetic backgrounds and ploidy states for most host fitness measures, except host lineage growth, were very few differences between C. albicans strains were detected. Taken together, these data support that C. albicans ploidy does contribute to its virulence phenotypes, but whether it attenuates or enhances virulence depends on its genetic background.
Host immune status and pathogen genetic background contribute to virulence phenotypes
We have previously established that C. albicans and other non-albicans Candida species cause more severe infections in an immunocompromised C. elegans host model compared to healthy C. elegans hosts 38 . Given that we see pathogen genetic background dependent ploidy patterns on virulence phenotypes in wildtype, healthy hosts, we wanted to assess if we could detect comparable patterns in immunocompromised hosts. For each C.
albicans genetic backgrounds significantly reduced host fitness compared to uninfected controls, the only exception was the laboratory homozygous strains did not significantly delay host reproduction. The laboratory homozygous background is also less virulent than the clinical oral/vaginal genetic background for host. Together, these results suggest that global differences in pathogen genetic background are revealed in hosts with compromised immune function for both lethal and non-lethal measures of host fitness.
To assess how pathogen ploidy impacts virulence in immunocompromised hosts, we compared all the diploid strains to the tetraploid strains for host survival (Fig. 3E), host lineage growth (Fig. 3F), host brood size (Fig. 3G), and host reproductive timing (Fig. 3H). Similar to healthy wildtype hosts, we did not observe a significant difference between these two ploidy states for and of the host fitness measures. However, when we account for C. albicans strain background, differences between pathogen ploidy emerge but depends on strain genetic background and a significant interaction between genetic background and ploidy is detected for host lineage growth ('interaction' p=0.0012, two-way ANOVA; Fig. S2B), brood size ('interaction' p=0.0090, two-way ANOVA; Fig. S2C), and reproductive timing ('interaction' p=0.0167, two-way ANOVA; Fig. S2D). While we cannot directly test for an interaction using host survival curves, we do detect differences between diploids and tetraploids for each C. albicans genetic background ( Fig. S2 and Table S2). Taken together, these results further support the observation that there is no global correlation between ploidy state and virulence, but ploidy does have an important contribution to virulence phenotypes within pathogen genetic backgrounds regardless of host immune status.
When we look at specific diploid-tetraploid pairs, representing different C. albicans genetic background, we see significant differences in virulence in immunocompromised hosts between the diploid and tetraploid state for at least one fitness measure, for all C. albicans genetic backgrounds except for the laboratory homozygous (Fig. 4). For the laboratory heterozygous genetic background, the tetraploid counterpart was more virulent than its diploid counterpart when differences between ploidy states were detected (lineage growth and brood size), similar to the pattern observed in healthy hosts (Fig. 2). Furthermore, the clinical bloodstream diploid was more virulent than its tetraploid counterpart for immunocompromised host survival and brood size ( Fig. 4A and C), similar to the pattern observed in healthy hosts.
However, the oral/vaginal strain had significant differences between its diploid and tetraploid counterparts for host survival and reproductive timing ( Fig. 4A and D), however the tetraploid was more virulent in the former, while the diploid was more virulent in the latter. We also performed every pairwise comparison between treatments for host survival (Table S2), lineage growth (Table S3), brood size (Table S4), and reproductive timing (Table S5) and find significant differences in virulence between different C. albicans genetic backgrounds and ploidy states for most host fitness measures, except host reproductive timing, were very few differences between C. albicans strains were detected. These results emphasize that while global differences
albicans infection regardless of genetic backgrounds or ploidy. A) Survival curves for
sek-1 C. elegans populations that are either uninfected (exposed to just an E. coli food source, grey) or when infected with different C. albicans strains (indicated in legend). Statistical significance was tested using pairwise comparisons of survival curves with Log-rank (Mantel-Cox) test. Astrisks denote statistical differences compared to the uninfected control (* indicates p <0.05, **** indicates p < 0.0001). C. albicans treatments that share letters are not significantly different, whereas treatments with differing letters are stastically different. B) Box and whiskers plot of host lineage growth which represents the total population size (representing the number of F1 and F2 progeny) produced within 7 days from a single founder sek-1 C. elegans host infected with C. albicans. Boxes indicate the 25-75th quartiles with median indicated. Error bars are the normalized range of the data and circles indicate outliers. The mean and 95% CI of the uninfected control treatment are indicated by the grey dashed line and shaded grey box. Statistical significance was tested using one-way ANOVA. Astrisks denote statistical differences compared to the uninfected control (* indicates p <0.05, *** indicates p < 0.001). C. albicans treatments that share letters are not significantly different, whereas treatments with differing letters are stastically different, post-hoc Dunn's multiple comparison test. C) Total brood size and D) Average percentage of host progeny produced during days 1-3 of adulthood (normal reproductive timing) of sek-1 C. elegans infected with C. albicans. Data and statistical analysis are the same as (B). E) % sek-1 host survival on Day 7 for diploid (dip) and tetraploid (tet) C. albicans strains (colored symbols indicate specific C. albicans genetic background). Statistical significance between diploid and tetraploids was tested using Wilcoxon matched-pairs signed rank test and p values are indicated. between C. albicans genetic backgrounds or ploidy states may be difficult to identify in immunocompromised hosts (Fig. 3), ploidy-specific and genetic background differences in virulence are detectable across multiple host fitness measures.
Ploidy-specific interactions between host and pathogen genotypes.
Finally, we were curious if host immune status impacted the virulence relationship between each diploid-tetraploid pair of strains. Since there are significant differences for most host fitness measures between healthy and immunocompromised hosts even when uninfected (Table S6), we normalized each C. albicans-infected host fitness metric to that of the uninfected control to directly compare the severity of C. albicans infection between host genotypes. This analysis shows that immunocompromised hosts are more significantly more susceptible to C. albicans infection and show larger reductions in survival, brood size and lineage growth compared to those observed in healthy hosts, while the amount of delayed reproduction caused by C. albicans infection is similar (Fig. 5A-D). We also detect significant interactions between C. albicans strain and host immune status for lineage growth ('interaction' p=0.0004, two-way ANOVA), brood size ('interaction' p=0.0042, two-way ANOVA; Fig. S2C), and reproductive timing ('interaction' p=0.0001, two-way ANOVA). sek sek-1 Normal Reproductive Timing (rel. to uninfected) sek-1 N2 sek-1 N2 sek-1 N2 sek-1 Figure 5: Ploidy-specific interactions between healthy and immunocompromised hosts. A) Relative impact on host survival from Day 7 survival data (C. albicans D7 survival/uninfected D7 survival for each host type) for all C. albicans strains (colored symbols indicate specific C. albicans genetic background) in healhty (N2) and immunocompromised hosts (sek-1). The mean and 95% CI of the uninfected control treatment are indicated by the grey dashed line and shaded box. Statistical significance between host genotypes was tested using Wilcoxon matched-pairs signed rank test and p values are indicated. B) Box and whiskers plot of relative host lineage growth, C) Brood size, and D) Reproductive timing between healthy. Data and statistical analysis are the same as (A). E) Relative impact of C. albicans ploidy on host survival. Relative virulence of diploid (solid lines) and tetraploid (dotted lines) C. albicans lab homozygous (pink), lab heterozygous (green), bloodstream (orange), and oral/vaginal (blue) genetic backgrounds in healthy (N2) and immunocompromised hosts (sek-1). Y-axis scale bar is the same as in (A). F) Relative host lineage growth, G) brood size, and H) reproductive timing between healthy and immunocompromised hosts across C. albicans genetic backgrounds. Symbols represent the mean value and error bars +/-SD. Y-axis scale bar is the same as in (B, C and D) respectively. Stastistical significance was tested by two-way ANOVA and 'interaction' p value indicated.
We next examined whether the relationship between ploidy-specific virulence differences changed depending on host immune status. To do this, we plotted the relative impact of the diploid (solid lines) and tetraploid (dotted lines) in healthy (N2) and immunocompromised (sek-1) host backgrounds for all four C. albicans genetic backgrounds (Fig 5E-H). In general, there was a high degree of similarity in the relationship between diploid and tetraploids across for both host genotypes, as indicated by a non-significant interaction term measured by two-way ANOVA. However, there were a couple of notable exceptions, particularly in the bloodstream pair of strains. In healthy hosts, we observed the diploid displaying more severe virulence phenotypes than its tetraploid counterpart for lineage growth and reproductive timing, yet these differences are diminished in immunocompromised hosts. Thus, we detect a significant interactions between host immune status and C. albicans ploidy for the bloodstream genetic background. Strikingly, we detect the reverse pattern for host survival with the C. albicans bloodstream diploid and tetraploid strains, in which no detectable differences are observed in healthy hosts, but the diploid is significantly more virulent than the tetraploid in immunocompromised hosts, yet regardless of the host context or specific fitness measure, the diploid is more virulent than its tetraploid counterpart. From these results, we conclude that interactions between host immune status, pathogen genetic background and ploidy state determine the severity of virulence phenotypes in C. albicans.
Discussion
Here, we sought to understand how the genetic background and ploidy of the human fungal opportunistic pathogen Candida albicans contributes to virulence phenotypes in the nematode host C. elegans. Overall, we found that most of the strains we tested were virulent for at least one measure of host fitness, but cannot generalize any over-arching patterns in how ploidy or genetic background contribute to virulence phenotypes. Rather, we have found that there is a significant interaction between C. albicans ploidy and its genetic background. We detect this interaction in both healthy and immunocompromised host contexts.
Immunocompromised hosts display more severe fungal infections compared to healthy hosts, regardless of C. albicans ploidy and/or genetic background.
C. albicans was considered an 'obligate' heterozygous diploid for many years [40][41][42][43] , and its diploid-tetraploid parasexual cycle only discovered and characterized in the last two decades 14,44 . Much of the research regarding C. albicans ploidy states have focused on the mechanisms involved with the parasexual cycle and how the ploidy reduction process that tetraploids undergo promote cellular heterogeneity and genetic variation 25,[45][46][47][48][49] 36 and that other fungal pathogens display extensive ploidy variation 13,14 , assessing how ploidy impacts virulence has newfound clinical relevance. We think our systematic approach of comparing paired diploid and tetraploid strain across genetic backgrounds and host contexts provides a more comprehensive analysis of ploidy and virulence. Furthermore, our results support the contradictory findings reported earlier, as we observe that sometimes certain tetraploids are less virulent than diploids, sometimes there are no differences between ploidy states, and sometimes certain tetraploids are more virulent than diploids (Tables S2-S5).
Importantly, not only does our analysis account for differences in C. albicans genetic background, but, by utilizing C. elegans as an infection system we can assess multiple measures of host fitness beyond host survival. As an opportunistic pathogen, C. albicans causes a wide-range of infections with the most severe infections occurring in immunocompromised individuals and only a fraction of these severe infections result in patient death. As such, we need to broaden our scope to investigate fungal virulence beyond a lethal phenotype. We have previously demonstrated that C. albicans infection of C. elegans not only results in reduced survival, but also has reduced fecundity and delays in reproduction 38 . Here, we have leveraged this infection system to screen for differences in virulence between C. albicans ploidy and genetic backgrounds. While we did not find any overall pattern in virulence between diploids and tetraploids, we did identify ploidy-specific differences depending on genetic background (Figs. 2 and 4). Importantly, these specific patterns were consistent across multiple host fitness measures and host immune status (Fig. 5). Furthermore, we found that immunocompromised hosts had significantly more severe infections than immune competent hosts (Fig. 5), similar to what is observed clinically.
It is important to note that the clinical C. albicans genetic backgrounds, bloodstream and oral/vaginal, are not completely isogenic between the diploid and tetraploid strains, and that some allelic variation exists in addition to their differences in ploidy 36 . It is feasible that some of the differences in virulence that we observe between the diploid and tetraploids isolates in these two clinical C. albicans backgrounds (i.e. the bloodstream diploid is more virulent than its tetraploid, whereas the oral/vaginal tetraploid is more virulent than its diploid) is due to these allelic differences. By also measuring virulence phenotypes in laboratory-derived genomes that only differ in the number of chromosome sets they contain, we can directly assess the impact ploidy has on virulence. Here, we still observe different patterns of ploidy-specific virulence, depending on genetic background. In the laboratory heterozygous genome, we consistently observe the tetraploid as more virulent than its diploid in both healthy (Fig. 2) and immunocompromised (Fig. 4) host contexts. However, in the laboratory homozygous genome, we frequently failed to find any significant differences between diploid and tetraploid for any of the host fitness measures, the only exception being healthy host survival, in which the tetraploid was avirulent and the diploid was virulent ( Fig. 2A). This result is consistent with previous findings that C. albicans homozygous genomes do not show significant growth differences in vitro or in vivo between ploidy states 26,46 .
In this work we found that ploidy and genetic background interact to contribute to C. albicans virulence (Figs. S1 and S2). We also observe significant interactions between C.
albicans strains and host immune status (Fig. 5). These results indicate that virulence is not simply a binary 'avirulent/virulent' classification, but rather a complex trait and we need to start dissecting fungal virulence from this perspective. Recently, genome analysis of clinical isolates revealed genomic features that differ between clinical isolates and SC5314, the laboratory reference strain of C. albicans, and/or identify genetic determinants of antifungal drug resistance 21,37,[50][51][52][53] . Only a small number have attempted to identify the genetic loci underpinning virulence and only two genes were identified and validated, EFG1 20,51 and PHO100 46 .
Importantly, these approaches may overlook other factors and contexts, such as ploidy or host immune status, that contribute to virulence. While it is clear that there variation in virulence occurs across C. albicans genetic backgrounds, there is still much work to do in elucidating the drivers of virulence.
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Domain: Biology Medicine
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Frequency and Antimicrobial Resistance Pattern among Bacterial Clinical Isolates Recovered from Different Specimens in Egypt
Antimicrobial resistance (AMR) is a global public health threat resulting in high mortality rates. Current study aimed to identify the most prevalent pathogens among variable infection sites and their AMR pattern. Data concerning cultures and antibiotic susceptibilities were retrieved from Microbiology Department’s records and statistically analyzed. Out of 554 bacterial isolates, Gram negative isolates (68.4%) were predominant. Urine specimens showed the highest incidence of recovery of total isolates (41.5%, n=230) followed by blood (23.1%, n=128), while sputum specimens exhibited the least frequency (17%, n=94). E. coli (30.7%, n=170), S. aureus (21.1%, n=117) and Klebsiella spp (20.9%, n=116) were the most frequently isolated pathogens. Recovery of isolates was significantly more frequent among males (P<0.05) except in case of urine specimens. Highest incidence of resistance in both Gram positive and Gram negative isolates was recorded in case of cephalosporins and penicillin/β-lactamase. Gram positive isolates exhibited the least resistance to linezolid (10.8%) and vancomycin (9.5%) whereas colistin was the most effective against Gram negative isolates as it recorded 16.4% resistance. Higher frequency of multiple drug resistance (MDR) was also observed in Gram negative isolates compared to Gram positive ones. Resistance to uropathogens and MDR were significantly more frequent in males. Although E. coli was the most prevalent uropathogen but it showed the least incidence of MDR however Pseudomonas spp exhibited the highest MDR rate. The high incidence of resistance in the current study is alarming and highlights the necessity of routinely monitoring the local prevalence of resistance for selecting the best antimicrobial treatment and as a guide for empirical therapy.
Introduction
Emergence of antimicrobial resistance to different antibiotics is a critical problem that leads to a real danger of post antibiotic era [1]. During the last decade, many reports have documented the doubling or even tripling in the resistance rates of nearly all groups of serious pathogens [2] in addition to the progressive emergence of MDR isolates [3]. The lack of proper and early identification of the causative pathogens especially in patients with serious infection led to the administration of broad spectrum antibiotics. Such issue resulted in dramatic emergence of resistant strains that the magnified the problem of resistance [1]. The Center for Disease Control and Prevention (CDC) reported that high rates of infection with resistant pathogens all over the world resulted in passive influence on the global economy, loss in productivity and elevated death rates [4]. Data concerning the endemic antimicrobial resistance are usually unavailable especially in the areas of the world where antibiotics are available over the counter [5]. Despite that many reports demonstrated the incidence and the resistance patterns of many pathogens, few studies are available to estimate the endemic antimicrobial resistance profile in low and middle income countries [6]. Thus an evidence based knowledge regarding the local antimicrobial resistance pattern is considered an essential guide for treatment of specific pathogens as well as for empirical antimicrobial therapy [5]. This guide is also of significant importance in the implementation of the effective antimicrobial stewardship [1] as well as in the design of national and international research programs [5]. Therefore, the present study aimed to identify the microbial spectrum and the antimicrobial resistance pattern of the most prevalent pathogens recovered from variable infection sites in addition to determination of the prevalence of multiple drug resistance.
Study Design
Retrospective study was conducted utilizing the microbiology laboratory records of in-patients in an Egyptian hospital in Cairo. Microbiology records were reviewed and records showing mixed cultures or unidentified microorganisms as well as duplicate records for the same patients were excluded [7]. Records for specimens other than blood, sputum, urine and wound specimens were also excluded. Information regarding the identified bacterial isolate, specimen type, patient's gender and antimicrobial susceptibility was collected and recorded.
Identification of the Isolated Organism
Sample processing, identification of the microorganism to the genus and/or species level was performed according to the standard operating procedures of the hospital in place. Briefly, bacterial isolates were identified based on morphological characters, Gram stain and confirmatory biochemical test. Gram positive bacteria were identified using catalase reaction, coagulase test as well as via testing the hemolytic activity on blood agar. Identification of Gramnegative bacteria was carried out through inoculation on MacConkey agar plates, followed by biochemical tests such as oxidase and urease tests.
Antimicrobial Susceptibility
Antimicrobial susceptibilities of the bacterial isolates were determined using Kirby-Bauer disk diffusion method using Mueller Hinton agar plates according to the Clinical Laboratory Standards Institute (CLSI) guidelines. The tested antimicrobial discs were routinely supplied from Oxoid and Bioanalyse.
Multiple Drug Resistance (MDR)
MDR isolates were identified according to the guidelines recommended by European Centre for Disease Prevention and Control (ECDC) and the CDC. MDR isolates were identified as isolates showing resistance to at least one antimicrobial agent in three or more antimicrobial classes [8].
Statistical Analysis
Data were presented as counts and percentage. Statistical analysis was performed using statistical package for social sciences (SPSS) computer software (version 25), IBM software, USA. Pearson Chi-square test was performed to identify the significant effect of each antibiotic on different isolates as well as the relation between gender and prevalence of different isolates. Chi-square and Fisher exact tests were used to test the association between gender and resistance to different antibiotics. Statistically significant difference was considered at p value ≤ 0.05.
Gram negative isolates showed predominance of E. coli (44.9%, n=170) followed by Klebsiella spp (30.6%, n=116) and Pseudomonas spp (10.6%, n=40). On the other side, Proteus spp (6.3%, n=24), non-lactose fermenters (NLF) (6.1%, n=23) and Acinetobacter spp (1.6%, n=6) were less frequent. The majority of Gram negative isolates were recovered from urine (51.5%, n=195) with the predominance of E. coli (58.2%, n=99) and Klebsiella spp (55.2%, n=64). Wound and sputum specimens were also found as another source for recovery of E.coli, where they showed frequency of recovery in the order of 18.2% and 14.7%, respectively. Pseudomonas spp also showed high incidence of recovery from urine (40%, n=16) followed by wound (27.5%, n=11). Recorded data revealed higher incidence of total Gram negative isolates (60.7%) in male patients. Gram negative isolates in male patients was significantly (P<0.05) more common than females among different specimens except in case of uropathogens where the difference between the incidence of recovery of these isolates was statistically nonsignificant among male and female patients ( Table 2). Percentage of isolates recovered from males, % F: Percentage of isolates recovered from females, % a : percentage of each isolate relative to N, *; Statistically significant difference between the incidence of recovery of isolates from males and females.
Nitrofurantoin showed potential antimicrobial activity against E. coli, where the percentage of resistance against it was 17.2%. Meanwhile, Klebsiella spp, NLF, Pseudomonas spp and Proteus spp recorded high resistance rates to nitrofurantoin in the order of 60.7%, 77.8%, 92.9% and 100%, respectively. Moreover, E. coli showed high resistance rates to most antimicrobial classes with lower resistance to piperacillin/tazobactam (43.3%, n=67) and amikacin (34.6%, n= 104), followed by cefoperazone/sulbactam (27.8%, n=90) and meropenem (23.3%, n=120). On the other side, the least resistance was observed in case of colistin (6.7%, n=15). Higher degree of resistance was recorded in case of Klebsiella spp compared to E. coli. Pseudomonas spp also followed similar resistance profile with the least resistance to both imipenem and colistin (12.5%, n=8). In addition, an elevated resistance in case of other bacterial isolates such as Proteus spp, Acinetobacter spp and NLF was also recorded. It was also obvious that, colistin was the most promising antimicrobial agent either against each Gram negative isolate or in case of total isolates, where it exhibited a resistance rate in the order of 16.4%. Data also revealed that some antimicrobials showed a statistically significant difference in their antimicrobial activities to different bacterial isolates as presented in Table 4.
Multiple Drug Resistance
Recorded data revealed that MDR occurs in 58.9% of total Gram positive isolates with a significant prevalence of MDR in males (66%). S. aureus exhibited the highest incidence of MDR (59.8 %), followed by Streptococcus spp (58.3%) and CoNS (54.5%). MDR was more common in blood (60.9%, n=87) and urine (62.9%, n=35) isolates, with lower frequency in wound (50%, n=28) and sputum (56%, n=25). Isolates recovered from blood, sputum and wound showed also a significant higher frequency of MDR among male patients, except in case of urine isolates where there was no significant difference between the prevalence of MDR among male and female patients (Table 5). Higher incidence of MDR (77%) was recorded in Gram negative isolates compared to Gram positive ones. E. coli showed the least percentage of MDR (67.6%), while Pseudomonas spp exhibited the highest incidence of MDR (95%). A significant higher frequency of MDR was observed in males (63%) compared to females (37%). Blood, sputum and wound isolates exhibited similar profile but the difference between the incidence of MDR in male and female uropathogens was non-significant (p>0.05) ( Table 6).
Prevalence of Resistance Among Uropathogens
Higher incidence of resistance to most antibiotics was significantly observed in isolates that were recovered from males compared to females (Table 7).
Discussion
Evaluating the altitudes of the problem of AMR is a challenge as the levels of antimicrobial resistance vary among healthcare settings and geographical regions. Infections with MDR pathogens result in postponed therapy which causes negative impact on the patient's health especially in case of immunocompromised individuals [9]. Moreover, adequate recognition of the proper use of antibiotics in each community is a key factor in the progress of resistance [10]. Current study aimed to determine the most predominant pathogens in our community and their antimicrobial resistance pattern.
In the present study, urinary tract infection was the most prevalent followed by blood stream infection with least frequency in case of respiratory tract infection. Gram negative isolates were mostly involved in urinary tract infections while Gram positive isolates were responsible for blood stream infection (BSI). Resembling our findings, a study reported that all the recovered uropathogens were Gram negative whereas 60% of the isolates causing BSI were Gram positive with highest incidence of S. aureus [2]. In the mean context, it was reported that urine specimens contributes in the recovery of 55.2% of bacterial isolates whereas blood, wound and sputum cultures were responsible for 25.3%, 16.2%, and 3.3% of isolates, respectively [11]. Moreover, a study demonstrated that Gram negative isolates were more common (61.3%, n=57) with the predominance of E. coli (n=36) [2]. S. aureus (22.8%, n=100), Klebsiella pneumoniae (14.8%, n=65) and E. coli (9.3%, n=41) were also reported as the most common pathogens among variable specimens in another study [12]. In agreement with the current study E. coli and Klebsiella pneumoniae weren't only the most frequently isolated pathogens among Gram negative isolates [13] but they also represented the most predominant pathogens relative to other uropathogens [14][15][16].
Although many studies reported that E. coli was as the most predominant isolate recovered from urine specimens but on the contrary to our results Klebsiella spp was categorized as the sixth most common uropathogen in one study [17] and S. aureus was the second pathogen involved in urinary tract infection (UTI) in another study [18]. The similarities as well as the variation in the type and frequency of these pathogens among different studies could be related to many factors such as environmental conditions, health practices, patient conditions, personal hygiene, number of patients involved in each study and laboratory procedures [19].
E.coli is not only one of the major pathogens responsible for UTI but it also plays a key role in wound and respiratory tract infection. Similar to current findings, E. coli was the most frequently isolated from urine specimens (85.9%) followed by wound (8.4%) and sputum specimens (5.6%) [20].
In the same context to the current results, Pseudomonas spp was one of the most prevalent Gram negative pathogens associated with urinary tract infections as well as in surgical sites [21]. In addition to other studies which reported that pus is the major source from which pseudomonas could be recovered [10,22].
Regarding BSI, the current data highlights the participation of Gram positive pathogens in this type of infection with higher rate of recovery of Gram positive isolates by about 2 folds compared to Gram negative isolates. Whereas the incidence of MDR among BSI was higher in Gram negative isolates compared to Gram positive ones. That was also supported by a study which demonstrated that among BSI, 59% of bacterial isolates were Gram positive however the frequency of MDR in Gram positive isolates was low (19.4%) compared to that in case of Gram negative isolates (34.2%) [23].
It is also important to point out the involvement of S. aureus and CoNS in BSI where both pathogens were reported as the most frequently isolated from blood specimens [17,12], respectively. Despite that our study revealed the superior contribution of S. aureus in the occurrence of BSI compared to CoNS. Another study demonstrated that among Gram positive isolates participating in BSI, CoNS (38.8%, n=72) was the most common pathogen followed by S. aureus (20.8%) [23].
Concerning the antimicrobial resistance pattern, the effectiveness of vancomycin against S. aureus was obvious in the current study in addition to other studies [11]. For example, it was reported that all S. aureus isolates were 100% susceptible to vancomycin [24].
In coincidence with the present study, higher incidence of resistance was recorded in Gram negative isolates compared to Gram positive ones [10]. In addition, E. coli demonstrated elevated resistance rates to ciprofloxacin and third generation cephalosporins compared to lower resistance towards nitrofurantoin [12]. In another study, E. coli exhibited elevated resistance to nalidixic acid and ceftriaxone [20]. Also in a study carried out in Mansoura University Hospitals (Cairo), it was observed that E. coli was highly resistant to cefuroxime (96%), ceftriaxone (92%), cefaclor (90%) and ciprofloxacin (76%) whereas lower resistance was recorded against meropenem (40%), imipenem (30%) and amikacin (16%) [25]. Also in agreement with our results, it was reported that E. coli exhibited the lowest percentage of MDR despite that it was the predominant uropathogen [18].
Resembling our findings, resistance to β-lactam antibiotics was reported as a major problem in a study carried out by Ibrahim and Hameed [13]. But on the contrary to the present study, they demonstrated lower resistance levels of Gram negative bacteria to amikacin, gentamicin and doxycycline in addition to high sensitivity of Gram positive isolates to macrolides and clindamycin [10]. The variation in the resistance pattern between the current study and other studies indicates this profile is influenced by variable determinants such as the diversity among different geographical regions [23], time during which each study was carried out as well as the study population [11].
Regarding resistance profile of Pseudomonas species and in agreement with the present study an elevated resistance rate was recorded against piperacillin/tazobactam and cefipime whereas higher sensitivity was observed to amikacin in addition to maximum sensitivity to imipenem [26]. Despite that another study reported that Pseudomonas aeruginosa was most commonly isolated from male patients, but it showed no resistance either to imipenem or colistin. The same study pointed out low antimicrobial resistance towards ceftazidime, piperacillin/tazobactam and cefipime [22], but these records weren't consistent with the current findings. This may be attributed to the variation between the detected pathogens in both studies and may indicate emergence of resistance in our community.
The recorded high incidence of MDR among Pseudomonas spp may be justified by the reported selective pressure due to mutations in chromosomal genes that led to production of extended spectrum β-lactamases (ESBL) as well as hyper expression of AmpC gene and the role of the efflux pumps. In addition to another resistance mechanism which is mediated through horizontal transfer of transposable elements that are coding for metallo-β-lactamases. Pseudomonas spp may also gain resistance to antibiotics as a consequence of interference with antibiotic permeability to the cell surface due to biofilm formation [21].
Elevated incidence of resistance to third generation cephalosporins and aztreonam as well as lower resistance rates to carbapenems in the present study might indicate the emergence of ESBL producing organisms in our community due to antibiotic abuse [10]. This is dependent on the fact that ESBLs are defined as Gram-negative bacteria that produce βlactamases resulting in resistance to first, second and third generation cephalosporins as well as aztreonam whereas they aren't able to confer resistance to carbapenems. ESBLs are also antagonized by inhibitors of β-lactamase such as clavulanic acid [27]. This could justify the obvious decrease in resistance which was recorded in the current study when cefoperazone (third generation cephalosporin) was combined with sulbactam (β-lactamase inhibitor) compared to the recorded elevated resistance against cefoperazone alone.
On the other side, the resistance to carbapenems may be related to efflux pumps and mutations in penicillin binding proteins. These mechanisms might enhance the resistance in case of Klebsiella pneumoniae, P. aeruginosa and Acinetobacter baumannii [28]. Thus the recorded higher resistance in case of Klebsiella spp compared to E. coli in the present study may be related to infections with Klebsiellaproducing carbapenemase-2 (KPC-2) or Metalloproteinase-1 producing K. pneumoniae [11].
Current study also recorded the emergence of resistance against colistin although it is considered the last line of defense against carbapenemase-producing Enterobacteriaceae. That might be attributed to the expression of plasmid-mediated colistin-resistant genes [29]. Also in consistence with our study, the bacterial uropathogens that were recovered from males showed higher incidence of resistance compared to females [18].
The rapid emergence of resistance is a global disaster that coincides with the regression in the discovery of new antibiotics [30]. It is worth to highlight that unreasonable consumption of antibiotics as well as transmission of resistant isolates among patients accounted for the progress in AMR rates [20]. Thus effective infection control measures [31], identification of the resistance mechanisms and the rational use of antibiotics through implementing effective antimicrobial stewardship are essential concerns. This stewardship should depend on assessment of the local prevalence of pathogens and their resistance profile so it could potentially manage the danger of AMR through reducing the selective pressure exerted on sensitive strains [32].
Conclusions
Gram negative isolates were more prevalent compared to Gram positive ones. Urinary tract infection was the most common followed by blood stream infection with highest incidence of E. coli, S. aureus and Klebsiella spp among total isolates. E. coli was the most common isolate accounting for urinary tract and wound infection whereas S. aureus was most frequently associated with blood stream infection. Males were more frequently subjected to different types of infections compared to females.
Highest incidence of resistance was associated with cephalosporins, followed by penicillin/β-lactamase inhibitors. However Gram positive isolates exhibited the lowest resistance to linezolid and vancomycin whereas colistin was the most effective antimicrobial agent against Gram negative isolates. Despite that the discovery of nitrofurantoin isn't recent but it retained most of its potentials especially against E. coli as well as Gram positive isolates.
Elevated frequency of MDR was obvious among Gram negative isolates. Although E. coli was the most prevalent pathogen but it showed the least incidence of MDR. Contrarily, Pseudomonas spp exhibited the highest MDR rate. Prevalence of MDR was higher in males except in case of uropathogens. The elevated resistance rates in case of pathogens that were recovered from males reflect the necessity of considering the patient's gender in case of empirical prescription of antimicrobials. Also, the emerging resistance to carbapenems and colistin should also be taken into account and spot light on the importance of effective control measures.
It is necessary to note that antimicrobial therapy should take into account the data regarding the local prevalence of causative pathogens and their antimicrobial resistance profile rather than the universal guidelines. The present study presents a whole vision regarding the antimicrobial resistance pattern for the most frequent bacterial isolates among different specimens as well as essential considerations during empirical antimicrobial therapy. This local prevalence will also aid in establishing an effective antimicrobial stewardship to preserve the potentials of the current antimicrobial agents.
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Domain: Biology Medicine
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Prevalence of exfoliative toxins and toxic shock syndrome toxin-1 encoding genes among coagulase positive S taphylococcus isolated from human and animal sources
1 Department of Medical Laboratory Sciences, College of Sciences, Al-Balqa‟ Applied University, Al-Salt, 19117 Jordan. 2 Department of Biology, College of Sciences, Al-Anbar University, Al-Anbar, Iraq. 3 Department of Biotechnology, Faculty of Agricultural Technology, Al-Balqa‟ Applied University, Al-Salt, 19117 Jordan. 4 Faculty of Allied Medical Sciences, Arab American University, Jenin-Palistine, P. O. Box 240 Jenin, Palestine.
INTRODUCTION
Staphylococcus sp. are one of the most commonly found pathogenic bacteria in human environment. The epidemiology of Staphylococcus species in animals has gained interest in the last years, not only for their importance in veterinary medicine, but for its increasingly evidenced zoonotic potential. The genus includes both human and animal pathogens, generally coagulasepositive staphylococci (CPS) such as S. aureus, S. intermedius, S. delphini, S. hyicus, S. schleiferi subsp.coagulans, and S. pseudintermedius, and coagulase negative staphylococci (CNS) such as S. equorum, S. xylosus, S. saprophyticus, S. succinus, S. warneri, S. epidermidis, and S. lentus (Devriese et al., 2008). Staphylococcus aureus is a dangerous human pathogen responsible for a wide variety of diseases. Other species are difficult to identify as frank human pathogens, but a few reports have described other coagulase positive staphylococci as causing opportunistic infections (Van Hoovels et al., 2006).
Nearly all S. aureus strains secrete exoproteins such as coagulase, nucleases, proteases, lipases, hyaluronidase and collagenase. Staphylocoagulase (SC) causes coagulation of plasma and is regarded as a marker for discriminating S. aureus from other less pathogenetic staphylococci. The nuclease enzyme is the major regulator of S. aureus virulence determinants (Olson et al., 2013) and the amplification of the nuc gene has a potential for the rapid diagnosis of S. aureus infections (Costa et al., 2005). Staphylococci secrete a wide spectrum of diverse extracellular proteins that are coordinately expressed during different stages of infection by a network of virulence regulators which render the bacterium virulent (Cotar et al., 2010). These include adhesion factors, toxic proteins/enzymes, and exotoxins including exfoliative (eta, and etb), staphylococcal enterotoxins (SEs), and toxic shock syndrome toxin-1 (TSST-1) (Nemati et al., 2013).
As major virulence factors of S. aureus, TSST-1, and ETs (A and B) are pyrogenic toxins that have been implicated in host colonization, invasion of damaged skin and mucus, and evasion of host defense mechanisms. They are responsible for specific acute staphylococcal toxemia syndromes (Udo et al., 2009). Exfoliative toxins (also known as "epidermolytic" toxins) are particularly interesting virulence factors of S. aureus. These extremely specific serine proteases recognize and cleave desmosomal cadherins only in the superficial layers of the skin, which is directly responsible for the clinical manifestation of staphylococcal scalded skin syndrome (SSSS) (Bukowski et al., 2010). However, a significant increasing rate of ETs was recorded for nasal and clinical isolates (Daği et al., 2015). The production of eta and etb has been examined in S. aureus strains and other strains associated with SSSS (Plano, 2004). Other CPS species, such as S. intermedius and S. hyicus, produce similar toxins (Ahrens and Andresen, 2004). However, it has not been fully understood whether the toxins are produced only by human strains or whether animal strains also produce them. Direct contact between animals and humans is a relevant factor to take into account to understand the prevalence and the evolution of Staphylococcus species. As S. aureus could pass from livestock to humans, it could be public health problem.
Clinically, toxic shock syndrome is closely associated with strains of S. aureus carrying the gene encoding for tst and associated mostly with tampon use (McDermott and Sheridan, 2015). The gene encoding tst is a chromosomal, and the toxin is symptomatically related to the staphylococcal enterotoxin group of toxins which are included in the pyrogenic toxin superantigen families (PTSAgs). PTSAgs exert their virulence by binding to the major histocompatibility complex (MHC) class II molecules and the Vβ chain of the T-cell receptor (TCR) from the outside in a nonspecific manner. This leads to the stimulation of T-cell proliferation, the release of inflammatory cytokines, and ultimately the suppression of the host immune system (Larkin et al., 2009).
Due to the high sensitivity and specificity they provide, molecular markers are an alternative tool for accurate identification and classification of Staphylococcus species. Molecular assays targeting some genes such as hsp60, 16S rRNA gene, femA, dnaJ (Hauschild and Stepanovic, 2008), and catalase (kat) gene have been used for reliably identifying and classifying staphylococci (Blaiotta et al., 2010). The aim of the present study was to characterize CPS isolated from human and animal sources in Jordan and to investigate the prevalence of ETs and tst genes among the isolates. In addition, PCR-RFLP analysis of the kat gene was employed for a genotypic study.
Collection of samples
A total of 753 samples were collected from human and animal sources during the period from October 2012 to May 2013.
Human sources
One hundred healthy students (53 female and 47 male) at Al-Balqa' Applied University were enrolled as volunteers. Two swabs were obtained from each student, one from nasal, and the other from nail. A written informed consent was obtained from all the volunteers in the study. Swabs were incubated on Tryptic Soy Broth (TSB) supplemented with 7% NaCl for 24 to 48 h at 37°C. In addition, 73 S. aureus isolates from routine microbiological specimen were collected from different hospitals in Jordan. The isolates were obtained from cultures of different specimens including blood infection, urinary tract infection, abscess, and septicemia. Suspected colonies of S. aureus from primary culture plates of Blood agar (BA), and Mannitol Salt Agar (MSA) were confirmed, by Gram reaction, positive catalase, tube coagulase and *Corresponding author. E-mailAuthor(s) agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License (Fabiano et al., 2008) and were kept at 4°C for identification (Abdul Aziz et al., 2013).
Animal sources
Four hundred and eighty samples were collected from three central slaughterhouses in Jordan which represent the population of animals in Amman city (capital of Jordan). Samples were imported meat (Romanian, Australian) from177 sheep, 47 goats, and 16 cows. The age of the animals ranged from 1.5 to 3 years. Two samples were obtained from each animal, one from nares before slaughtering and others from various muscle sites by collection of segments of the muscle or from swabs of slaughtered animals. Samples were enriched in TSB containing 7% NaCl for 24 to 48 h of incubation at 37°C (Isenberg, 2004). A portion of the enrichment cultures were then streaked on MSA and BPA and incubated at 37°C for 24 to 48 h. The suspected colonies were maintained on Staphylococcal 110 (SM1110) media and kept at 4°C for identification.
Extraction of genomic DNA
Bacterial culture was grown overnight on nutrient broth; 2 ml of the culture was transferred into a microcentrifuge tube and spun at 5,000 x g for 5 min. The pellet was resuspended in 567 μl of Tris-EDTA (TE) buffer to which 30 μl of 10% SDS and 3 µl of 20 mg/ml proteinase K were added. The mixture was inverted gently and incubated for 1 h at 37°C. After incubation, 100 µl of 5M NaCl was added and mixed thoroughly. Then, eighty microliter of 10% Cetyl trimethylammonium bromide (CTAB) -0.7 M NaCl solution was added and the tubes were incubated for further 10 min at 65°C. Equal volume of phenol/chloroform/isoamyl alcohol (25:24:1) was added, mixed well and centrifuged at 10,000 rpm for 10 min. The upper aqueous phase was transferred to a new tube and an equal volume of chloroform/isoamyl alcohol (24:1) was added and centrifuged at 10,000 rpm for 10 min. The upper aqueous phase was then transferred to a new tube and 0.8 volume of isopropanol was added, mixed gently until the DNA was precipitated. The DNA was washed with 70% ethanol and resuspended in 50 μl TE buffer (Rallapalli et al., 2008).
Detection of tst, eta, and etb toxins genes by PCR
The primers used for detection of tst, eta, and etb genes are listed in Table 1 (Johnson et al., 1991). Each polymerase chain reaction (PCR) contained 2.5 μl of 10X PCR Buffer, 1.0 μM MgCl2, 200 μM dNTP, 1U Taq DNA polymerase, 10 pmol of each primer, and 50 ng/ul of template DNA. The final volume was adjusted to 25 μl by adding sterile ultrapure water. DNA amplification was performed using the following amplification conditions: initial denaturation for 5 min at 94°C followed by 30 cycles of denaturation (94°C for 2 min), annealing (50°C for 1 min), and extension (72°C for 1 min). A final extension step (72°C for 5 min) was employed after the completion of the cycles (Rall et al., 2008).
Identification of CPS species by PCR-RFLP
Two oligonucleotide primers were used in this part: CPSK1F (CARAAYAACTGGGATTTCTGGAC) and CPSK6R (GCATCRCCRTAWGAGAATAAACG) from the highly conserved region of S. aureus kat gene sequences found in GenBank. Targeting positions were 487-509bp and 1031-1009 bp of the kat gene of S. aureus subsp.aureus Mu50 (BA000017) which allowed the amplification of 544 bp fragment. PCR amplifications were performed with a total volume of 50 μl, including: 5 μl of template DNA, 5 μl of 10X buffer PCR buffer, 2.5 μl of 50 mM MgCl2, 0.5 μl of dNTP mix (25 mM of each dNTPs), 0.2 μl of each primer (0.1 mM), and 0.2 μl of Taq DNA polymerase solution (5 U/μl). PCR amplification conditions consisted of an initial denaturing step (95°C (Blaiotta et al., 2010).
Identification of CPS species and subspecies by DNA sequencing
From the highly conserved region of kat gene sequences of CPS found in genbank two oligonucleotide primers were selected: CPSK1F7 (CARAAYAACTGGGATTTCTGGAC) and CPSK6R (GCATCRCCRTAWGAGAATAAACG) (Macrogen Inc. Seoul, South Korea). According to the variation in biochemical results and PCR-RFLP band patterns, strains were choice to sequence. Analysis of DNA similarity was performed using BLAST programs (Basic Local Alignment Search Tool).
Phylogenetic analysis
Using the keyword "catalase Staphylococcus", sequences of catalase genes from different Staphylococcus species and isolates were retrieved from the National Center for Biotechnology Information (NCBI) site (www.ncbi.nlm.nih.gov). A phylogenetic tree of catalase genes was constructed using Molecular Evolutionary Genetics Analysis (MEGA) version 5.2 (Tamura et al., 2011). The Neighbor-joining (NJ) method of tree generation was used to assess the evolutionary relationships (Saitou and Nei, 1987), and the significance of clustering was evaluated by bootstrapping with 1000 replications.
RESULTS
In this study, a total of 753 samples were obtained from 273 human (100 nasal swabs, 100 nail swabs, and 73 clinical samples), and 480 animal sources (240 nasal swabs, 240 meat (pieces and swabs). Samples distribution in respect to their livestock sources was shown in Table 2. According to colonies morphology and coagulase test, only 167 isolates were characterized as CPS (Table 3). The prevalence of CPS colonizing humans was 115 including: 26(26%) nasal swabs, 16(16%) nail swabs. CPS are present in seven cases in both the nail and the nasal of the same person. However, 52 of CPS was isolated from animal sources and distributed as follows: nasal swabs isolates was detected in 13(5.4%),meat pieces in 34(24.2%),and meat swabs in 5(5%) of the tested samples.
Characterization of coagulase positive staphylococcus
One hundered and sixty seven CPS isolates obtained from human and animal sources were characterized by different biochemical tests (Table 4). All of CPS isolates were able to grow on P agar supplemented with acriflavine, able to produce catalase, reduce tellurite, and did not produce oxidase or amylase enzymes. However, CPS isolated from human sources produced more virulance factors than animal isolates.
The prevalence of toxin genes (eta, etb, and tst) in CPS isolates
The eta, etb and tst genes positive isolates produced 119, 200 and 350 bp, respectively. The gene coding for eta toxin was the most frequent among isolates obtained from human sources which showed 23 and 49.3% for nasal and clinical., respectively. However, 38.4% of meat isolates showed positive results for the eta toxin. On the other hand, only 7.6% of humans expressed etb gene in the noses. The possession of various genes combination was found in 15 (8.98%) of all the isolates obtained from human sources. Nasal (15.38%) and clinical (13.7%) showed eta plus tst, whereas, (3.8%) of nasal isolates showed eta plus etb genes (Table 5). The prevalence of eta, etb, and tst among human isolates are more than animal isolates.
Differentiation of CPS species by PCR-RFLP approach
A fragment of 544 bp for catalase gene was amplified from 167 isolates. A clear differentiation at the species and subspecies levels was achieved by using PCR-RFLP analysis that was performed using PCR products from all isolates (Figure 1). Accordingly, 163 of the isolates were identified as S. aureus subsp.aureus, and 4 were identified as S. pseudintermedius. However, returning to the source of CPS, all S. pseudintermedius isolates were obtained from animal sources including 3 obtained from sheep and one from goats. Phenotypic characterization revealed that Staphylococcus aureus subsp.aureus produced more virulence factors comparing with S. pseudintermedius. This is demonstrated by the high percentages of betahemolysin produced on blood agar supplemented with 5% (v/v) human blood, lipase, lecithinase, acetoin production, DNase and Thermonuclease activity (TNase). However, all S. pseudintermedius isolates were able to ferment mannitol and produce protease, alpha hemolysin and lysostaphin susceptibility, but unable to produce lecithinase enzyme and βhemolysin. Toxigenicity study revealed that all of the exotoxin-producing isolates were 6).
Sequencing and bioinformatics analysis
For bioinformatics analysis to confirm the PCR-RFLP results, a set of PCR products representing different species was used for sequencing. The BLAST analysis of the sequencing results classified some isolates as S. aureus subsp.aureus and others as S. pseudintermedius. To determine the phylogenetic relationship of catalase sequences with the sequences of different Staphylococcus species and isolates, a bootstrap phylogenetic tree was constructed using the neighbor joining method. The phylogenetic analysis clustered together the sequences of the same species (Figure 2). However, S. pseudintermedius identified in our study does not seems to be clustered with the other members of the intermedius group. Our findings confirms that the isolates characterized in this study represent S. aureus subsp.aureus and S. pseudintermedius.
DISCUSSION
S. aureus is one of the most commonly found pathogenic bacteria responsible for a broad range of nosocomial and community acquired infections due to an impressive array of toxins and other virulence determinants (Plata et al., 2009). It colonizes the skin and mucosa of human and several animal species. Although multiple body sites can be colonized in human beings, the anterior nares of the nose is the most frequent carriage site for S. aureus (Wertheim et al., 2005a). Extra-nasal sites that typically harbor the organism include the skin, perineum, and nails (Wertheim et al., 2005b). Accurate and rapid detection is important not only for choosing appropriate antibiotic therapy for the individual patient, but also for control of the endemicity of S. aureus infection.
The pathogenicity of Staphylococcus is related to the production of many virulence factors from which coagulase was considered as the most important one. In the present study, 26% of CPS was isolated from human nasal and 16% from nail samples. These results are in agreement with results obtained by other researchers (Al-Zahrani, 2012;Walther et al., 2012). However, Crosssectional surveys of healthy adults populations have reported S. aureus nasal carriage rate of approximately 27% since 2000 (Wertheim et al., 2005a;Bischoff et al., 2004). This rate is much lower than the earlier reported prevalence of 35% which included studies since 1934 (Kluytmans et al., 1997). Improved personal hygiene and changes in socioeconomic class might explained this decline.
Strains present in the nose often contaminate the back of hands, fingers and face and so, nasal carriers can easily become skin carriers (Al-Zahrani, 2012). However, the prevalence of CPS among animal samples differed according to the sites of isolation. Nasal prevalence showed 5.4%, while meats give 16.25% (24.2 and 5% for pieces and swabs, respectively). These results were similar to the data from others (Abd El-Hamid and Bendary, 2013;Goja et al., 2013). Phenotypic characterization of CPS to species level was achieved applying growth on media supplemented with acriflavine, oxidase, mannitol utilization, hemolysin production, acetoin production, and amylase activity. However, 163 (97.6%) of CPS were characterized as typical S. aureus, while 4 (2.39%) of CPS were biochemically atypical by their production of αhemolysin, the absence of clumping factor, and lecithinase production. The differences could be due to the diversity in the origin of the isolates (mainly animals), or might be due to some mutations that occur in the genes thus affecting the metabolic activity of the species. In addition to the fact that most phenotypic identification systems have been developed for human health care and validated using clinical isolates obtained from human infections and thus might misclassify isolates from livestock (Zadoks and Watts, 2008).
Staphylococcus aureus can cause localized and invasive infections in humans. This is attributed to its ability to produce a variety of enzymes and toxins. Whereas nearly all strains of S. aureus produce enzymes that contribute to their pathogenicity, it has been generally accepted that only some strains produce ETs and PTSAgs (Bohach and Foster, 2000). In this study, the toxins genotypes of CPS were demonstrated. From one hundred and fifteen CPS isolates obtained from human source, 38.4% nasal and 26% clinical isolates possessed the gene for tst. These results are in accordance with previous findings that many healthy individuals are carriers of tst-producing strains of S. aureus (Mehrotra et al., 2000). The isolation of S. aureus strains possessing one of the pyrogenic toxins genes was previously described (Bawadi et al., 2009). In addition, half of the clinical isolates (49.3%) harbored the eta gene in comparison to (23%) for nasal. The notable higher prevalence of tst gene among clinical isolates indicates that the possession of this gene in particular seems to be a habitual feature of S. aureus. The resulted percentages are agreeable with the earlier reports and could be correlated with the transfer of this gene at high frequency (Moore and Lindsay, 2001).
On the other hand, only two human isolates harbored etb toxins genes in the nose (7.6%) in comparison to (0%) for other isolates. However, Becker et al. (2003) found that none of the clinical isolates were etb positive, while 1% of the nasal isolates were etb positive. Others also reported the absence of the genes encoding etb in the clinical isolates (Abd El-Hamid and Bendary, 2013). A geographic variation in the prevalence of different ETs isoform was reported. The majority of these reports confirmed that eta was the predominant ETs isoform in Europe, North America, and Africa which was similar to findings of this study, whereas etb-producing isolates were shown to be more frequent in Japan (Nishifuji et al., 2008). Screening for etb gene in larger samples is necessary to give better results concerning their prevalence"s in different population. The possession of more than one toxin gene was found in 8.98% of human (clinical and nasal) isolates. However, eta plus tst and eta plus etb was found in 12.17 and 0.86%, respectively. Similar coexisting pyrogenic genes combination was reported by others (Becker et al., 2003). The contribution of pyrogenic genes combination to the overall pathogenicity potential of CPS should be investigated further.
The current study revealed that the gene coding for eta toxin, was the most frequent among human than animal isolates followed by tst gene. The higher prevalence of the eta gene in staphylococci could be explained by its greater immunogenicity (Yamasaki et al., 2005). However, the absence of etb toxin gene among animal isolates of CPS indicates that the gene cannot be held responsible for the diseases that may be induced by in animal and human. Strains that expressed eta and tst genes might form an alert for public health if they pass from poultry to human. A recent study by Nemati et al. (2013) reported the absence of ETs and tst genes in S. aureus isolated from animals. However, others reported the rare prevalence of exfoliative toxins among S. aureus isolates from animals (Endo et al., 2003). This indicates that these genes cannot be held responsible for the zoonotic diseases that may be induced in human. Moreover, Adesiyun et al. (1991) reported that ETs genes were observed in 3.9% of the examined animal"s origin isolates. The present study confirms the relatively low prevalence of eta, tst encoded by genes in CPS isolated from animals and reported by others (Nemati et al., 2013). Although the importance of tst on animal health was not explained completely, it may play a role in the pathological mechanisms of bovine mastitis with its superantigenic functions (Zschock et al., 2000). Therefore, large-scale studies are required to determine the presence and role of tst in S. aureus isolates originating from livestock.
The accuracy of conventional methods for species identification and taxonomic classification of staphylococci based on phenotypic characteristics is limited (reported to be range from 50 to 70%) (Kloos and Bannerman, 1995). The use of nucleic acid targets, with their high sensitivity and specificity, provides an alternative technique for the accurate identification and classification of Staphylococcus species. Earlier results have been obtained by comparing sequences of certain genes such as hsp60, sodA, rpoB, tuf, and gap (Ghebremedhin et al., 2008). Blaiotta et al. (2010) evaluated the catalase (kat) gene performance as a new target for phylogenetic analysis of staphylococci and identification at the species level. The kat genes display a high level of restriction endonuclease polymorphism, offering good opportunities for rapid, and accurate species-level identification of staphylococcal isolates. All CPS isolates that have been identified phenotypically were confirmed genotypically by amplification of kat genes. In this study, the catalase gene of all CPS isolates was amplified by universal primers, allowing the amplification of a 544-bp region of kat containing polymorphic TaqI restriction sites of the various CPS isolates. Based on their PCR-RFLP patterns, 163 isolates were identified as S. aureus subspecies aureus, and only four isolates were identified as S. pseudintermedius. The cat gene sequence that determined in this study was similar to that already described by others (Blaiotta et al., 2010). To confirm the identification, some strains reclassified on the basis of their TaqI PCR-RFLP patterns were subjected to sequencing of the kat gene (544-bp fragment). The comparison of resulting sequences with those from reference strains indicated an agreement with those of the PCR-RFLP analysis (S. aureus subspecies aureus showed 99% homology, while S. pseudintermedius showed 82% homology to the references). It seems that the S. pseudintermedius identified in this study does not clustered with the other members of the intermedius group. S. pseudintermedius colonization is uncommon in humans, even among people with frequent contact with animals (Talan et al., 1989). They are also rare among CPS isolates from hospitalized humans (Mahoudeau et al., 1997). Their importance as a zoonotic pathogen is therefore much smaller than that of other species. However, several cases of zoonotic transmission between companion animals and humans have been reported. In some cases humans were only colonized or contaminated, but in other cases transmission resulted in human infections (Guardabassi et al., 2004).
Despites the fact that the main host of S. pseudintermedius is the dogs and cats (Moodley et al., 2014), the results of this pilot study revealed that the four strains of S. pseudintermedius were isolated from livestock including 3(5.76%) from sheep and 1(1.92%) from goats. To our knowledge, this is the first report of S. pseudintermedius strains originating from sheep and goats samples worldwide. Although, Vasil (2007) reported the isolation of S. intermedius from sheep milk samples there is a high possibility, that these isolates were S. pseudintermedius and not S. intermedius since he depends on the biochemical tests only for identification. Direct contact between animals-animals and animals-humans is a relevant factor to take into account in understanding the epidemiology and evolution of this species. Van Hoovels et al. (2006) reported the first case of S. pseudintermedius infection in a human. However, Sasaki et al. (2007) also identified two strains from humans as being S. pseudintermedius strains.
Identification of Staphylococci to species level in microbiology is important to inform therapeutic intervention and management (Geraghty et al., 2013). In human, S. pseudintermedius is an opportunistic pathogen and a leading cause of skin and ear infections, postoperative wound infections in animals mainly the dogs and cats (Weese and van Duijkeren, 2010). This species has been recognized on a few occasions as a pathogen of rhinosinusitis, a catheter-related bacteremia, and an implantable cardioverter-defibrillator infections (Stegmann et al., 2010;Chuang, 2010). However, veterinary dermatologists and small animal clinical staff are sometimes considered as nasal carriers of S. pseudintermedius (Morris et al., 2010). The spectrum of S. pseudintermedius diseases have been expands which emphasizes the risk of zoonoses mainly in imunocompromised subjects (Savini et al., 2013).
Although, a limited knowledge concerning their pathogenecity was published, various virulence factors are known to be produced by this bacterium, (Fitzgerald, 2009) with the ETs as a major virulence factor (Iyori et al., 2010). This was demonstrated by the presence of 25% of the S. pseudintermedius strains that harbored eta toxin gene. By highlighting the virulence properties of the investigated strains, it has been found that all expressed high levels of protease, mannitol utilization, capable of anaerobic fermentation, and lysostaphin sensitivity. However, it is differed from S. aureus subsp.aureus by the absence of clumping-factor, and by their partial hemolytic activity (produced αhemolysin by two strains). Unexpectedly, and although initially describing S. pseudintermedius as β -haemolytic, Awji et al. (2012) instead stated that the organism can be presumptively differentiated from S. aureus as the former lacks betahaemolysis (using sheep blood agar). However, accurate phenotype observation remains crucial to reaching a conclusive bacterial diagnosis (Savini, 2013). Accordingly, the diagnostic algorithm of CPS should be reconsidered. It is likely that human and veterinary S. pseudintermedius isolates have been misidentified as S. aureus, S. intermedius, or other species (Bond and Loeffler, 2012). This fact should be considered when a patients' history includes contact with animals, the potential role of S. pseudintermedius as the agent of zoonoses has to be taken into account, and a correct identification may be performed. The isolation of livestock associated S. pseudintermedius in this pilot study could under line their possibility as a risk factor participating in human infections and emphasizing the need for correct species identification in clinical laboratories that handle samples of both human and animal origin. However, more useful genome-based investigations such as matrix-assisted laser desorption ionization-time of flight mass spectrometry could be used for profiling of staphylococcal strains using a large collection of staphylococci of diverse origins (David et al., 2010).
Although Livestock-associated and human-associated strains shared some virulence factors, but distinct virulence factors appeared to be important in host adaptation. Exchange of genes encoding these virulence factors between strains may expand the host range and thereby threaten public health (Fluit, 2012). More studies should be done to characterized animal isolates of CPS and prevent transferring species to health care settings. In conclusion, S. aureus subspecies aureus isolated from human seems to be different phenotypically and genotypically from livestock isolates.
Figure 1 .
Figure1. Agarose gel electrophoresis (2%) showed the TaqI RFLP analysis of Kat gene. Lane M, 1.25 Kb DNA marker; lane 1, negative control from CNS isolates, lane 2, PCR product for S. aureus isolated from human nail; lane 3, PCR product for S. aureus isolated from animal nose; lanes 4-5, PCR products for S. aureus isolated from meat; lanes 7-9, PCR products for S. aureus isolated from clinical sources, lane 6, product for S. pseudintermedius isolate from meat source, and lane 10, positive control (S. aureus ATCC 25923).
Figure 2 .
Figure 2. A phylogenetic tree based on the alignment of nucleotide sequences of kat gene from Staphylococcus species. The tree was constructed using the neighbor joining method provided in the MEGA 5.2 software. Numbers at each branch indicate the percentage of times a node was supported in 1,000 bootstrap pseudoreplication. The scale bar evaluates the sequence divergence. GenBank accession numbers are shown in parentheses and asterisks indicate sequences identified during this study.
Table 1 .
Base sequences, locations within the genes, and predicted sizes of amplified products for the Staphylococcal toxin-specific oligonucleotide primers.
Table 2 .
Distribution of animal samples according to their sources.
Table 3 .
Numbers and percentages of coagulase positive Staphylococcus isolates collected from different sources.
for 3 min) and 40 amplification cycles: a denaturing step for 10s at 95°C and an annealing-extension step for 45 sat 56°C. After amplification, 15 μl of each PCR mixtures were tested by electrophoresis on 1.5% (w/v) agarose gel at 100 V for 1 h. The remaining part (35 μl) of the PCR product was digested in a total volume of 50 μl by 20 U of TaqI restriction endonuclease at 65°C for 2 h. Restriction fragments were resolved by electrophoresis on 2% (w/v) agarose gel at 100V for 2 h
Table 4 .
Phenotypic characteristic of coagulase positive Staphylococcus isolated from human and animal sources.
Table 5 .
The prevalence of eta, etb, and tst genes among CPS isolated from human and animal sources.
Table 6 .
Differences in phenotypic and genotypic characteristics produced by S. aureus, and S. pseudintermedius isolated from human and animal sources.
S. pseudintermedius was able to produce eta toxin (Table
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Domain: Biology Medicine
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Estimation of hepatitis B virus cccDNA persistence in chronic infection using within-host evolutionary rates
Hepatitis B virus (HBV) infection is a major global health problem with over 240 million infected individuals at risk of developing progressive liver disease and hepatocellular carcinoma. HBV is an enveloped DNA virus that establishes its genome as an episomal, covalently closed circular DNA (cccDNA) in the nucleus of infected hepatocytes. Currently available standard-of-care treatments for chronic hepatitis B (CHB) include nucleos(t)ide analogues (NA) that suppress HBV replication but do not target the cccDNA and hence rarely cure infection. There is considerable interest in determining the lifespan of cccDNA molecules to design and evaluate new curative treatments. We took a novel approach to this problem by developing a new mathematical framework to model changes in evolutionary rates during infection which, combined with previously determined within-host evolutionary rates of HBV, we used to determine the lifespan of cccDNA. We estimate that during HBe-antigen positive (HBeAgPOS) infection the cccDNA lifespan is 61 (36-236) days, whereas during the HBeAgNEG phase of infection it is only 26 (16-81) days. We found that cccDNA replicative capacity declined by an order of magnitude between HBeAgPOS and HBeAgNEG phases of infection. Our estimated lifespan of cccDNA is too short to explain the long durations of chronic infection observed in patients on NA treatment, suggesting that either a sub-population of long-lived hepatocytes harbouring cccDNA molecules persists during therapy, or that NA therapy does not suppress all viral replication. These results provide a greater understanding of the biology of the cccDNA reservoir and can aid the development of new curative therapeutic strategies for treating CHB.
INTRODUCTION
We propose the within--host evolutionary rate of HBV can be used to estimate cccDNA lifespan. We developed a novel mathematical model to determine the relationship between HBV evolutionary rate and the lifespan of cccDNA, and combined with published mutation and evolutionary rates 10,14,16 , we inferred the lifespan of cccDNA during different phases of CHB. To the best of our knowledge, these are the first estimates of cccDNA lifespan in treatment naïve subjects and provide important insights into the HBV reservoir that will be valuable for the design and evaluation of future treatment interventions. A: Simplified HBV replication cycle. A virus particle containing relaxed circular DNA (rcDNA) enters a hepatocyte (blue circle) and is uncoated. The rcDNA is transported to the nucleus (purple circle) and repaired to generate cccDNA. This cccDNA is the transcriptional template for all viral RNAs, including pre--genomic (pgRNA), which is transported to the cytoplasm, encapsidated, and converted into rcDNA by error--prone reverse transcription. The encapsidated rcDNA can be transported back into the nucleus to form more cccDNA (intra-cellular amplification), or enveloped and released as virions that can infect hepatocytes (extra--cellular amplification). B: Structure of the mathematical model. This is a single compartment model representing the burden of cccDNA in the liver, Y, over the course of infection. The cccDNA burden can increase due to amplification (intra--and extra--cellular), where b is a measure of the within--host replicative capacity of cccDNA. cccDNA can be cleared from the liver due to natural cell death, at rate d, cytolytic immune responses at rate δ, and non--cytolytic immune responses at rate c. Proliferation can also result in loss of cccDNA at rate (1--q)(d+δY), where q is probability that an individual cccDNA survives mitosis. C: Representation of the model dynamics and key results, where the numbers give the most likely values inferred by fitting the mutation and evolutionary rates to the model. The darker the colours on the figure the higher the cccDNA burden (reds) and the stronger the immune response (blues).
RESULTS
We developed a mathematical model describing the number of cccDNA molecules in the liver that is independent of infected cell frequency, and accounts for intra-- and extra--cellular cccDNA amplification and loss of cccDNA during hepatocyte mitosis (Fig 1B and Methods, Eqs 1 and 2). Using this model we derived expressions for the viral generation time, defined as the typical time for one cccDNA molecule to generate another cccDNA molecule at time t since infection, g(t) (Eq 5), and the neutral rate of evolution at time S(t) (Eq 6). At equilibrium, we show that the lifespan of cccDNA, ! , is equal to the virus generation time, ! , which is given by the neutral mutation rate divided by the neutral rate of evolution, ! (Eq 8). The notation used throughout is given in Table 1. Per capita replicative capacity, defined as the per capita growth rate of cccDNA when few cells are infected and in the absence of infected cell death or loss of cccDNA due to non--cytolytic immune responses.
R0
The basic reproductive rate of cccDNA (the number of cccDNA molecules a single cccDNA produces in its lifetime in an otherwise uninfected population of hepatocytes) b= βΚ Replicative capacity of cccDNA (a rescaled measure of the per capita replicative capacity) d Natural death rate of hepatocytes δ Additional death rate of infected hepatocytes due to cytolytic immune responses c Loss rate of cccDNA due to non--cytolytic immune responses q Probability that a cccDNA molecule survives mitosis µ Mutation rate of cccDNA (substitutions per site per reproduction) Lifespan of cccDNA The lifespan of cccDNA molecules most likely changes over the course of HBV infection, and will be influenced by host and viral factors 18 , including the rate of hepatocyte proliferation 19,20 . Early in infection cccDNA is transcriptionally active and translation of pre--core/pgRNA results in detectable levels of hepatitis B e antigen (HBeAg) in the periphery that associates with high HBV DNA levels (viral load --VL) 21 . In later stages of infection after seroconversion and genesis of anti--HBe antibodies there is a loss of HBeAg and more efficient immune targeting of infected cells 22 , leading to a reduction in VL and a shortening of cccDNA lifespan. This HBeAg NEG phase of infection is often associated with the emergence of precore mutations that limit HBeAg expression 23 . The higher hepatocyte death rates during HBeAg NEG CHB infection will induce hepatocyte proliferation 21 . Although the extent to which cccDNA is lost during hepatocyte mitosis is uncertain 8 , unless all cccDNA episomes survive mitosis, the increased proliferation rate of infected cells will shorten the average lifespan of cccDNA 20,24,25 . From the published estimates for the mutation 10 The distributions for cccDNA in stable HBeAg POS and HBeAg NEG chronic infection are based on the neutral mutation rate and rate of neutral evolution (orange and blue lines, respectively). If the cccDNA burden during HBeAg NEG infection is not stable, but gradually falling (i.e. the basic reproduction number, R 0 , is less than one) the lifespan will be slightly less than inferred here. The upper estimate reflects the maximum likely cccDNA lifespan when few cells are infected, based on the neutral rate of evolution during HBeAg--postive infection and assuming no cccDNA survives mitosis (q=0; green line). The shorter lifespan of cccDNA during HBeAg NEG compared to HBeAg POS infection can be explained by higher rates of cccDNA clearance (Eq 9). This may reflect changes in the immune environment due to HBe--antigen seroconversion that is associated with increased cytolytic and non--cytolytic immune responses ( and c respectively). Mutational changes in the virus that limit HBeAg expression may also affect HBV replication and stability of cccDNA 23 . Increased host immune responses during HBeAg NEG infection could push the basic reproduction number, R 0 , of cccDNA below one (Eq 3) due to the higher clearance rates of cccDNA molecules, and also due to . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
(which was not peer-reviewed) The copyright holder for this preprint . [URL]/10.1101/2020.02.04.20020362 doi: medRxiv preprint reduced replicative capacity, b, of cccDNA. If R 0 <1, the number of cccDNA will not reach a stable level but will continually decline. In this non--equilibrium situation the lifespan of cccDNA may be less than our inferred 26 days since the viral generation time will be greater than the lifespan of cccDNA (Fig 3 and methods). Our model suggests cccDNA lifespan can be up to two times longer when few cells are infected compared to when most cells are infected cells (see methods; compare Eqs 9 and 10). When few cells are infected there is less cell death due to cytolytic immune responses, a lower rate of hepatocyte proliferation to maintain the number of hepatocytes, and consequently reduced loss of cccDNA via mitosis of infected cells. This is of more than theoretical interest, because when estimating how long it will take to deplete the cccDNA reservoir on treatment, it is the lifespan of cccDNA when relatively few cells are infected that is important since treatment is known to reduce the cccDNA load. The maximum expected cccDNA lifespan, corresponding to HBeAg POS infection, few infected cells, and no cccDNA surviving mitosis, is 123 days (71--472 days; Fig 2, green line). Reports for duck hepatitis B virus (DHBV) show a high proportion of cccDNA survives mitosis 25 . In contrast, for HBV recent experimental 8,24 and modelling 20 results suggest that relatively few cccDNA molecules survive mitosis, making this longer lifespan a reasonable expectation.
Dynamics of the mathematical model
To demonstrate the behaviour of our model we present examples of the dynamics when no cccDNA survives mitosis (q=0, Fig 3; see S1 Fig for model dynamics when q=1). We used parameters that are compatible with our estimated cccDNA generation times (61 days during HBeAg POS infection and 26 days during HBeAg NEG infection). Since hepatocytes are long--lived we defined the natural death rate as d=0.002 per day throughout and, for simplicity, we set c=0 under the assumption that cytolytic responses have greater antiviral activity than non--cytolytic responses. We assume a neutral mutation rate =2x10 --5 s/s/c 10 . The model dynamics when q=1 are similar to the case where q=0, apart from the lifespan of cccDNA in the early stages of infection is predicted to be higher if q=1 (see below). A graphical representation of the results is given in Fig 1C, and a summary of the parameters in Table 2.
HBeAg POS infection
The replicative capacity of cccDNA, b, was chosen to be 0.3/day so that the peak number of cccDNA molecules in the liver is reached at approximately 3 months since infection, in line with reported observations 26 . The death rate of infected cells due to cytolytic immune responses, , was determined assuming a cccDNA generation time at equilibrium of 61 days, and solving Eq 9 for (giving =0.006 per day if q=0; the associated R 0 is 30). Under these assumptions, during the first few months of infection the cccDNA burden (number of cccDNA divided by the maximum number of cccDNA) increases rapidly, leading to a short viral generation time predicted by the model of 3.3 days (Eq 11, Fig 3, S1 Fig). A recent study estimated an eclipse period of approximately 3 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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The copyright holder for this preprint . [URL]/10.1101/2020.02.04.20020362 doi: medRxiv preprint All rates are given per day, and generation times are listed as days 2 Where three values are given these refer to the alternative parameters used for the different trajectories presented in The neutral rate of evolution is also predicted to be high during this early stage of infection due to the short generation time.
As infection progresses, the viral generation time increases due to fewer susceptible target cells (Eq 5), in line with results in epidemiology 27 , and this in turn reduces the evolutionary rate (Eq 6). This dependency of evolutionary rate on epidemiological dynamics has been noted in a previous simulation study on within--host viral infection 28 , but is generally an underappreciated factor influencing evolutionary rates. At equilibrium, the estimated viral generation time and cccDNA lifespan are the same, and it is this equivalency that enables us to determine these parameters from the neutral rate of evolution, independent of the parameters of the model (see Methods). Due to the long lifespan of infected hepatocytes, a high cccDNA burden is reached in the model. This is in line with observations that most hepatocytes are infected at peak infection 29 .
. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
HBeAg NEG infection
We assumed the transition from HBeAg POS to HBeAg NEG occurs after an arbitrary amount of time after HBeAg POS equilibrium is reached and associates with a reduced cccDNA generation time from 61 to 26 days. If this reduced generation time is not accompanied by a decrease in replicative capacity, only a modest fall in the cccDNA burden is predicted (Fig 3, . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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The copyright holder for this preprint . [URL]/10.1101/2020.02.04.20020362 doi: medRxiv preprint estimated 10--fold reduction in the ability of cccDNA to reproduce during HBeAg NEG compared to HBeAg POS infection. In Fig 3, the orange line shows the model dynamics given this decline in b, and when R 0 =1 during HBeAg NEG infection (i.e. = 0.034 per day and b=0.038 per day). In this case, the cccDNA burden falls at a relatively modest rate. Perhaps more likely is that R 0 < 1 and the number of cccDNA molecules continues to decline. The green line shows the dynamics if R 0 =0.7 ( =0.050). However, even with this modest increase in , the number of cccDNA is predicted to fall rapidly. The difficulty in explaining low but steady VL using standard within--host virus models, and the sensitivity of VL to model parameters when R 0 is close to one, have been acknowledged previously, particularly in relation to HIV--1 infections [32][33][34] . Possible explanations for the low numbers of cccDNA during HBeAg NEG infection and low rates of spontaneous cure include the existence of a small number of hepatocytes that are susceptible to infection, resulting in low numbers of cccDNA molecules even if R 0 is high 32 , or the existence of a metapopulation--type partitioned structure in the liver, which enables the cccDNA to persist when R 0 is low 34 .
Estimated time to eradicate cccDNA on treatment
When few cells are infected, the inferred cccDNA lifespan is 123 days during HBeAg POS infection if q=0. Even with this longer estimate for cccDNA lifespan, if there are 10 12 cccDNA molecules at the start of treatment (see methods), we would expect the reservoir to be depleted after less than ten years of treatment (Eq 13, Fig 4A). Moreover, if treatment is initiated during HBeAg NEG CHB the time to eradicate cccDNA is predicted to be even faster (only 1.5 years) with a lifespan of 26 days, and a lower number of cccDNA molecules (2x10 9 ) in the liver at the start of treatment. However, these predictions are in stark contrast to what is observed in the clinic, where a high proportion of individuals remain infected after many years of continuous treatment 35 and there is no appreciable difference in treatment mediated cure in HBeAg NEG or HBeAg POS patients 36,37 . The discrepancy may arise due to our estimated cccDNA lifespan being too short. An estimated lifespan of 236 days during HBeAg--postive CHB still lies within our 95% confidence interval, and would give a time to eradication, and hence sterilizing cure between 18 and 36 years (Eq 13). However, this does not explain the long time to eradicate cccDNA during HBeAg NEG infection. Alternative explanations include ongoing (albeit reduced) cccDNA amplification during NA treatment (b>0) 37,38 , or the presence of a long--lived subset of infected hepatocytes 24,39 . To evaluate these two scenarios, we modelled cccDNA dynamics in CHB patients on treatment assuming different levels of viral replication (Fig 4B) or a subset of long-lived cells (Fig 4C, S2 Fig). The dynamics of cccDNA are sensitive to the amount of replication, making it unlikely that ongoing amplification alone explains the failure of treatments to eliminate cccDNA. Apart from a narrow range of replicative capacities, either a high and steady cccDNA burden, or relatively rapid cccDNA elimination, is predicted on treatment. The existence of a long--lived population of infected hepatocytes is more robust to differences in model parameters, with a gradual increase in the time to eradicate cccDNA as the death rate of long--lived cells is . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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The copyright holder for this preprint . [URL]/10.1101/2020.02.04.20020362 doi: medRxiv preprint increased, making a long--lived population a more parsimonious explanation for the slow decline in the HBV reservoir. However, since the decay dynamics of the reservoir on treatment can be complex, and differ between individuals 38 , a combination of factors most likely explains the clinical observations.
DISCUSSION
We provide a new model to estimate the HBV cccDNA lifespan based on reported mutation and within--host evolutionary rates 10,14,16 . The lifespan of cccDNA is an important component of the half--life of the cccDNA reservoir, which describes how the population of cccDNA molecules in an individual declines over time. We predict an average cccDNA lifespan of 61 days during HBeAg POS CHB compared to only 26 days in the HBeAg NEG phase of infection. Although estimates for the mutation and evolutionary rates for HBV are associated with high levels of uncertainty, our predicted lifespan is in agreement with in vitro studies showing a 40 day half--life of HBV cccDNA 2 and an estimated half--life of 33--57 days in woodchucks and ducks in vivo 40,41 . As far as we are aware, this is the first time cccDNA lifespan has been estimated during untreated infection. The lower lifespan during HBeAg NEG infection is consistent with a study in which VL data during therapy was fitted to a mathematical model, concluding that the turnover of infected cells is higher if therapy is initiated during HBeAg NEG infection 22 , although our predictions for cccDNA persistence are longer 22 . The shorter cccDNA lifespan during HBeAg NEG CHB may reflect host immune responses, with our model suggesting a doubling of the clearance rate compared to HBeAg POS infection. However, this increased clearance rate is predicted to have a modest effect on the total number of cccDNA molecules. As well as inferring the lifespan of cccDNA, we inferred cccDNA replicative capacity (a combined measure of intra and extra--cellular amplification). Our results predict an approximate ten--fold reduction in replicative capacity between HBeAg POS and HBeAg NEG phases of infection. This can explain the lower cccDNA levels reported in HBeAg NEG CHB 30,31 , and is consistent with observations that the replicative capacity of cccDNA in the HBeAg NEG � � � . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
(which was not peer-reviewed)
The copyright holder for this preprint . [URL]/10.1101/2020.02.04.20020362 doi: medRxiv preprint phase of infection is reduced compared to HBeAg POS infection 30 . This may reflect immune control at the level of the viral epigenome, but without cell death 42 . Our estimates for cccDNA lifespan have implications for curative treatment strategies. If NA therapy inhibits all cccDNA amplification, we would predict HBV to be cured after 1 to 10 years of continuous treatment. However, this is not observed in the clinic, with only 1% of individuals clearing HBsAg each year 35 . Possible explanations for this discrepancy are that NAs do not inhibit all intra--and extra-cellular amplification 37,38 , or the existence of long--lived infected cells 24,39 . Our model is consistent with the presence of long--lived infected cells providing the most parsimonious explanation for sustained infection on treatment. There is growing evidence that there is negligible intra--cellular cccDNA amplification in human HBV infection 8 , and since NA treatment will inhibit the genesis of viral particles this will prevent extra--cellular amplification. Furthermore, the dynamics of cccDNA clearance is sensitive to the assumed amplification rates, and therefore if amplification alone explains the dynamics we would expect to see a proportion of individuals clearing infection within 1--2 years of starting treatment. The presence of long--lived HBV infected cells has parallels with the HIV reservoir, where long--lived latent--infected CD4 + T cells prevent cure 43 . Distinguishing between residual amplification and long-lived infected cells will help define the expected impact of treatment strategies that prevent cccDNA replication, compared to those directly targetting cccDNA. As HBV evolution will only occur if there is cccDNA amplification, it may be possible to distinguish between these two mechanisms by measuring the rate of cccDNA evolution whilst on treatment. Our estimates of cccDNA persistence and amplification provide insights into mechanisms underlying CHB and will inform our understanding of how spontaneous or therapeutic clearance may be achieved. Given different infection profiles among individuals, and limited datasets available for our model, the confidence intervals of our estimations are wide. Our analysis exemplifies the power of modelling as a tool to inform therapeutic interventions and highlights the need for genomic studies to determine HBV evolutionary rates in CHB.
METHODS
To derive estimates of HBV cccDNA lifespan using the neutral mutation rate and the rate of evolution we developed a deterministic mathematical model describing the dynamics of cccDNA during the course of treatment naïve CHB. We used this model to derive expressions for viral generation time and neutral rate of evolution, both of which are predicted to change during the course of infection. Finally, we derived expressions for the lifespan of cccDNA during (i) stable CHB and (ii) when the proportion of infected cells is low, as would be expected in early stages of infection or in the first few months of NA treatment. A within--host model of HBV dynamics HBV cccDNA can replicate via intra--cellular and extra--cellular routes (Fig 1A), with a reported copy number between 1--50 molecules within a single hepatocyte nucleus 2,24,44-47 (the higher estimates tend to be for DHBV and lower estimates for human . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
(which was not peer-reviewed)
The copyright holder for this preprint . [URL]/10.1101/2020.02.04.20020362 doi: medRxiv preprint HBV 8 ). Since cccDNA can be lost during mitosis, we modelled the number of cccDNA copies in the liver, rather than the number of infected cells. To do this, we implicitly assume that viral production is proportional to the number of cccDNA molecules. This is a reasonable assumption since VL has been reported to associate with increasing cccDNA copy numbers 30,48 . We describe the number of copies of cccDNA in the liver at time t since infection, N(t) as: where the first term describes the increase in cccDNA due to intra--and extra--cellular amplification. We assume that the rate of increase is density dependent, with a maximum per capita growth rate per day and a maximum possible number of cccDNA, K. We assume K is constant since proliferation ensures the number of hepatocytes in the liver remains stable during infection 21 , and since the maximum number of copies of cccDNA that can persist within each hepatocyte is virally controlled [48][49][50] . The second term describes the rate at which cccDNA is lost due to the natural death of hepatocytes and the host immune response, under the assumption that cccDNA is randomly distributed among infected hepatocytes. We assume that hepatocytes, and therefore cccDNA, have a natural death rate d per day. Infected hepatocytes (and hence cccDNA) have an additional death rate per day due to cytolytic immune responses, and cccDNA is lost at rate c per day due to non--cytolytic immune responses. The final term describes the loss of cccDNA due to cell proliferation. Uninfected and infected hepatocytes are assumed to proliferate at the rate ( ) per day, and hence cccDNA will be exposed to proliferation at rate, ( ), with a probability q that a cccDNA molecule will survive mitosis. Since the maximum possible number of cccDNA, K, is constant, proliferation and cell death are balanced, hence: = + ( ) (Eq1b) A complete expression for the dynamics of N(t) can be found by solving Eq 1b for and substituting into Eq 1a. To simplify further, we consider the cccDNA burden in the liver, ( ) = ( ) , rather than the total number of cccDNA molecules, giving us: is a rescaled measure of cccDNA replicative capacity. From this equation we can calculate the basic reproductive rate of cccDNA, R 0 , which is defined as the number of new cccDNA molecules a single cccDNA molecule will produce in a susceptible population of hepatocytes: . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
(which was not peer-reviewed)
The copyright holder for this preprint . [URL]/10.1101/2020.02.04.20020362 doi: medRxiv preprint (Eq3) If R 0 <1, then the infection cannot be sustained in the long term. At equilibrium, the cccDNA burden is given by: which is equivalent to the cccDNA burden during stable chronic infection. Our model considers the number of cccDNA molecules independent of their distribution within cells. This is similar to the "single copy" modelling assumption used in 25 , in which only one cccDNA molecule can persist in a cell, and which was shown to produce almost identical dynamics to one in which multiple copies of cccDNA are explicitly modelled within infected cells 25 .
An expression for the neutral rate of HBV evolution
In a large well--mixed viral population, and in the absence of selection, the rate of evolution at time t is given by ( ) = ( ), where is the (neutral) mutation rate, measured per site per viral generation, and ( ) is the generation time 51 . For our within--host model of HBV infection, g is equivalent to the typical amount of time it takes for one cccDNA molecule to replicate another molecule. This is similar to the meaning of generation time in demography and epidemiology 27,52,53 , and which from Eq 2 is given by: ( ) = 1 1 − ( ) (Eq5) At time t since initial infection, the neutral substitution rate is therefore given by: Since intra--and extra--cellular amplification involve an error--prone reverse transcription step, we have assumed they have similar mutation rates. Substituting ! into Eq 6, we can find an expression for the neutral rate of evolution rate at equilibrium:
Lifespan of cccDNA during steady state infection
In our model, at equilibrium the generation time of HBV will be equal to the typical cccDNA lifespan, ! . At equilibrium the number of cccDNA molecules remains constant, and therefore the rate at which cccDNA is produced is equal to the rate at which cccDNA is lost due to infected cell death, non--cytolytic clearance of cccDNA, and proliferation of infected cells. Since the reciprocal of the production rate is equal to the generation time, and the reciprocal of the rate cccDNA is lost is the typical lifespan of cccDNA, at equilibrium, viral generation time and cccDNA lifespan are . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
(which was not peer-reviewed)
The copyright holder for this preprint . [URL]/10.1101/2020.02.04.20020362 doi: medRxiv preprint identical ( ! = ! ). This relationship holds because of our assumption of constant death rate and hence exponentially distributed lifetimes of cccDNA 27 ; see 27,52 for how this changes for different distributions. Using the equivalence of ! and ! , the lifespan of cccDNA at equilibrium can be determined from the mutation and neutral evolution rates by rearranging the first part of Eq 6: ! = ! ! ! (Eq8) Substituting the expression for S E from Eq 7 into Eq 8, we can write an expression for the lifespan of cccDNA at equilibrium based on the model parameters:
The lifespan of cccDNA when few cells are infected
If infection increases the death rate of hepatocytes, then the level of proliferation (to replace eliminated cells) will be larger the more cells are infected. Consequently, the lifespan of cccDNA when few cells are infected (e.g. during early phases of infection or during spontaneous clearance of infection, or after prolonged successful suppressive treatment) may differ from the lifespan during HBeAg POS or HBeAg NEG steady state infection. By setting Y<<1 in equation 2, we can derive an expression for cccDNA lifespan when the copy number or burden is low: (Eq10) Comparing the expressions for ! and !≪! , we can see that if all cccDNA survives mitosis (q=1) or infection has a minimal effect on the death rate of infected cells ( =0), then cccDNA lifespan remains unchanged during infection (as long as d and c don't change). However, if these conditions are not met, then the lifespan of cccDNA when few cells are infected, !≪! , can be up to double the lifespan during chronic stable infection, ! , for identical model parameters (e.g. when q=c=d=0, and b>> ). As we noted above, the cccDNA lifespan is only equivalent to the generation time at equilibrium. Using equation 5, when few cells are infected, the generation time is given by: (Eq11) This has also been observed in the epidemiological literature 27 . Combining equations 3, 10 and 11 we see that: . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
(which was not peer-reviewed)
The copyright holder for this preprint . [URL]/10.1101/2020.02.04.20020362 doi: medRxiv preprint If ! > 1 and few cells are infected (i.e. the number of cccDNA is increasing) the life expectancy of cccDNA will be greater than the viral generation time, whereas if ! < 1 the life expectancy will be less than the viral generation time. This might be the case if, for example, increased immune responses associated with HBeAg NEG infection push ! below one. Estimating the generation time and lifespan of cccDNA from within--host evolutionary rates. During stable chronic infection, the lifespan of cccDNA, L E , equals the viral generation time, g E , with ! = ! (Eq 6). Although the mutation rate of HBV has not been determined, for avian hepadnavirus it has been estimated at 2x10 --5 s/s/c (in the range 0.8x10 --5 to 4.5x10 --5 ; 10 ). Since we are interested in the neutral rate of evolution, we assume that a third of all mutations in non--overlapping reading frames are synonymous, and that synonymous mutations are neutral or nearly neutral 54 , giving a neutral mutation rate of around 0.67x10 --5 s/s/c (0.3x10 --5 to 1.5x10 --5 ) in non-overlapping reading frames. To incorporate the uncertainty associated with this estimate, we assumed the probability of the true mutation rate is log--normally distributed with mean 10 --5.2 and standard deviation 10 0.2 . Using longitudinal HBV sequence data, rates of evolution for non--overlapping regions of the genome were generated using a relaxed clock method, inferring 16.1x10 --8 (8.1 x10 --8 , 25.5 x10 --8 ) substitutions per site per day (s/s/day) for HBeAg POS and 38.9 x10 --8 (27.2 x10 --8 , 51.5 x10 --8 ) for HBeAg NEG chronic infection (the numbers in brackets give the 5% and 95% highest posterior density (HPD) intervals; see Table 5 in 14 ). In a separate study, using data from 55 , the synonymous rate of evolution in non--overlapping genomic regions was estimated as half of the overall rate of evolution 16 . Assuming synonymous mutations are neutral, and that the ratio of synonymous to nonsynonymous evolutionary rates is constant during infection, we therefore take the neutral within--host rates of evolution during the HBeAg POS and HBeAg NEG phases of infection to be half the rates of evolution reported in 14 for non-overlapping reading frames. This gives a neutral rate of evolution of 8.0x10 --8 (4.0x10 --8 , 12.7x10 --8 ) s/s/day during the HBeAg POS phase, and 19.5 x10 --8 (13.6 x10 --8 , 25.8 x10 --8 ) s/n/day during the HBeAg NEG phase. We assumed the probability distributions of these rates are normally distributed, with the standard deviation calculated using the difference between the estimated rate and the 5% HPD. We randomly sampled from each of the probability distribution functions (PDFs) for the mutation rate and substitution rates, and used these values to calculate the generation time of cccDNA during HBeAg POS and HBeAg NEG CHB. This was repeated 100,000 times, from which the probability distributions for cccDNA generation time during HBeAg POS and HBeAg NEG chronic infection were estimated using the built in SmoothKernalDistribution function in Mathematica 56 . Assuming the number of cccDNA rapidly reaches equilibrium during HBeAg POS and HBeAg NEG infection, the virus generation will provide an approximation of the cccDNA lifespan during stable chronic infection (Fig 3).
. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
Time to cccDNA eradication on treatment
Apart from when treatment is first initiated, the number of infected cells on treatment will be relatively low. Assuming eradication in our model is achieved when fewer than one cccDNA molecule persists, and there is no cccDNA replication whilst on treatment, the time to eradication can be approximated by: (Eq13) where !"!# is the number of cccDNA when therapy is initiated and Ln is the natural logarithm. To determine reasonable values for !"!# , we multiplied the number of hepatocytes in a human liver by the number of cccDNA per hepatocyte during untreated infection. There are about 1.4x10 8 hepatocytes per gram of human liver 57 , and an adult human liver is around 1.5kg, giving approximately 2x10 11 hepatocytes in total. In a recent study, an average of 6.3 copies of cccDNA per hepatocyte were found during chronic HBeAg POS infection, and 0.01 per hepatocyte during HBeAg NEG infection 30 , which gives a total of approximately 1x10 12 copies of cccDNA during HBeAg POS infection and 2x10 9 copies of cccDNA during HBeAg NEG infection.
Model assuming a subset of long--lived hepatocytes
If a proportion, , of hepatocytes are long--lived, the dynamics of cccDNA in 'normal' infected cells, Y[t], and in long--lived infected cells Z[t], are given by: where ! , ! and ! represent the natural death rates, cytoloytic death rate and clearance rates of the very long--lived cccDNA.
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Domain: Biology Medicine
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The Role of Cytokines and Chemokines in the Development of Basal Cell Carcinoma
The immune system plays an important role in surveillance against tumor development, and it is widely known that cancer cells protect themselves against the host's anti-tumor immune defense. Cancer cells have several means of evading the antitumor immunity, one of which is the production of immune modulators such as cytokines and chemokines. These factors can either promote or block immune responses. Many of these molecules are used by cancer cells to promote tumor progression including cell proliferation, cell migration, matrix remodeling, immune suppression and angiogenesis. On the other hand, some molecules are involved in immunotherapeutics for the purpose of enhancing and modifying antitumor immune responses.
Introduction
The immune system plays an important role in surveillance against tumor development, and it is widely known that cancer cells protect themselves against the host's anti-tumor immune defense. Cancer cells have several means of evading the antitumor immunity, one of which is the production of immune modulators such as cytokines and chemokines. These factors can either promote or block immune responses. Many of these molecules are used by cancer cells to promote tumor progression including cell proliferation, cell migration, matrix remodeling, immune suppression and angiogenesis. On the other hand, some molecules are involved in immunotherapeutics for the purpose of enhancing and modifying antitumor immune responses.
In basal cell carcinoma (BCC), several cytokines and chemokines and their receptors are associated with the development of this cutaneous cancer. There are varying degrees of inflammation in BCC. The majority of peritumor inflammatory cells are lymphocytes and most are T cells (1). It has been proposed that the tumor microenvironment of BCC is generally Th2 dominant. T regulatory cells and immature dendritic cells mediated by Th2 cytokines cause immunosuppression and decreased immunity to BCC (2). Tumor-associated macrophages (TAM), which are polarized to M2 type, are associated with tumor invasion and angiogenesis in BCC (3). In this chapter, we focus on cytokines and chemokines which may influence and enhance these immunosuppressive networks.
IL-6
Interleukin-6 (IL-6) is a pro-inflammatory cytokine, which can induce tumor progression by manipulating immune responses in the tumor microenvironment. IL-6 is directly related to epidermal hyperproliferation in psoriasis (4). There are many experimental evidences that IL-6 is associated with BCC. Overexpression of IL-6 in BCC cell lines increases anti-apoptotic activity and tumorigenic potency (5). The phosphotidyl inositol 3-kinase (PI3K)/Akt signal pathway is involved in such anti-apoptosis (6). On the other hand, IL-6 induces bFGFdependent angiogenesis in BCC cell line via JAK/STAT3 and PI3k/Akt pathways (7). IL-6 is also involved in CXCL-12 (SDF-1)-enhanced angiogenesis via activating ERK1/2 and NF-κB (8). IL-6 expression is associated with a significant increase of IL-8 (CXCL8) expression in www.intechopen.com BCC (9, 10), one of which functions is tumor angiogenesis. The expression of these two cytokines shows a significant positive correlation (10).
A single nucleotide polymorphism (SNP) in the promoter regions of IL-6 gene (IL6) is associated with the risk of BCC. The promoter region of IL6 contains several SNPs, including -634G>C, -597G>A and -174G>C. It has been reported that IL6 -597 G>A is significantly associated with BCC risk (11). However, others reported that there was no difference for genotype distributions of SNPs in the promoter region of IL6 between the BCC cases and controls, while linkage disequilibrium was observed between the -174 and -597 alleles in the IL6 (12).
IL-10
Interleukin-10 (IL-10) is a major immunosuppressive cytokine that plays a critical regulatory role in several areas of the immune system. It contributes to immunosuppression in the tumor microenvironment and may render it permissive for infiltration of cancer cells. IL-10 is upregulated in both melanoma (13)(14)(15) and non-melanoma skin cancer including BCC (2,16,17). The presence of IL-10 in BCC is associated with the lack of expression of HLA-DR, ICAM-1, CD40 and CD80 and the inconsistent expression of HLA-ABC in BCC (17). BCC is regarded as an indolent (slow growing) cancer with limited metastatic potential. While IL-10 expression by melanoma cells correlates with melanoma progression and development of metastatic competence (18), there is no clear correlation between IL-10 expression and tumor invasiveness of BCC. IL-10 can be detected by both aggressive BCC and nonaggressive BCC such as superficial BCC. However, there are discrepant results regarding IL-10 expression in superficial BCC. Urosevic et al. found that superficial BCC cells were uniformly negative for IL-10 expression at baseline and showed little change after imiquimod treatment (19). www.intechopen.com SNP in the promoter regions of IL-10 gene (IL10) is associated with the risk of BCC. IL10 -1082G>A is detected in BCC (12), and this polymorphism, as well as tumor necrosis factor-alpha (TNF-α) gene TNF -308G>A polymorphism, is more prevalent in aggressive BCC (20). However, others reported that there was no significant association between BCC and IL10 -1082 (11).
CXCL9, CXCL10, CXCL11 and CXCR3
CXCL9 (MIG), CXCL10 (IP-10) and CXCL11 (I-TAC) are chemokines that are induced by interferon during inflammatory responses. These chemokines bind to a common receptor, CXCR3. They can promote chemotaxis of activated T cells and NK cells through binding to CXCR3. The most recent attention has been given to the role of these chemokines in tumorigenesis of BCC. It has been reported that CXCL9, CXCL10, CXCL11, and their receptor CXCR3 are significantly upregulated in BCC. CXCR3, CXCL10, and CXCL11, but not CXCL9, colocalize with keratin 17, which is a BCC keratinocyte marker. Exposure of BCC cells to CXCL11 in vitro enhances keratinocyte cell proliferation (23). CXCL9, CXCL10 and CXCL11 promote expression of functional indoleamine 2,3-dioxygenase (IDO), which also colocalizes with keratin 17 (24). Thus, CXCR3 and its ligands may be important in tumorigenesis of BCC.
IL-8 (CXCL8)
Interleukin-8 (IL-8, CXCL8) is a chemokine produced by inflammatory cells and other cell types. This chemokine is one of the major mediators of the inflammatory response. It functions as a chemoattractant, but is also known as an angiogenic factor. IL-8 is associated with tumor angiogenesis in many solid tumors. It has been reported IL-8 is highly expressed in BCC (25). As described earlier in this chapter, IL-8 expression is associated with a significant increase of IL-6 expression in BCC (9, 10), and is positively correlated with IL-6 expression (10). However, the detailed mechanisms of IL-8 involved in the development of BCC are not fully understood.
CCL27
CCL27 is a chemokine that functions as a chemoattractant by interacting with its receptor, CCR10. This chemokine regulates T cell homing under homeostatic and inflammatory www.intechopen.com conditions, and plays a role in T cell-mediated inflammation of the skin. In BCC, the downregulating of CCL27 expression is associated with tumor immune escape. A significant decrease in CCL27 expression is also observed in squamous cell carcinoma and actinic keratosis. These skin tumors may evade T cell-mediated antitumor immune responses by down-regulating the expression of CCL27 through the activation of epidermal growth factor receptor (EGFR)-Ras-MAPK-signaling pathways (26).
IFN-γ
Interferon-gamma (IFN-γ) is a cytokine that is critical for immune responses against cancer. IFN-γ binding to the receptor activates the JAK-STAT pathway. In BCC, The expression of IFN-γ receptor is significantly decreased on the cancer cells compared with the overlying epidermis. The absence or paucity of IFN-γ receptor and the absence of intercellular adhesion molecule-1 (ICAM-1) may explain the lack of tumor-infiltrating cells and the lack of an active cell-mediated immune response in BCC (27).
On the other hand, Th1 cytokines including IFN-γ play a role in spontaneously regressing BCC. Some cases of BCC may show spontaneous regression in the absence of therapy. Such spontaneous regression is mediated by activated CD4+ T cells, and IFN-γ is elevated in actively regressing BCC (28). There is a significantly increased number of CD4+ T cells infiltrating regressing tumors, and the expression of IL-2 receptor, which is an early activation marker for T cells is also increased (29). Abundant CD8+ T cells and interferon signal transduction is associated with partial host antitumor response (2).
Imiquimod has been shown to be efficacious as a topical treatment for BCC. Imiquimod is a Toll-like receptor 7 (TLR7) agonist, which induces interferon and other cytokines through the immune system and stimulates innate and adaptive cell-mediated immunity. Clinical studies have demonstrated clinical and histological clearance of superficial BCC after treatment with imiquimod 5% cream (30)(31)(32). Imiquimod treatment is associated with the early appearance of lymphocytes and macrophages. This early response tends to be a mixed cellular response of CD4 cells, activated dendritic cells and macrophages, with later infiltration by CD8 T cells (33). Application of imiquimod induces a cascade of Th1 cytokines including IFN-α, TNF-α, IL-1α, IL-12, and IFN-γ, with profound effects on innate and adaptive immunity and on immunologic memory and antigen presentation. IFN-γ is produced by CD4 and CD8 T cells. IFN-γ is associated with the enhanced expression of ICAM-1, promoting the influx of immune cells. Imiquimod treatment also induces a massive increase in macrophage peritumoral and intratumoral infiltration (19). Thus, the TLR7agonist plays an important role in inducing a lymphocytic infiltrate by promoting specific Th1 cellular immune response capable of eliminating cancer cells (34).
FasL (CD95L) and Fas (CD95)
Fas ligand (FasL, CD95L) belongs to the tumor necrosis factor (TNF) family. FasL binds to its receptor, Fas (CD95), and induces apoptosis. Apoptosis via FasL/Fas pathway plays an important role in the regulation of the immune system. FasL expressed by cacncer cells induces apoptosis of infiltrating lymphocytes and they can evade immune surveillance, contributing to cancer progression. BCC has been reported to lack Fas expression (19,35), while they commonly retain the expression of FasL (36). In normal skin, Fas is expressed by keratinocytes in the basal layer. Fas expression is up-regulated in chronically sun-damaged skin. Actinic keratosis does not express Fas. Squamous cell carcinoma focally expresses Fas at the sites of contact with lymphocytes (35).
It has been suggested that BCC can evade host immune surveillance by expressing FasL (37). However, different results were obtained for the FasL expression in BCC (19,38,39), and the issue of FasL expression in BCC is still debatable. After imiquimod treatment, the infiltrating cells demonstrate an increase in Fas/FasL expression, while Fas expression by BCC cells remains unaffected and FasL expression demonstrates either an increase or a decrease in different cases (19). After intralesional IFN-α treatment, BCC cells become Fas-positive with signs of tumor regression as a result of tumor cell apoptosis (36). Thus, Fas/FasL pathway may be associated with tumor regression by such treatments.
Substance
Alternative
Conclusions
There is much more work to be done in order to adequately characterize the clinical significance of cytokines, chemokines and related molecules in BCC. Studies thus far show that the factors described in this chapter play an integral role in BCC development and immunosuppression. A better understanding of these interactions may facilitate development of more potent immune-based treatment for BCC.
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Domain: Biology Medicine
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Bacterial DNAemia is associated with serum zonulin levels in older subjects
The increased presence of bacteria in blood is a plausible contributing factor in the development and progression of aging-associated diseases. In this context, we performed the quantification and the taxonomic profiling of the bacterial DNA in blood samples collected from forty-three older subjects enrolled in a nursing home. Quantitative PCR targeting the 16S rRNA gene revealed that all samples contained detectable amounts of bacterial DNA with a concentration that varied considerably between subjects. Correlation analyses revealed that the bacterial DNAemia (expressed as concentration of 16S rRNA gene copies in blood) significantly associated with the serum levels of zonulin, a marker of intestinal permeability. This result was confirmed by the analysis of a second set of blood samples collected from the same subjects. 16S rRNA gene profiling revealed that most of the bacterial DNA detected in blood was ascribable to the phylum Proteobacteria with a predominance of the genus Pseudomonas. Several control samples were also analyzed to assess the influence of contaminant bacterial DNA potentially originating from reagents and materials. The data reported here suggest that para-cellular permeability of epithelial (and, potentially, endothelial) cell layers may play an important role in bacterial migration into the bloodstream. Bacterial DNAemia is likely to impact on several aspects of host physiology and could underpin the development and prognosis of various diseases in older subjects.
P s e u d o m o n a s A r t h r o b a c t e r A c i n e t o b a c t e r E s c h e r i c h i a -S h i g e l l a P h y l l o b a c t e r i u m P a r a c o c c u s T e p i d i
Escherichia-Shigella Cluster_10 Arthrobacter Solirubrobacterales 67-14 genus Caulobacteraceae genus Cluster_364 Burkholderia Clostridium Fig. S5. Abundance of 16S rRNA gene copies of taxonomic units detected in second set of blood samples (n=42) that significantly correlated with the serum levels of zonulin. ρ, Spearman's rank correlation coefficient; P, P value of the Kendall's rank correlation. Taxa that resulted significantly correlated with zonulin also from the analysis of the first set of blood samples are indicated in bold and red color.
Fig. S6. Correlations of the taxonomic units detected in blood (expressed as relative abundances) toward age, BMI, and metabolic and functional markers
determined in blood of the older subjects under study (n=43). This figure only includes taxa whose abundance significantly correlated with at least one parameter.
Technical issues concerning zonulin quantification
In this study, zonulin quantification in serum samples was carried out by means of the most commonly used commercial ELISA kit. Recently, the specificity of this and others ELISA assays has been questioned 2 and, consequently, it was suggested to interpret with caution data collected as direct assessment of intestinal permeability 3 . In this context, it is noteworthy that already Scheffer and colleagues 4 , previously identified through the use of the same kit a variety of proteins structurally related to zonulin (in particular properdin).
Consequently, the authors suggested that although the assay was not specific for pre-haptoglobin2 quantification, other members of permeability-regulating proteins belonging to the mannose-associated serine protease family could be determined 4 .
Technical issues concerning the detection and taxonomic profiling of bacteria DNA in blood
In a recent publication, circulating cell-free DNA isolated from human blood plasma was subjected to massive shotgun sequencing 5 ; more than half of the identified contigs had little or no homology with sequences in available databases and, interestingly, were assigned to hundreds of entirely novel microbial taxa. In our study, we did not find such a large presence of unknown microorganisms. Nonetheless, two main aspects distinguish the research by Kowarsky et al. from ours: (i) we performed 16S rRNA gene profiling and not shotgun metagenomic sequencing and (ii) we analyzed DNA isolated from whole blood and not plasma. This second aspect is particularly important considering the presence of bacterial DNA in blood cells such as erythrocytes and antigen-presenting cells 6,7 .
In this study, the bacterial DNA isolated from blood was taxonomically profiled through MiSeq sequencing of 16S rRNA gene amplicons. We presented above numerous similarities, both quantitatively (i.e., abundance of 16S rRNA gene copies) and qualitatively (i.e., detected taxa), between the results of our study ad what reported in several other studies available in literature. However, none of the papers we referenced above focused specifically on the evaluation of potential contaminant DNA, originating from any possible experimental step. The use of 16S rRNA gene profiling for the bacterial taxonomic characterization of low microbial biomass samples, such as blood, has been criticized as being at high risk of microbial contamination that may occur at any step of the protocol, from sample collection until sequencing 8,9 . In our study, we analyzed several control samples to assess the potential presence of contaminants in labware (e.g. vacutainer and EDTA tubes) and reagents (e.g. solutions used during extraction, library preparation, sequencing, and qPCR). According to qPCR experiments, we always detected in control samples a quantity of bacterial DNA much lower than that quantified in blood samples, suggesting the potential contaminants should not have significantly affected the taxonomic profiling of blood samples. However, the confirmation of a significant correlation between zonulin and 16S rRNA gene copies in blood (total and ascribed to Pseudomonas) also in the second set of blood samples investigated supports the conclusion that the bacterial DNA detected in blood largely do not derive from contamination. Nonetheless, it is also important to mention that most of the bacterial
Supplementary Material
13 genera detected in blood in our study have been reported as contaminants occurring during microbiome research in other studies (reviewed in 8 ).
Considering the relative abundance of bacterial taxa detected in blood and control samples, we hypothesize that the most probable contaminants belong to the families Enterobacteriaceae, Micrococcaceae and Moraxellaceae (the second, third and fifth most abundant families detected in blood, respectively), whereas at least most part of the DNA ascribed to Pseudomonadaceae (the most abundant family detected in blood) is less likely to derive from contaminants. Lists of bacterial taxa that were identified in negative controls during different independent studies have been proposed 8,10 , cataloging up to 70 different genera to be considered as potential contaminants 8 . These lists contain numerous Proteobacteria including Pseudomonas, which was found to be the most prevalent and abundant bacterial genus in the blood samples investigated in our study. Pseudomonas is a ubiquitous bacterium, which colonizes numerous environments, such as soil, water and various plant and animal organisms, due to minimal survival requirements and remarkable adaptation ability 11 . Notably, Pseudomonas is also one of the microorganisms most frequently isolated from patients with bacteremia, particularly the species P. aeruginosa 12 . In this report, the partial sequence of the 16S rRNA gene belonging to the most prevalent and abundant OTUs found in the analyzed blood samples (Cluster 1 and Cluster 3, Fig. 5) shared 100% similarity with P. fluorescens and other species of the same phylogenetic lineage. Although far less pathogenic than P. aeruginosa, P. fluorescens has been often reported as the aetiologic agent of opportunistic infections in lungs, mouth, stomach, urinary tract, skin, and, most commonly, blood 13,14 . Notably, P. fluorescens is recognized as the most important cause of iatrogenic sepsis, attributed to contaminated blood transfusion or contaminated equipment used in intravenous infusions [15][16][17] .
Although the literature evidence discussed above suggests that P. fluorescens and related species can be contaminants (see Supplementary Discussion in Additional file 1), on the other hand, these bacteria were also reported to possess numerous functional properties that support their survival and growth in mammalian hosts 14 . Furthermore, an interesting association was found between the presence of serum antibodies against the I2 peptide encoded by P. fluorescens and Crohn's disease 18 , celiac disease 19 , ankylosing spondylitis 20 , and chronic granulomatous disease 21 . In addition, P. fluorescens was reported to be regularly cultured from clinical samples even in the absence of acute infection 14 . Finally, P. fluorescens was demonstrated to induce zonulin expression and decreased intestinal permeability in a time dependent manner in an in vitro model of intestinal epithelium 22 . In the same study, the authors found increased zonulin levels and higher abundance of Pseudomonas 16S rRNA gene copies (as determined through qPCR with genus-specific primers) in coronary artery disease (CAD) patients compared to non-CAD subjects 22 . Altogether, these reports support the hypothesis that human-adapted P. fluorescens strains constitute low-abundance indigenous members of the microbial ecosystem of various body sites, such as the lungs, mouth, and stomach 14,[23][24][25] . Contextually, we can speculate that certain P. fluorescens-related strains are highly adaptable and poorly pathogenic members of the microbiota in several body sites that may frequently translocate into the bloodstream, providing a dominant contribution to bacterial DNAemia. However, we are conscious that our results do not conclusively demonstrate the actual presence of Pseudomonas (cells or free DNA) in blood. We believe that DNA-\===
Domain: Biology Medicine. The above document has
* 2 sentences that start with 'Bacterial DNAemia is',
* 2 sentences that start with 'In this context',
* 2 sentences that start with 'In our study, we',
* 2 paragraphs that start with 'Technical issues concerning',
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Remote immune processes revealed by immune-derived circulating cell-free DNA
Blood cell counts often fail to report on immune processes occurring in remote tissues. Here, we use immune cell type-specific methylation patterns in circulating cell-free DNA (cfDNA) for studying human immune cell dynamics. We characterized cfDNA released from specific immune cell types in healthy individuals (N = 242), cross sectionally and longitudinally. Immune cfDNA levels had no individual steady state as opposed to blood cell counts, suggesting that cfDNA concentration reflects adjustment of cell survival to maintain homeostatic cell numbers. We also observed selective elevation of immune-derived cfDNA upon perturbations of immune homeostasis. Following influenza vaccination (N = 92), B-cell-derived cfDNA levels increased prior to elevated B-cell counts and predicted efficacy of antibody production. Patients with eosinophilic esophagitis (N = 21) and B-cell lymphoma (N = 27) showed selective elevation of eosinophil and B-cell cfDNA, respectively, which were undetectable by cell counts in blood. Immune-derived cfDNA provides a novel biomarker for monitoring immune responses to physiological and pathological processes that are not accessible using conventional methods.
Introduction
Circulating biomarkers for monitoring inflammatory or immune responses are an essential part of diagnostic medicine and an important tool for studying physiological and pathological processes. These include, among others, counts of specific immune cell types in peripheral blood, RNA expression profiles in blood cells (Maas et al., 2002;Tuller et al., 2013), and levels of circulating proteins such as C-reactive protein (CRP) (Gabay and Kushner, 1999;Sproston and Ashworth, 2018). A major limitation of circulating immune cell analysis is that it often fails to report on immune processes taking place in remote locations. Conversely, CRP and similar proteins do reflect the presence of tissue inflammation but are highly non-specific with regard to tissue location and the nature of inflammatory process (Gabay and Kushner, 1999).
Dying cells release nucleosome-size fragments of cell-free DNA (cfDNA), which travel transiently in blood before being cleared by the liver (Heitzer et al., 2019). Analysis of the sequence of such fragments is emerging as a powerful diagnostic modality. Liquid biopsies using cfDNA have been applied to reveal the presence of mutations in a fetus as reflected in maternal cfDNA (Bianchi et al., 2014;Fan et al., 2012;Lo et al., 1997), identify and monitor tumor dynamics via the presence of somatic mutations in plasma (Wan et al., 2017), and detect the rejection of transplanted organs when the levels of donor-derived DNA markers are elevated in recipient plasma (De Vlaminck et al., 2014;De Vlaminck et al., 2015). More recently, we and others have shown that tissue-specific DNA methylation patterns can be used to determine the tissue origins of cfDNA, allowing to infer cell death dynamics in health and disease even when no genetic differences exist between the host and the tissue of interest (Cheng et al., 2019;Lehmann-Werman et al., 2016).
Although the majority of cfDNA in healthy individuals is known to originate in hematopoietic cells (Lehmann-Werman et al., 2016;Moss et al., 2018;Sun et al., 2015), it has often been regarded as background noise, against which one may look for rare cfDNA molecules released from a solid tissue of interest. We hypothesized that identification of immune cell-derived cfDNA could open a window into immune and inflammatory cell dynamics, even in cases where peripheral blood counts are not informative. Here, we describe the development of a panel of immune cell type-specific DNA methylation markers, and the use of this panel for cfDNA-based assessment of human immune cell turnover in health and disease. We show that immune cell cfDNA measurement can provide clinical biomarkers in multiple disease and treatment conditions, otherwise undetectable by cell subset enumeration in blood.
Results
Identification of cell type-specific DNA methylation markers for immune cells Using a reference methylome atlas of 32 primary human tissues and sorted cell types , we searched for CpG sites that are uniquely methylated or unmethylated in a specific immune cell type. Notably, across the entire atlas, the vast majority of such unique loci are hypomethylated in the cell type of interest and methylated elsewhere (typically marking cell type-specific enhancers), while just a small minority are methylated in a given cell type and hypomethylated elsewhere (manuscript in preparation). We identified dozens of uniquely hypomethylated CpG sites for most cell types examined, qualifying these as biomarkers for DNA derived from a given cell type ( Figure 1A). Based on this in silico comparative analysis, we selected for further work 17 different CpG sites, whose combined methylation status could distinguish the DNA of seven major immune cell types: neutrophils, eosinophils, monocytes, B-cells, CD3 T-cells, CD8 cytotoxic T-cells, and regulatory T-cells (Tregs). For each marker CpG we designed PCR primers to amplify a fragment of up to 160 bp flanking it, considering the typical nucleosome size of cfDNA molecules. Amplicons included additional adjacent CpG sites, to gain enhanced cell type specificity due to the regional nature of tissue-specific DNA methylation (Lehmann-Werman et al., 2016). We then established a multiplex PCR protocol, to co-amplify all 17 markers from bisulfite-treated DNA followed by next-generation sequencing (NGS) for assessment of methylation patterns (Neiman et al., 2020). Methylation patterns of amplified loci across 18 different human tissues validated the patterns inferred from in silico analysis and supported the ability of this marker cocktail to specifically identify the presence of DNA from each of the seven immune cell types ( Figure 1B). We also assessed assay sensitivity and accuracy via spike-in experiments. We mixed human leukocyte DNA with DNA from the HEK-293 human embryonic kidney cell line and used the methylation cocktail to assess the fraction of each immune cell type. The markers quantitatively detected the presence of DNA from specific immune cell types even when blood DNA was diluted 10-to 20-fold ( Figure 1C and Figure 1-figure supplement 1). These findings establish . For each immune cell type we chose the top 10 CpGs that are hypomethylated (yellow) in the specific immune cell type and hypermethylated (blue) in other tissues and cells. This yielded 70 cell-specific CpG sites (rows) for seven different immune cell subtypes -B-cells, CD8 cytotoxic T-cells, CD3 T-cells, regulatory T-cells, eosinophils, monocytes, and neutrophils. (B) Methylation patterns of 17 loci, selected from the 70 shown in panel A, based on the presence of multiple adjacent hypomethylated CpGs within an amplicon of up to 160 bp. Each methylation marker (columns) was assessed using genomic DNA from 19 different tissues and cell types (rows). All 17 markers were amplified in one multiplex PCR. Shades of gray represent the percentage of fully unmethylated molecules from the indicated marker in DNA from the indicated cell type. (C) Spike-in experiments assessing assay sensitivity. Human leukocyte DNA was mixed with DNA from HEK-293 cells (human embryonic kidney cells) in the indicated proportions. Colored lines show the inferred percentage of DNA from the indicated immune cell type in the mixture, as a function of the percentage of leukocyte DNA in the mixture. The percentage of DNA from each immune cell type was calculated using markers specific to neutrophils (NEUT1, NEUT2, NEUT3), monocytes ( specificity, sensitivity, and accuracy of the methylation marker cocktail for detection of DNA derived from the seven selected immune cell types.
Immune cfDNA reflects cell turnover rather than counts of circulating blood cells To validate assay accuracy, we applied the immune cell methylation markers to genomic DNA of blood cells, expecting to observe signals that agree with cell ratios as determined by complete blood counts (CBC). We obtained 392 blood samples from 79 healthy individuals at different time points, and simultaneously tested for CBC, and methylation marker cocktail both in DNA extracted from whole blood and in cfDNA extracted from plasma. Theoretically, immune cell cfDNA could be a mere reflection of the counts of each cell type (e.g. if it is released mostly from blood cells that have died during blood draw or preparation of plasma). In such a case, immune cfDNA should correlate well with CBC (and with immune methylation markers measured in genomic DNA from whole blood). Alternatively, if cfDNA reflects cell death events that took place in vivo, the correlation to cell counts is expected to be weaker.
Comparing the CBC to DNA methylation pattern, we observed a strong correlation between assessments of specific cell fractions in the two methods (Pearson's correlations; r = 0.67-0.83, p-value < 0.0001, Figure 2A and Figure 2-figure supplement 1), supporting validity of the methylation assay for identifying fractions of DNA derived from specific immune cell types, consistent with previous findings (Baron et al., 2018). However, comparing cfDNA methylation markers in plasma to blood DNA methylation markers and CBC, we observed that proportion of cfDNA from specific immune cell types did not correlate with the proportion of the same markers in circulating blood cells and with CBC (Pearson's correlations; r = 0.14-0.53, Figure 2B-C and Figure 2-figure supplement 1). These findings suggest that immune cfDNA levels are the result of biological processes beyond immune cell counts.
We reasoned that over-or under-representation of DNA fragments from a specific immune cell type in plasma compared with blood counts most likely result from differences in cell turnover. The concentration of cfDNA from a given cell type should be a function of the total number of cells that have died per unit time (turnover rate), which is derived from the number of cells and their lifespan: The larger the population of a given cell type (both circulating and tissue-resident), the more cfDNA it will release; similarly, the shorter is lifespan, the more DNA will be released to plasma. The cfDNA findings were consistent with this model. For example, the fraction of lymphocyte cfDNA in plasma was always smaller than the fraction of lymphocyte DNA in circulating blood cells or the fraction of lymphocytes in CBC ( Figure (Macallan et al., 2005;Michie et al., 1992). Conversely, the fraction of monocyte cfDNA was larger than the fraction of monocyte DNA in genomic DNA from whole blood or the fraction of monocytes in CBC ( Figure 2B-C), consistent with the shorter half-life of monocytes (Patel et al., 2017).
To further examine the relative presence of immune cfDNA in plasma and whole blood, we employed an independent set of samples and an independent technology to measure and interpret methylation markers. Specifically, we performed deconvolution of methylomes obtained by whole genome bisulfite sequencing (WGBS) (85× average coverage), on genomic DNA from whole blood, and matched plasma cfDNA from 23 healthy donors (see Materials and methods). This analysis, based on genome-wide methylation patterns, also revealed that lymphocyte and monocyte cfDNA was under-and over-represented, respectively, relative to the abundance of DNA from these cells in blood (lymphocytes, p-value = 0.002; monocytes, p-value = 0.0005, Kruskal-Wallis) ( Figure 2D).
These findings support the exciting idea that cfDNA levels from a given immune cell type integrate total cell number and the lifespan of that cell type, and can provide information on processes not evident from circulating cell counts. For example, if the level of cfDNA from a specific immune cell type increases while the circulating counts of this cell type are unchanged, this can indicate either growth in the size of a tissue-resident population, or increased cell turnover, both of which are important immune parameters that cannot be easily obtained otherwise. Below, we provide evidence that such information can be extracted following perturbations of immune homeostasis.
We also conducted a longitudinal study, to understand how immune-derived cfDNA is changing over time in the same individual. We collected weekly blood samples from 15 healthy donors over a period of 6 weeks. For each sample we obtained CBC and measured immune DNA methylation markers in genomic DNA of blood cells and in plasma cfDNA. We then calculated the coefficient of variation among CBC, blood methylation markers, and cfDNA methylation markers, within and between individuals. In circulating blood cells, the inter-individual variation in immune methylation markers and CBC was always higher than the intra-individual variation in these markers ( Figure 2E and . This is consistent with previous reports that blood cell counts among individuals are more similar to themselves than to others, indicating distinct set points per person for the total number of specific immune cell types circulating in blood (Alpert et al., 2019;Carr et al., 2016). Strikingly, cfDNA values of the same immune methylation markers varied to the same extent among samples of the same individual and among samples of different individuals ( Figure 2F). This argues that unlike cell counts, cfDNA of immune cells has no individual set point. Rather, cfDNA levels appear to reflect homeostatic maintenance of cell number, whereby cell birth and death are modulated to maintain a desired cell count.
Elevation of B-cell-derived cfDNA after influenza vaccination precedes changes in cell counts and correlates with specific antibody production
We hypothesized that upon perturbations of the immune system, cfDNA markers will reveal information about immune cell dynamics that is not present in peripheral blood cell counts, for example, extensive cell death during the process of affinity maturation, which repeatedly selects for B-cell clones with increased antibody-target affinity. To test this hypothesis, we examined longitudinal blood samples from healthy individuals who received an annual quadrivalent influenza vaccination (Nakaya et al., 2011;Voigt et al., 2018). The influenza vaccine response is mediated mostly by the humoral immune system (B-cells) aided by CD4 T-cells (Gage et al., 2018). Changes in circulating cell counts occur a week after vaccination, reflecting processes such as plasma cell formation (Victora and Wilson, 2015). cfDNA responses to vaccination were not previously reported. We recruited 92 healthy volunteers (age range 20-73, mean age 37.4) who received the vaccination in 2018 or 2019.
Note that cfDNA proportions of immune cells differ from the proportions of these cell types in peripheral blood. (D) Deconvolution of cfDNA and white blood cell (WBC) methylomes generated using whole genome bisulfite sequencing (WGBS) of 23 healthy donors. Note under-representation of lymphocyte DNA and over-representation of monocyte DNA in cfDNA compared with blood DNA (lymphocytes, p-value = 0.0021; monocytes, p-value = 0.0005, Kruskal-Wallis). (E-F) XY scatter plots showing the average of inter-individual coefficient of variation (X-axis) and intra-individual coefficient of variation (Y-axis) for each immune cell type in whole blood (E) and in cfDNA (F) based on methylation markers. Black line represents perfect correlation between inter-and intra-individual dual variation. Dots below the black line reflect greater inter-individual variation and dots that are above reflect greater intra-individual variance. A smaller intra-individual variation in whole blood suggests a set point for proportions of blood cell types in each individual. By contrast, cfDNA levels of immune markers vary similarly within and between individuals.
The online version of this article includes the following figure supplement(s) for figure 2: From each volunteer we obtained blood samples a day before vaccination (day 0, D0), and at day 3, 7, and 28 post-vaccination. Consistent with previous reports, B-cell counts (measured by methylation analysis of DNA from whole blood) were moderately but significantly elevated on day 7, and persisted to day 28 (p-value = 0.0048, Kruskal-Wallis) ( Figure 3A; Li et al., 2012). Surprisingly, B-cellderived cfDNA levels increased as early as day 3, peaked on day 7 and returned to baseline levels on day 28 (p-value < 0.0001, Kruskal-Wallis) ( Figure 3B and D), suggesting that cfDNA reveals an early increase in the turnover of B-cells following vaccination, which is not portrayed in circulating B-cells. We observed a similar trend in the ratio of B-cell cfDNA to B-cell counts in each individual ( Figure 3C and p-value=0.016, Kruskal-Wallis, B-cell counts calculated from methylation markers in whole blood). Of note, this response was specific to B-cell-derived cfDNA; total cfDNA levels did not change over the time course of vaccination, nor did cfDNA levels of other immune cell types (Figure 3-figure supplement 1). Taken together, this strengthens evidence that cfDNA changes reflect processes beyond alterations in absolute circulating cell counts in a cell-specific manner.
To ask if the elevation of B-cell cfDNA has functional significance in the development of an immune response, we obtained information on the production of antibodies. We classified all volunteers into responders or non-responders according to the hemagglutinin antibody titer measured at 28 days post-vaccination, and asked if B-cell cfDNA or B-cell counts correlated with antibody production. Responders had a significantly higher peak elevation of B-cell cfDNA relative to their pre-vaccination baseline levels compared with non-responders (p-value = 0.044, Mann-Whitney, AUC = 0.7, p-value = 0.04) ( Figure 3E and F and Figure 3-figure supplement 2). Peripheral B-cell counts were not different between responders and non-responders (p-value = 0.2, Mann-Whitney) ( Figure 3G). It is well established that influenza vaccination is more effective in younger individuals (Del Giudice et al., 2015;Ranjeva et al., 2019;Siegrist and Aspinall, 2009;Wagner et al., 2018). To examine the relationship between age, antibody production and cfDNA we plotted the fold elevation of B-cell cfDNA from baseline as a function of donor age, and marked responders and non-responders. Nonresponders to vaccination in our cohort were all above 35 years and tended to have a minimal elevation of B-cell cfDNA above baseline even when compared to people in their age group ( Figure 3H and Figure 3-figure supplement 2, peak elevation of B-cell cfDNA in responders versus non-responders p-value = 0.089), suggesting that B-cell cfDNA dynamics report on a biological process independent of age. We conclude that B-cell turnover (as reflected in B-cell cfDNA but not in B-cell counts) captures an early response of the immune system to influenza vaccination that predicts a functional outcome, suggesting cell-specific cfDNA could serve as a sensitive biomarker of functional immune changes.
Selective elevation of eosinophil-derived cfDNA in patients with eosinophilic esophagitis
To test the hypothesis that immune-derived cfDNA can reveal pathological inflammatory processes in remote locations, we studied patients with eosinophilic esophagitis (EoE). EoE is a chronic inflammatory disease characterized clinically by esophageal dysfunction, and histologically by eosinophilpredominant inflammation of the esophagus (Liacouras et al., 2011). Diagnosis of EoE requires an invasive endoscopic biopsy. Notably, most patients do not show peripheral eosinophilia (Aceves et al., 2007;Dellon et al., 2009). We analyzed blindly immune cfDNA markers in plasma samples from patients with active EoE (N = 21), patients with EoE in remission (N = 24), and healthy controls (N = 14). Patients with active EoE had elevated levels of eosinophil cfDNA (mean = 115 GE/ml) compared with both healthy controls (mean = 34 GE/ml, p-value = 0.0056) and patients with inactive EoE (mean = 36 GE/ml, p-value = 0.0003, Kruskal-Wallis), while other immune cfDNA markers were not elevated in active EoE patients ( Figure 4A and B and Figure 4-figure supplement 1). The fraction of eosinophils in blood was not significantly elevated in EoE patients ( Figure 4C and p-value=0.1, Kruskal-Wallis), consistent with restriction of eosinophil abundance to the esophagus and further supporting the idea that immune cfDNA is not a reflection of circulating immune cells.
Among a small subset of donors for which we had access to plasma, PBMC and CBC (12 active EoE, 8 inactive EoE, 3 controls), elevated eosinophil counts and elevated eosinophil cfDNA levels were observed in non-overlapping groups of EoE patients (elevated eosinophil counts in 4/12 patients with active EoE, 2/8 patients with inactive EoE, as previously reported [Dellon et al., 2009]; elevated eosinophil cfDNA in 5/12 patients with active EoE), suggesting that counts and cfDNA reflect different biological processes ( Figure 4D). Finally, we generated receiver operating characteristic (ROC) curves to test our ability to identify active EoE patients. Eosinophil cfDNA could distinguish active EoE from healthy controls ( Figure 4E, AUC 0.83, p-value = 0.001) and from patients with inactive disease ( Figure 4F, AUC 0.84, p-value = 0.0001), with high specificity and sensitivity. These findings suggest that cell type-specific cfDNA can be used to detect clinical inflammation occurring in solid tissues that is not reflected in peripheral cell counts.
B-cell-derived cfDNA elevation in patients with B-cell lymphoma
Hematological malignancies occurring in remote immune organs such as the bone marrow, spleen, and lymph nodes are often undetectable in peripheral blood (Conlan et al., 1991). We reasoned that increased turnover of cancer cells in hematological malignancies would release cfDNA molecules carrying methylation marks of the normal cell type from which the tumor originated, informing on tumor presence and dynamics. In support of this idea, previous plasma methylome analysis by Sun et al., 2015, demonstrated elevated B-cell-derived cfDNA in a pregnant woman that unknowingly had B-cell lymphoma. In addition to tumor-derived cfDNA, cell type-specific cfDNA markers could reveal collateral damage incurred by the tumor to normal adjacent cells (Ménétrier-Caux et al., 2019;Ray-Coquard et al., 2009). To test this idea we examined blood samples from patients with B-cell lymphoma, a disease which often requires imaging and invasive biopsies for diagnosis and monitoring (Barrington et al., 2014;Laurent et al., 2017). We studied plasma and blood cells from 17 newly diagnosed (treatment-naïve) B-cell lymphoma patients (diffuse large B-cell lymphoma, n = 6; Hodgkin's lymphoma, n = 5; follicular lymphoma, n = 6) and age-matched healthy controls (Supplementary file 1). Lymphoma patients (mean = 264.4 GE/ml) had dramatically elevated levels of B-cell-derived cfDNA compared with controls (mean = 18.3 GE/ml, p-value < 0.0001), while B-cell counts in peripheral blood were actually decreased (control; mean = 0.162, lymphoma; mean = 0.079, 10 9 /l, p-value = 0.0059, Mann-Whitney) ( Figure 5A-C). We observed that the level of B-cell cfDNA accurately distinguished B-cell lymphoma patients from healthy controls, much better than did B-cell counts (cfDNA, AUC = 0.98, p-value < 0.0001; B-cell counts, AUC = 0.75, p-value = 0.006; Figure 5D and E). Total levels of cfDNA as well as the levels of other immune cfDNA markers were also elevated in lymphoma patients, consistent with reports on alterations in non-B-cells in lymphoma (Simone, 2013). We observed the strongest response in the levels of B-cell cfDNA (14.4-fold increase compared with controls), CD8 cytotoxic T-cells (10.7-fold), and Tregs (13.8-fold) ( Figure 5F, Figure 5-figure supplement 1). Lymphocyte counts were decreased, such that the ratio of cfDNA to cell count for each cell type was dramatically elevated in lymphoma patients ( Figure 5G). In an additional cohort of lymphoma patients that were monitored before and after treatment, we observed a decrease in B-cell-derived cfDNA in most patients (n = 16, p-value = 0.0032, Kruskal-Wallis test), similarly to what have been observed in other cancers following treatment Figure 5H). Initial analysis did not reveal a correlation between B-cell cfDNA and clinical outcome of treatment as defined by PET-CT (e.g. the few patients whose B-cell cfDNA were not reduced after treatment did not stand out as having worse prognosis), suggesting more complex relationships between B-cell cfDNA dynamics and clinical phenotype. To validate these findings, we performed the analysis on plasma samples from a second, independent cohort of untreated lymphoma patients and healthy controls. As in the first cohort, we observed higher levels of B-cell cfDNA in patients (lymphoma n = 10, mean = 1473 GE/ ml; control n = 34, mean = 15 GE/ml, p-value < 0.0001, Mann-Whitney), accompanied by lower B-cell counts and higher T-cell cfDNA ( Figure 5-figure supplement 2).
These findings indicate that lymphoma growth causes an elevation in the levels of B-cell cfDNA. In addition, a massive loss of normal T-cells leads to extensive release of cfDNA, potentially reflecting an immune response against the tumor or collateral damage. Taken together across all three conditions (influenza vaccination, EoE, and lymphoma), immune cell dynamics in remote locations that are not evident in peripheral blood are detectable via cell-specific methylation markers in plasma.
Discussion
We describe a novel method for monitoring turnover dynamics of the human immune system, using cell type-specific cfDNA methylation markers. The assay opens a window into aspects of human immune and inflammation biology that are not reflected in blood cell counts or gene expression patterns. Specifically, the concentration of cfDNA derived from a given immune cell type is a function of the total number of cells of that type (circulating and remote pools, combined), the lifespan of this population, determinants of cfDNA release (e.g. efficiency of phagocytosis) and determinants of cfDNA clearance from plasma (e.g. liver uptake). While many of these parameters are typically unknown, in some cases cfDNA dynamics allow to infer a change in cell turnover or in total cell number outside systemic circulation. For example, when analyzing cfDNA from different cell types in the same sample, it is fair to assume that they were subject to the same clearance kinetics.
We propose that a deeper qualitative and quantitative understanding of the fundamental rules governing cfDNA release and clearance will enrich our ability to relate liquid biopsy data to physiological processes taking place in vivo.
Since the method relies on highly stable methylation marks of cell identity (Dor and Cedar, 2018), it is expected to be universal, with the same markers allowing to accurately monitor immune cell dynamics in all individuals. While our current assay uses a panel of 17 methylation markers specific to seven key immune cell types, future improvements should increase the resolution of analysis to target essentially all immune cell types. For example, identification of methylation markers specific to memory B-cells, plasma cells, T-cell subtypes, and tissue-specific macrophages is likely feasible, and could greatly increase the information gained from immune cfDNA analysis. We note however that dynamic cellular states may involve changes in gene expression that do not involve reprogramming of DNA methylation patterns, representing a limitation of the approach. In other words, methylation markers can inform on the turnover dynamics of cell types, not cell states. Analysis of more dynamic aspects of the epigenome, such as the profile of histone marks on circulating nucleosomes (Sadeh et al., 2021), may allow inference of transient gene expression programs in cells prior to death and release of cfDNA.
Our cross-sectional and longitudinal analysis of immune cfDNA in healthy individuals begins to define the normal range among the population, an essential step toward using the assay for identifying deviations from health. More extensive characterization of immune cell cfDNA in healthy individuals is necessary to interpret trends that were revealed by our healthy cohorts. For example, we noticed lower levels of neutrophil cfDNA in adult females compared with adult males, suggesting that neutrophils in females live longer; we speculate that such differences in lifespan explain why women have a higher steady-state neutrophil count (Bain and England, 1975) (and data not shown). Additional observations of healthy immune cfDNA dynamics that merit further investigation regard age-related changes, such as elevated monocyte cfDNA and reduced lymphocyte cfDNA in individuals older than 60. Finally, the intra-and inter-individual variation in immune cfDNA levels shows that unlike blood cell counts, cfDNA levels vary wildly, apparently with no regulatory mechanism that attracts them to a certain set point typical to an individual. We propose that varying cfDNA levels reflect the action of regulated cell death as a homeostatic mechanism by which the healthy body maintains cell numbers within a desired range (model, Figure 6). curve for the diagnosis of lymphoma based on B-cell cfDNA levels in healthy subjects and patients with B-cell lymphoma. (E) ROC curve for diagnosis of lymphoma based on B-cell counts. (F) Levels of immune cell type-specific cfDNA in lymphoma patients and healthy controls (mean lymphoma/ mean control). (G) The ratio between the percentage of cfDNA from a given immune cell type and the percentage of cells from this population in blood according to complete blood counts (CBC), in each donor among the healthy volunteers (n = 23, blue bars) and patients with lymphoma (n = 17, red bars). Boxes represent 25th and 75th percentiles around the median, whiskers span min to max. (H) B-cell-derived cfDNA in lymphoma patients (n = 16) before and after treatment (p-value = 0.0032, Kruskal-Wallis).
The online version of this article includes the following figure supplement(s) for figure 5:
Figure 5 continued
This model has some practical implications. For diagnostic applications of immune cfDNA, one should use the healthy population baseline levels of each methylation marker. Deviation from the normal range would indicate abnormality that requires attention (similar to the situation with blood counts). Our study suggests that fluctuations of specific cfDNA markers within norm levels are not indicative of pathology, but are rather part of a normal homeostatic process.
Beyond the healthy baseline, we studied immune cfDNA dynamics in three settings of immune system perturbation. First, post influenza vaccination, we identified an early elevation of B-cell cfDNA, preceding an increase in circulating B-cell counts and correlating to the effectiveness of antibody production. The correlation was statistically significant and independent of the known age-related risk of non-responsiveness. We propose that elevated B-cell turnover (and elevated B-cell cfDNA as its readout) reflects early stages in the successful response of B-cells to the vaccine, including the process of affinity maturation whereby large numbers of B-cells are generated and eliminated within lymph nodes as a result of insufficient binding to the target epitope. More work is needed to accurately define the population of B-cells that release cfDNA after vaccination, and to understand the physiological driver of this response. Practical applications, if the association between B-cell cfDNA and vaccination outcome proves robust, may include an early personalized indication for the success of vaccination. Second, we examined immune cfDNA dynamics in EoE, a model for an inflammatory disease in which one tissue is damaged by infiltration of a specific immune cell population, while leaving a minimal mark on peripheral cell counts. cfDNA analysis revealed the selective elevation of eosinophil turnover in active EoE, in some cases even when circulating eosinophil cell counts are unchanged. Larger scale studies are warranted to determine if eosinophil cfDNA can be a sufficiently sensitive and specific biomarker for assisting the clinical diagnosis and monitoring of EoE, ultimately relieving the need for invasive biopsies of the esophagus. Lastly, cfDNA dynamics in patients with B-cell lymphoma revealed the impact of disease on the turnover of B-cells (consistent with a previous study by Sun et al., 2015), and the reflection in B-cell cfDNA of the response to treatment. This analysis also revealed the involvement of turnover of other immune cell types in lymphoma. As with EoE, cfDNA in lymphoma provides a systemic biomarker of immune processes taking place in remote locations. However in lymphoma, these processes include both tumor dynamics and host responses -either bystander effects (collateral damage) or an immune response to the tumor. Potential uses of immune methylation markers in this field include early diagnosis of hematological malignancies, detection of minimal residual disease, Figure 6. A schematic view of immune marker variance within individuals. Intra-individual variance of immune cell count (blue) and immune-derived cell-free DNA (cfDNA) (red) in multiple time points. Our findings suggest that while immune cell counts are stable and typical within an individual, immune cell cfDNA levels vary greatly, reflecting changes in cell turnover that help maintain the cell count set point. and monitoring response to treatment (although our findings suggest that the latter may involve complex relationships between B-cell cfDNA and clinical response). Beyond hematological malignancies, immune-derived cfDNA dynamics can inform on the response to immune checkpoint inhibitors.
It is important to put the novel assay of immune cfDNA in the context of other emerging immune monitoring tools. In conditions that involve activation of the adaptive immune system, sequencing of the B-and T-cell receptor repertoire can be highly informative regarding the nature of the involved B-and T-cell clones, and the nature of the specific target epitopes. An important current limitation of immune repertoire analysis is that it requires large genomic fragments, which can only be derived from whole cells. This precludes cfDNA-based analysis, and limits access to T-and B-cell dynamics taking place in remote locations. Expression profiling of leukocytes, using single cell RNA sequencing and CyTOF, is also emerging as a powerful tool for understanding immune cell dynamics, revealing rich information about the transcriptome of circulating cells (Bucasas et al., 2011;Jiang et al., 2013;Kurtz et al., 2015;Voigt et al., 2018;Meng et al., 2017). We propose that both T/B repertoire analysis and expression profiling can be complemented by cfDNA profiling, which will provide nonoverlapping information about immune cell turnover (and potentially also gene expression patterns associated with cell death) (Sadeh et al., 2021) in remote locations.
In summary, analysis of specific immune cell methylation markers in cfDNA allows for monitoring of human immune cell dynamics, providing temporal and spatial information not accessible via circulating cell counts. We propose that this novel tool can illuminate healthy and pathological immune processes, including non-immune diseases having an inflammatory component such as cancer, rejection of transplanted organs, metabolic and neurodegenerative disease.
Subject enrollment
This study was conducted according to protocols approved by the Institutional Review Board at each study site, with procedures performed in accordance with the Declaration of Helsinki. Blood and tissue samples were obtained from donors who have provided written informed consent. When using material from deceased organ donor those with legal authority were consented. Subject characteristics are presented in Supplementary file 1.
Healthy controls
A total of 234 healthy volunteers (56% females, 44% males, age range 1-85 years) participated in the study as unpaid healthy controls. All denied having any chronic or acute disease.
Vaccination cohort and determination of anti-hemagglutinin antibody titers
Ninety-two healthy volunteers that received the annual influenza vaccination (55 females, 37 males, age range 20-73 years) gave blood samples a day before vaccination, and after 3, 7, and 28 days (±2 days).
The anti-hemagglutinin antibody titers were determined using hemagglutination inhibition (HI) assay. Serum samples obtained from vaccinated and non-vaccinated individuals were stored at −20°C until tested treated with receptor destroying enzyme (RDE) (Sigma C8772, diluted 1:4), for 16 hr prior to heat inactivation (30 min, 56°C). Absorption with erythrocytes was performed to remove non-specific hemagglutination, in accordance with a modified WHO protocol (Rowe et al., 1999). Serial 2-fold dilutions (1:20-1:2560) of sera in 25 μl PBS were prepared in V-shaped well plates, and an equal volume of four hemagglutinin units of viral antigen was added. The mixture was then incubated at room temperature for 1 hr. Fifty microliters of 0.5% chicken erythrocytes suspended in PBS, were added to the wells, and mixed by shaking the plates on a mechanical vibrator. Agglutination patterns were read after 30 min and the HI titer was defined as the reciprocal of the last dilution of serum that fully inhibited hemagglutination. The cut-off value selected for a positive result was 1:40. The influenza antigens for 2018-19 and 2019-20 winter seasons were supplied by the WHO.
Responders were defined as people who did not have at baseline antibodies against at least one of the four strains (2018-19: H1N1, H80, YAMA, VIC;2019-20: H1N1, YAMA, VIC, H3; no antibodies defined as influenza strain antigen titer <40) and developed antibodies after vaccination (influenza strain antigen titer >40). One person had antibodies against all strains at baseline and was excluded.
EoE cohort
Twenty-one active EoE patients, 24 EoE patients in remission, and 14 controls were recruited to the study at Cincinnati Children's Hospital. Diagnosis of EoE patients was made based on an histological biopsy taken from the distal esophageal tissue.
Lymphoma cohort
Twenty-seven newly diagnosed lymphoma patients that came for treatment in the hematological daycare unit in Hadassah Medical Center were recruited to the study in two cohorts (17 patients in cohort #1, 10 patients in cohort #2). Diagnosis was made by PET-CT. In addition, we recruited 16 newly diagnosed lymphoma patients who were monitored before and after receiving treatment. Response to treatment was assessed based on PET-CT.
Sample collection and processing
Blood samples were collected by routine venipuncture in 10 ml EDTA Vacutainer tubes or Streck blood collection tubes and stored at room temperature for up to 4 hr or 5 days, respectively. Tubes were centrifuged at 1500× g for 10 min at 4°C (EDTA tubes) or at room temperature (Streck tubes). The supernatant was transferred to a fresh 15 ml conical tube without disturbing the cellular layer and centrifuged again for 10 min at 3000× g. The supernatant was collected and stored at -80°C. We note that immune cfDNA analysis is particularly sensitive to conditions of plasma isolation, given the potential confounding effects of DNA released from lysed leukocytes. Our experiments indicated that the isolation protocol described above is optimal. While the speed of second centrifugation (3000 × g or higher) did not have a major effect of yield and purity, consistent with previous work (Risberg et al., 2018;Ungerer et al., 2020), it was important to minimize the time spent between blood drawing and centrifugation when using EDTA tubes. cfDNA was extracted from 2 to 4 ml of plasma using the QIAsymphony liquid handling robot (Qiagen). cfDNA concentration was determined using Qubit double-strand molecular probes kit (Invitrogen) according to the manufacturer's instructions.
DNA derived from all samples was treated with bisulfite using EZ DNA Methylation-Gold (Zymo Research), according to the manufacturer's instructions, and eluted in 24 μl elution buffer.
Selection of immune cell methylation markers
Immune cell-specific methylation candidate biomarkers were selected using comparative methylome analysis, based on publicly available datasets Moss et al., 2018), to identify loci having more than five CpG sites within 150 bp, with an average methylation value for a specific cytosine (present on Illumina 450K arrays) of less than 0.3 in the specific immune cell type and greater than 0.8 in over 90% of tissues and other immune cells. As noted above, such putative marker loci are far more abundant in the genome than loci that are methylated in the cell type of interest and unmethylated elsewhere. From our previously described atlas of human tissue-specific methylomes (Lehmann-Werman et al., 2016;Moss et al., 2018), we identified ~50 CpG sites that are unmethylated in specific immune cells and methylated in all other major immune cells and tissues. We selected arbitrarily two to three of these sites for neutrophils (i.e. NEUT1, NEUT2, NEUT3), monocytes (i.e. MONO1, MONO2), eosinophils (i.e. EOSI1, EOSI2, EOSI3), B-cells (i.e. B-CELL1, B-CELL2), T-cells (i.e. T-CELL1, T-CELL2), CD8 T-cells (CD8A, CD8B), Tregs (TREG1, TREG2), and designed primers to amplify ~100 bp fragments surrounding them using the multiplex two-step PCR amplification method (Neiman et al., 2020). Marker coordinates and primer sequences are provided in Supplementary file 2.
The validation of markers was done using DNA extracted from different cells and tissues, and the methylation status of the CpG block was assessed. Some markers were more sensitive if one CpG site was allowed to be methylated differently than other CpGs in the block. as indicated in Supplementary file 2.
PCR
To efficiently amplify and sequence multiple targets from bisulfite-treated cfDNA, we used a twostep multiplexed PCR protocol, as described recently (Neiman et al., 2020). In the first step, up to 17 primer pairs were used in one PCR reaction to amplify regions of interest from bisulfite-treated DNA, independent of methylation status. Primers were 18-30 base pairs (bp) with primer melting temperature ranging from 58°C to 62°C. To maximize amplification efficiency and minimize primer interference, the primers were designed with additional 25 bp adaptors comprising Illumina TruSeq Universal Adaptors without index tags. All primers were mixed in the same reaction tube. For each sample, the PCR was prepared using the QIAGEN Multiplex PCR Kit according to manufacturer's instructions with 7 μl of bisulfite-treated cfDNA. Reaction conditions for the first round of PCR were: 95°C for 15 min, followed by 30 cycles of 95°C for 30 s, 57°C for 3 min and 72°C for 1.5 min, followed by 10 min at 68°C.
In the second PCR step, the products of the first PCR were treated with Exonuclease I (ThermoScientific) for primer removal according to the manufacturer's instructions. Cleaned PCR products were amplified using one unique TruSeq Universal Adaptor primer pair per sample to add a unique index barcode to enable sample pooling for multiplex Illumina sequencing. The PCR was prepared using 2× PCRBIO HS Taq Mix Red Kit (PCR Biosystems) according to manufacturer's instructions. Reaction conditions for the second round of PCR were: 95°C for 2 min, followed by 15 cycles of 95°C for 30 s, 59°C for 1.5 min, 72°C for 30 s, followed by 10 min at 72°C. The PCR products were then pooled, run on 3% agarose gels with ethidium bromide staining, and extracted by Zymo GEL Recovery kit.
NGS and analysis
Pooled PCR products were subjected to multiplex NGS using the MiSeq Reagent Kit v2 (Illumina) or the NextSeq 500/550 v2 Reagent Kit (Illumina). Sequenced reads were separated by barcode, aligned to the target sequence, and analyzed using custom scripts written and implemented in R. Reads were quality filtered based on Illumina quality scores. Reads were identified as having at least 80% similarity to the target sequences and containing all the expected CpGs. CpGs were considered methylated if 'CG' was read and unmethylated if 'TG' was read. Proper bisulfite conversion was assessed by analyzing methylation of non-CpG cytosines. We then determined the fraction of molecules in which all CpG sites were unmethylated. The fraction obtained was multiplied by the concentration of cfDNA measured in each sample, to obtain the concentration of tissue-specific cfDNA from each donor. Given that the mass of a haploid human genome is 3.3 pg, the concentration of cfDNA could be converted from units of ng/ml to haploid GE/ml by multiplying by a factor of 303.
Methylation markers were calibrated compared to CBC to give a more accurate quantitative number, using linear regression. When necessary, methylation values were multiplied by a coefficient number derived from our spike-in calibration curves, to reflect the actual concentration of DNA from the relevant cell type. The fraction of neutrophil-derived cfDNA was obtained by dividing methylation fraction by 0.69.
Our entire dataset of PCR sequencing reactions used in this study is available upon request. The computational pipeline used to interpret sequence reads as well as a representative set of data (sequences that gave rise to Figure 1C) were uploaded to GitHub ( [URL]:// github. com/ Joshmoss11/ btseq; swh:1:rev:efc75ddd347c20392cf0a034706a7b5b6090be75, Moss, 2021) .
Deconvolution
We obtained 46 WGBS datasets from 23 healthy adult individuals. For each of these donors, we extracted genomic DNA from white blood cells (WBC) and cfDNA from plasma, and performed WGBS at an average depth of 85×. Methylation data was uploaded to GEO (Accession number GSE186888). WGBS data were converted to an array-like format by calculating the average methylation at 7890 CpGs from the Moss et al. methylation atlas . We then ran the deconvolution algorithm , [URL]:// github. com/ nloyfer/ meth_ atlas) for each WBC and cfDNA sample, to assess the relative presence of each blood cell type.
Statistics
To assess the correlation between groups, we used Pearson's correlation test. To determine the significance of differences between groups we used a non-parametric two-tailed Mann-Whitney test. For multiple comparisons, a Kruskal-Wallis multiple comparison test was used. p-Value was considered significant when < 0.05. To detect outliers in the healthy population we applied a multiple outlier detection ROUT-test (Q = 5%) (Motulsky and Brown, 2006). Samples that were detected as outliers were excluded. All statistical analyses were performed with GraphPad Prism 8.4.3.
Intra-individual and inter-individual variation
Intra-individual coefficient of variation for each immune cell type in CBC, whole blood, and cfDNA was calculated for each person across six different time points. The inter-individual coefficient of variation for each immune cell type was calculated for each time point across all individuals. The average of the intra-individual coefficient of variation was calculated. To prevent a bias due to difference in sample size (intra-individual variation, six time points; inter-individual variation, 15 individuals), we used R (version3.6.1) to sample all different combinations of a randomly selected six-person group and calculated the inter-individual coefficient of variation. Coefficients of variation of the different combinations were averaged. • Supplementary file 2. Genomic coordinates of immune cell type-specific methylation markers used in this study, and primer sequences used to amplify these loci after bisulfite conversion.
• Transparent reporting form
Data availability
All data generated or analyzed during this study are included in the manuscript and supporting files. The whole-genome bisulfite sequencing data reported in the paper, from 46 samples, is uploaded to GEO as described. The paper also reports data from PCR reactions that were analyzed by massively parallel sequencing. This is a very large set of data that is extremely low in information content and is of little interest to readers or even to people interested in replicating our results or interrogating them further. The key information (methylation status) in each sample is provided in the supplementary information, and we also uploaded the analysis algorithm and some sequence data. The entire set of raw sequencing data is available in the Dor lab to anyone interested. Please contact Prof. Yuval Dor dor@ huji. ac. il. All information will be shared. There is no need for any paperwork. Code is uploaded to GitHub as described in the paper. The methylation status of each marker in each sample is provided in Supplementary file 1. This data was used to generate the graphs shown in the paper. Sheets in this file indicate which figure they relate to.
The following dataset was generated: Author (
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Domain: Biology Medicine
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Assessment of Protection Induced by DNA and Live Vaccine Encoding Leishmania MHC Class I Restricted Epitopes against L. major Challenge in Balb/c Mice Model
Neglected tropical diseases (NTDs) are a complex of viral, bacterial, parasitic and helminthic diseases most common in Middle East, North Africa and South America among low-income populations. The term “neglected” is used not exactly because poor people are more affected but because the mortality rate is less considerable than morbidity rate. However debilitating effects reflected as DALY (disability adjusted life year) remark NTDs as serious global health problem more than ever. Unfortunately, effective vaccine is still lagging behind due to some challenges as antigen discovery, pre-clinical development, clinical trials in resource-poor countries and intricacies of host-parasite interaction [1,2].
Introduction
Neglected tropical diseases (NTDs) are a complex of viral, bacterial, parasitic and helminthic diseases most common in Middle East, North Africa and South America among low-income populations. The term "neglected" is used not exactly because poor people are more affected but because the mortality rate is less considerable than morbidity rate. However debilitating effects reflected as DALY (disability adjusted life year) remark NTDs as serious global health problem more than ever. Unfortunately, effective vaccine is still lagging behind due to some challenges as antigen discovery, pre-clinical development, clinical trials in resource-poor countries and intricacies of host-parasite interaction [1,2].
Leishmaniasis, both as cutaneous (CL) and visceral (VL), falls within NTDs which highly affect Middle East and in particular Iran [3]. Considerable effort without satisfying outcomes was made to control the disease by leishmanization and killed Leishmania vaccine [4]. However, due to ample evidence showing vaccine feasibility, subunit and live attenuated vaccines are still under massive investigations, but both demand more efforts. Recent advancements in computational immunology (immunoinformatics) and also full genome sequence availability from different species of Leishmania has introduced a new concept of "genome-based-vaccines" in "reverse vaccinology" era versus "conventional vaccinology" [5][6][7]. This concept will hopefully revolutionize both subunit and live attenuated Leishmania vaccine through genome mining for new antigens (new subunit candidates) and targeted gene manipulations respectively.
Leishmaniasis is a Th1-immune-response demanding infection since Leishmania is an obligatory intracellular parasite residing within host macrophages [8]. CD4 + Th1 type cytokines and in particular IFN-γ play a dual role in intracellular infection control. They potentiate pathogen killing within infected macrophages by up regulating toxic nitrogen-oxygen metabolites and activating CD8 + T-cell mediated apoptosis of infected cells [9]. CD4 + and CD8 + T-cells are activated by sensing short peptides presented within MHC class II and class I context respectively. Researchers now harness Immunoinformatics tools to predict potential CD4 + /CD8 + T-cell epitopes in silico and then select in vitro/in vivo evaluated immunogenic ones to arrange them together in a polytope construct [10,11]. Polytopes or T-cell vaccines as novel subunit vaccines are highly preferred since they carry minimal immunogenic part of the protein where whole protein might have gift by Prof. Genevieve Milon, Pasteur Institute of Paris) were cultured for weeks to concentrate secreted antibody in about 1500 ml medium. Filtered medium was loaded on protein G coated column (HR 10/10 Pharmacia biotech) and IgG was extracted by acidic buffer (pH=2.7). Acidic buffer was immediately neutralized and further exchanged by PBS through dialysis in 4°C. Poly-ethylene glycol concentrated antibody was aliquoted (500 µg for each injection) and stored in -80°C until use. Intra-vascular (i.v.) injection of 500 µg antibody followed by flow cytometry 48 h later with 488-argon ion-laser equipped BD FACScalibur instrument confirmed 98% reduction in CD8 population after anti-CD8 treatment compared to untreated mice. (As observed in Figure S3, even 7 weeks after anti-CD8 treatment, the CD8 population was only 5-6% of total CD3 + T cells instead of 25% in untreated mice.
Mouse immunization protocol and challenge
Female Balb/c mice (6-8 weeks old) were divided in groups (20 mice per group) to be immunized by DNA-DNA or DNA-Live regimens in right hind footpad (summarized in Table 1). Mice were boosted three weeks after priming. Then three weeks after booster injection, all mice were challenged sub-cutaneously (s.c.) in the left footpad (2 x 10 5 stationary phase promastigotes per mouse). DNA immunizations were preceded by electroporation (BTX ® -Harvard Apparatus (ECM 830)) right before DNA inoculation with pre-set pulsing voltages (66 volts, 21 milliseconds). For this, mice were humanly anesthetized by Ketamine (10%) -Xylazine (3%) mixture in normal saline. Mice in G2 were depleted of CD8 + T-cells by i.v. inoculation of rat anti-mouse CD8 antibody (purified from H35 hybridoma), 8 hours before and 48 hours after infectious challenge with L. major EGFP .
Footpad swelling measurement by metric caliper
Started one week after infectious challenge, mice in different groups were weekly monitored for footpad swelling by metric caliper. Swelling was measured as mean of left footpad thickness and wideness after subtraction of pertinent baseline values of right footpad.
In situ imaging of anesthetized mice for EGFP fluorescence
Anesthetized mice were fixed one by one on imaging stage of KODAK imaging system (In-Vivo imaging system F Pro) after position adjustments with door open (exposure time was set on 30 second and filter wavelengths on 470/535 nm). The image was captured in less than a minute. Pixel counting and measurement of the lesions were performed using KODAK molecular image software version 5.3. Results were reported as "net intensity", a quantitative measurement defined as the number of green pixels in a given area (region of interest) multiplied by the average intensity of each pixel [22,23]. This experiment was performed 7 weeks post challenge since earlier imaging barely discriminates fluorescent signal difference among groups. adverse effects like suppressors of cytokine signaling proteins [12], contain immune regulatory epitopes or Tregitopes [13] and even potentially stimulate Th2 responses such as Leishmania meta-1 protein [14].
CD8 + T-cells' contribution is undoubtedly necessary for VL control but remains controversial regarding CL control [15,16]. Our previous study clearly evaluated CD8 + T-cells stimulation by a polytope construct containing H-2Kd restricted epitopes from non-vaccine protein candidates of Leishmania major [17]. Included epitopes originated from a list obtained by Leishmania genome mining for H-2Kd restricted epitopes using immunoinformatics tools [18]. Here we evaluated the protective effect of this construct in Balb/c model of CL by DNA-DNA and DNA-live immunization. Both regimens induced partial protection in Balb/c model further potentiating the CD8 + T-cells' role in primary CL infection control. These results are in concordance with a premise postulated by Uzonna et al. that stimulation of CD8 + T-cells early after infection is so critical for immune deviation toward Th1 response [19,20]. In our study protection was clearly compromised by CD8 + T-cell depletion at the time of infectious challenge resulting in long lasting Th2 responses. These preliminary results for polytope constructs seem as sparkles of hope in Leishmania vaccination and even therapeutic research field while post genomics is flourishing more than ever to influence vaccine design [21].
Ethics statement
All mouse experiments including maintenance, handling program, blood sampling and euthanasia were approved by Institutional Animal Care and Research Advisory Committee of Pasteur Institute of Iran, based on the Specific National Ethical Guidelines for Biomedical Research issued by the Research and Technology Deputy of Ministry of Health and Medicinal Education of Iran (issued in 2005). All mice were housed in plastic cages with free access to tap water and standard rodent pellets in an air-conditioned room under a constant 12:12 h light-dark cycle at room temperature and 50-60% relative humidity.
Polytope construct
Here we studied the protective effect of a previously designed and evaluated polytope construct composed of few peptides in tandem [10,17]. Briefly the polytope included HLA-A2 restricted epitopes from 4 known Leishmania vaccine candidates (CPB and CPC, 5 peptides, LmSTI-1, 4 peptides and LPG-3, 4 peptides) and 4 recently reported H-2Kd restricted epitopes from non-candidates. Peptides were separated by short spacers. Mouse ubiquitin sequence was added to N-terminus and Tetanus Toxoid Th epitope (TT 830 ) was added to the C-terminus. This package (received as pUC-PT from BIOMATIK-Canada) was further cloned in pcDNA3.1 + (Invitrogen) making pcDNA-PT and was purified to be endotoxin free by Endofree Plasmid Giga kit (QIAGEN). We also evaluated a live vaccination strategy by using recombinant Leishmania tarentolae parasite stably transfected with polytope sequence in 18srDNA locus and in non-secretory form (L. tar PT-EGFP ). Polytope structure and cloning pathways are summarized in Figure S1A and S1B respectively.
Anti-CD8 purification from H35-17.2 hybridoma cell
Rat H35-17.2 hybridoma cells produce monoclonal anti-mouse CD8 beta IgG2b antibodies and secrete it into culture medium. Protein-G affinity chromatography columns are then used to specifically purify IgG antibodies from supernatants. H35-17.2 hybridoma cells (a
Real-Time PCR measurement of lymph node parasite burden
Real-time PCR was used to quantify parasite burden in draining lymph nodes at both early and late phase of infection as described before [24]. Genomic DNA was extracted from homogenized lymph nodes of 4 individual mice. Two sets of primers targeting a region of kinetoplastid minicircle named RV1 (forward: 5′-CTTTTCTGGTCCCGCGGGTAGG-3′) and RV2 (reverse: 5′-CCACCTGGCCTATTTTACACCA-3′) were used. Absolute copy number of the target sequence was extrapolated from a standard curve using Applied Biosystems 7500 real time PCR application. Standards were prepared from genomic DNA extracted from 2 × 10 7 L. major parasite serially diluted 10 folds up to 6 dilutions. PCR reactions were prepared in duplicate including 50 ng genomic DNA, 5 pmol of each forward and reverse primers, 12.5 µl Qiagen QuantiFast SYBR Green Master Mix to a total volume of 25 µl. PCR amplification cyclings included: 1 cycle of 95°C for 2 min; 40 cycles of 95°C for 15 s, 58°C for 30 s, and 72°C for 40 s. Data was extrapolated on standard curve using 7500 system SDS software.
Antigenic stimulators
The polytope construct contained 4 H-2Kd restricted 9-mer peptides which were used in in vitro assays ( Table 2). A 10-mer peptide from Hepatitis C virus' NS-3 protein (KLSGLGLNAV-kindly provided by Dr. Arash Memarnejadian, Pasteur Institute of Iran) was used as irrelevant control. All peptides were synthesized by BIOMATIK-Canada.
Cytokine and antibody ELISA
Secreted IFN-γ and IL-5 were quantified by DouSet ELISA-Mouse IFN-γ and DouSet ELISA-Mouse IL-5 kits (R&D Systems) respectively as indicated by instructions. Briefly, maxisorb ELISA plates (Greiner-Bio one) were coated by anti-cytokine antibody, incubated overnight at ambient temperature (RT), washed and coated with PBS-1% BSA (1 h at RT). After two hours of incubation at 37°C of standard dilutions (100 μl) and supernatants (200 μl), plates were washed and biotin conjugated anti-cytokine antibody was added (2 h at 37°C). Final reaction was revealed by streptavidin conjugated horse-radish-peroxidase (20 min at RT) and enzyme substrate (ABTS ® Peroxidase Substrate System (KPL)). Reaction was stopped by 1% SDS solution and absorbance was measured at 405 nm with reference filter.
CD8 + /IFN-γ + T cells detection by intra-cellular cytokine assay using flow cytometry
Herein standard plate based protocol with some modifications was used [10,25]. Briefly, pre-stimulated splenocytes were harvested from 6 well plates. Cells were washed and restimulated in vitro again by relevant peptides (10 µg/ml), irrelevant control (10 µg/ml) and PMA/Ion (Sigma) along with Golgiplug (BD-Biosciences) to prevent excretion of elevated cytokines while incubated at 37°C-5% CO 2 incubator overnight. After overnight culture, plate was centrifuged at 2000 rpm for 5 min to pellet the cells. Cells were washed in staining buffer (PBS 1x, 0.5% FCS and 0.1% NaN 3 ) and re-suspended in the same buffer containing PE-Hamster anti-mouse CD3e and PerCP-Rat anti-mouse CD8 antibodies. After 30 min at 4°C, cells were pelleted and washed in staining buffer then fixed and permeabilized with 200 µl of Cytofix-Cytoperm buffer (BD Cytofix-Cytoperm kit, BD biosciences) while incubated for further 20 minutes at 4°C. After adequate washing with saponin containing wash buffer (BD Cytofix-Cytoperm kit, BD biosciences), cells were re-suspended with FITC-Rat anti-mouse IFN-γ antibody for 30 min at 4°C then washed and prepared for data acquisition in staining buffer with BD FACScalibur flow cytometer. 100,000 events were acquired and analyzed by FlowJo 7.5.3 (TreeStar, USA). Live lymphocytes were gated on CD3 + T cells then CD8 + /IFN-γ + T cell population was reported in percent in CD3 + gate of lymphocyte region. PMA/Ion stimulated cells were used as control.
CD8 + T-cell proliferation determination by CFSE flow cytometry assay
Pre-stimulated splenocytes were harvested from 6 well plates in 14 ml falcon tubes filled up to 14 ml with RPMI medium. Cells were washed and resuspended in 1 ml PBS-0.1% BSA then very gently mixed with 1 ml freshly prepared 5-(and-6)-carboxy fluorescein diacetate succinimidyl ester (CFSE-20 µM) solution. Cells were incubated 10 min in 37°C-5% CO 2 incubator and immediately neutralized by 10% FCS supplemented cold RPMI (5 min incubation on ice). Cells were properly washed twice to remove un-integrated CFSE and restimulated with relevant peptides (10 µg/ml), irrelevant peptide (10 µg/ml) and ConA (5 µg/ml). Cells without CFSE staining were used as control [26]. 48 h later cells were re-harvested and stained for CD3 (PE-Hamster anti-mouse CD3e, BD biosciences) and CD8 markers (perCP Rat anti-mouse CD8α, BD biosciences) after 30 min incubation at 4°C in staining buffer. Cells were washed and prepared for data acquisition in staining buffer with BD FACScalibur and analyzed by FlowJo 7.5.3. 100,000 events were acquired and live lymphocytes were gated on CD3 + T cells then CD8 + T cells. CFSE dilution was analyzed as dividing and was reported in percent after subtraction of background proliferation of control peptide stimulated cells. ConA stimulated cells were used as control. CFSA proliferation was fulfilled seven weeks after challenge.
Statistical Analysis
Statistical analysis was performed using Graph-Pad Prism 5.0 for Windows (San Diego, California). The data were analyzed with Student's t-test unless the F value was statistically significant. In this case data were analyzed with Mann-Whitney-U test. The results were considered statistically significant with a precision of p<0.05 (One asterisk, 0.05>p>0.01; two asterisks, 0.01>p>0.001; three asterisks, p<0.001). All over the paper, the results have been considered significant only if the difference of the test groups (G1 or G5) was significant versus all relevant control groups (G2 and G3 and G4 for DNA-DNA experiment, G4 and G6 for DNA-Live experiment). In each case, date presented is the numerical difference of actual stimulation by relevant peptides and background stimulation by irrelevant peptide. All indicated data are representative of two rounds of experiment.
In vitro footpad swelling measurements coupled with in vivo qualitative footpad imaging
Started one week after infectious challenge, weekly measurements of footpad swelling (as a clinical sign of infection) by metric caliper showed a significant control (p<0.05, t-test analysis) in lesion size in DNA-DNA vaccinated group (G1) lasting during infection ( Figure 1A). The difference between G1 and CD8 + T-cell depleted G2 group (0.01>p>0.001, unpaired t-test analysis) is very important (G2 has comparable size during infection with G3 and G4). DNA-Live vaccination induced a non-significant difference in lesion size in G5 group early after infection (compared to corresponding controls) which did not last long ( Figure 1B). Seven weeks after challenge, 6 randomly selected mice from each group were subject to in vivo imaging using fluorescence property of L. major EGFP as challenge parasite. As shown in Figure 1C, net intensity comparison of selected ROI of left footpad in G1 was significantly lower at late infection after DNA-DNA immunization (0.01>p>0.001, unpaired t-test analysis). This difference with G2 group (which has comparable parasite load with controls) is remarkably important. However no significant difference was detected in DNA-Live vaccinated groups ( Figure 1D). All footpad images from G1-G6 groups captured by in vivo imaging are illustrated in Figure 1E.
Lymph node parasite burden evaluated by Real-Time PCR
Parasite burden is routinely measured by limiting dilution but where applicable more sensitive approaches as RT-PCR are substituted. Dissected, homogenized and weighed draining lymph nodes at 3 and 7 weeks post challenge were assayed for absolute quantity of parasite in each individual tissue by means of quantitative real-time PCR (based on standard curve). As shown in Figure 2B, significant lower parasite load was detected at 7 weeks post-infection in G1 group (p<0.05, unpaired t-test) in comparison to controls (the difference was not significant at 3 weeks post challenge as shown in Figure 2A). This observation was quite consistent with footpad in vivo imaging results at 7 weeks point, which is a direct measurement of local fluorescent signals ( Figure 1E). Higher level of parasite number in G2 group seven weeks after challenge remarks a significant role for CD8 + T-cells (p<0.001, unpaired t-test). Parasite level was even higher in G2 at 3 weeks post challenge but not significant (Figure 2A). DNA-Live immunization confirmed a nonsignificant difference between G5 and control groups 3 weeks after challenge ( Figure 2C) which was negligible at 7 weeks ( Figure 2D). As a marker of parasite proliferation, lymph nodes' weight was lower in G1 group ( Figure 2E) 7 weeks post-challenge but not in G5 group ( Figure 2F).
IFN-γ production by peptide stimulation
Before challenge, 3 weeks and finally 7 weeks after, 6 mice per group were randomly selected and humanly euthanized. Spleens were dissected to prepare single cell suspensions of splenocytes. 3 x 10 6 cells were distinctly stimulated in vitro with 10 µg/ml of each relevant P1-P4 peptides and irrelevant control peptide (10 µg/ml). IFN-γ level was calculated after subtracting background secretion by cells stimulated with control peptide. Before challenge ( Figure 3A), IFN-γ was detected in response to P2, P3 and P4 stimulation at a low but significantly different level (0.05>p>0.01, Mann-Witney U test) in G1+G2 group (G1+G2 refer to the results from these 2 groups before anti-CD8 treatment, both immunized with pcDNA-PT). Three weeks after challenge, IFN-γ was detectable only in response to P4 stimulation but not statistically significant ( Figure 3B). At the end, all 4 peptides induced remarkable IFN-γ compared to controls especially G2 with very low cytokine level (0.05>p>0.01, Mann-Witney U test) ( Figure 3C). The same analysis was performed for DNA-Live vaccination and as observed, no significant IFN-γ was detected neither before challenge ( Figure 4A) nor weeks after at the late phase ( Figure 4C). However 3 weeks analysis revealed detectable IFN-γ after P3 and P4 stimulation (0.05>p>0.01, Mann-Witney U test) ( Figure 4B).
Th1/Th2 ratio determined by cytokine ELISA after F/T stimulation 3x10 6 cells were distinctly stimulated in vitro with Leishmania major F/T derived from multiple consecutive freezing and thawing action as described in previous section. Total Th1/Th2 ratio was verified after F/T stimulation by ELISA determined IFN-γ and IL-5 level. As shown in Figure 5A, three weeks after challenge of DNA-DNA immunized group (G1), the immune response was roughly skewed towards Th1. However Th1 polarization was quite dominant 7 weeks after challenge due to high levels of IFN-γ to IL-5 in this group ( Figure S2-A). CD8 + T-cell depletion in G2 clearly compromised this effect where Th2 response dominated even 7 weeks after challenge. This was directly the result of higher levels of IL-5 compared to IFN-γ in this group ( Figure S2-A). It was then concluded that Th1 polarization was a direct effect of CD8 + T cells' contribution. DNA-Live immunization (G5) in contrast was not able to induce Th1 polarization ( Figure 5B) although higher levels of IFN-γ were detected both 3 and 7 weeks after challenge ( Figure S2-B).
CD8 + /IFN-γ + T cell quantification by intra-cellular cytokine staining
Single cell suspensions of splenocytes were in vitro stimulated with individual relevant and irrelevant peptides plus rh. IL-2 to moderately augment frequencies of responding clones. As observed in Figure 6, ICCS successfully detected CD8 + /IFN-γ + T cells in G1 group against P1 and P4 stimulations (0.05>p>0.01, Mann-Witney U test) both early after infection (6A) and later (6B). So IFN-γ detected in ELISA in response to P1 and P4 was directly attributable to CD8 + T-cells. However CD8 + T-cells' frequency in response to P2 and P3 might have been less than enough to be adequately sensed by ICCS even at late phase of infection in contrast to ELISA results. The lower avidity of P2 and P3 could have been compensated by complementary rounds of in vitro stimulation and IL-2 supplementation. CD8 + T-cell depletion was clearly reflected in flow cytometric results both early and late infection. Furthermore ICCS analysis after DNA-Live immunization revealed higher but not statistically significant CD8 + /IFN-γ + T cell frequencies in G5 group compared to relevant controls ( Figure 7A and 7B). Representative plots of ICCS strategy are illustrated in Figure S3.
CD8 + proliferation in response to peptide stimulation evaluated by CFSE
CFSE stained splenocytes were restimulated in vitro for further 48 hours and evaluated for CD8 + T-cell proliferation in response to individual P1-P4 epitopes and control peptide. As shown in Figure 8A, 7 weeks post-challenge DNA-DNA immunization stimulated CD8 + T-cell proliferation against P1 and P4 epitopes (0.05>p>0.01, Mann-Witney U test) but not P2 and P3. These results further confirmed ICCS and higher avidity of P1 and P4 compared to P2 and P3. In contrast no considerable proliferation was observed against P1-P4 peptides after DNA-Live immunization ( Figure 8B). This was in good concordance with ICCS results in DNA-Live immunized mice. Representative plots of CFSE dilution are illustrated in Figure S4. So based on common results of ELISA, ICCS and CFSE at 7 weeks post infection, we concluded that P1 and P4 have effectively raised CD8 + T cell responses and have contributed in partial protection but P2 and P3 might have less effectively contributed due to lower avidity.
Humoral immune response evaluation by ELISA as Th1/Th2 deviation marker
Serum IgG2a and IgG1 antibodies were evaluated before and 5 weeks after challenge as a reliable marker of Th1 and Th2 directed response respectively. P1 and P4 were pooled together since were supposedly stronger peptides. P2 and P3 were then pooled together. Before challenge no significant difference was detected within groups (data not shown). As shown in Figure 9A, IgG2a to IgG1 ratio stands at least 2 folds higher in DNA-DNA immunized group (G1) compared Homogenates were subject to genomic DNA extraction and RT-PCR with Leishmania kinetoplast specific primers. The A and B plots represent results from DNA-DNA immunization at 3 and 7 weeks post-challenge respectively. Stars represent significant difference (p<0.05, unpaired t-test). The C and D plots represent results from DNA-Live immunization at 3 and 7 weeks post-challenge respectively. E and F represent lymph nodes' weight during infection (Each dot represents mean+SD of lymph node weight in each group). Columns represent mean+SD of absolute parasite load in each group. ns: not significant. Before infectious challenge (column A), 3 weeks (column B) and 7 weeks (column C) after challenge, 6 mice per group were randomly selected and humanly euthanized. Spleens were dissected to prepare single cell suspensions of splenocytes. 3 x 10 6 cells were distinctly stimulated in vitro with relevant P1-P4 peptides (10 µg/ml each) and irrelevant control peptide (10 µg/ml). IFN-γ level was calculated after subtracting background secretion by cells stimulated with control peptide. G1+G2 refer to the results from these 2 groups before anti-CD8 treatment. In each plot, columns represent mean+SD in each group. Stars represent significant difference between groups analyzed by Mann-Whitney-U test (0.05>p>0.01). challenged with high doses (10 4 -10 7 ) of L. major parasite. Most of the mice species as CBA and C57BL/6 are resistant to infection due to Th1 immune deviation except than Balb/c mice which succumbs to high dose challenge and is known as susceptible in this context [27]. Although leishmaniasis is an intracellular infection, the contribution of CD8 + T-cells as immune correlates of disease at primary infection remained to be addressed [28,29] until the data from low dose experimental challenge in both Balb/c and C57BL/6 mice was extrapolated. The data from Balb/c mice infected by low dose challenge was controversially CD8 + T-cell dependent, but these mice were able to elevate Th1 type immune response and control the primary and secondary infection [30][31][32]. However data from C57BL/6 mice clearly shed light on CD8 + T-cells as contributors to CL control. CD8 + T-cells depletion at primary infection abolished resistance in C57BL/6 mice infected intra-dermally by 100-1000 metacyclic promastigotes (approximation of low dose natural infection) [33]. Uzonna et al. further elucidated that IFN-γ secreted by CD8 + T-cells is important to direct early Th2 type responses towards Th1 and establish protection which will end in a long term memory protecting against subsequent infections [19,20].
Unraveling CD8 + T-cells' contribution in leishmaniasis control could profoundly impress vaccine design procedure. CD8 + T-cell epitopes are 8-10 small fragments easily predictable by online immunoinformatics software. These epitopes if evaluated immunogenic in vitro and in vivo, which is the precise mark of previous activation following natural infection, could be further benefited in polytopes or T cell vaccines. Although both unassembled epitopes used individually in cocktail or assembled peptides in tandem are effective in stimulating relevant T cell clones, nucleic acid based constructs or DNA polytopes expressing epitopes in tandem are potentially preferred due to selfadjuvanting characteristics of plasmid DNAs and their intrinsic potential to induce both CD4 + and CD8 + T-cells [34,35]. Before infectious challenge (column A), 3 weeks (column B) and 7 weeks (column C) after challenge, 6 mice per group were randomly selected and humanly euthanized. Spleens were dissected to prepare single cell suspensions of splenocytes. 3 x 10 6 cells were distinctly stimulated in vitro with relevant P1-P4 peptides (10 µg/ml each) and irrelevant control peptide (10 µg/ml). IFN-γ level was calculated after subtracting background secretion by cells stimulated with control peptide. In each plot, columns represent mean+SD in each group. Stars represent significant difference between groups analyzed by Mann-Whitney-U test (0.05>p>0.01). to controls. Higher IgG1 level in CD8 + T-cell depleted group (G2) compared to G1 is noteworthy ( Figure S5). These results notify a clear Th1 dominant response in DNA-DNA vaccinated group versus Th2 dominant response in CD8 + T-cell depleted G2 group. As observed, the ratio was not too big. This could reflect the function of ubiquitin molecule which directs the polytope into proteasome right after synthesis reducing the chance for B-cell stimulation. This promisingly validates the ubiquitination concept in polytope vaccine design. Furthermore humoral response is detected against both peptide pools including P2 and P3. This further confirms that P2 and P3 peptides could raise the recall response in ICCS and CFSE by complementary rounds of stimulations and IL-2 supplementation in vitro. Analysis after DNA-Live immunization did not discriminate any remarkable Th1 polarized response ( Figure 9B). IgG1 and IgG2 levels are separately illustrated in Figure S5.
Discussion
It has been a consensus for years that CD4 + T cells are the pivotal immune correlates of leishmaniasis control which determine the fate of infection. Such a premise was based on data from laboratory mice Based on these relevant data, we proposed a polytopic DNA vaccine encompassing small 9-mer fragments from 4 different non-vaccine candidates previously predicted by immunoinformatics mining of Leishmania major genome [18]. We hypothesized that if polytope constructs induce multiple CD8 + T-cell clones [17] and if CD8 + / IFN-γ + T cells effectively direct Th1 responses early after infection [19], then a multi-CD8 inducing polytope could induce protection in animal models of CL. So, here we described the protective efficacy of a homologous (DNA-DNA) and also a heterologous prime-boost regimen (DNA-Live) with a rationally designed polytope encoding four H-2Kd restricted immunogenic peptides after high dose challenge with L. major EGFP in Balb/c mice (the immune response is absolutely investigated against H-2Kd restricted peptides and not the others restricted to human HLA-A2 with low affinity for H-2Kd allele).
DNA-DNA immunization (preceded by electroporation) induced partial protection which was immunologically correlated with CD8 + / IFN-γ + T cell clones. Immune response was totally skewed towards Th1 at late weeks after challenge. This was further confirmed by humoral response evaluation at mid infection (about 5 weeks after challenge). We concluded that peptide affinity is the detrimental factor in protection efficiency. Focusing on ICCS responses we found P1 and P4 as high avidity peptides since both induced CD8 + /IFN-γ + during early and late phases of infection detected in ELISA, ICCS and CFSE. But response to P2 and P3 was barely detected in ICCS and CFSE showing that these 2 peptides demand further in vitro stimulations. P1-P4 was selected among high in silico scored peptides (predicted by SYFPEITHI/BIMAS - Table 2) with high in vivo IFN-γ production potential after peptide immunization. This was previously reported by Herrera-Najera et al. [18]. In our previous experiment, the DNA construct encoding the same peptides was used to immunize Balb/c mice and we found all 4 peptides immunogenic in ELISpot assay [17]. In this study our results confirmed that after in silico prediction, peptide immunogenicity should be evaluated by infectious challenge to include high avidity epitopes in vaccine construct like P1 (SYSSLVSAL) and P4 (FYQEAAELL) because these 2 were highly promising at pre-and post-challenge conditions quite contrary to P2 and P3. However this is almost a difficult task in Balb/c mice due to disease progression instead of healing after infection. In vivo cleavage of peptides included is another important determining factor which was precisely managed by ubiquitin complementation and spacer inclusion for optimal proteasomal degradation. Clinical evaluations by observational methods (footpad swelling and imaging) marked milder disease progression in G1 compared to the rest. Real-Time PCR technique revealed much restricted parasite load at late infection. Totally this is a promising result regarding high dose L. major primary infection control in susceptible Balb/c mice by two immunizations. Further boosters (3 or even 4) could potentiate the results.
In line with our results, concerning DNA vaccine efficiency inducing protection against high dose parasite challenge in Balb/c mice by priming CD8 + T-cells, Guranathan et al. demonstrated that immunization with a plasmid encoding Leishmania LACK protein was more efficient than immunization with recombinant LACK protein and recombinant IL-12, as induced protection was more durable against L. major challenge. They clearly demonstrated CD8 + T-cell contribution in this effect [36][37][38]. Furthermore, Campos-Neto et al. proved that vaccination with a plasmid DNA encoding both TSA and LmSTI-1 fusion proteins confers protection against Leishmania major high dose challenge in Balb/c mice. This effect was attributed to CD8 + T-cells' stimulation by TSA protein [39]. Also a cocktail of 4 plasmids encoding L. infantum histone proteins cross protected against high dose L. major challenge in Balb/c mice with both CD4 + and CD8 + T-cells as contributors [40]. Heterologous prime-boosts are proof of concept in this regard since this type of immunization is fully accepted where CD8 + T-cells' activation is necessary [41]. Jayakumar et al. examined a prime-boost immunization with DNA-MVA encoding tryparedoxin peroxidase (TRYP) with TLR1/2 adjuvant and induced CD4 + /CD8 + /IFN-γ + T cell related protection in Balb/c mice against L. panamensis infection [42]. Same results were obtained by LACK DNA-MVA immunization reported by Sanchez-Sampedro [43]. Overally these results suggested that DNA vaccines as equivalents of low dose parasite or antigen administration are able to induce Th1 skewed immune responses against primary high dose parasite challenge. This happens because of CD8 + T-cell activation. Antigen dose effect of DNA vaccine could have been augmented by electroporation which demands further elucidation. G2 group provided the proof of concept in this study. Partial protection was compromised while CD8 + T-cells were efficiently depleted at infectious challenge time after pcDNA-PT prime-boost immunization. Th2 response was evidently dominant and resulted in significant difference in clinical features. Since CD8 + / IFN-γ + T cells were detected in G1 both at early and late phase; the difference was attributed to the function of these cells. Similar findings were reported in studies in which depletion of CD8 + T-cells abolished induced protection by impeding the frequency of IFN-γ producing CD4 + T cells and reducing the level of IL-12 production and it᾽s receptor. Jayakumar et al. clearly demonstrated that immunity in Balb/c mice against L. panamensis high dose challenge induced by DNA-TRYP priming with Pam3CSK4 is compromised by CD8 + but not CD4 + T-cell depletion at the time of infectious challenge [42]. However our study was characterised by peptidic stimulants instead of whole proteins in vaccine construct which is proposed as novel subunit vaccine for Leishmania infection. Polytope ensembles are applicable since peptide epitopes from one potential protein or different proteins of one strain or conserved proteins from different strains of a species are easily assembled together.
Heterologous prime-boost vaccine regimens as mentioned above have proven protective by CD8 + T-cell induction against intracellular pathogens such as Leishmania. Among possible vector combinations, Human Adenovirus [44], Modified Vaccinia Ankara [42,43] and Salmonella [45] have received significant attention. However antivector immunity is the main drawback linked with these vectors. Recently introduced live non-pathogenic Leishmania tarentolae (L. tarentolae) [46], could be a promising surrogate for viral/bacterial vectors in Leishmania vaccine research due to high resemblance to pathogenic L. major strain [47]. Herein a recombinant L. tarentolae stably expressing 4 H-2Kd restricted peptides in non-secretory form (intra-cytoplasmically) was used in DNA-Live regimen which roughly protected against high dose challenge early after infection. The results indicated dominant Th2 response which was consistent with clinical manifestations. Humoral immunity 5 weeks after challenge also indicated that the balance was not clearly in favor of Th1 response. Totally both clinical and immunological parameters failed to support protection.
Previously L. tarentolae has proven promising by long lasting protection while expressing multiple candidate antigens (all among potential vaccine candidates as A2) [24,48] especially along with sandfly salivary immune-stimulatory proteins [49]. This is a confirmatory proof that peptides included must be among high avidity ones previously evaluated in in vivo infectious challenge. Besides, the recombinant L. tarentolae used in this study expresses an ubiquitinated polytope which guides the newly synthesized polypeptide into proteasome for cleavage. It is recommended to replace the cytoplasmic form of synthesis with secretory to further expose relevant peptides to immune system. For this ubiquitin could be replaced by leader sequences. This way the nascent polytope will be efficiently translocated into endoplasmic reticulum both for degradation and secretion out of the cells. It is also recommended to fulfill a dose escalation for L. tarentolae immunization to meet the requirements of CD8 + T-cell activation (in this study while polytope construct is expressed under the control of a strong promoter (18s rRNA), the recombinant parasite is inoculated in 2 x 10 7 parasite per mouse). Furthermore promising adjuvants are now available such as CpG oligonucleotides that are used to potentiate live Leishmania vaccine and help CD8 + T-cell induction in vivo [50].
As a whole, DNA-Live regimen roughly protected against high dose challenge due to weak CD8 T cell induction. In contrast, DNA-DNA immunization correlated with a partial protection where immune correlates were induced early after infection and expanded to the end. The polytope construct included only 4 H-2Kd restricted epitopes, 2 of which were lower in avidity. As previously mentioned, these peptides were predicted by immunoinformatics and were evaluated to induce IFN-γ producing clones after peptide immunization in Balb/c mice and not after infectious challenge. As evidenced, Balb/c mice are highly susceptible to Leishmania infection and complete protection by vaccine is somewhat imaginary and not fully achievable. Therefore the limited level of protection observed in this model by this construct is not negligible and is noteworthy to be further potentiated by increasing the number of high-avidity peptides and by advantaging competent adjuvants and delivery systems.
A literature review on T-cell vaccines for Leishmania gives very few results. Most of them are at pre-vaccine in silico prediction-in vivo validation stage to predict potential MHC class I/II restricted peptides from known [51][52][53][54][55] or genome wide vaccine candidates [18,56,57].
Since CD4 + T-cells are known as important contributors of immunity, the few vaccine outputs are confined to MHC II epitopes [58][59][60]. Very recently Das et al. published the results from an innovative polytope design from conserved regions of 5 known Leishmania candidate antigens. CD4 and CD8 inducing pentadecapeptides evaluated immunogenic in human populations of endemic regions were mapped onto sequence of the original antigens and redesigned to include as many as possible T cell epitopes. This was to meet the requirements of MHC polymorphism in human population with large number of epitope inclusion. A cocktail of 5 different DNA constructs based on each individual peptidic region protected against VL in Balb/c mice. This data is the very first report of a DNA-based T cell vaccine development in Leishmania [61]. Here we reported the results from a prototypic polytope vaccine specifically inducing CD8 + T-cell responses with previously evaluated H-2Kd epitopes. The source proteins of used peptides are among non-vaccine candidates introduced by L. major genome mining using immunoinformatics. P4 is also derived from a hypothetical protein so is noteworthy to be further characterized as novel vaccine candidate. Therefore the partial protection conferred by DNA-DNA or DNA-Live (lower efficiency compared to DNA-DNA) regimens in a susceptible CL model could change vaccine concepts. First, new vaccine candidates with both CD4 + and CD8 + T-cell inducing potential could be introduced. Second, polytopic constructs with multiple high avidity epitopes provoking both CD4 + and CD8 + T-cell responses could enter the Leishmania vaccine research. In this context non-pathogenic live vectors with high resemblance to pathogenic L. major could end in promising results.
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Domain: Biology Medicine
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A Multi-Scale Study of Thalamic State-Dependent Responsiveness
The thalamus is the brain’s central relay station, orchestrating sensory processing and cognitive functions. However, how thalamic function depends on internal and external states, is not well understood. A comprehensive understanding would necessitate the integration of single cell dynamics with their collective behavior at population level. For this we propose a biologically realistic mean-field model of the thalamus, describing thalamocortical relay neurons (TC) and thalamic reticular neurons (RE). We perform a multi-scale study of thalamic responsiveness and its dependence on cell and brain states. Building upon existing single-cell experiments we show that: (1) Awake and sleep-like states can be defined via the absence/presence of the neuromodulator acetylcholine (ACh), which controls bursting in TC and RE. (2) Thalamic response to sensory stimuli is linear in awake state and becomes nonlinear in sleep state, while cortical input generates nonlinear response in both awake and sleep state. (3) Stimulus response is controlled by cortical input, which suppresses responsiveness in awake state while it ‘wakes-up’ the thalamus in sleep state promoting a linear response. (4) Synaptic noise induces a global linear responsiveness, diminishing the difference in response between thalamic states. Finally, the model replicates spindles within a sleep-like state, drastically changing its responsiveness. The development of this novel thalamic mean-field model provides a new tool for incorporating detailed thalamic dynamics in large scale brain simulations.
Introduction
The thalamus, a well preserved structure found in all mammals [1], serves as the core relay hub of the central nervous system. Diverse thalamic nuclei function as transmitters of sensory information from the periphery to the cortex and other central nervous system structures, while also facilitating the transfer of motor commands from the cortex to various regions of the body [2, p. 4-5]. Each of the relatively independent thalamic nuclei comprises at least two cell types: excitatory (glutamergic) principal relay cells, featuring extensive axonal projections to various nervous system structures, but rarely to other principal cells, and local inhibitory (GABAergic) interneurons [3,4].
The primary source of activity in thalamic nuclei arises from direct pathways, operating in both peripheral-to-central and central-to-peripheral directions. Additionally, cortical feedback projections exert a strong influence on the thalamus. Notably, the number of thalamo-cortical outgoing axons is approximately one-tenth of the number of cortico-thalamic incoming axons [3,5], and the cortex is the major source of synapses within the thalamus, for example accounting for 50% of synapses in the lateral geniculate nucleus (LGN) [3]. This extensive feedback loop between the thalamus and cortex indicates a substantial modulating role of the cortex in thalamic relay functions [6].
During attentive wakefulness, thalamic relay neurons display tonic firing. However, membrane hyperpolarization leads to bursting behavior via low-threshold Ca + channels [7]. Bursting occurs in deep sleep states (NREM) and general states of low attention [8], in which hyperpolarization is generated by a low level of the neuromodulator acetylcholine (ACh) [9].
Surrounding the thalamus, the thalamic reticular nucleus (TRN) contains GABAergic reticular cells (RE) that broadly inhibit thalamic nuclei through axonal, and themselves through dense axonal and dendritic connections [3,7]. RE neurons can be activated through feedforward signals from thalamic nuclei or feedback from the cortex. These neurons consistently exhibit bursting behavior and can induce similar patterns in thalamic relay cells via hyperpolarization. This recurrent network allows the cortex and thalamus itself to actively modulate thalamic response and transfer of information, rendering the thalamus as a gate, with the TRN as the gatekeeper.
In addition to its gating function, there is also evidence of the thalamus playing a principal role in whole brain dynamics, such as spindle oscillations or slow waves in NREM sleep or anaesthesia [10][11][12]. It is suggested that the intrinsic loop between thalamocortical (TC) relay and RE cells plays a pivotal role in all of these behaviours by acting as a pacemaker and oscillator. The crucial mechanism at play is the rebound bursting of relay cells via hyperpolarization induced by RE inhibition.
These oscillatory behaviors primarily manifest during sleep-like brain states, where the TC cells show a prevalence for bursting [13]. Additionally, in newer studies it was shown that thalamic integration with cortical pathways suggests a significant role of the thalamus in many higher brain functions, including sensation, attention, and cognition [14,15].
Investigating the interaction between thalamic reticular and relay neurons at various levels is therefore crucial for deciphering the interplay of the brain with the outside world. To this end it is necessary to analyse these neuron interactions and their corresponding population activity via large-scale models. One feasible approach for scaling upwards is to employ networks of single-cell neuron models, but the computational demand rapidly increases as the network size is taken to the scale of anatomical subdivisions of the brain. For larger scales and even whole-brain simulations, it is necessary to decrease computational complexity. This can be achieved by reducing the degrees of freedom and describing homogeneous populations of neurons as the smallest units. A viable option is to use a mean-field theory to model population dynamic statistics.
Most existing neuronal field models can be separated in two groups: either phenomenological models (e.g. [16][17][18]), or more abstract mathematical models (e.g. [19][20][21]). Phenomenological models replicate biological behaviour and are capable of modelling particular brain regions, cell types or whole brain recordings. However, these can not couple significant effects or characteristics to model parameters which makes it impossible to use such models far of the fitting point and renders analytical analysis impractical. Conversely, abstract mathematical models couple the dynamical aspects of neuronal activity directly to model parameters and allow analytical or fast-forward numerical analysis, but model parameters are often not well linked to biological observables.
To strike a good balance between these two options, we develop in this paper a biologically realistic mean-field model of the thalamus that also allows analytical analysis. To achieve this biological realism with a firing rate model, our formalism follows a bottom-up approach, starting at the single-cell level and incorporating cellular and structural specificities of the thalamic circuits [22]. Our approach incorporates three crucial biological features: (1) Irregular spiking activity of neurons is believed to be important for transfer efficiency [23] and the correct baseline for neurons in both awake-like asynchronous (AI) states [24] as well as in sleep-like synchronous (SI) states [25]. (2) Synaptic conductances allow for realistic bi-stability and self-sustained activity [26] as well as modeling the fluctuation-driven regime [27]. (3) Adaptation mechanisms are the main generators of the different firing behaviors in the brain and important to include into models for generating realistic firing rate saturation and especially the bursting behavior of thalamic cells.
Using this novel mean-field model we investigate the state-dependent responsiveness of the thalamus, integrating the interplay between multiple scales (from single-cell level to the mesoscale). Building upon existing single-cell experiments we show that: First, the transition from tonic to burst firing of TC cells via ACh renders thalamic response nonlinear in sleep state (Section 3.2). Second, sensory stimuli generate a linear response, while cortical inputs generate a nonlinear response of the thalamus (Section 3.3). Third, cortical input and synaptic noise modulate thalamic response and synaptic noise diffuses thalamic state transitions and removes thalamic response dependency on both voltage and frequency (Section 3.4). Finally, we demonstrate that the proposed model is capable of generating self-sustained spindle oscillations, drastically altering responsiveness in this state (Section 3.5).
Methods
In this section we describe the single-cell, network, and the mean-field model. The chosen network and connectivity structure as well as cell and synaptic parameters are described.
Spiking neuron model
For both single-cell and network simulations we employ the Adaptive exponential integrate and fire model (AdEx) (as defined in [28] and analysed in [29]). This conductance based model numerously proved to be a good balance between computability and biological realism in terms of capturing all firing modes observable in real cells [30] and significantly in thalamocortical cells [31]. Importantly, it allows for a systematic fit of real cell traces. The dynamical system is the two equations describing membrane potential v and adaptation current ω of a given cell µ with the cell parameters listed in Table 1 and where I syn models all incoming synaptic currents. It consists of two currents dependent on excitatory G e syn and inhibitory G i syn membrane conductances and is defined as where G syn is modeled such that each time a spike (t s ) arrives these conductances experience an increment Q and exponentially relax again with time constant τ . As a baseline we use Q e = 1nS and Q i = 5nS [22]. Additional to the integration of this ODE set comes the usual spike mechanism employed in integrate and fire models: A spike of neuron µ is counted if v µ > V thr = −20mV, then the membrane potential is reset to V r = {−55mV for RE, −50mV for TC} for a refractory period of 5ms.
Network architecture and model parameters
We model one thalamocortical relay (TC) and one connected reticular (RE) population of a generic lateral thalamic nucleus. We neglect interneurons, as it can be assumed that they only yield minor contribution to population dynamics [32]. One of the main potential application of the thalamus mean-field is to be incorporated into large or whole brain models with already developed cortical and sub-cortical mean-field models and related implementations [22,[33][34][35][36][37][38][39]. As a reference, these previous works on cortical circuits describe typically populations of ∼ 10 4 neurons, corresponding to the size of a single cortical column. To keep the scale difference between cortex and thalamus proportional, we employ a scale of 1/10 [3,5,40] and therefore use N = 500 neurons per population. This allows to build a basic realistic-scale thalamo-cortical loop with just two mean-field models.
The network with its connections is depicted in Fig. 1a. We consider a random connected Erdos-Renyi network comparable to the statistical assumptions of the meanfield (Table S1). TC and RE populations form a loop of excitation and inhibition. TC cells do not excite other TC cells but RE cells (next to outgoing axons to the cortex). In contrast, RE are connected in an inhibitory loop and also inhibit TC cells. We propose two external drives serving as inputs to the model: The cortical drive P (going to both populations) and the sensory drive S (going only to TC cells) modeling cortical signals and sensory stimuli to the thalamus, respectively. For the synaptic and connection parameter values, we start with a connection probability between TC and RE populations of p = 5%, which captures the sparse connectivity between the two populations [4]. To model the dense net of locally selfinhibiting RE neurons in the TRN [2,41,42], we use p = 30%. There are 2 to 10 times more axons projecting from cortex to TC than from cortex to RE cells, but the amplitude of connection to RE is stronger, keeping a strong inhibitory corticothalamic modulation via the TRN [3,7]. Last, the number and convergence of axons from RE to TC cells ensures sparse but strong inhibition [4,7]. See Fig. 1a for all the parameter values.
Moving to cell parameters, we model two states of the thalamus corresponding to high or low levels of the excitatory modulator acetylcholine (ACh). In McCormick and Prince [9] and [43], it was shown that low levels of ACh change the firing patterns of TC cells to inhibit single tonic firing and to promote bursting. Because of the capability of ACh to act as a switch between tonic and bursting mode in the TC cells relay, and its role in controlling the overall physiological brain state [32,44,45], we define here these two states as awake state (ACh present; wakefulness, REM sleep) and sleep state (ACh absent; NREM sleep, low attention).
We will follow the approach from a preceding study [46] to analyse the parameter space and choose realistic cell parameters. The parameter values are based on the values proposed in Destexhe [31] with taking into account experimentally evident ranges (see [46] for details). The AdEx (1) was fitted on recorded cell traces of TC and RE neurons with and without ACh present from [9,43]. The resulting parameters are shown in Table 1 and their fit is validated on the whole parameter space in Fig. S4-S7. The most critical difference to [46] lies (1) in a hyperpolarised E L for RE cells in sleep state. This choice does deviate from a best fit as is evident from the increased error as shown in the second row in Fig. S6. However, this was necessary to guarantee biologically realistic stable and inhibition controlled AI dynamics of the full network (see Section 3.1). And (2) in a stronger spike adaptation b for TC cells, also in sleep state. This choice is required for TC cells to burst also in network simulations (see Fig. 3.b) and leads to stronger bursting at the single-cell level, but does not significantly increase the error of the single-cell fit.
To show that the cells inherit the correct behaviour, using the AdEx (1) with the proposed cell parameters, in Fig. 1b four exemplary single cell traces of RE and TC are shown. In there a constant-time gated-current was injected in to the cell to invoke a firing response of the cell. This was done by setting a rectangular pulse as I syn in (1) (I syn generates tonic and burst firing via two different bifurcations depending on excitability state, see [29]). The top row shows the wanted response types for the TC cell: Tonic firing with awake parameters (modulating ACh), and burst firing with sleep parameters (low-level of ACh). In the bottom row, RE cell's respond via burst firing in both parameter states, but the burst duration decreases in sleep state while keeping the same amount of spikes (increased burstiness).
Mean-field model
El Boustani and Destexhe [47] developed a second-order mean-field formalism of differential equations describing the firing rate statistical moments of spiking networks. This general framework closes the statistical hierachy at second order and is applicable to any arbitrary neuron models as long as a characteristic transfer function can be defined. It is assumed that the network is a sparse and randomly connected Erdos-Renyi model. It is derived with the assumption of the system being in an E-I balanced AI state. This formalism is extended by including the slow dynamic effects of adaptation [22] so that the system is fully described by mean firing rate ν µ and adaptation ω µ for each neuron population µ. The differential equation system for this framework where F µ is the transfer function of cell population µ and c µν the covariance between two populations. The indices {µ, ν, λ, η} run over the set of populations, e.g. in our case of two populations the set of {e, i} for excitatory TC and inhibitory RE. The derivatives are defined as ∂ µ = ∂ ∂νµ . Important to note is the role of T which marks the adiabatic time step such that dynamics with smaller time resolutions are not captured and which has to fulfil the requirements of Table S1.
The core of this formalism is the transfer function F and so the main task in constructing a mean-field of the thalamus is to get the transfer function of TC and RE cells in the two states of awake and sleep.
To derive the transfer function we follow the semi-analytical approach of Zerlaut et al. [48] which combines the seminal studies of [49,50]. In there the firing rate is written as a probabilistic function counting the spikes in term of the membrane potential v(t) being above a certain spike threshold potential V θ in each time bin of duration τ V which resembles the membrane potentials autocorrelation time. In the Gaussian limit we get a function dependent on the membrane subthreshold fluctuation statistical moments and define that as our transfer function: where µ v is the mean and σ v the standard deviation of the (subthreshold) membrane potential. In the second step, the constant threshold V θ is replaced with a phenomenological one acting as a function dependent on -and therefore accounting for -different cell properties. Because there is no theoretical form, a general second order polynomial dependent on the set {µ V , σ V , τ V } was proposed [50]: with x, y ∈ {µ V , σ V , τ N V } and where cm is the non-dimensionalised autocorrelation and the parameters space is normalised to limit the fluctuation driven regime, with mean x 0 and deviation δx 0 .
Here either single cell simulations or experimental clamp data can be used to get values for the unknown amplitudes {P }. This fitting has to be performed for each distinctive cell type, so in our case for TC and RE neurons. Because the two states awake and sleep are mostly changes in adaptation parameters, and it was shown in [22] that the mean-field is predictive even far from its fitting point, we just need one fit per cell type. This also is biologically realistic, for the changes induced by e.g. ACh would not change the cell morphology, and we consider the threshold membrane potential to stay the same for both states.
The set of {µ V , σ V , τ V } can be calculated purely analytically by using Campbell's theorem and assuming Poissonian distribution of incoming spikes as the generator of subthreshold fluctuations [49] (as is the case in the AI regime). The mean or static synaptic conductances are calculated then as a function of incoming spike frequencies {ν e , ν i } in terms of their mean and standard deviation: where ) Then we can calculate the mean membrane potential µ V from the first order approximation of (1) as a function of incoming spike frequencies Taking (3) as the general synaptic input, we can calculate the form of a single postsynaptic potential (PSP). And via shotnoise theory get the density power spectrum of membrane fluctuations P V (q) as a response to a stimulation (3). Then the variance of fluctuations with taking the integral in frequency domain σ 2 V = q P V (q), follows to where µ G (E (e,i) − µ V ) is the effective synaptic drive. Finally, the autocorrelation time τ V completes the framework which is defined in terms of the power spectrum as where in case of only one synaptic event this would reduce to τ V = τ m + τ (e,i) . With ( 13), (14), and ( 15) the transfer function with effective threshold ( 8) is now dependent only on the incoming firing rates at excitatory and inhibitory synapses F (µ V , σ V , τ V ) → F (ν e , ν i ), closing our firing-rate based mean-field formalism.
Transfer function fit
To get the transfer functions we fit {P } on single cell simulations of TC and RE cells (in awake state) using the AdEx equations (1). The formalism translates excitatory and inhibitory input firing rates {ν e , ν i } of a neuron into its fluctuation statistics {µ V , σ V , τ V } and then to its output firing rate.
The advantage of this semi-analytic approach is that-given either simulated or experimental data-we can calculate the phenomenological threshold V eff thr via reordering of (8). Then the employed procedure is to first fit (9) linearly in the threshold space (depending on the topography of the space to capture, this fit can be done nonlinearly too). However, here (13) has to be adjusted because the adaptation ω is unknown. Therefore, the (stationary) solution to (7) will be used to calculate ω from the firing rate data. The resulting values for {P } are following used as initial guesses for the fully nonlinear fit of (8) in the original firing rate space.
For the fit we normalised the fluctuation regime the same way as done in previous works [22,50]; to ensure comparability:
Results
The results of this paper are structured as follow: First the mean-field model will be compared with simulated spiking network dynamics and validated in and far of the fitting point (Section 3.1). Then thalamic responsiveness and how it depends on different external and internal states will be investigated (Bursting in Section 3.2, Inputs 3.3, and Noise 3.4). Lastly, spindle oscillations in a sleep-like state in the employed models are shown (Section 3.5).
Fitting and validation
In this section, we validate the mean-field model and demonstrate its suitability for modeling both awake and sleep state of the thalamus by comparing it with spiking networks.
The fit parameters of the mean-field's transfer function via (9), obtained using our fitting technique (as described in Section 2.4), are depicted in Table 2. These parameters are applied to both awake and sleep states (ACh absent/present; see Section 2.2) and are used throughout this and the following three sections (but not in Section 3.5). In Fig. 2a, we show the fitted transfer functions F for TC cells (top, blue) and RE cells (bottom, red) across the full range of excitatory input frequencies (ν e ) and a subset of three inhibitory input frequencies (ν i ). Each dot represents the averaged output frequency from the single-cell simulations over 5 seconds. The sigmoid shape of the transfer function ( 8) is evident. Certain deviations from the fitted predictions via F are observed only at very high firing rates; a region of lesser biological relevance for the phenomena studied in this paper. To improve statistics, the single-cell firing rates were averaged over 100 runs.
A direct validation of the mean-field model is to compare the predicted mean firing rates and their standard deviations with those of the full spiking network, both modeling the entire thalamic substructure (Fig. 1a). This comparison is shown in Fig. 2c. Both populations receive an external constant cortical input of P = 4Hz and a split-Gaussian sensory stimulus S (definition in Section S.2). The spiking network provides the membrane potential evolution and spiking times t s for all cells. The spikes of all neurons are shown in the upper raster plot. By averaging the number of spikes over a specific bin time T bin , we calculate the time-dependent averaged firing rate of the spiking population. We use T bin = 5ms for all simulations except stated otherwise.
To compare to the mean-field, in the formalism we have to employ a similar time window for the mean-fields time constant T , and we set T = T bin (in accordance with the formalism requirements, Table S1). The spiking network and mean-field show the wanted balanced excitation-inhibition (E-I) state in AI regime with RE activity being dominant.
In Fig. 2d, we vary the cortical drive P and compare the equilibrium or stationary population firing rates (methodology in Section S.3) for TC and RE populations in both the spiking network and mean field over a 10-second simulation. This analysis reveals four distinct regimes of TC response: The first regime with no activity. The second regime with a fast response to changes in P . The inhibited third regime with limited responses. And the fourth regime with strong TC cell responsiveness due to (biologically unrealistic) saturated RE cell activity. This justifies using a cortical drive 1 < P < 10Hz for most simulations, ensuring a stable low-activity AI state, comparable to in-vivo experiments.
In Fig. 2e, we compare the distribution of firing rates and membrane potentials. In the latter the refractory states are removed to get a realistic comparison with the mean-field. The fit between mean-field and spiking network distributions only diverges 330 at high firing rates of close to 100Hz due to the discontinuous nature of spiking models. The good agreement in not only firing rate but also membrane potential is significant, because equations ( 13) and ( 14) predict accurately the spiking populations membrane potential statistics and can henceforth be used to compare with electrophysiological data and methods.
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In the same figure, top-right, there is depicted a comparison of (normalised) autocorrelations τ ac of TC and RE population activity in the stationary state corresponding to P = 4Hz, showing a strong independence of population activity as expected from a inhibition-controlled network without excitatory-excitatory connections. This also agrees with the models being in AI state and the choice of T = T bin = 5ms > τ ac is justified.
Finally, we assess the robustness of the mean-field by varying global parameters (in Fig. S3). This is done for adaptation parameters {b, a}, which exhibit the significant change between awake and sleep state (Table 1), and synaptic excitatory conductance Q e to validate its change for simulations in this study (Section 3.4). We demonstrate that even far of the actual fitting point, the mean-field remains effective in capturing network dynamics. This validation allows us to use the mean-field approach for parameter space analysis and the study of the transition between awake and sleep states with just one mean-field parameter fit.
Tonic and burst firing modes
We explore how bursting (the state of ACh neuromodulation) impacts the response of thalamic neurons and their network. Based on the fit to biological bursting TC cells from [43] we can already state that the employed parameter set with the AdEx shows bursting of single TC cells in the ACh-depleted or sleep state (as evident from the cell traces in Fig. 1).
We want to investigate the stability of those regimes and their dependence on model parameters. With the employed models (Section 2), the mechanism generating bursting is the slow adaptation current of the AdEx (1). In Section S.5 we derive an analytic metric quantifying firing adaptation using the transfer function of our meanfield framework. With this metric and with single-cell scans, we show in Fig. S1 that the awake and sleep states are well separated. While they are stable to small perturbations, they are also close to the phase transition which ensures richer dynamics.
Following, we aim to replicate experiments at single cell level on tonic and bursting states of TC cells, as documented in Sherman and Guillery [7, ch. 6]. These experiments involved manipulating the membrane potential of recorded TC cells to force either a tonic mode (around −65mV, resting state) or a bursting mode (around −75mV, hyperpolarised state), in the absence of external stimuli. A grating retinal stimulus was applied, leading to an oscillatory firing rate response. There, TC cells in tonic mode exhibited a linear response, while TC cells in bursting mode showed responses primarily during the initial phase of each stimulus period.
We recreated this behaviour computationally in our proposed spiking network with an oscillatory sensory drive S, with amplitude of 10Hz and frequency of 2Hz. The network was set in awake state emulating a lightly anaesthetised state as in experiment.
To model the thalamus in-vivo, a constant external cortical drive of P = 4Hz was applied (the inhibited regime, Fig. 2d). Subsequently, we recorded one single cell with each awake and sleep parameters. While the proposed awake and sleep states are not identical to the artificially set tonic and bursting modes in the experiment, the switch via acetylcholine (ACh) generates a similar polarization.
The recorded cell's response was calculated by averaging the spike times over 40 simulations for a time bin of 15s. This firing rate is depicted in the first row of each Fig. 3a,b for the awake state and sleep state, respectively. We observe the same response patterns as in the experiment for awake and sleep parameters, although with slightly lower response amplitudes in the sleep state compared to the hyperpolarized state of the experiment. This can be attributed to the absence of T-channels and lowthreshold spikes in the AdEx model [7]. In Fig. 3b there is also depicted, in light blue, 385 the response in sleep state with adaptation parameters of the preceding study [46] (b = 20pA), which does not show the correct behavior.
Moving to population-level, the second row of Fig. 3a,b superimposes the spiking network's and mean-field's responses. In the awake state the entire TC population faithfully tracks the stimulus, as do single TC cells (top row). In sleep state, the 390 response amplitude and also RE activity are greatly reduced, both showing phase locking while keeping the shape of the stimulus. The phase shift is created by the delay of slowly activating adaptation mechanisms and reactive RE inhibition. Phase locking was found in sleep state for all amplitudes of stimuli.
The effects, however, are quite small and functionally not so different between awake and sleep states. We would expect stronger effects of bursting in the responsiveness when adaptation effects are significantly slower than changes in the input and subsequently membrane potential (as is the case for single cells, Fig. 1b for a rectangular pulse). To investigate this at the network level, thalamic response to faster changing stimuli is tested. In Fig. 3c, a split-Gaussian with steep left-hand std. is depicted (at t 0 = 1.5s with std.σ l = 2ms and amplitude A = 20Hz, see Section S.1; inhibited regime). TC response is two-fold at an initial peak and then quickly adapts. This response curve is nonlinear and does not follow the shape of the stimulus faithfully anymore. This peak response is a direct effect of TC cells bursting at the onset of the stimulus, as shown in the inset for a random TC cell of the spiking network simulation. Similar to the single cells definition of showing bursting (Fig. 1b), also the TC population activity vanishes after the initial peak for a sustained input (no cortical drive, Fig. 3d). The initial bursting of TC cells is captured by the mean-field mainly via its second order moments, namely autocovariance c (blue shaded areas in plot) and autocorrelation C next to a smaller increase in mean firing rate ν.
To analyze the dependence of thalamic response for both tonic and bursting TC cells (awake and sleep state) on the shape of the stimulus, in Fig. 3d, there is depicted the peak response amplitude of the thalamus as a function of the std.σ l of a split-Gaussian stimulus (σ r = 0.2s and A = 10Hz), representing the change or 'shape' of a generic stimuli. In awake state the peak response is nearly constant, does not depend on how fast the stimulus changes, and the thalamus magnifies the input amplitude nearly two-fold. In contrast, in sleep state, only steep slopes or fast changing stimuli are generating a substantial response, whereas for slowly changing stimuli the response is drastically reduced (Fig. 3b).
In conclusion, both single-cell and population-level response of TC cells appears linear in awake state (ACh present) with enhanced stimulus amplitude, while in sleep state (ACh absent) response is linear but of reduced amplitude for slowly changing stimuli, and nonlinear for quickly changing stimuli. In addition, and as evident from Fig. 3c, both spiking network and mean-field model capture the bursting of TC cells, resulting in a "bursting" population response. This enhances stimulus detection in low attention states for significant sensory inputs and the transmission of mostly time-dependent information such as oscillations in sleep state.
Cortical and sensory input
We will proceed with how thalamic responsiveness depends on background activity and how the two different biological inputs to the thalamus modulate it's behaviour.
Referring back to Fig. 3d, we see a modulating role of cortical input, which in sleep state can render the usually highly nonlinear TC response linear by removing the dependence on stimulus change at high cortical drives (gray lines in plot). This could allow the cortex to generate a time window where outside information temporally is transferred faithfully during usually non-attentive states.
Moving on, we are interested in the differences between the two drives. In Fig. 4a, the (stationary) firing rate response of the TC cell population in the mean-field model for different constant inputs in awake state is displayed, with both cortical and sensory drives. We applied a small constant cortical input P = 1Hz to be in a low activity AI state comparable to in-vivo (Fig. 2d). In case of sensory stimuli, the response is strongly proportional to the input, and we identify that the slope of this response is influenced by the cortical drive P . In Fig. 4b we see this dependency is inversely proportional, where we conducted simulations for varying cortical drive amplitudes and observed that the gain (slope of the linear response curve) decreases as P increases. In the sleep state, the response remains relatively constant, slightly decreasing with P , contrasting the awake state's high gain for all cortical inputs. The cortical drive removes the firing rate-dependency of thalamic response to stimuli in awake like states but does not alter it in sleep state. This is in agreement with studies which assumed the cortical role in the thalamus to be modulating thalamic response similar to noise [51], and with our study on synaptic noise (see next Section 3.4).
For cortical input, the response is nonlinear but exhibits multiple linear regions, as seen in Fig. 2c. The threshold at around 25Hz serves as a turning point (the end of the inhibited regime, at which the RE population firing rate saturates). Inputs below this threshold do not provoke a strong sustained response, while inputs above do. The RE population's strong response to changes in the inhibited regime nearly nullifies TC and therefore thalamic response.
These behaviors are evident in the TC population's response to a rectangular pulse stimulus from either P or S in Fig. 4c. Notably, low cortical inputs can even be repressive, with only larger amplitudes triggering robust and sustained responses, particularly in the awake state (in agreement with studies such like Crandall et al. [6]). In sleep state for both inputs or with low cortical inputs in awake state, responses are highly nonlinear, emphasizing the transfer of gradients rather than absolute values. The initial activity spikes at the onset of the input are created by the delay it takes the RE population to react to both stimulus and TC excitation to inhibit TC activity and -to a lesser extent-by the delayed adaptation mechanisms of both RE and TC populations. This is magnified in sleep state by stronger adaptation effects and resulting single cell bursting (see last Section 3.2). This mechanism allows the thalamus to respond to cortical input and modulation despite its strong inhibiting effect via the TRN. Concluding, only in awake state and for sensory input, or with cortical control for sensory input at sleep state, thalamic responsiveness is linear while only temporal information is transferred for cortical input and sensory input at sleep states without cortical control.
Synaptic noise
We have analyzed so far how the responsiveness of the thalamic cells depends on the different firing modes and input sources. However, it has been shown that the level of synaptic noise (background activity) can significantly change these responses. We analyse in this section the role of noise as background synaptic and subthreshold activity and how it influences response and firing modes. We start by replicating single cell findings from Wolfart et al. [52]. They observed that synaptic noise controls TC neurons response and behaviour and that such noise removes the dependency of TC cells response on voltage and input frequency.
We recreated this computationally at single cell level. Fig. 5a shows the response of single TC cells in awake state to a Poissonian spike train of 5Hz with varying excitatory synaptic strength (Q e ), reflecting the experimental setup. We observe the same step-like function in the static case without external synaptic noise: going from no activity to single spike response to double spike response or bursts at high conductances (regions separated by dashed lines). With noise the response function becomes smoother and the partition of the aforementioned regimes becomes blurred. The timedependent noise was implemented as an Ornstein-Uhlenbeck (OU) process entering the cells membrane potential as a synaptic current (see Section S.2).
To translate this behaviour to the population level we did simulations of the full spiking network of the employed thalamic substructure. A constant Poissonian input of 15Hz was inserted into all cells, coming from just one source; comparable to dynamical patch clamps at single cell level. The stationary firing rate output of the TC population was measured for different synaptic strengths Q e . The resulting response function is depicted in Fig. 5b for the static and noisy case for both spiking network and mean-field. Shaded area is the standard deviation induced by small conductance noise (5nS), and orange the average. Reproducing Fig. 5b of [52].
For the mean-field, the noise-dependent shape of the response function is passively included in the definition of the transfer function (8), with its slope being controlled by the standard deviation of the subthreshold membrane potential (σ V ). However, to recreate the experiment, which employed a time-dependent external noise, we extended the formalism by adding two additional static synaptic conductances μG(e,i) . Those are modelled as OU-type functions averaged for each time bin equal to the mean-field's time constant T (see Section S.2).
Both spiking network and mean-field show that the TC populations response function has its maximum slope at the same place as the first step at single cell level from no activity to single spike response (the first dashed line in Fig. 5a and the dashed line in Fig. 5b, respectively). Furthermore, the effect of synaptic noise is the same for population response as in the single-cell experiment, decreasing the response functions maximum slope.
How the maximum slope of the response function depends on this noise is depicted in Fig. 5c. Here instead of the injected noise the noise-dependent membrane potential subthreshold fluctuations averaged over all runs (σ V ) is shown. In sleep state the population response slope is ∼ 20% less steep for the static case or small noise. Strong synaptic noise and subsequent membrane potential fluctuations decrease the slope as expected. Additionally, synaptic noise diffuses the response differences of awake and sleep state at intermediate noise levels and removes nearly all dependence of thalamic response on conductance at high noise levels (σ V > 10mV), where the response function is nearly constant (at a value dependent on the ratio of excitatory and inhibitory noise μGe /μ Gi ).
Additionally, the effect of synaptic noise on the firing adaptation F of TC cells was tested in Fig. 5d. Noise diffuses the state transition between no firing adaptation and strong firing adaptation for different levels of membrane potential polarization. As in Section 3.2 we can refer to the similarity of F to burstiness, and hypothesize that strong noise allows for firing adaptation and also bursting for membrane potential levels showing no bursting without noise, this would be in agreement with experimental studies ([52] Fig. 5b therein). Previously, we showed that synaptic noise modifies thalamic response dependency on voltage and conductance. There, input frequency was fixed. Further following [52], we proceed to investigate how noise changes the thalamus' response in respect to input frequency.
For this, single TC cells were simulated for extended duration with incoming Poissonian spike trains of 10Hz, modelling a generic input from retinal ganglion cells in-vivo. Here the retinal input conductances were fixed. During simulation, for each output spike of the recorded TC cell, the interspike interval (∆ ISI) of the retinal input between the spike which results in the spike response and the preceding one is measured. This way the spike probability or response can be measured as a function of input frequency. Because of the increasingly more rare occurrence of large ISI's (∆ > 400ms) in a Poissonian spike train of 10Hz, the following plots are cut of at 550ms. Until then reasonable long simulation times provide distinguishable uncertainties. Fig. 6a shows the results for a TC cell without additional synaptic noise. At resting potential (awake state, E L = −65mV with Q e = 14ns) spike response only occurred at summed input spikes with ∆ < 50ms with an all-or-none character. At hyperpolarized potential (awake state, E L = −70mV with Q e = 24ns) not only input spike summation evoked a response but also ISI's with duration longer than 300ms. These even show higher spike probability compared to spike summation at low ISI's. The difference in input conductances Q e was necessary to account for equal number of spikes between both states, where the high conductance in the hyperpolarized state captures the effects of T-channels. In the presence of synaptic noise this changes drastically and both TC cells at resting and at hyperpolarized levels exhibit the same spike response, completely independent of input frequency and possible spike summation. Remarkably, spike probability is significantly lower than without noise. These results exactly reproduce the experimental results of [52].
Moving to population level, we present thalamic stimulus response as a function dependent on synaptic noise. Noise acts in a similar way on the frequency dependent response as cortical input (see Section 3.3). In the same manner, in Fig. 6c the slope of the linear response of the TC population as a function of input amplitude is depicted (gain). As with modulating cortical input (refer Fig. 4), noise decreases response. However, different to the control of cortical input, where the gain saturates at 0.7Hz −1 , noise linearly reduces gain until a complete banishment of frequency dependence at very high noise levels (σ V > 12mV). This holds true for all states. In the awake state the loss of gain per membrane fluctuation is (−0.12±0.01)gain/mV. For the sleep state the loss is (−0.028±0.003)gain/mV. Finally, we see that the noise required to equalize the dependence on frequency between awake and sleep state is significantly higher than for equalizing the dependence on voltage (induced subthreshold fluctuations of 12mV and 4mV, respectively).
Spindle oscillations
Spindle oscillations are one of the main activity dynamics of the thalamus during NREM sleep or anesthesia [53], strongly influencing the responsiveness of the thalamus in such states. These originate from the superposition of multiple cellular and circuit properties, with especially the mechanism of RE-induced rebound bursts in TC cells in ACh depleted or sleep-like states (see Section S.6 for more details).
To enhance this rebound bursting in our sleep state we promote burst firing by adjusting the reset membrane potential (V r ) below the sodium spike threshold onset: V r = −48mV for TC and V r = −42mV for RE cell (see [29] for the significant role suggested Andronov-Hopf bifurcation that occurs when gradually increasing the connection probability in the network, for spiking network and mean-field. This corresponds to a parameter shift from the parameters used in [31] with γ = 1 to the parameters used in this paper with γ = 10. of V r ). This yields sustained burst firing without sustained activation, mimicking Tchannel like activation and IPSP barrages in RE cells, which we could not capture with the AdEx sleep state (Section 3.2). Accordingly we re-calibrate the mean-field fit to accommodate the change in V r (Table S2).
We observe spindles in the proposed models within this adjusted sleep state and when applying an initial kick to evoke activity. Fig. 7a,b show self sustained oscillations of both full spiking network and mean-field, respectively. Their frequency spectrum and phase space are compared in Fig. 7c.
In a previous study [31] only small AdEx networks generated spindles in SR-like dynamics, while at larger scales of N > 40 neurons, population activity showed selfdriven steady states with AI dynamics. In Fig. 7d we show a bifurcation diagram transitioning between connection and balancing synaptic parameters from [31] (γ = 1) to our parameter values (Table 1, γ = 10). Increasing connection probability creates a supercritical Andronov-Hopf bifurcation, showing that sufficient connections are necessary for generating and keeping stable spindles at larger network scales. The spindles produced by our mesoscale network show realistic SI dynamics (see Section S.6). This self-sustained oscillation is remarkably robust in regards to perturbations of all kinds of inputs, producing spindles of same frequency. This renders the thalamus' responsiveness in this spindle-adjusted sleep state highly independent of external input. Only prolonged and constant inputs of a duration longer than multiple spindle periods destroy the synchronisation and create steady state AI dynamics, with however spindle oscillations starting as soon as the input stops. The bifurcation diagram in connection with [31], shows that thalamic function and responsiveness can be drastically altered depending on specific order parameters, as seen here with connection probability and synaptic conductance.
Discussion
In this study, we investigated the state-dependent responsiveness of the thalamus at micro to meso scale. For this we introduced a biologically realistic mean-field model of the thalamus, which captures the population dynamics of thalamocortical relay neurons (TC) and thalamic reticular neurons (RE) in two physiological states: Awake state (high level of ACh neuromodulation, wakefulness and REM sleep) and sleep state (low level of ACh neuromodulation, NREM sleep; Section 2.2 and [9,43]).
The mean-field model employs the master-equation formalism introduced by El Boustani and Destexhe [47] and incorporates adaptation mechanisms [22]. We constructed it using a bottom-up approach following the formalism described by Zerlaut et al. [50], which includes a subthreshold-dependent transfer function [49].
In Section 3.1 we successfully validated the mean-field's predictive accuracy through comparison with the spiking network, confirming its ability to replicate the dynamic behavior and population distribution of thalamic cells. We also demonstrated that the mean-field model is capable of predicting the network's subthreshold activity and proved its validity beyond the fitting point. This allows the use of modeling experiments using intracellularly injected currents in combination with this model.
Thalamic responsiveness and it's dependence on internal and external state was investigated in three steps: First, in Section 3.2, we analyzed the important role of bursting in TC cells which provides a mechanism by which the thalamus modulates the transmission of sensory information to the cortex, extending the single cell findings of Sherman and Guillery [7]. We showed that in sleep state response is highly reduced, except for significant (fast changing) stimuli where mainly their timing is transmitted via a strong and fast thalamic response, which is generated by TC cell bursting and delayed inhibition of RE cells. This is in agreement with the hypothesis that bursting plays a role in generating wake-up calls during low-attention states [7], and also supports that the thalamus generates and distributes oscillations in NREM sleep states [53]. Additionally, we state as an important validation that the mean-field model is successfully capturing the important nonlinear thalamic feature of bursting.
Second, in Section 3.3, we examined the influence of external states on thalamic response. We demonstrated that in this model, and in accordance with experiments [6], there is an important distinction in the origin of inputs: sensory-like stimuli experience a more linear response and are therefore transferred more faithfully than cortical-like inputs, which generate a nonlinear response. In sleep-like state the relay of information becomes strongly nonlinear regardless of input origin. Additionally, we identified the modulatory effect of cortical input to (1) repress thalamic response in awake state, via activation of the inhibiting TRN, and (2) to promote a linear response to sensory stimulus in sleep state.(2) would allow the cortex to wake-up the thalamus in order to faithfully transfer sensory input, e.g. after a preceding wake-up call of a potentially significant stimulus.
Third, in Section 3.4, we investigated the role of synaptic noise in thalamic response. The experimental findings of Wolfart et al. [52] were as a first time successfully modeled. We showed that synaptic noise acts as a controller for response also at the population level. The TC cells' step-like response function for single spikes translates well into their collective response at population scale, sharing the same conductance threshold. This allows the thalamus to fine-tune its responsiveness to external stimuli at cell and population level. Additionally, noise diffuses transitions between states of tonic/bursting firing at single cell level and awake/sleep at the population level. We find that in equal manner for single cell and population level, noise banishes the thalamic response dependency on both voltage and frequency. We state the interesting similarity between synaptic noise and cortical input in how both control stimulus transfer and render stimulus response less dependent on stimulus frequency, whose similarity is often only presumed [51]. These insights pronounce the importance of integrating conductance-based subthreshold fluctuations dynamics into meso to macro scale modeling approaches.
Finally, in Section 3.5, the successful reproduction of spindle-like oscillations in a sleep-like state is an important validation for our thalamic model. We emphasize the necessity of specific substructures within the thalamus for generating realistic oscillations at all scales ( [54], see Section S.6). In this state thalamic responsiveness to inputs is highly suppressed. Only strong and prolonged cortical inputs temporarily create AI dynamics during their activation.
In conclusion, our study underscores the value of integrating single-cell dynamics with thalamic specific structure at population-level in understanding the complex role of thalamic responsiveness. With these findings and with offering a biologically realistic and experimentally grounded mean-field model of the thalamus, which captures the effects of bursting, neuromodulation, and fluctuation, we provide here an essential starting point for: (1) Further investigation of thalamic function and sensory processing.(2) Large-scale modeling (especially the thalamo-cortical loop with already developed cortical mean-fields [22,33]), while integrating micro-scale cell and synaptic effects with physiological states.
Transfer function
The transfer function F was defined as the complementary error function with erf : R → (−1, 1). The statistical interpretation is that for a normal distributed random variable ξ with ⟨ξ⟩ = 0 and
Mean-field correlation
The differential equation describing the correlation of populations {µ, ν, λ} ∈ {e, i} to close the second order statistical moments of the mean-field formalism is given by where τ is the time lag and ν 0 marks the stationary solution of the firing rate.
Gaussian stimulus
The split-Gaussian stimulus employed as a general sensory input was defined as , where θ(t) is the Heaviside step-function, and with A the amplitude and σ l,r the slopes of the left and right side of the split-Gaussian, respectively.
Synaptic noise model
Ornstein-Uhlenbeck type noise can be defined as where µ is the mean reversion level, θ the mean reversion rate, and σ the amplitude of the standard Wiener process W (t), or white noise.
The conductance noise was implemented in the mean-field via adding the mean of OU-type noise (x) in a time window corresponding to T to the static conductances µ G (see methods). The resulting conductances with including noise are then: μG(e,i) = µ G(e,i) + x(t) T (e,i) .
The specific parameters for x(t) used for the case of synaptic noise case in the simulations are µ = 0nS, θ = 5ms −1 , and for the amplitudes σ i = 200nS and σ e = 60nS.
S.3 Dynamical analysis
The steady and equilibrium points of the spiking network are taken as the averaged population activity over long simulation times for constant inputs where oscillations are ruled out either by observation or by spectrum analysis. For the mean-field these are calculated as the populations activity after a transient up to a point where the Jacobian (J) eigenvalues are negative. The Jacobian is defined on the variable set X ∈ {ν e , ν i , ω e , ω i } and calculated via numerical derivatives.
For the bifurcation diagram (Fig. 7) the same technique was used up to the bifurcation point, except that now X ∈ {ν 1 e , ν 2 e , ν 1 i , ν 2 i , ω 1 e , ω 2 e , ω 1 i , ω 2 i }. For the mean-field the Jacobian eigenvalues are complex up to the bifurcation point, suggesting a supercritical Andronov-Hopf bifurcation. After the bifurcation point for the spiking network, instead of the averaged firing rates, the extrema are averaged over a long time simulation, mimicking the real sub-plane of the suggested complex-space Andronov-Hopf bifurcation. The same was done for the mean-field.
The maximum Lyapunov exponents are calculated employing the Rosenstein algorithm [55].
S.4 Numerical simulations
All simulations were conducted in Python. The spiking network simulations additionally used the Python package Brian2 [56]. Constructing the network, all neuron connections to random to ensure a statistical model comparable to the mean-fields assumptions. All numerical integrations in spiking network simulations employed Heun's method, while for mean-field simulations Euler's method was used.
S.5 Firing adaptation
We introduce firing adaptation F as a metric describing how adaptation shapes the response. Because the AdEx generates bursting-like behaviour via its adaptation current ω, we will see a good agreement between F and burstiness.
We use the transfer function F (8). For the effect of adaptation mechanisms on the firing rate of a neuron type we can identify two crucial states described by F : The noadaptation fixpoint (F 0 ): This state represents the firing rate of a cell in the absence of adaptation and corresponds to the cell's firing rate at the onset of a stimulus where the (slow) adaptation mechanisms have no impact yet. And the real-adaptation fixpoint (F ω ): Representing the cell's firing rate when it has fully adapted to its own and the stimulus influence. Then F 0 − F ω reflects the change in firing rate of a cell from initial activity at the onset of a stimulus as it transitions towards full adaptation and slowing its firing rate.
Necessarily, the transfer function is firing-based and needs non-zero firing rate inputs (ν e , ν i ) to yield results. Therefore, we calculate the fixpoints for a constant ν e = 1Hz. When considering high firing rates as decreased ISIs this metric is comparable to experimental methods measuring burstiness (such as [52]). Concluding, we define the firing adaptation metric as With this, we investigate the stability of bursting in TC cells and the dependence of bursting on model parameters. For this we use the level of firing adaptation to quantify the effect of adaptation mechanisms on the firing rate of neurons. The dependence of TC firing adaptation on membrane and spiking adaptation parameters and membrane polarization is shown in Fig. S1a,b. Firing adaptation is strongest at high membrane adaptation levels and hyperpolarized membrane potentials. The awake state experiences nearly no firing adaptation, while the sleep state shows strong firing adaptation. We want to improve on defining the states of ACh as tonic/bursting states and subsequently as awake and sleep states. For this we conducted a parameter scan using spiking network simulations of single TC cells mapping the different firing modes. Similar to [46] (and Fig. 1) we injected the cell with a constant current for 1s. We classified firing patterns based on the number of spikes on a time scale relative to the adaptation time constant (τ ω ≃ 200 ms). The external current applied was proportional to E L in order to induce activity (with I = {200nA for E L = −50mV, 400nA for E L = −85mV}). Fig. S1d presents the results, highlighting the four possible firing modes. The ACh-absent or sleep state exhibits stable bursting not susceptible to either adaptation or voltage perturbations while the ACh-present or awake state is deep in the tonic regime.
The scan shows a similar tendency of increased bursting as with increased firing adaptation. Note that the firing adaptation is not taking into account if there is actually a non-zero response to account for the single spikes or no activity response types of the single cell scan (Fig. S1d). When integrating with F the actual response amplitude we get however the same strip-region of bursting as in the single-cell scan (see Fig. S1c, the same holds for the scan in b).
S.6 Spindle mechanism
Spindle oscillations are one of the main activity dynamics of the thalamus during slowwave sleep or anesthesia [53], strongly influencing the responsiveness of the thalamus in such states. These originate from the superposition of multiple cellular and circuit properties. In ACh depleted conditions such as during sleep, T-channel currents promote bursts after hyperpolarization (as modeled in [57] for a Hodgkin-Huxley model), and barrages of IPSPs occur in strongly connected subgroups within the TRN [58]. This creates two self-sustained loops: an inhibitory self-loop within TRN neurons, and a loop connecting RE and TC cells (see colored connections in Fig. S2e). The rebound bursts caused by this circuit arrangement generate spindle oscillations, as shown by experiments and computational models [53,59].
Computational models demonstrated that a 4-neuron structure of TC and RE neurons exhibits self-sustained oscillations with all characteristics of spindles, as shown first for Hodgkin-Huxley type models [60] and later using AdEx models [31]. These oscillations rely on a loop where a TC cell triggers a burst in an RE cell which, in turn, causes a rebound burst in another TC cell, completing the cycle. In their study however the oscillations waned with increased network size.
In this study, we showed that the proposed models in a sleep-like state indeed show spindles at all scales, validating them as thalamic models. In contrast to a previous study [31], we aimed for robust spindles at population scales. To enhance rebound bursting we promoted burst firing by adjusting the reset membrane potential (V r ) below the sodium spike threshold onset: V r = −48mV for TC and V r = −42mV for RE cell. This yields sustained burst firing without sustained activation, mimicking Tchannel like activation and IPSP barrages in RE cells. Accordingly we re-calibrated the mean-field fit to accommodate the change in V r .(The original fit produces spindles with unrealistic amplitudes.)The resulting fit parameters are depicted in Table S2.
The Jacobian eigenvalues calculated at the steady points of the mean-field up to the bifurcation point are complex. This together with the transition into a stable limit cycle suggests a supercritical Andronov-Hopf bifurcation as the origin of spindles (Fig. 7d). In Fig. S2a oscillatory dynamics in a small network of 20 neurons are illustrated, revealing a generating 4-loop structure inside the network (b). We employed the burstadjusted sleep parameters but with synaptic parameters from [31]. To invoke activity, an initial kick of Poissonian input of high frequency to only a random subset of neurons was applied.
For the mean-field to produce self-sustained oscillations we employ the 4-structure as seen at single cell level (from [31]) and justify its need also at the population level. This is further supported by studies such as [45]. They showed the mechanism behind the generation and specific frequency of oscillations in the thalamus is the delay in the propagation of activity between different TC neurons. Thalamic relay cell clusters have no connection to themselves and so RE neurons serve as intermitter. The slow adaptation mechanisms governing rebound bursts are therefore generating the slow ∼ 10Hz spindle oscillations.
In order to implement this, we construct two RE and two TC populations and keep all connections active, but reduce the connection probability of links which are antagonistic to this propagation via rebound bursts. This can be seen as two close but locally separated subgroups of relay neurons. The resulting structure is depicted in Fig. S2e.(Note: If the connections are set homogeneous then this network is acting exactly like a mean-field with just two populations.)The main links for the propagation are colored and labeled with their mechanism of propagation: RE induced rebound bursts and TC excitation. To initiate the spindles here also a kick of high activity going to only one or two populations was necessary, in agreement with the spiking network. Additionally, T was set to 15ms to have similar phases and shapes with the spiking network.
The spindle-adjusted sleep state differs quite substantially from the sleep state in the rest of this study. The adapted leakage and reset parameters, modelling Tchannel like activation and IPSP barrages to invoke stronger rebound bursts, create this significant difference, suggesting two distinct states. One representing a sleep state similar to N2 in which spindles are observed, while the other could represent a less-deep sleep state [2, ch. 44]. This suggests that the concentration of ACh alone is not sufficient for investigating awake and sleep separation, but the mechanisms behind rebound bursts are specifically important to include. Especially how these connect to ACh and other, in this study neglected, neuromodulators like dopamine, norepinephrine, or serotonin. Additionally, we remark that the shown spindles inherit the correct underlying mechanisms, as evident from experiments, but do show a frequency at the lower end of the usually defined spindle frequency, potentially being more close to Delta waves. All this hints at interesting future work in connecting physiological brain states of awake and sleep with multi-scale thalamic models.
S.7 Supplementary Tables and Figures
Table S1 Formal requirements and restrictions for the employed approximations of the mean-field formalism.
Fig. 1
Fig. 1 Network structure and single cell dynamics.(a) The chosen network structure of two thalamic populations (TC and RE cells of each N = 500), their synaptic increments Q, and connection probabilities p for all connection between the TC and RE population. The external inputs are shown in grey: The cortical drive P (N = 8000) and the sensory stimulus drive S (N = 500). The arrows mark the direction of synaptic transmission and if they act excitatory (blue) or inhibitory (red).(b) Single cell traces from AdEx IF neurons (see Methods for details) of TC and RE for a timed constant input current (grey line). The left column shows TC and RE response to the injected current in awake state (with ACh) and the right column the same in sleep state (no ACh). The cells membrane potential v and adaptation current ω are shown in color for TC (blue) and RE cell (red), respectively.
Fig. 2
Fig. 2 Validating the mean-field with spiking networks.(a,b) The fitted transfer functions for RE and TC cell for three different inhibitory inputs each with their corresponding single cell simulations. Top (a, blue) is for TC and bottom (b, red) for RE cell-type (in awake state). The dots each represent the averaged firing rate of a cell over 100 runs.(c) Comparison of the firing rate of the mean-field and the spiking network for constant cortical drive P = 4Hz and a split-Gaussian stimulus coming from S. Top is the raster plot showing all spiking times {ts} for all neurons in the spiking network simulation. Bottom is the averaged mean firing rate of spiking network (blue/red lines) and predicted mean firing rate of the mean-field ν (black line) with its standard deviation (shaded blue/red areas).(d) Comparison of the equilibrium firing rate of the spiking network and of the mean-field over a range of cortical inputs. Each dot represents a spiking network simulation for 10s where the steady long time mean is calculated. The black lines correspond to the mean-fields fixpoints ν 0 (e,i) , with the shaded areas being the standard deviations. The inhibited regime between ca. P ≃ (1, 20)Hz marks the standard activity employed. The inset shows a zoom at the low-drive regimes where activity is first silent and then controlled by TC until P ≃ 1Hz.(e) Left column: The firing rate distributions of spiking network (histogram) and mean-field (line) for P = {2, 4}Hz. Bottom-right: Comparison of membrane potential distribution for 4Hz. Top-right: Autocorrelation τac of TC and RE population for spiking network (grey lines) and mean-field (blue/red lines).
Fig. 3
Fig. 3 Bursting of TC cells renders thalamic response nonlinear in sleep state.(a) Top row: Single cell and population response to a strong oscillatory sensory drive S in awake state. Bottom row: Activities of spiking network (TC population, blue) and mean-field (TC population, black line with color-shaded std., RE population, red line). The grading stimulus is pictured in light grey. The single cell recording of the top rows is taken from this network simulation.(b) The same setup as in (a) but in sleep state (TC bursting, see main text). The single cell recording is done in the network of (a) which was in awake state to be close to the experiment.(Dark blue is sleep state and light blue is sleep state with lower adaptation b = 20pA.)The single cell traces in (a) and (b) reproduce the experiments of Sherman and Guillery [7, ch.6]. (c) Thalamic response of spiking network and meanfield to a fast changing stimulus (split-Gaussian with steep left-hand std.σ l ). Inset shows trace of 3 random TC cells of the spiking simulation, showing bursting at the onset of the stimulus (t 0 = 1.5s; mV per s).(d) Rectangular stimulus in absence of cortical drive showing that TC activity vanishes after initial burst.(e) The maximum amplitude of response (peak), relative to the incoming stimulus amplitude, as a function of the 'slope' of stimulus (left std.σ l of split-Gaussian; amplitude 10Hz and right std. 0.2s). Depicted is the response for awake and sleep state and in sleep state for different applied cortical constant drives (from black to gray: {1, 2, 4, 10}Hz).
Fig. 4
Fig. 4 The thalamus' responsiveness depends on external input origin.(a) The steady state output firing rate of the TC population after reaching equilibrium for different drives P and S. Blue is for inputs coming from cortical drive P , where solid marks the inhibited regime and dashed the blow-up regime. Black are for sensory drive S, with varying degrees of cortical input. TC cells respond linearly for sensory stimuli, whereas cortical stimuli are nonlinear only showing a strong proportionality after ∼ 20Hz.(b) Cortical drive removes thalamic response dependency on stimulus frequency. The gradients of the sensory input response curves (black in (a)) as a function of cortical input P for awake and sleep state.(c) The TC populations response to a rectangular stimulus of varying amplitude coming from either drive in both states. In the bottom-right there are also depicted 10 randomly chosen single cell traces to connect the population spike with single cell burst-like behaviour (mV per s).
Fig. 5
Fig. 5 Synaptic noise modulates the dependence of thalamic responsiveness on voltage.(a) Response to a 5Hz Poissonian spike train for different values of excitatory synaptic strength Qe for simulated spiking single cells in awake state. The noise was injected as an OU-like current in the membrane potential via Isyn. The dots represent each the spikes per receiving incoming spike, averaged over 100 runs for 10s each. The lines correspond to sigmoidial fits, where the blue dashed lines mark the shift fit parameter depicting the center of the slope. Reproducing Fig. 4a of [52]. (b) The same setup but with the full thalamic spiking network (squares) and the mean field (lines), showing the relative response to a 10Hz Poissonian spike train. The dotted line marks the slope center of the single cell simulations going from no spikes to a one-to-one spike response. For the mean-field the synaptic noise was added as an additional time-dependent conductance into the formalism (see main text).(c) The (maximum) slopes of the mean-field response curves (b) plotted against the standard deviation of the membrane potential predicted by the mean-field. Showing a proportionality between fluctuations and the slope of the response function. At high noise levels the difference between awake and sleep state vanishes. The two cases from (b) are drawn as empty blue/black boxes.(d) Synaptic noise reduces the dependency of TC firing adaptation (sim.burstiness) on cell state (polarisation). Shaded area is the standard deviation induced by small conductance noise (5nS), and orange the average. Reproducing Fig.5bof[52].
Fig. 6
Fig. 6 Synaptic noise removes the dependence of thalamic response on frequency.(a) A single TC cell's spike response probability dependent on the interspike intervall (∆ ISI) between input spikes. The input is a Poissonian spike train with a mean frequency of 10Hz, comparable to an in-vivo retinal input. For both resting (E L = −65mV, blue curve) and hyperpolarized (E L = −70mV, orange curve) states the spike response is nearly 100% at low ISI's and therefore only reacting to summed input spikes. In hyperpolarized state, with T-channel adjusted synaptic conductance (see main text), the TC cell responds to also high ISI's with a nearly one-to-one spike probability. This reproduces Fig. 4 in [52]. (b) Same setup as in (a) but with additional synaptic noise (see main text). Frequency-dependent response is nearly removed.(c) Thalamic stationary response slope (gain per increase of input, see main text) of the mean-field to gated sensory stimuli of 10Hz as a function of synaptic noise via noise induced membrane potential fluctuations. For the awake state, and the sleep state with b ∈ {20, 200}pA. Regardless of state, noise linearly leads to a reduced thalamic response dependency on stimulus frequency. The shaded lines are linear fits.
Fig. 7
Fig. 7 Spindle oscillations in a sleep-like state generate a highly unresponsive thalamic state.(a) Raster plot of the full-scale spiking network with 1000 neurons for spindle parameters (ACh/sleep state with rebound burst).(b) Mean-field oscillations: Firing rates and standard deviations of both TC and RE populations.(d) Fourier spectrum for spiking network (grey) and mean field (blue) of the TC population activity. Inset right: Phase plane in the TC and RE firing rate space. Yellow is the stable limit cycle and black the transient.(d) Bifurcation diagram showing thesuggested Andronov-Hopf bifurcation that occurs when gradually increasing the connection probability in the network, for spiking network and mean-field. This corresponds to a parameter shift from the parameters used in[31] with γ = 1 to the parameters used in this paper with γ = 10.
Fig
Fig. S1 Firing adaptation and dynamics of TC cells.(a) Parameter scan for firing adaptation F for TC cells via the fitted transfer function (8) for resting potential and membrane adaptation a. Depicted are the parameter values for sleep and awake state.(b) Same as in (a) but for spiking adaptation b.(c) The scan of (a) but the firing adaptation F is multiplied by the firing rate during the response (also via transfer function), revealing a split of bursting dynamics for the same input.(d) Parameter scan of TC single-cell simulations, showing the possible firing modes of tonic, bursting, single spikes, or no spikes as a response to a input current (see text). Similarity to firing adaptation in (a) and (c) is evident.
Fig. S2
Fig. S2 Spindle oscillations are generated by sparse TC-RE connections.(a) Raster plot of a simulation for the small network showing all 20 neurons.(b) Four neurons showing antiphase burst firing with rebound bursts of the TC cells as a possible generator of spindles. Cell traces are taken from the simulation in (a) with a small network (N = 20). The arrows show the loop of activity propagation between RE and TC cells with rebound bursts and direct excitation. These pathways translate to the four colored connections in (e).(e) The 4-structure used for the mean-field connections. The colored arrows mark the main pathway of propagation with rebound bursts (RB) and direct excitation (E) as transmitters. The non-significant connections are marked in grey with their respective relative strength shown in percentages of the original connection probability. The RE populations keep their local self-loop.
Fig. S3
Fig. S3 Global parameter analysis for mean-field and spiking network. Black markers represent equilibrium population firing rates of the spiking network. Colored line and shaded area represent the mean-fields mean and standard deviation, respectively.(a) Spiking adaptation parameter.(b) Membrane potential adaptation parameter as shift of the original parameters to keep the difference between TC and RE adaptations, securing stable dynamics.(a) and (b) ensure the meanfields fit validity between awake and sleep state.(c) Synaptic exponential delay time constant of both populations.(d) Excitatory incremental synaptic conductance Qe of the TC population (3). The good fit allows for modeling dynamic clamp-like techniques and suggests that the mean-field is able to capture the non-trivial firing rate saturation of the spiking network (also supported by Fig. 2d).
Table 1
Cell and synaptic parameters for TC and RE cells in awake (ACh) and sleep (no ACh) states. Connection parameters see Fig.1a. The last two parameters are for the spiking network only and "-" means the same value as in awake state.
Table 2
The fitting parameters of V eff thr and their values. All values in mV.
fitting parameter values for recreating spindles with sleep parameters and included rebound burst mechanism.(All values in mV.)
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Domain: Physics Biology
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Effects of Individuals and Behaviors on Acoustic Features of Ultrasonic Vocalizations in Rats
The goal of this study was to investigate how spectrographic features of ultrasonic vocalizations (USVs) in rats vary among individuals and behaviors. Eighteen pairs of rats were allocated to individual pair cages. Each pair’s behaviors and vocalizations were recorded during the 900s a known cage-mate was returning to the cage. The effects of individuals, behaviors, and the interaction between individuals and behaviors (individuals×behaviors) were tested on the duration and peak frequencies. There was difference in the duration and peak frequency: i) among individuals (p<0.0001 and p<0.0001, respectively); ii) among behaviors (p = 0.0667 and p< 0.0001, respectively); iii) among individuals×behaviors (p<0.0001 and p<0.0001, respectively). The frequency of ultrasonic vocalizations changed with a frequency ranging from 40 to 71 kHz which were emitted by individuals, whereas the frequency of ultrasonic vocalizations changed with a frequency ranging from 60 to 70 kHz which were emitted by behaviors. The peak frequency of call on ‘contact’ behavior was lower than that of call on other behaviors, but call duration of call on ‘contact’ was longer than on other behaviors. Especially, 40 kHz calls were found on ‘contact’ and ‘other’ behaviors. We suggest that ultrasonic vocalizations need to be subdivided and the effects of individuals and behaviors must be considered to assess emotional state of rats because these may influence the features of ultrasonic vocalizations. (
Rats vocalize under a range of conditions including sexual interaction (Paredes and Alonso, 1997) and aggressive encounter (Sales, 1972). The 22 kHz calls are associated with defensive postures (Portavella et al., 1993), and produced when rats are exposed to a predator and under aggressive encounters (Sales and Pye, 1974;Blanchard et al., 1991). The 50 kHz calls are apparently produced in anticipation of social contact (Brudzynski and Pniak, 2002). However, much of the previous research on this topic has focused on just a few call features, and more studies need to be conducted to describe the behaviors related to the vocalized calls in various situations.
Few studies have described the spectrographic features of these calls (Brudzynski et al., 1999;Kehoe et al., 2001;Brudzynski and Pniak, 2002) and none to date has shown how these call features vary in various individuals and behaviors. Therefore, the aim of this study was to describe how spectrographic features of ultrasonic vocalizations of the rat vary in various individuals and behaviors.
Subject
Eighteen pairs of female rats (Wistar; 400-500 g) were obtained from the UBC (University of British Columbia) Animal Care Center Rodent Breeding Unit as surplus supply stock. Animal room temperature was kept at 21°C and the light was turned on and off every 12 hours in turn. Rats could freely access food (Lab Diet 5001, PMI Nutrition International, Richmond, USA) and tap water. All testing was conducted during the period the light was on.
Experimental procedure and analysis
Each of the 18 pairs was allocated to an individual cage (45.5 cm×24 cm×20.5 cm; 20 L polypropylene cage). Because rats are highly social animals, each experimental animal was housed with a companion to encourage USV production. Behaviors were recorded using a Panasonic WV-BP330 camera and AG-6720A VCR. Each pair was recorded continuously during the first 0-300 s and 600-900 s a known cage-mate was returning to the cage after an ovariectomy performed as part of another study. In that study, analgesics were administered one hour prior to surgery. Following treatment, rats were allowed to recover in an incubator for one hour. The recorded behavioral patterns are described in Table 1. Behaviors were scored for frequency and duration using the Observer (Noldus, Netherlands).
Rat vocalizations were collected with a 1/4" condenser microphone (Bruel and Kjaer, Type 4135, Denmark), connected to a preamplifier (Bruel and Kjaer, Type 2619, Denmark) and a measuring amplifier (Bruel and Kjaer, Type 2636, Denmark). The signal was recorded directly to a high-capacity hard disk at a rate of 250 kHz using a 330 kHz PCI-DAS1200/JR data acquisition card (Computerboards Inc.) and CBDisk 1.4 Software (Engineering Design, Belmont, MA, USA). High (above 100 kHz) and low frequency room noise were filtered out by a Krohn-Hite band pass filter. Sound analysis was done by SIGNAL 4.0 (Engineering Design, Belmont, MA, USA).
To divide into individuals, vocalizations could be detected using the Signal and then we confirmed the individual behavior using the Observer at that time.
Vocalizations were subjected to spectrographic analysis to determine call duration and peak frequency (Niel and Weary, 2006). These parameters may be useful for comparing ultrasonic calls as they varied considerably in call shape (Callahan et al., 1996;Brudzynski et al., 1999), frequency, and duration (Sales, 1979).
Statistical analysis
In order to compare correctly, some pairs (emitting 22 kHz call continuously or call nothing; 13 pairs) were excluded from all data analyses in this study (Figure 1). 5 pairs and 397 ultrasonic vocalizations were analyzed in this study. These ultrasonic vocalizations were grouped according to individuals and behaviors. Data were tested for normality using the Univariate procedure (SAS, 2000). Because the durations were not a normal distribution, they were corrected through a Log10 transformation. The GLM procedure was used to compare the acoustic parameters. The effects of individuals, behaviors, and interaction between individuals and behaviors (individuals×behaviors) were tested on the durations and peak frequencies. Parameters are expressed as means±standard error.
RESULTS
There was difference in the duration and peak frequency: i.e. i) individuals (4 df; p<0.0001 and p<0.0001,
Effects of individuals on acoustic features of USVs
We found that there were differences in the call duration and in the peak frequency among individuals (p<0.0001). Call duration of the 1st pair (35.9±0.04 ms; mean±SE) was the longest and that of the 14th pair (11.9±0.05ms) was the shortest. Peak frequency of the 14th pair (71.0±1.48 kHz) was the highest and that of the 12th pair was the lowest (54.6±1.52 kHz; Figure 2). Especially, call durations of the 1st pair were the longest and call peak frequencies of the 14th pair were the highest throughout the whole behavior, except during bouts of 'contact' (Table 2; p<0.0001).
Effects of behaviors on acoustic features of USVs
Our findings showed that there was no difference in the call duration (p = 0.0667), but there was difference in the peak frequency among behaviors (p<0.0001). Call duration and peak frequency during bouts of 'contact' were longer (27.0±0.03ms; mean±SE) and lower (59.8±1.06 kHz) than during bouts of 'move' and 'other' behaviors. On the other hand, call duration during 'other' behavior (18.2±0.03ms) was the shortest and peak frequency during bouts of 'move' (68.6±1.04 kHz) was the highest compared to other behaviors (Table 2 and Figure 3; p<0.0001).
Effects of individuals×behaviors on acoustic features of USVs
This result showed that there was difference in the call duration and in the peak frequency among the individuals and behaviors (p<0.0001). Call duration and peak frequency were the longest and the lowest during 'contact' behavior. Call peak frequency of the 14th pair was the highest throughout the whole behavior and call duration of the 14th pair was the shortest during bouts of 'contact' (Table 2; p<0.0001). During 'move' behavior, peak frequencies of calls of entire pairs were near 70 kHz. Sales (1979) reported that call duration tended to be shorter during isolation and call frequency tended to be longer in frequency than during handling calls. This was similar to data reported by Wöhr et al. (2008) in which 50 kHz calls were found in relatively high numbers during short isolation. In these studies, there was no difference in the call duration, but there was difference in the peak frequency among the behaviors. This observation was most likely due to no detection of the 20 kHz call in this study and this is comparable to that reported by Sales (1972) with short pulse. This result contributes further evidence that frequency of call is associated with duration of call.
DISCUSSION
Frequencies of ultrasonic vocalizations emitted by individual rats changed, ranging from 40 to 71 kHz, in this study. This result is consistent with earlier findings that call frequency of 35-70 kHz is known as a 50 kHz call (Sales, 1972;Blanchard et al., 1990;Kaltwasser, 1990;Brudzynski and Pniak, 2002;Burgdorf et al., 2007). According to Knutson et al. (1999) and Knutson et al. (2002), long 22 kHz ultrasonic vocalizations may indicate a state of negative activation, whereas short 50 kHz ultrasonic vocalizations may indicate a state of positive activation. However, we found that frequencies of ultrasonic vocalizations changed with a frequency ranging from 60 to 70 kHz which were emitted by behaviors and there was a difference in the peak frequency among the behaviors (p<0.0001). Thus, it is necessary to classify ultrasonic vocalizations into behaviors, although ultrasonic vocalizations in the 60-80 kHz range have been obtained in response to injection of antimicrobials (Dinh et al., 1999). Likewise, Fu and Brudzynski (1994) found that 50 kHz calls were recorded by injection of glutamate. We therefore suggest that high frequency ultrasonic vocalizations need to be subdivided and effects of individuals and behaviors must be considered to assess emotional state of rats.
This result is comparable to that of Brudzynski and Pniak (2002), in which 50 kHz calls were produced in anticipation of a social contact, although peak frequency of call during 'contact' behavior was lower than that of call during other behaviors in this study. This showed that call frequency is no different when a rat contacts a known cagemate compared with an unknown cage-mate, so it seems that cage-mate had no effect on the contact behavior-related ultrasonic vocalization. In this experiment, 40 kHz calls were often found during bouts of 'contact' and 'other'.40 kHz ultrasonic vocalizations predominate during infancy (Noirot, 1968) and when pups are separated from their mother (Miczek et al., 1991). Brudzynski et al. (1993) reported that repeated hand touch applied to the neck of rats induced ultrasonic vocalizations, 2.6% of which were within 44-67 kHz. However, this result does not seem to be associated with previous studies. Rather, the 40 kHz calls in this study may be associated with behaviors because 'contact' and 'other' were not movement as shown in Table 1. High frequency ultrasonic vocalizations were also detected more on 'move' and 'other' behavior than during 'contact' in this study. This observation was similar to data reported by Knutson et al. (1998) who showed that high frequency ultrasonic vocalizations were linked to a motivational state rather than specific play behaviors or general activity.
Based on this experiment, it is not clear why the call frequency of the 12th pair was lower than that of others. However, it could merely be explained by individual difference because all of the environmental factors were controlled for all pairs. The variety of individuals makes it difficult to understand the ultrasonic vocalization. Hence, USVs as a robust indicator can be used to assess emotional states and welfare if we find the cause of the individual difference.
In conclusion, these results showed the spectrographic features of rat ultrasonic vocalizations among individuals and behaviors. Moreover, our findings show that acoustic features of ultrasonic vocalizations are influenced not only by individual but also by behavior. Our study indicates that effects of individual and behavior should be considered to assess emotional state using ultrasonic vocalizations.
Table 1 .
The mutually exclusive categories used for behavior observation Figure 1. Effect of individuals and behaviors on acoustic features of ultrasonic vocalizations.
Table 2 .
The number of the ultrasonic vocalizations and average (±SE) call duration and frequency at peak amplitude. Call features are described separately for each of the behavior categories described in Table1, and for each pair of rats observed * I: Individuals, B: Behaviors, I×B: Interaction between individuals and behaviors. Figure 2. Effect of individuals on acoustic features of ultrasonic vocalizations.
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Domain: Psychology Biology
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Short review on the aggressive behaviour: genetical, biological aspects and oxytocin relevance
. In this mini-review we were interested in describing the main genetic, biological and mechanistic aspects of the aggressive behaviour in human patients and animal models. It seems that violent behaviour and impulsive traits present a multifactorial substrate, which is determined by genetic and non-genetic factors. Thus, aggressivity is regulated by brain regions such as the amygdala, which controls neural circuits for triggering defensive, aggressive or avoidant behaviour. Moreover, other brain structures such as the anterior cingulate cortex and prefrontal cortex regions could modulate circuits involved in aggression. Regarding the genetic aspects, we could mention the mutations in the monoamine oxidase or the polymorphisms of the genes involved in the metabolism of serotonin, such as tryptophan hydroxylase. Also, besides the low levels of serotonin metabolites, which seem to be associated with impulsive and aggressive traits, there are good evidences that deficiencies in glutamate transmission, as well as testosterone, vasopressin, hypochloesterolemia or oxytocin modifications could be related to the aggressive behaviour. Regarding oxytocin we present here in the last chapter the controversial results from the current literature regarding the various effects exhibited by oxytocin administration on the aggressive behavior, considering the increased interest in understanding the role of oxytocin on the main neuropsychiatric disorders.
Moreover, the ethological vision on the concept of aggression is also interesting. From this perspective, aggression refers to the interaction and evolution of animals in natural environments. The ethologists analyze human aggression from the perspective of animal aggression. Thus, biologically, people are a highly evolved animal species. In the animal world aggression includes three functions: equal distribution of the species, the selection of the fittest and the protection of the young and defenceless [4].
In such cases in animals, aggression can involve physical violence, hitting, biting or pushing, and most conflicts are resolved through menacing and intimidation stances or strokes that do not cause physical damage. The stereotype signals may include threatening and hostile facial expressions; vocalizations, such as birdsongs and the release of chemicals [5]. In animals, aggressive behaviour might confer biological advantages. Moreover, aggression can ensure gaining territories, resources of food and water, provides opportunities for mating, for self-defense or protection of offspring and leads to the natural selection of more vigorous animals [6]. Also, an individual from a group is more likely to become aggressive when the other members exhibit aggressive behaviours that are similar [7].
In analogy with the aggressive behaviour in animals, the human aggressiveness retains an important role in the survival instinct and exhibits. Still, a variety of the roles of the aggressive behaviour are no longer valid today. Also, people can turn aggressive energy by sublimating it into work, play, sports competition or art. Given the specific energy of this pulsing, aggression seems to be unavoidable, as it may manifest spontaneously, regardless of situational particularities. Aside from its obvious internal nature, aggression can be decreased or maintained by the course of development in the context of culture and environment [8,9].
In this way, aggression is a characteristic of all living beings throughout their evolutionary scale, having its origin in the central nervous system primitive structures. Moreover, it is a form of destructive behaviour intended to cause damage, either material, psychological, moral or mixed. Thus, the act of aggression may be directed against objects such as a house, furniture, kitchen utensils, against human beings or against one's ownself as autolytic behaviour. Thus, aggression can take several forms: aggression against other persons is referred to as hetero-aggression, which can be physical or verbal. The milder variant of this form of aggression is hostility and the extreme variant is murder. Classic aggression is directed against objects and things from the environment and is aimed to destroy the objects, while directing the autolytic behaviour against one's ownself is called self-aggressiveness, presenting a broader spectrum of manifestations, from self-hostility and self-sabotage to self-mutilation and reaching suicidal tendencies and/or completed suicide. When it occurs as an impulsive reaction, the manifestation of aggressiveness has the role to release the tension, to psychologically defuse an individual with an excessively high psychoenergetic burden. In some cases, the planned hetero-aggressiveness can indicate the presence of psychopathic traits of personality and self-aggression could be an indicator of depression [10].
Also, certain sources of aggression are related to the personality traits of the individual, while others to external conditions, such as frustration, which is one of the most common triggers of aggression along with the attack or direct provocation, verbal and physical, pain, in its physical and moral forms, heat and crowds. The most common and well-known forms of aggressive behaviour that have a social and community impact are particularly delinquency and crime. Thus, aggression is a behaviour oriented to produce damage, injuries, prejudice and physical harm to objects, people or one's own self. Also, aggressiveness can be manifested in different forms and intensities, from simple ideas and thoughts, physical arousal, anger, competitive type traits and dominance to verbal aggression and serious violence acts.
In this way, the researches on adult aggressive behaviour demonstrated two types of aggression: proactive aggression usually calculated and making use of tools, aimed to obtain rewards and reactive aggression, which is generally impulsive as a stress adaptation response for unexpected events and can be a potential hazard [11].
Epidemiology
There are relatively few studies in the literature regarding the general aggressive behaviour. In this way, in a study from 2000 performed on 1269 patients with various psychiatric disorders, an overall rate for aggressive behaviour of 13.7% was reported. The highest rates in terms of aggressive behaviour occurred more often in patients with bipolar disorders (2.81%) and schizophrenia (1.96%). Moreover, the patients at increased risk were those under 32, with episodes of psychosis or substance abuse [12].
Regarding the way people manifest aggressive behaviour, it is known that this specific behaviour is exhibited in various degrees in individuals varying by gender, age, cultural aspects, as well as by biological and genetic peculiarities or presence of certain affections.
Thus, in children there is an almost constant degree of aggressiveness, manifested either as a healthy assertive, competitive behaviour or as a pathological trait frequently involving violent behaviour, delinquency and criminality. Also, it is known that in boys and males in general, the level of aggressivity is higher than in women and is primarily geared towards persons of the same age. Also, the predisposing precipitating factors for aggressive behaviour in children are different, depending on their age. Thus, in little children the lack of attention and physical discomfort can be causes of violent explosions of anger. Later, insults, criticisms or social comparison are triggers for aggressive behaviour, while in adolescents, the frustrations may be hidden under a rather masked form as breaking rules, stealing, lying, cheating or the need for social dominance [13].
Genetical aspects
The genetic substrate has also a particularly important role in the expression of aggressive behaviour and in the presence or absence of the personality traits associated with aggression. In this way, the studies on twins or adoptions suggested that heredity is involved in aggresivity, in varying proportions (e.g. from 44% to 72%) [14].
However, not a single gene has been identified as to be clearly associated with this type of behaviour, but rather a polygenetic substrate formed from multiple genes that regulate the activity of some neurotransmitters such as serotonin or genes responsible for the structural components of brain areas critical for aggression. Moreover, this genetic polymorphism may contribute to individual differences and susceptibility to aggressive behaviour. Thus, the mutations in the monoamine oxidase (MAO) gene which are associated with the alteration of catecholamines metabolism, or polymorphisms of the genes involved in the metabolism of serotonin, such as tryptophan hydroxylase of the 5-HT1B, the 5-HT2A and 5-HT1A receptor have been identified [15]. Also, one allele of the tryptophan hydroxylase gene was associated with the suicide attempts of violent delinquents and with aggressive behaviour in some patients with personality disorders [16].
Also, the genetic predisposition for aggressivity appears to be deeply affected by genetic polymorphic variants of the serotonergic system affecting the level of serotonin in the central and peripheral nervous system, the biological effects of this hormone, the serotonin production rate, the synaptic release and degradation. In this way, some functional polymorphisms of monoamine oxidase A (MAOA) and the serotonin transporter (5-HTT) are of particular importance considering the connections between the aformentioned polymorphic variants and anatomical changes in the limbic system of aggressive persons. Furthermore, the functional variants of the 5-HTT and MAOA genes can intervene in how the environmental factors influence the aggressive traits [17].
Biological mechanisms for aggressiveness
The neurobiological bases of aggressive behaviour consist from a complex of molecules and neural circuitry designed to convert motivation into action. Thus, the exposure to various frustrating stimuli such as abuse, frustration or hostility can stimulate certain brain region that process emotional and cognitive stimuli and increase psychic excitability. Moreover, it has been shown that impulsiveness and violence are associated with specific brain regions, such as the limbic system. In this way, brain structures which are considered essential in triggering aggressive behaviour are represented by the amygdala, the ventromedial hypothalamus, the limbic system, the motor cortex and orbitofrontal cortex [18]. Moreover, in patients with dementia the level of agitation and aggression are directly proportional to the level of atrophy in certain brain key areas for aggressive behaviour, such as the frontal lobe, amygdala, cingulate gyrus or the hippocampus. Also, the amygdala responds to threats and provocative stimuli by stimulating the motor cortex which further initiates the motor component of the aggressive act [18].
The emotional component is also associated with the cingulate cortex, which analyzes the negative emotional stimuli. Additionally, the amygdala has connections with the hippocampus and is involved in releasing certain factors that have the role of changing the homeostasis of the body (e.g. as to prepare it for action). Also, the system limiting the aggressive behaviour has its origin in the prefrontal cortex and in particular in the orbital prefrontal cortex, inhibiting the limbic regions involved in the generation of the aggressive behaviour [19]. Moreover, in experimental animals, stimulating ventromedial hypothalamus causes aggressivity and inhibits the natural structures responsible for natural aggressivity inhibition [20].
Further evidence is also provided by studies of borderline personality disorder manifested through aggressive behaviour, impulsivity, physical aggression directed towards others, acts of selfmutilation, and family violence, showing changes in the serotonergic system of these patients. Also, a link between temporal lobe epilepsy and violent and impulsive behaviour was described, and an association between aggressive behaviour in patients who have a history of head injury and brain organic changes [21].
Of course, the way in which aggressive behaviour is expressed is also based on the various specific neurotransmitter systems. In this way, the most studied system involved in aggression is the serotonin system. In this way, a decrease in the serotonergic transmission, which can be induced by the inhibition of serotonin production or by antagonizing its effects, determines a decrease of the negative consequences or of the relevance of punishment for a certain type of behaviour. Thus, restoring serotonin through the administration of L-tryptophan (e.g. a serotonin precursor) or drugs that increase serotonin level could strength the behavioural effects of punishment. Moreover, the restoration of serotonergic activity by administration of L-tryptophan or drugs that increase serotonin could perhaps help recover control over violent tendencies [22]. Also, as serotonin seems to facilitate the inhibition of prefrontal cortex, insufficient serotonergic activity can lead to increased aggression. The decrease of serotonin levels, as demonstrated by identifying low levels of a serotonine metabolite, has been associated with impulsiveness and violent behaviour. In this way, studies on the serotonergic neurotransmitter system show that serotonin metabolite, 5-hydroxyindoleacetic acid (5-HIAA) is found in low concentrations in the cerebrospinal fluid in depression and could be accompanied by violence and suicidal behaviour [23].
Moreover, other authors demonstrated that there is a correlation between the level of 5hydroxyindoleacetic from the cerebrospinal fluid and impulsive and violent behaviour. Thus, a low concentration of the serotonin metabolite is found in people with aggressive behaviour. It also seems that a low level of 5-HIAA is present in delinquents or people with a history of violence [24].
In addition, it is believed that low levels of serotonin metabolites seem to be associated with impulsive and aggressive traits. As mentioned before, it seems that serotonin depletion is associated with increased aggressiveness and impulsivity. In this way, a 2013 study on transgenic mice showed that a chronic reduction in the levels of serotonin is associated with increased aggresivity. Moreover, pharmacological intervention on serotonergic neurons, aimed to suppress the neurotransmitter discharge, resulted in increased levels of aggression [25]. These data confirmed the fact that low serotonin activity is decreasing the threshold for aggressive behaviour and supports the idea of a direct association between low serotonin levels and increased aggressiveness.
Another relevant aspect in this matter could be represented by some childhood experiences such as trauma or abuse, in relation to the emergence of serotonergic system abnormalities. In this way,
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some studies showed that sexually abused women are experiencing genetic changes associated with a low activity of monoamine oxidase A allele, a gene involved in serotonin synthesis [26]. Moreover, these women have subsequently increased rates of antisocial traits. Thus, changes in the serotonin system may be actively contributing to strengthening hostile, aggressive and impulsive personality traits, especially when exposed to negative experiences [26]. Also some researchers reported an interaction between genetic, environmental and gender factors, especially during the critical early stages of development, which causes pathological manifestations that reflect changes in serotonin homeostasis. Additionally, the serotonin system involvement in aggressive behaviour could be the outcome of various homeostatic imbalances for the 5-HT system [27].
Also, some clinical studies suggested that the increased reactivity of noradrenergic and dopaminergic system may facilitate aggression. Thus, reduced levels of norepinephrine may be responsible for triggering excessive irritability in response to a stressful, unpredictable factor. Biological, biochemical and genetic investigations of the enzyme responsible for the metabolism of catecholamines, the MAO-A, have also shown that low levels of MAO-A activity are associated with susceptibility to react violently and with impulsive behaviour [28,29]. Moreover, it appears that in males the antisocial characteristics are negatively correlated with the activity of MAO [30].
In addition, the involvement of glutamate in aggressive behaviour has been investigated in several studies, as some theoretical models indirectly link impulsivity and aggression to glutamate. As know, glutamate is the most abundant excitatory neurotransmitter in the vertebrate nervous system and is released from presynaptic vesicles after stimulation of the presynaptic neurons, acting on specific receptors, the N-methyl-D-aspartate (NMDA) receptor, and α-amino-3-hydroxy-5methyl-4-isoxazolepropionic acid (AMPA) [31]. Also, beside the preclinical studies which are suggesting that central glutamate receptors stimulation could increase the aggressive behaviour [32], in some experimental animals the administration of glutamate directly into the central gray matter induces defensive hostility, while the treatment with a glutamate antagonist such as the kynurenic acid results in the same response, of even defensive aggressiveness [33]. In fact, there is also a considerable number of glutamatergic neurons within the projections between the anteromedial hypothalamus and central gray matter, which could represent the structural support for the link between glutamate and aggressiveness [32]. These data are in fact supported by a study published in 2013 showing that there is a positive correlation between the CSF glutamate levels and levels of impulsive aggressivity in human patients [34]. Regarding the endocrine system involvement in the aggressive behaviour, it is believed also that testosterone and mainly its most active metabolite dehydroepiandrostenedione (DHEA) could be implicated. Thus, it was showed that testosterone levels are higher in people with aggressive behaviour, as in the case of the convicts which have committed violent crimes [35]. Also, high levels of testosterone occur in sport teams that have an aggressive, dominant component or in various confrontations [36]. The testosterone acts centrally through the activation of amygdala for example, triggering aggressivity, while peripherally it increase muscle mass to achieve specific motor behaviour. Also, a large number of receptors for androgens and estradiol are found in the neurons from the prefrontal area, hypothalamus and especially the amygdala. Moreover, the effect of testosterone on the brain begins in early embryonic life, leading to anatomic and organizational changes that produce, in fact, the masculinization of the brain. Also, the antiandrogenic agents appear to reduce the level of aggressivity [37].
We should also mention in this context the relevance of the hypothalamic-pituitary-adrenal (HPA) axis, as well as the importance of cortisol and their relations to serotonergic system, which antagonize the effects of testosterone [38,39]. Moreover, it seems that a major role in the increase or decrease of the impulsivity is played by the imbalances between testosterone and serotonin or testosterone and cortisol (e.g. high levels of testosterone and low concentrations of cortisol), which is explicable considering the reduced activity of the control and emotion self-regulating neural circuitry.
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Moreover, hypochloesterolemia has been associated with aggressive behaviour and aggressive suicidal attempts, while lipid-decreasing drugs administration correlates with increased irritability states [40]. Also, experiments on primates have shown that reducing cholesterol through diet could actually lead to increased aggressiveness and reduced activity for the central serotonin, which is associated with the risk of violence in both humans and animals [41].
In addition, there are also studies demonstrating that vasopressin increases aggression behaviour in most mammals including humans. It appears that this effect could be linked to the aforementioned serotonin system effects, manifested through a modulating effect on the aggressive behaviour [42]. Also, some data supports the correlation between the level of CSF vasopressin and a personal history of aggressivity [43]. As already mentioned, vasopressin exerts uninhibitory actions via the serotonin system, aspects which is somehow supported by the fact that vasopressin antagonists can reduce aggressive behaviour [44].
Oxytocin and aggression
Another peptide related to vasopressin is oxytocin (e.g. the vasopressin/oxytocin signalling systems are involved in a variety of functions such as reproduction, immunity and thermoregulation, but with focus on the social manifestations connected with affiliation and aggression [42]) and lately, there is increased interest in understanding the role of (especially intranasal) oxytocin on the main neuropsychiatric disorders such as autism [45], schizophrenia [46], anxiety [47], depression, Prader Willi syndrome [48] or even psychopathy [49] and the variety of behaviours exhibited by the relevant central areas, including aggression [50][51][52].
In this way, it was demonstrated several times, by authors such as Bosch et al., in 2005 that oxytocin is critically involved in the regulation of maternal aggression [53]. Moreover, the aforementioned author did manage to find a very significant correlation between the aggressive behaviour showed as a part of maternal defence in specific behavioural testing and oxytocin release from both the paraventricular nucleus and the central nucleus of the amygdala. Also, mechanistically speaking it seems that the most important aspect in oxytocin modulation for the maternal aggressive behaviour is represented by the differences in the central release patterns of oxytocin [53]. Moreover, in a subsequent publication in 2013 the same group is stressing the aforementioned connection between oxytocin and arginine vasopressin in modulating maternal aggression in rats, developing also a further hypothesis on the commune role of these neuropeptides in anxiety perception and how this can be correlated to maternal aggression [54], considering also the recent reports regarding the significant effects of intransal oxytocin administration in anxiety patients [47].
Interestingly, it was also recently showed that in the high trait aggressive people the administration of oxytocin can result in an increased aggression towards a close person (e.g. intimate or romantic partner), possibility as a way of maintaining the current status/relationship [55]. In this way, in 2014 the group of Nathan DeWall showed in a in a double-blind, placebocontrolled study of high trait aggressive subjects that oxytocin (24 International Units) is actually stimulating aggression only in subjects prone to physical aggression (for example exhibiting behaviours such as hitting or throwing objects etc.) [55].
Similar aspects were also showed in various other experimental species such as dogs, fish or piglets. In this way, the Topal group showed very recently in 2015 that dogs receiving intranasal oxytocin showed less friendly first reaction and individual differences in aggression to an unfamiliar experimenter, as compared to placebo, in a specific design behavioural task for dogs called Threatening Approach Test [50].
Also, in piglets, which are considered to be much more closer in brain anatomy, growth and development to the human brain, as compared for example to the classical rodent models [56], the group of Rault et al. in Australia showed that neonatally oxytocin-treated piglets received and performed more aggressive behaviours, then the controls, possibly by reducing the HPA axis [51].
Moreover, in fish such as Neolamprologus pulcher, a cooperatively breeding cichlid fish, Hellmann group showed also in 2015, that after temporarily removing a subordinate individual, it
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was more likely for this to receive increased aggression, when returned back to the group, if it was treated with isoctocin, an analogue version of oxytocin, versus the treatment with saline [52]. However, there are still a lot of controversial results in this area of research, since for example other groups failed to find any significant effects for the administration of the intranasal oxytocine on aggressive behaviour for example in subjects with antisocial personality disorder. In this way, in a recent study from September 2015 it was found that intransal oxytocine generated little effects in aggression and anyway not related to dose of oxytocin administrated, as judged by a well-validated laboratory task of human aggression called point subtraction aggression paradigm [57].
Even more, it was showed that in non-lacting female rats (so outside the already classical and well known perspective that oxytocin is implicated in defensive maternal aggression [53,54]) there is surprising potential for an anti-aggressive effect of synthetic oxytocin administration, as determined through an original behavioural approach such as novel female resident-intruder test for spontaneous female aggressive behaviour [58]. In fact, there are previous correlative studies showing a significant correlation between reduced oxytocin concentration in the cerebrospinal fluid of some patients and aggressive behaviour [59], while some authors are strongly believing that the aforementioned intranasal administration of oxytocin is exhibiting pro-social behaviours [60].
There are authors stating that these different effects of oxytocin on aggression, but also on other related superior behaviours, could be explained by the different ways of oxytocin administration (peripheral vs. intranasal), different dosage or by different experimental setup (e.g. looking at outor in-groups members or which is the basic level of aggressive responding in that individual) [50,[61][62][63].
Conclusions
Thus, it seems that violent behaviour and impulsive traits present a multifactorial substrate that is determined by genetic and non-genetic factors. In this way, aggressivity is regulated by brain regions such as the amygdala, which controls neural circuits for triggering defensive, aggressive or avoidant behaviour, while the dysfunction of certain neural circuits responsible for emotional control seems to induce violent behaviours. Moreover, besides the amygdala, other brain structures such as the anterior cingulate cortex and prefrontal cortex regions seem to modulate circuits involved in aggressive behaviour. Regarding the genetic aspects, we could also mention the mutations in the monoamine oxidase or the polymorphisms of the genes involved in the metabolism of serotonin, such as tryptophan hydroxylase. Also, besides the low levels of serotonin metabolites which seem to be associated with impulsive and aggressive traits it seems that reduced levels of glutamate, as well as testosterone, vasopressin hypochloesterolemia or oxytocin modifications could be related to the aggressive behaviour.
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Domain: Psychology Biology
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Regulation of Morphological and Functional Aspects of Sexual Dimorphism in the Brain
Sexual dimorphism of the adult brain regulates sex-dependent functions including reproductive and neuroendocrine activities in rodents. It is determined by sex steroid hormones during a critical perinatal period in female and male rodents. Sex steroids act on each nuclear receptor in the brain and control different physiological and neuroendocrine functions and behaviors. Several regions of the brain show evident morphological sex differences that are involved in their physiological functions. This review addresses and focuses largely on the role of sexdependent differences in the brain, and their crucial functions in animal models. Particularly, recent intriguing data concerning the diversity of neuronal functions and sexual dimorphism are discussed.
Introduction
Sexual dimorphism is characterized by morphological and physiological changes driven by sex steroids. In the rodent brain, it occurs during a critical period characterized by higher plasticity of neurons allowing changes in neuronal circuits and connectivity. For instance, hormonal manipulation during this time window, such as castration in males or replacement therapy in females (injections of androgen or estrogen), resulted in the conversion of intrinsic features and alteration of structures and functions of neural circuits in the brain. In rodents, critical time span for brain sex differentiation extends from embryonic day (ED) 18 to the postnatal day 10 [1], while in human it is exclusively embryonic day (ED12 -22). Post this critical period, neuronal plasticity is lost and the effects of sex steroids can be diverted to activational effects in the brain. The mechanisms involved in defining the timing and duration of the neonatal critical period for the brain sexual differentiation remains to be determined. It is proposed that epigenetic modifications such as DNA methylation and histone acetylation might control the expression of genes implicated in brain sexual dimorphism [2].
The neuroendocrine systems, which control the action of sex steroids, including that on neural circuits, are differentiated in a sex-dependent manner, resulting in the regulation of reproductive and sex-specific behaviors. The actions of sex steroids in masculinization and feminization of the brain are mediated by steroid hormone receptors. In both human and nonhuman primates, young male and females show sex differences in toy preferences [3,4]. Girls with congenital adrenal hyperplasia (CAH) show to preference toward toys of males and to have decreased female-typical behavior [5]. These results argue that behavioral sex differences are caused by sex steroids. Estrogen is produced locally in the brain from testosterone by the aromatase cytochrome P450 enzyme [6,7] and affects sexual differentiation by biding to estrogen receptor (ER) in rodents. Maternal and fetal estrogen can be bound by the α-fetoprotein produced by fetal liver cells and yolk-sac cells, thereby preventing their passage through the blood-brain barrier [8]. This mechanism results in female brain being free from estrogen. In contrast, in males, testosterone crosses the bloodbrain barrier and is converted by aromatase to elicit sexual differentiation of the brain [6,7,9]. The effects of testosterone and its enzymatic derivative, estradiol, on their receptors are therefore critical for the sexual differentiation of the brain.
Sex-specific differences in the anteroventral periventricular nucleus
The crucial role of estrogens in the sexual differentiation of the brain is mediated by estrogen receptors subtypes ERα [10,11] and ERβ [12]. The amount of steroid hormone receptors differentiated and available development differs between sexes. The anteroventral periventricular nucleus (AVPV) is greater in size and cell number in females than in males [13,14]. In the AVPV, the distribution of ERα is similar in both sexes, but its expression levels are higher in females than in males in prepubertal and adult rats [15]. In contrast, the distribution pattern for ERβ detected by nonisotopic in situ hybridization and immunohistochemistry is different between sexes [16]. Specifically, in females, a vast majority of ERβ-positive cells is located in the most medial portion of the AVPV, whereas the ERβ-containing cells in males are dispersed more laterally in the AVPV (Figure 1). The distribution of ERβ is reversed by neonatal hormonal manipulations [16]. Therefore, sex-specific physiological functions are predictable for sexual dimorphism in the AVPV.
ERβ sexual dimorphism in the AVPV
Steroid-mediated organization of the brain might involve cell apoptosis, cell migration, neurogenesis, cell differentiation and synaptogenesis. Estrogen and androgen induce programmed cell death [17] by the sequential activation of cysteine-dependent asparate-specific proteases (caspase) during the development of the hypothalamus [18] in the dimorphism of dopaminergic neurons in the AVPV [19][20][21]. The total number of ERβ-positive cells within the AVPV is not different between intact females and males [15,22]. This is assumed to be caused by mechanisms other than apoptosis namely the sexual dimorphic expression of ERβ in the AVPV. The sexual dimorphic features of the brain caused by sex steroids do not always coincide with larger nuclei exclusively in one sex. Indeed, a region-specific ERβ gene expression is observed in the AVPV [22]. Moreover, the steroids might act on specific regions in the brain [22]. In brain slices from developing mouse brain, estradiol but not dihydrotestosterone induces and modulates neuronal migration [23,24]. These results suggest that sexual dimorphism of ERβ in the AVPV might contribute to migration rather than apoptosis or neurogenesis.
Functional implications in ERα and ERβ localization in the AVPV
In the AVPV of female rats, a majority of ERβ-positive cells also express ERα [16]. It has been shown that ERα together with kisspeptin regulates ovulation, while ERβ is rather modified by these events [25]. At the molecular level, ERs bind to an estrogen responsive element (ERE) [26] after heterodimer formation [27], which allows the integration and collaboration of various signaling pathways for the completion of ovulation. The experimental infusion of antisense oligonucleotides in females results in decreased ERβ expression in the AVPV and consequently a persistent estrous [16]. Moreover, ERβ-positive cells and dopaminergic neurons have comparable distribution patterns in the AVPV [16]. Both ERα and ERβ have a role in the sexual dimorphism of dopaminergic neurons in the AVPV in both sexes [16,28]. The secretion of luteinizing hormone (LH) is controlled by dopaminergic projections to neurons producing the gonadotropin-releasing hormone (GnRH) [29]. The cycle of female rats stalls ovulation state by small lesions of the AVPV [30]. Altogether, these data suggest that ERα and ERβ are colocalized with GnRH and are involved in LH secretion [31,32]. In particular, ERα exerts a positive role for GnRH neurons, while ERβ exerts a negative control of those neurons [25]. Nonclassical ERE-independent ERα effects are involved in negative regulation on pulsatile GnRH secretion, while ERβ effects are involved in positive regulation on that secretion [31,33]. It is still controversial to
Sexual dimorphism in the AVPV. ERβ positive cells aggregated densely in females (A-C), whereas the ERβcontaining cells in males (D-F
) dispersed more laterally in the AVPV in the AVPV. Scale, 100 μm. From [16].
regulate GnRH neurons by ERs. Considering the inherent male distribution pattern of ERβ, a peculiar characteristic of the dopaminergic innervation in the AVPV [28] might be responsible for the GnRH secretion in the brain of males.
Formation of the sexually dimorphic nucleus in the preoptic area
The sexually dimorphic nucleus in the preoptic area (SDN-POA) was first characterized by Nissl staining, revealing in a larger volume in the brain of male rats than that in the brain of female rats [1,34]. The volume of this nucleus is altered by gonadal steroids during the perinatal critical period [1]. Somatostatin might also be involved in sexual dimorphism in the SDN-POA. Indeed, during development, cells positive for somatostatin are expressed in a sex-dependent manner in the SDN-POA. Sex reversal of the dimorphism of somatostatin expression is observed in orchidectomized males and estrogen treated female pups [35]. The somatostatin mRNA-positive cells are significantly more in males than in females, but eventually the difference recedes. Somatostatin expression in females is steady during the postnatal development. The transcription of somatostatin is transient and seems to contribute to the development of the SDN-POA. Somatostatin might prompt neuronal differentiation and survival via the somatostatin receptor.
Immunostaining against calbindin D28k, a major cytoplasmic calcium-binding and buffering protein, has been successfully used to identify the rat hypothalamus [36], SDN-POA [35,37] and provides an alternative to Nissl staining [37]. Distribution of calbindin-labeled cells in the SDN-POA is similar to somatostatin in both sexes. It has been suggested that apoptosis has a role in sexual differentiation of the SDN-POA [38]. However, no difference in the total numbers of calbindin positive cells was observed in the SDN-POA after perinatal administration of bromodeoxyuridine in both sexes [39]. On the contrary, in the postnatal SDN-POA, these neurons still show an aggregated distribution in females, while they are dispersed laterally in males [39]. Altogether, these data suggest that, besides apoptosis, cell proliferation and migration might contribute to the morphological difference in the rat SDN-POA. Moreover, ERα are reported to be expressed in the SDN-POA [40], suggesting the presence of estrogenic action in the SDN-POA sexual dimorphism.
Moreover, Nissl stained SDN-POA had not been reported in mouse until recently identified by calbindin immunohistochemistry [41] (Figure 2). The morphological sexdependent differences of the mouse SDN-POA were first demonstrated and established in terms of morphology and linked to gonadal steroid hormones during the prenatal critical period. Male mice have a greater number of calbindin-positive cells than females [41]. Similar differences within medial POA/anterior hypothalamic area (AHA) are observed in sheep, which are smaller in females than in males [42]. The volume of this nucleus in males is smaller in male-oriented than in female-oriented individuals. In humans, interstitial nuclei of the anterior hypothalamus (INAH) are considered comparable to those of rodent and sheep. The INAH is smaller in females than in males and smaller in homosexual men than in heterosexual men [43]. These results suggested that the sexual dimorphic nucleus in the two species is involved in sexual orientation. The male mice copulatory behavior and the preference for females is attributed to this difference in the SDN-POA [44][45][46][47]. Further functional analysis is required to completely understand the mechanisms involved in the sexual dimorphism of the SDN-POA.
Sexual dimorphic expression and function of ERs in the preoptic area
In the preoptic area (POA), ERα expression is much higher in females than in males [48]. This sex difference occurs during the perinatal period. After birth, the Regulation of Morphological and Functional Aspects of Sexual Dimorphism in the Brain DOI: [URL] of ERs is down regulated in the POA by estrogen [49]. The decreased ERα expression occurs in both sexes but the differential expression in the POA between females and males persists throughout life. Although the ERα levels
Sexual dimorphism in the SDN-POA. Calbindin (CB)-immunoreactive cells in the mouse SDN-POA in males (A-E) and in females (F-J) in the rostral-caudal direction. In males (B-D), but not females, a cell aggregate of CB-positive cells is prominent.
Sale, 400 μm. From [41].
are higher in females than in males, a comparable distribution pattern of ERα is observed [16,48]. The POA has been implicated to be involved in steroid activation of the male copulatory behavior [50]. In particular, dopamine neurons in the mPOA prompt male sexual behavior [51]. ERα and oxytocin containing neurons in the mPOA participate to control copulatory behavior in male rats [52][53][54]. In females, the POA and the adjacent bed nucleus of the stria terminalis (BNST) is considered essential for controlling maternal [55][56][57][58] and mating behaviors [59]. ERα in the mPOA is involved in the regulation of maternal care, maternal aggression and sexual behavior [56]. ERβ is detected by in situ hybridization and immunohistochemistry in the medial preoptic nucleus (mPOA) and more caudally in the BNST [16] (Figure 3). In males, ERβ in the mPOA is involved in aggressive behavior [60]. Overall in rodents, identical brain regions control specific behaviors depending on the sex. Recently, it is shown that the male-typical mounting behavior and female-typical pup retrieval behavior are induced by ERα located in the same region of the POA [61]. These data suggest that the sex specific neural circuits are able to control opposite behaviors. Therefore, sex-typical behaviors are likely induced by the harmonic expression of sex specific receptors together with sex steroid. Besides the neural circuit with a high degree of plasticity in the sexual dimorphic nervous system assuring precise sex-specific behavior events, there may be a possible the involvement of circumstances in ensuring responsiveness of the sex steroids.
Functional diversity of the ventromedial hypothalamus
The volume of the ventromedial hypothalamus (VMH) is larger in males than in females [62,63]. ERα and ERβ are expressed in the VMH of rodents [22,48]. However, ERα expression is abundant in the ventrolateral portion of the VMH and is higher in female rats than in male rats [48]. The sex difference is most likely due to the conversion of testosterone into estrogen, which downregulates ERα expression. Moreover, the aromatase signal in males is more robust than in females [64]. A sex difference in ERβ expression is observed in both postnatal day 14 and in the adult rat brain, indicating that the sexual dimorphism is also maintained throughout life [22]. ERβ expression in the adult VMH is downregulated by estrogen or testosterone administration. The difference in expression is reversed by administration of estrogen in female rats or orchidectomy male rats. This sexual dimorphism is entirely attributable to the effects of sex steroids on the brain organization and plasticity during the critical neonatal period of the brain. Estrogen, converted from circulating androgen in males, downregulates ERα and ERβ expression in the VMH [22,48] and consequently physiological functions. Estrogen together with progesterone in the VMH induces female sexual reproductive behavior such as lordosis, sexual receptivity and odor preference [65].
In adult males, the expression of ERα is lower than that in females. Cells in the male VMH are activated during fighting [66]. In these processes, ERα is involved in sexual [67] and aggressive behaviors in mice [68,69], whereas ERβ is assume to be inhibitory to the aggressive behavior [68]. Other studies have demonstrated that male sexual behavior is not affected by ERβ in the VMH [70], but is profoundly regulated by ERα and the androgen receptor (AR), suggesting a possible distinct role for ERβ and ERα on each behavior. Opposing social behaviors, such as mounting and attack, are regulated by ERα [67] or progesterone receptor [71] cells located in discrete regions of the VMH. Sex steroid receptor expression in the VMH is induced by environmental hormonal milieu during the critical period and in turn controls the dynamic action of the sex hormones on sex-specific behavior in adults [66,72]. These data suggest that males and females seem to exhibit identical neural circuits in the VMH, but the activated receptors might contribute to inducing the sex-typical behavior. The sex-specific neural circuit dictated by sex steroids could work in conjunction with estrogen-mediated ERs.
Alternative mechanism for sexual dimorphism in the medial amygdala
The medial amygdala (MeA) is larger volume in males than in females [73] and this difference is abolished after castration in males and androgen treatment in females [74]. In adults, the size and volume of neurons is modified by circulating androgen. After castration of adult male rats, the cell soma size in the posterodorsal MeA (MePD) is similar to the one observed in females [74]. However, the number of MeA neurons in both sexes is not affected by adult androgens [75]. Steroid hormones also influence the organization of the MePD during the neonatal period [76,77] and its metabolite, estrogen, results in masculinization of the MePD. ERα and ERβ are abundantly expressed in the MePD [22,48,78,79] where the aromatase enzyme is also detected [80,81]. ERs mediate estrogen-induced modifications in the MePD associated with masculinization and male-specific behaviors [82]. However, the masculinization in the MePD in the adult brain is mostly driven by circulating androgen [82]. Both the action of ER [82] and AR [83] in the MePD on the size of neuronal somas and in the sexual behavior mostly occurs in the adults [82]. In adults, there is no sex-dependent difference in ER subtype and expression in the MePD, but there is a sexual dimorphic expression of ERβ but not ERα in newborns [84]. Neonatal hormonal manipulations could not reverse the sex differences in ERβ in both sexes, suggesting that ERβ-mediated estrogen actions are not involved in the sexual dimorphism in the MePD. Furthermore, ERβ is highly expressed in the MePD of adult female and male rats and is not affected by gonadectomy or estrogen treatment in both sexes [22]. Therefore, the ERβ expression also acts independent of activity in this structure.
In the MePD, sexual dimorphism involves mechanisms distinct from other regions of the brain. The MePD receives inputs from the olfactory and pheromonal systems, suggesting a functional role of this structure in sex arousal and regulation of adult social behaviors, including mating, aggressive [85,86], and territorial behavior [87]. Acquisition of mating stimuli induces Fos in the ERs in the MeA [88]. Finally, the mechanisms induced by the ERs in the MeA and those involved in sexual stimuli [89], gonadotropin secretion [90], ovulation [91] sex and courtship behaviors [87], onset of puberty [92], parenting, and reproduction [85,89,93] still remain to be identified.
Conclusion
Sexual dimorphism is characterized by morphological differences in several regions of the brain. Morphological sex differences in the POA/AHA and the INAH were revealed in sheep and human brains, which are assumed to be important for determining sexual orientation. Expression of the phenotypes i.e., behavioral sex differences, are suggested to be derived from morphological sex differences in the brain. However, the morphological sex differences are subtly evident in other human brain regions; hence, their association with functional sex differences in the human brain remains controversial. Consequently, CAH results in masculinized female brain, thereby leading to male-typical preferences, which are the congenital characteristics inherently caused by steroid and not acquired by learning. Striking sex differences in animal models contribute in establishing the mechanisms of sexual dimorphism in the brain of all living beings.
ER expression levels contribute substantially to the physiological and behavioral differences. However, the extent to which the amounts of ER control the development of sexual dimorphism remains to be clarified. Sex-specific neural circuits activated by sex steroids might contribute to the functional role of ERs activated by estrogens. Recently it was evidenced that a high neuronal plasticity rate in neural circuits is necessary to ensure precise sex-specific responsiveness to sex steroids. The mechanism involved in the regulating the local action of sex steroids remains to be elucidated. Particularly, the expression and regulation of genes implicated in sexual dimorphism must be investigated.
Figure 1. Sexual dimorphism in the AVPV. ERβ positive cells aggregated densely in females (A-C), whereas the ERβcontaining cells in males (D-F) dispersed more laterally in the AVPV in the AVPV. Scale, 100 μm. From[16].
Figure 2. Sexual dimorphism in the SDN-POA. Calbindin (CB)-immunoreactive cells in the mouse SDN-POA in males (A-E) and in females (F-J) in the rostral-caudal direction. In males (B-D), but not females, a cell aggregate ofCB-positive cells is prominent. Sale, 400 μm. From[41].
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Domain: Psychology Biology
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Altered Sensory Neuron Development in CMT2D Mice Is Site-Specific and Linked to Increased GlyRS Levels
Dominant, missense mutations in the widely and constitutively expressed GARS1 gene cause peripheral neuropathy that usually begins in adolescence and principally impacts the upper limbs. Caused by a toxic gain-of-function in the encoded glycyl-tRNA synthetase (GlyRS) enzyme, the neuropathology appears to be independent of the canonical role of GlyRS in aminoacylation. Patients display progressive, life-long weakness and wasting of muscles in hands followed by feet, with frequently associated deficits in sensation. When dysfunction is observed in motor and sensory nerves, there is a diagnosis of Charcot-Marie-Tooth disease type 2D (CMT2D), or distal hereditary motor neuropathy type V if the symptoms are purely motor. The cause of this varied sensory involvement remains unresolved, as are the pathomechanisms underlying the selective neurodegeneration characteristic of the disease. We have previously identified in CMT2D mice that neuropathy-causing Gars mutations perturb sensory neuron fate and permit mutant GlyRS to aberrantly interact with neurotrophin receptors (Trks). Here, we extend this work by interrogating further the anatomy and function of the CMT2D sensory nervous system in mutant Gars mice, obtaining several key results: (1) sensory pathology is restricted to neurons innervating the hindlimbs; (2) perturbation of sensory development is not common to all mouse models of neuromuscular disease; (3) in vitro axonal transport of signaling endosomes is not impaired in afferent neurons of all CMT2D mouse models; and (4) Gars expression is selectively elevated in a subset of sensory neurons and linked to sensory developmental defects. These findings highlight the importance of comparative neurological assessment in mouse models of disease and shed light on key proposed neuropathogenic mechanisms in GARS1-linked neuropathy.
INTRODUCTION
Characterized by distal dysfunction of motor and sensory nerves, Charcot-Marie-Tooth disease (CMT) is a hereditary peripheral neuropathy that usually presents in adolescence and affects 1 in 2,500-5,000 people, which makes it the most common inherited neuromuscular condition (Pipis et al., 2019). Classically, the disease can be categorized as CMT1, typified by demyelination and thus reduced nerve conduction velocity, CMT2 in which there is axon loss but no diminished nerve conduction velocity, and intermediate CMT that shares features of both CMT1 and CMT2 (Reilly et al., 2011). Consistent with length-dependency, patients display slowly progressive, bilateral muscle weakness and sensory deficits predominantly in the extremities, typically starting in the feet. CMT is on a phenotypic spectrum with distal hereditary motor neuropathy (dHMN) and hereditary sensory/autonomic neuropathy (HSN/HSAN), which have mainly motor and sensory/autonomic involvement, respectively, and can be caused by mutations in the same gene (Pisciotta and Shy, 2018).
To date, mutations in more than 100 different genetic loci have been linked to CMT (Rossor et al., 2013;Pipis et al., 2019). Many causative CMT1 genes are selectively expressed by myelinating Schwann cells or have myelin-specific functions, providing mechanistic justification for pathology. However, CMT2-associated genes are involved in a variety of processes critical to general cell viability (e.g., mitochondrial dynamics, endolysosomal sorting, ubiquitination, heat shock response), and the pathomechanisms underlying neuronal selectivity remain relatively obscure. Fitting with this, the widely and constitutively active aminoacyl-tRNA synthetase (ARS) enzymes, which covalently bind specific amino acids to their partner tRNAs for protein translation (Ibba and Soll, 2000), represent the largest protein family implicated in CMT etiology. To date, dominant mutations in six ARS-encoding genes (GARS1, YARS1, AARS1, HARS1, WARS1, and MARS1) have been linked to CMT with varying degrees of evidence for pathogenicity (Wei et al., 2019).
Encoding glycyl-tRNA synthetase (GlyRS), which charges glycine, GARS1 is the first and best-studied ARS gene linked to CMT (designated CMT type 2D, CMT2D, OMIM: 601472; Antonellis et al., 2003). Uncharacteristically and contravening length-dependency, CMT2D patients frequently display upper limb predominance with weakness beginning in dorsal interosseus muscles of the hand and progressing to involve lower limbs in about only half of patients (Antonellis et al., 2018;Sivakumar et al., 2005). Genetic studies across yeast, Drosophila melanogaster and mouse models for CMT2D indicate that, although neuropathy-causing mutations can abolish canonical GlyRS function and loss-of-function pathogenesis hypotheses prevail (Meyer-Schuman and , the disease is most likely caused by a toxic gain-of-function (Boczonadi et al., 2018;Wei et al., 2019). Commensurate with mutant protein toxicity, wild-type GARS1 overexpression in CMT2D mice has no discernible rescue effect on neuromuscular pathologies, while the increased dosage of disease-causing Gars alleles causes more severe neuropathy (Motley et al., 2011). Moreover, all assessed GlyRS mutants possess a similar conformational opening that excavates neomorphic surfaces usually buried within the structure of the wild-type enzyme (He et al., 2011(He et al., , 2015. Given that GlyRS is secreted from several different cell types (Park et al., 2012(Park et al., , 2018Grice et al., 2015;He et al., 2015), these uncovered protein regions can mediate aberrant deleterious interactions both inside and outside the cell (He et al., 2015;Sleigh et al., 2017a;Mo et al., 2018), likely accounting for non-cell-autonomous aspects of pathology (Grice et al., 2015(Grice et al., , 2018. While some of these mis-interactions are with neuronallyenriched proteins, the pathomechanisms underlying neuronal selectivity in CMT2D remain unresolved. Nevertheless, recent studies indicate that impairments in the processes of axonal transport (Benoy et al., 2018;Mo et al., 2018) and protein translation (Niehues et al., 2015) may be playing a causative role.
Several different mouse models are available for CMT2D (Seburn et al., 2006;Achilli et al., 2009;Morelli et al., 2019), which have mutations in endogenous mouse Gars, causing phenotypes akin to human neuropathy. These mice display loss of lower motor neuron connectivity and disturbed neurotransmission at the neuromuscular junction (NMJ), causing muscle weakness and motor function deficits (Sleigh et al., 2014a;Spaulding et al., 2016). Furthermore, there appears to be a pre-natal perturbation of sensory neuron fate in dorsal root ganglia (DRG), such that CMT2D mice have more nociceptive (noxious stimulussensing) neurons and fewer mechanosensitive (touch-sensing) and proprioceptive (body position-sensing) neurons (Sleigh et al., 2017a). Perhaps causing this and providing a rationale for neuronal selectivity, mutant GlyRS mis-interacts with the extracellular region of tropomyosin receptor kinase (Trk) receptors. These largely neuron-specific transmembrane proteins mediate the development and survival of sensory neurons by binding with differential affinity to neurotrophins secreted from distal target cells/tissues (e.g., Schwann cells and muscles; Huang and Reichardt, 2003). Activated neurotrophin-Trk receptor complexes are internalized in the periphery, sorted into signaling endosomes, and then retrogradely transported along microtubules to neuronal somas, where they elicit transcriptional events fundamental to nerve survival (Villarroel-Campos et al., 2018).
The earliest manifestation of CMT2D in many individuals is transient cramping and pain in the hands upon cold exposure (Antonellis et al., 2018). In addition to muscle weakness, this is followed by compromised reflexes and loss of sensation to vibration, touch, temperature, and pin-prick (Sivakumar et al., 2005). Some of these symptoms are reflected in the phenotypes observed in Gars-neuropathy mice, highlighting their potential for studying sensory pathomechanisms (Sleigh et al., 2017a).
However, the motor symptoms of CMT2D patients are the focus of the clinical investigation, given their relative severity. Moreover, GARS1 neuropathy patients can show little to no sensory involvement and are thus diagnosed with dHMN type V (OMIM 600794; Antonellis et al., 2003). The pathological impact of mutant GlyRS on the sensory nervous system is therefore relatively under-studied and requires further attention if we are to elucidate the cause of its varied involvement in GARS1linked neuropathy. Here, we have thus extended our sensory analyses in CMT2D mice to better understand the importance of anatomical location to pathology and to assess the relevance of some proposed disease mechanisms in afferent nerves.
Animals
All experiments were carried out following the guidelines of the UCL Queen Square Institute of Neurology Genetic Manipulation and Ethics Committees and following the European Community Council Directive of 24 November 1986 (86/609/EEC). Gars C201R/+ (RRID:MGI 3849420) and SOD1 G93A (RRID:IMSR_JAX 002726) mouse handling and experiments were carried out under license from the UK Home Office following the Animals (Scientific Procedures) Act 1986 and were approved by the UCL Queen Square Institute of Neurology Ethical Review Committee. Gars Nmf249/+ (RRID:MGI 5308205) tissue was provided by Drs. Emily Spaulding and Robert Burgess (The Jackson Laboratory, Bar Harbor, ME, USA), as previously described (Sleigh et al., 2017a). Gars C201R/+ and Gars Nmf249/+ mice were maintained as heterozygote breeding pairs on a C57BL/6J background and genotyped as previously described (Seburn et al., 2006;Achilli et al., 2009). Both males and females were used in the analyses of mutant Gars alleles, as no clear sex-specific differences have yet been observed or reported. Genotyped using standard procedures (Gurney et al., 1994), transgenic male mice heterozygous for the mutant human SOD1 gene (G93A) on a mixed C57BL/6-SJL background [B6SJLTg (SOD1 * G93A)1Gur/J] and wild-type male littermate controls were used for the SOD1 G93A experiments. Gars C201R/+ mice sacrificed for 1-month and 3-month timepoints were 29-37 and 89-97 days old, respectively. The Gars Nmf249/+ mice used at 1 month were P31-32, while SOD1 G93A mice were P30-31 and P100-101.
Tissue Dissection
DRG were extracted from either non-perfused or saline-perfused mice as previously described (Sleigh et al., , 2020b. The most caudal pair of floating ribs and the large size of lumbar level 4 (L4) DRG and associated axon bundles were used as markers to consistently and accurately define the spinal level. The forepaws of embryonic day 13.5 (E13.5) embryos were removed between the wrist and elbow joints, as outlined elsewhere (Wickramasinghe et al., 2008).
Culturing Primary DRG Neurons
Twenty to twenty-four lumbar to thoracic DRG ( Figure 1B) were dissociated and cultured on 35 mm glass-bottom dishes (MatTek, P35G-1.5-14-C) in the presence of freshly added 20 ng/ml mouse glial cell line-derived neurotrophic factor (GDNF, PeproTech, 450-44) as detailed elsewhere (Sleigh et al., 2017a). To reduce variability, a wild-type and Gars C201R/+ littermate of the same sex were dissected and cultured in parallel for each experimental replicate.
In vitro Signaling Endosome Transport Assay
The atoxic binding fragment of tetanus neurotoxin (H C T) was bacterially expressed and labeled with the AlexaFluor647 antibody labeling kit (Life Technologies, A-20186) as previously outlined (Gibbs et al., 2016). Twenty-four hours post-plating of dissociated DRG neurons, H C T-647 was added to the neuronal media at a final concentration of approximately 1.5 µg/ml (30 nM), before gentle mixing by rotation and returning to 37 • C for 25 min. H C T-containing medium was then aspirated, the cells were washed with 2 ml pre-warmed medium, before being slowly flooded with 2 ml standard medium containing all supplements. Within 10-90 min of the media change, endosome transport was imaged on an inverted LSM780 laser scanning microscope (Zeiss) inside an environmental chamber pre-warmed and set throughout the experiment to 37 • C. An area containing a single neuronal process retrogradely transporting fluorescent endosomes was imaged using a 63× Plan-Apochromat oil immersion objective (Zeiss). Images were taken at 100× digital zoom (1,024 × 1,024, 1% laser power) every 2.4 s on average. Before selecting a neuronal process for analysis, it was first traced back to the cell body to confirm the directionality of transport and imaged for area measurement (see below). Cultures from wild-type and Gars C201R/+ mice were imaged in the same session and, to avoid introducing time-dependent biases, their order was alternated across replicates. Two males and two females of each genotype were analyzed at each timepoint.
Endosome Transport Analysis
Individual endosomes were manually tracked using Tracker (Kinetic Imaging Limited) as described previously (Sleigh et al., 2020a). Briefly, endosomes were included in the analysis if they could be observed for five consecutive frames and did not pause for >10 consecutive frames. Endosomes moving solely in the anterograde direction were infrequent and not included in the analysis. Individual frame-to-frame step speeds are included in the frequency histogram (an average of 2, 370 ± 135 frame-to-frame speeds per animal), meaning that an endosome tracked across 21 consecutive frames will generate 20 frame-to-frame speeds in this graph. To determine the endosome speed per animal, individual endosome speeds were calculated, and then the mean of these determined (an average of 95.1 ± 3.5 endosomes per animal). All speed analyses include frames and time during which endosomes may have been paused, i.e., the speed across the entire tracked run length is reported and not the speed solely when motile. An endosome was considered to have paused if it remained in the same position for two or more consecutive frames. The ''% time paused'' is a calculation of the length of time all tracked endosomes remained stationary, while the ''% pausing endosomes'' details the proportion of endosomes that displayed at least one pause while being tracked. An average of 26.2 endosomes was tracked per neuron, and at least three individual neurons were assessed per animal replicate.
Image Analysis
Cell body areas of neurons analyzed in endosome transport assays were measured using the freehand tool on ImageJ 1 to draw around the circumference of the somas. The diameters of neuronal processes imaged for transport were measured in ImageJ using the straight-line tool. The average of five measurements across the width of the process was calculated.
To determine in which cells GlyRS levels were highest in Gars Nmf249/+ lumbar DRG sections, all cells with increased GlyRS expression were first identified by eye in the single fluorescence channel. Cells positive for NF200 were then independently designated in the second channel. The percentage of GlyRSelevated cells also positive for N200 was then calculated. Similarly, the percentage of NF200 + cells without an increase in GlyRS was also determined. All sections used for GlyRS analysis were stained and imaged in parallel with the same confocal settings to permit side-by-side comparison.
Statistical Analysis
Data were assumed to be normally distributed unless evidence to the contrary could be provided by the D'Agostino and Pearson omnibus normality test. GraphPad Prism 8 (version 8.4.0, La Jolla, CA, USA) was used for all statistical analyses. Means ± standard error of the mean are plotted, as well as individual data points in all graphs except for those depicting western blot densitometry. Unpaired t-tests and two-way ANOVAs were used throughout the study. Rather than ANOVAs, unpaired t-tests were used to analyze the percentages of NF200 + and peripherin + neurons separately, because the two markers are not independently expressed. Similarly, western blot densitometry was also analyzed using unpaired t-tests; since expression was calculated relative to wild-type levels for each individual protein, the expression of proteins in wild-type animals are not statistically comparable.
Altered Sensory Development Occurs Specifically in Lumbar Segments of CMT2D Mice
In previous work, we showed that sensory neuron fate is altered during development in the mild Gars C201R/+ and more severe 1 [URL]/ Gars Nmf249/+ mouse models for CMT2D, the extent of which correlated with overall model severity (Sleigh et al., 2017a). In that study, by co-staining DRG for NF200, a marker of medium-large area mechanosensitive/proprioceptive neurons, and peripherin, which identifies small area nociceptive neurons ( Figure 1A), we determined that mutant Gars DRG had fewer touch-and body position-sensing (NF200 + ) neurons and a concomitant increase in noxious stimulus-sensing (peripherin + ) neurons. We reported that this phenotype was present at birth and did not change up to 3 months of age, suggesting it is developmental in origin and non-progressive. Ganglia assessed in these original experiments were isolated from lumbar level 1 (L1) to L5, which contains neurons that innervate the lower leg (Mohan et al., 2014); however, CMT2D patients frequently display upper limb predominance (Antonellis et al., 2018).
To determine whether the phenotype is also observed in forelimb-innervating ganglia (Tosolini et al., 2013), we isolated and immunohistochemically analyzed cervical level 4 (C4) to C8 DRG from 1-month-old wild-type and Gars C201R/+ mice ( Figure 1B). Co-labelling DRG for NF200 and peripherin ( Figure 1C) and calculating the percentages of neurons expressing each marker, we saw no difference between genotypes ( Figure 1D). Corroborating this, western blotting of cervical DRG lysates showed no difference in NF200 or peripherin protein levels (Figures 1E,F). Together, these data indicate that there is no impairment in sensory neuron identity in the C4-C8 ganglia of Gars C201R/+ mice.
To confirm and extend the lumbar phenotype, we assessed levels of the protein TrkB, which binds brain-derived neurotrophic factor (BDNF) and neurotrophin-4 (NT-4) to ensure the survival of a mechanosensitive sub-population of NF200 + neurons (Montaño et al., 2010). We found that lumbar ganglia of Gars C201R/+ mice have less total TrkB, consistent with there being fewer NF200 + neurons in the mutant DRG, whereas C4-C8 ganglia showed no difference (Supplementary Figure S1).
We then statistically compared the proportions of wild-type cervical DRG neurons with previously published data from 1-month-old wild-type L1-L5 DRG (NF200 + 40.7 ± 1.9%; peripherin + 61.5 ± 2.1%; Sleigh et al., 2017a). We found that the ratio of subtypes is more even in cervical ganglia, which possess significantly more NF200 + and significantly fewer peripherin + neurons than lumbar DRG (Supplementary Figure S2A).
In the past, we also identified a sensory neurodevelopmental phenotype in embryonic Gars C201R/+ hindlimbs (Sleigh et al., 2017a). Dissecting hind paws from embryonic day 13.5 (E13.5) mice and staining neurons for neurofilament (2H3), we observed impaired arborization of nociceptive neurons found in the developing dorsal floor plate. To evaluate whether this phenotype is also seen in forelimbs, we analyzed forepaws from E13.5 embryos (Figure 2A). Similar to the hind paws, there was no difference in sensory nerve growth between genotypes, assessed by measuring the distance from nerve growth cone to digit tip ( Figure 2B). However, CMT2D forepaws did not display the nociceptive nerve branching defect present in lower limbs ( Figure 2C). Therefore, similar to the DRG, developing sensory neurons originating at cervical FIGURE 1 | Sensory neuron development is not impaired in Gars C201R/+ dorsal root ganglion (DRG) at cervical spinal levels. (A) Neurons found in sensory ganglia can be classified into NF200 + cells, which are mainly medium-large in size and function as either mechanoreceptors or proprioceptors, and peripherin + cells that are generally small and nociceptive. (B) DRG used in this study were taken from cervical spinal level 4 (C4) to C8 and lumbar level 1 (L1) to L5 for immunofluorescence analysis and from thoracic to lumbar levels for primary cultures. The schematic was created with BioRender ( [URL]). (C) Representative immunofluorescence images of 1-month-old wild-type and Gars C201R/+ cervical DRG sections stained for NF200 (cyan) and peripherin (red). Scale bars = 200 µm. (D) There was no difference in the proportions of NF200 + (P = 0.812, unpaired t-test) or peripherin + (P = 0.885, unpaired t-test) neurons between genotypes in C4-C8 ganglia, n = 4. (E) Representative western blot of C4-C8 DRG lysates from 1-month-old wild-type and Gars C201R/+ mice probed for NF200, peripherin, and the loading control Gapdh. (F) Consistent with the immunofluorescence analysis (D), there was no difference between genotypes in levels of NF200 (P = 0.835, unpaired t-test) or peripherin (P = 0.702, unpaired t-test) protein, n = 5. NS, not significant; WT, wild-type. See also Supplementary Figures S1, S2.
spinal levels do not show the impairments found in lumbar afferent nerves.
Sensory Populations Are Unaltered in a Mouse Model of ALS
We believe that the small, yet physiologically relevant, distortion of sensory populations in lumbar ganglia of CMT2D mice may be associated with aberrant mutant GlyRS-Trk receptor binding during development. To see whether it extends to other mouse models of neuromuscular disease, we analyzed L1-L5 DRG from SOD1 G93A mice, an established model of SOD1-associated amyotrophic lateral sclerosis (ALS), which displays a plethora of defects and dysfunctional pathways in peripheral, albeit mainly motor, nerves (Kim et al., 2015;Nardo et al., 2016). Lumbar DRG were dissected and immunohistochemically processed from SOD1 G93A and littermate control males at P30-31 ( Figure 3A) and P100-101 (Figure 3C), representing pre-symptomatic and late disease stages, respectively. No distinctions in sensory populations were observed at either age (Figures 3B,D), suggesting that the sensory subtype switch is not observed in all mouse models of neuromuscular diseases. SOD1 G93A and Gars C201R/+ mice are maintained on different genetic backgrounds, and it appeared as though there may be a small difference in neuron populations between wild-types of the two strains. We, therefore, compared neuron proportions at 1 and 3 months in lumbar DRG from wild-type mice on a mixed C57BL/6-SJL background (SOD1 G93A control) vs. a pure C57BL/6J background (Gars C201R/+ control). We observed a small, but significant difference between strains in the percentage of NF200 + , but not peripherin + , neurons at 1 month, but not 3 months (Supplementary Figures S2B,C).
Long-Range Signaling Endosome Transport Is Unaffected in CMT2D Sensory Neurons
Axonal transport is reliant upon motor proteins traversing microtubule networks to deliver diverse cargoes from one end of an axon to the other (Guedes-Dias and Holzbaur, 2019). Anterograde transport from the cell body to the axonal terminal is key for delivering organelles, proteins, and RNAs towards peripheral synapses. Connecting the axon tip to the cell body, retrograde transport is needed for long-range delivery of autophagosomes and survival-promoting neurotrophic factors. Pre-symptomatic disturbances in axonal trafficking are thought to underlie, or at least contribute to, several neurological diseases (Sleigh et al., 2019). Indeed, primary DRG neurons cultured from 12 day old Gars Nmf249/+ mice display reduced retrograde transport speeds of nerve growth factor (NGF)-FIGURE 2 | Sensory neurodevelopment appears normal in the forelimb of Gars C201R/+ embryos. (A) A representative single confocal plane, tile scan image of the dorsal aspect of an E13.5 wild-type forepaw stained for neurofilament (2H3, green). The arrow depicts distance from a major nerve branch ending to the tip of a finger, which was measured for B. The nerves to the left of the dashed line were used for branch analysis in (C). Scale bar = 250 µm. (B,C) There was no difference between wild-type and Gars C201R/+ mice in sensory nerve extension into forepaw extremities (B, P = 0.328, unpaired t-test), nor in the amount of branching (C, P = 0.662, unpaired t-test), n = 3-8. NS, not significant; WT, wild-type.
loaded endosomes (Mo et al., 2018), while reduced mitochondrial motility was also identified in sensory processes of 12-monthold Gars C201R/+ mice (Benoy et al., 2018). Disruption of two different cargoes suggests a broad transport impairment (e.g., due to microtubule dysfunction); however, Gars C201R/+ neurons were cultured from late symptomatic mice, thus the defective trafficking observed in this model may simply be a secondary consequence of neuropathology.
To analyze Gars C201R/+ transport in early symptomatic sensory neurons, we cultured primary thoracic and lumbar DRG neurons from 1 and 3-month-old mice and assessed retrograde signaling endosome trafficking. These spinal levels were combined to obtain sufficient cell numbers for the assay (Figure 1B), and the time points were chosen to allow comparison with several other phenotypes assessed previously in this model (Sleigh et al., 2014b(Sleigh et al., , 2017a. Cultures were incubated with fluorescently labeled atoxic binding fragment of tetanus neurotoxin (H C T-647), which is taken up by neurons and loaded into signaling endosomes containing Trk receptors and p75 neurotrophin receptor (p75 NTR ), when applied to media (Deinhardt et al., 2006(Deinhardt et al., , 2007. Time-lapse confocal microscopy was performed to enable tracking of individual endosomes (Figure 4A). Sensory neurons cultured from DRG do not always display a visible axon initial segment and may bear several morphologically indistinguishable axon-like extensions/processes (Nascimento et al., 2018), thus the trafficking analyzed may not always be ''axonal'' transport per se.
Contrary to the previous studies, overlapping histograms of endosome frame-to-frame speeds suggest that there is little difference in endosome dynamics between genotypes and timepoints ( Figure 4B). This was confirmed by analyzing average endosome speeds (Figure 4C), the percentage of time that endosomes were paused (Figure 4B), and the percentage of pausing endosomes (Figure 4E) per animal. There was also no difference in transport parameters between wild-type and Gars C201R/+ when axons were used as the experimental unit (Supplementary Figure S3); however, irrespective of the genotype, older cultures did display a general slowing of endosome speeds linked to increased pausing when compared in this manner.
Transport was assessed in larger area neurons only, because there is a less frequent overlap of moving endosomes in wider processes, likely due to lower microtubule density (Ochs et al., 1978), permitting greater tracking accuracy. Although not confirmed immunohistochemically, analyzed cells were therefore very likely to be medium-large NF200 + sensory neurons (i.e., mechanosensitive or proprioceptive). Nonetheless, given the distortion in sensory subtypes in CMT2D lumbar DRG (Sleigh et al., 2017a) it is possible that different neuron populations were analysed between genotypes. Thus, we measured cell body areas and process diameters from the neurons in which endosome transport was assessed (Supplementary Figure S4). There were no differences in these morphological properties, indicating that similar neurons were analyzed across genotypes and timepoints.
Elevated GlyRS Levels in Select Neurons Are Linked to the Sensory Subtype Switch
GlyRS protein levels have previously been reported to be elevated in both Gars C201R/+ and Gars Nmf249/+ brains (Achilli et al., 2009;Stum et al., 2011), perhaps as a compensatory response to impaired protein function. To determine whether GlyRS levels are also altered in sensory neurons, we extracted and performed western blotting on C4-C8 and L1-L5 DRG from 1-month-old Gars C201R/+ mice ( Figure 5A). There was no difference in GlyRS levels in cervical ganglia; however, GlyRS was upregulated more than 2-fold in L1-L5 DRG ( Figure 5B). This was corroborated by GlyRS immunofluorescence analysis in C4-C8 ( Figure 5C) and L1-L5 (Figure 5D) ganglia. We also assessed L1-L5 DRG of 1-month-old Gars Nmf249/+ mice and saw the same pattern of enhanced GlyRS fluorescence in a subset of DRG neurons (Figure 5E), thus indicating that the increase of mutant GlyRS levels in L1-L5 DRG is an early event in CMT2D pathogenesis.
Upon closer inspection, GlyRS immunofluorescence is marginally higher in some of the smaller area neurons of wild-type lumbar DRG; however, in both Gars mutants, the upregulation appears to be in larger neurons. To better characterize this, we co-stained Gars Nmf249/+ lumbar DRG for GlyRS and NF200 ( Figure 6A). We found that ≈87% of neurons with increased GlyRS levels were also NF200 + (Figure 6B), which is a particularly high proportion considering that these mutant ganglia consist of only ≈22% NF200 + neurons (Sleigh et al., 2017a). This suggests that there is a preferential increase of GlyRS in mechanosensitive and proprioceptive neurons. We then quantified the percentage of NF200 + neurons that showed elevated GlyRS and found that ≈32% showed the phenotype (Figure 6B), indicating that GlyRS is differentially upregulated even within this neuronal population, perhaps in a subgroup with a particular function.
A global increase in ARS proteins would perhaps suggest dysfunction in a cellular process linked to aminoacylation, for instance, protein translation, which is impaired a gain-offunction manner in CMT2D fly models (Niehues et al., 2015). We, therefore, assessed whether upregulation is GlyRS-specific by analyzing lumbar DRG levels of Kars-encoded lysyl-tRNA synthetase (LysRS) and Yars-encoded tyrosyl-tRNA synthetase (TyrRS; Figure 7A). LysRS was chosen because, with GlyRS, it is the only other dual-localized synthetase functioning in both cytoplasm and mitochondria, while TyrRS was chosen because there is strong evidence that mutations in its encoding gene, YARS1, cause CMT (Wei et al., 2019). Gars C201R/+ L1-L5 DRG shows no change in LysRS levels, but a small yet significant rise in TyrRS ( Figure 7B). We confirmed the lack of LysRS upregulation by staining lumbar ganglia from Gars Nmf249/+ mice ( Figure 7C). TyrRS immunohistochemistry was attempted, but the resulting staining pattern was not consistent with a cytoplasmic tRNA synthetase, suggestive of non-specific staining.
Together, these data indicate that there is a selective increase in Gars expression that occurs predominantly in NF200 + cells and only at spinal levels displaying a developmental perturbation of sensory neuron fate.
Sensory Phenotypes Are Restricted to the Lower Limbs of CMT2D Mice
CMT2D mice display developmental phenotypes in sensory neurons innervating the hind paws (Sleigh et al., 2017a). To extend these analyses and determine whether the upper limb predominance of patients is replicated, we assessed afferent nerves from cervical spinal levels in Gars C201R/+ mice. The lumbar phenotypes of subtype switching and impaired axon branching were not present in sensory neurons targeting forepaws (Figures 1, 2, and Supplementary Figure S1), suggesting that anatomical location has a considerable bearing on neurodevelopmental pathology. Given that these phenotypes are developmental and do not progress in severity from P1 to 3 months, it is unlikely that they occur in cervical DRG later in the disease.
Although mutations in GARS1 frequently cause hands to be affected before and more severely than feet, there are examples where the opposite occurs (Sivakumar et al., 2005;Forrester et al., 2020); albeit it is unclear as to whether this DRG lysates from 1-month-old wild-type and Gars C201R/+ mice probed for GlyRS and the loading control Gapdh. (B) There was no difference between genotypes in GlyRS levels in cervical ganglia (P = 0.825; unpaired t-test; n = 5); however, GlyRS was elevated in mutant L1-L5 DRG (***P < 0.001, NS not significant; unpaired t-test; n = 4). (C,D) This was confirmed by immunofluorescence analysis of GlyRS in the cervical (C) and lumbar (D) DRG sections from 1-month-old wild-type and Gars C201R/+ mice, n = 4. GlyRS levels appear higher in many individual neurons with larger cell bodies in the mutant lumbar DRG (top right image). (E) These findings were replicated when comparing sections of L1-L5 ganglia from 1-month-old wild-type and Gars Nmf249/+ mice, n = 3. Scale bars = 200 µm. WT, wild-type. also applies to sensory symptoms. Similarly, the Gars C201R mutation could therefore simply preferentially impact lower limbs. However, restriction of weakness to CMT2D patient feet is rare. So, what could be driving the differential pathology between lumbar and cervical ganglia in mice? It may be due to a distinction in the proportions of sensory subtypes. In FIGURE 6 | GlyRS is preferentially increased in some, but not all, NF200 + sensory neurons of Gars Nm249/+ mice. (A) Representative immunofluorescence image of an L1-L5 DRG section from a 1-month-old Gars Nm249/+ mouse stained for GlyRS (yellow) and NF200 (cyan). Red arrows and magenta arrowheads highlight N200 + and NF200neurons, respectively, in which GlyRS was increased. Scale bar = 200 µm. (B) The majority of neurons in which GlyRS was upregulated, designated "(+)GlyRS," express NF200; however, not all NF200 + cells have increased GlyRS levels, n = 3.
wild-type mice, NF200 is expressed by ≈41% of neurons in L1-L5 DRG (Sleigh et al., 2017a), whereas ≈56% are N200 + in cervical ganglia (Supplementary Figure S2A). If mutant GlyRS aberrantly interacts with Trk receptors pre-natally, thus impacting sensory development and skewing the proportions of functional subclasses (Sleigh et al., 2017a), then GlyRS is likely to have less impact on ganglia that have a more equal balance between NF200 + and peripherin + cells, as is observed in C4-C8 DRG. Alternatively, spinal level distinctions may be caused by differences in the amount or kinetics of GlyRS secretion or Trk expression. DRG at different spinal levels develop asynchronously (Lawson and Biscoe, 1979) and possess divergent transcription factor profiles (Lai et al., 2016), which may also contribute to the lower limb predominance observed in CMT2D mice. Indeed, the transcription factors neurogenin 1 and 2, which drive two distinct waves of neurogenesis required for segregation of major classes of Trk-expressing sensory neurons, are differentially required by cervical and more caudal sensory ganglia (Ma et al., 1999).
Irrespective of the cause, experiments presented here highlight the importance of comparative anatomy in mouse models of neuromuscular disorders to enhance understanding of pathomechanisms. It remains to be seen whether such variations are also observed in the motor nervous system of CMT2D mice, although differential susceptibility of muscles to NMJ denervation has been previously reported (Seburn et al., 2006;Sleigh et al., 2014bSleigh et al., , 2020Spaulding et al., 2016).
Developmental Perturbation of Sensory Fate Is Not a Common Phenotype
A difference in sensory neuron populations has also been reported in lumbar DRG of mice modeling spinal muscular atrophy (SMA; Shorrock et al., 2018). We, therefore, aimed to determine whether this phenotype is a general feature of mouse models of neuromuscular disease, as this would cast doubt on the aberrant binding of mutant GlyRS to Trk receptors as being the cause of impaired sensory development in mutant Gars mice. SOD1 G93A mice modeling SOD1-linked ALS show a variety of defects in sensory neurons (Sassone et al., 2016;Vaughan et al., 2018;Seki et al., 2019); however, they do not display a subtype switch in lumbar DRG (Figure 3). Moreover, we recently found that mice modeling a developmental form of SMA caused by loss-of-function mutations in BICD2 also do not show this phenotype (Rossor et al., 2020). Together, these data suggest that perturbed sensory development is not observed in all mouse models of neurodegeneration.
We did, however, see a small difference in lumbar DRG between wild-type littermates of SOD1 G93A and Gars C201R/+ mice (Supplementary Figures S2B,C), which are maintained on different genetic backgrounds, suggesting that genetic background may subtly influence sensory neuron populations, likely contributing to previously reported disparities in sensation between strains (Crawley et al., 1997). This result is not overly surprising considering that mouse genetic background can influence even gross anatomical features such as the number of spinal levels (Rigaud et al., 2008).
Long-Range Transport Is Not Universally Impaired in CMT2D Sensory Neurons
Deficits in axonal transport contribute to many different genetic neuropathies (Prior et al., 2017;Beijer et al., 2019), and its early involvement in disease may be a common driver in peripheral nerve selectivity typical of many forms of CMT2. Indeed, disruption of long-range trafficking has been identified in sensory neurons cultured from CMT2D DRG (Benoy et al., 2018;Mo et al., 2018). Contrastingly, we found no difference in retrograde transport of signaling endosomes between wild-type and Gars C201R/+ at 1 and 3 months of age (Figure 4 and Supplementary Figure S3). Nonetheless, if thoracic DRG show limited to no pathology, by combining thoracic DRG with L1-L5 ganglia we may have masked a lumbar-specific DRG transport phenotype. Additionally, the medium and associated supplements in which DRG neurons were cultured vary across studies. Neuronal activity can impact the rate and quantity of axonal transport (Sajic et al., 2013;Wang et al., 2016), while proteins such as neurotrophic factors, which are present in primary neuron media, can affect neuronal activity (Dombert et al., 2017). Though unlikely, it is possible therefore that the medium in which our neurons were grown may have selectively enhanced Gars C201R/+ transport masking a trafficking deficiency. Mo et al. (2018) identified a slow-down in NGF-containing endosomes tracked in sensory neurons cultured from 12-day old Gars Nmf249/+ mice. Firstly, if transport disruption correlates with the overall disease burden, then the more severe mutant Densitometry analysis indicates that LysRS levels did not differ between genotypes. There was a small, but significant, increase in TyrRS levels in Gars C201R/+ lumbar DRG, but the change was much less than that observed for GlyRS (densitometry analysis from Figure 5 included for comparison). ***P < 0.001, **P < 0.01, NS, not significant; unpaired t-test, n = 4. (C) Representative immunofluorescence analysis of LysRS in L1-L5 ganglia sections from 1-month-old wild-type and Gars Nmf249/+ mice. Unlike GlyRS (Figure 6), but consistent with the Gars C201R/+ LysRS western blot (A,B), individual neurons did not display an increase in LysRS levels, n = 3. N.b., the positive LysRS signal observed in axons (wild-type image) was also seen in the secondary only control and was therefore likely to be non-specific. Scale bars = 200 µm. WT, wild-type.
allele is more likely to display a defect than the milder Gars C201R/+ mutant. Equally as important, the assayed neurons in the two studies were perhaps different. NGF binds to TrkA, which is expressed by nociceptors (Supplementary Figure S1A), thus transport was probably assessed in noxious stimulus-sensing peripherin + neurons from Gars Nmf249/+ mice. The atoxic binding fragment of tetanus neurotoxin that we used to assess transport is taken up into multiple populations of signaling endosomes (Villarroel-Campos et al., 2018); however, we focused our analyses on large area neurons with wide processes (Supplementary Figure S4), likely to be either TrkB + mechanoreceptors or TrkC + proprioceptors. Benoy et al. (2018) identified that in vitro sensory neurons cultured from 12-month-old Gars C201R/+ mice had an almost complete impairment in mitochondrial motility. The disparity with this study could simply reflect the analyzed cargo, i.e., mitochondrial, but not endosomal, transport is altered in this model. Alternatively, the difference may be due to the age at which the neurons were tested (1 and 3 months vs. 12 months). Supporting this idea, we have previously shown that Gars C201R/+ sensory neurons cultured from 1-month-old animals show normal neurite/process outgrowth (Sleigh et al., 2017a); however, this was defective in 12-monthold cells (Benoy et al., 2018). Decreased neuronal health may, therefore, be contributing to reduced mitochondrial motility, as may the process of aging. Indeed, we have previously reported that the dynamics of signaling endosome transport in vivo remain unaltered in aged wild-type mice Sleigh et al., 2020a), whereas mitochondrial transport is known to be altered in old animals (Mattedi and Vagnoni, 2019).
Our long-range retrograde transport data indicate that cargo trafficking is not globally disrupted in all CMT2D sensory neurons during early disease stages. To unravel the significance of axonal transport impairments to CMT2D etiology, it will be important to assess the trafficking of a variety of different cargoes both in sensory and motor neurons of mutant Gars models. Given the complexity of the in vivo environment, the kinetics of axonal transport is not always replicated in vitro (Sleigh et al., 2017c), hence the analysis of mutant Gars mouse transport should also be extended to peripheral nerves in vivo (Gibbs et al., 2016(Gibbs et al., , 2018.
GlyRS Elevation Is Not a Simple Compensatory Mechanism
Neuropathy-causing GARS1 mutations differentially impact the enzymatic activity, with some fully ablating it, whilst others having little effect (Oprescu et al., 2017). The charging function of GlyRS C201R and GlyRS P278KY in Gars C201R/+ and Gars Nmf249/+ mice, respectively, were originally reported as unaffected (Seburn et al., 2006;Achilli et al., 2009;Stum et al., 2011). However, a re-evaluation under Michaelis-Menten kinetic conditions suggests that GlyRS P278KY has severely decreased kinetics and cannot support yeast viability, commensurate with a loss-of-function (Morelli et al., 2019). Moreover, GlyRS C201R aminoacylation was analyzed indirectly in brain lysates that had a 3.8-fold increase in GlyRS (Achilli et al., 2009), which could mask a charging deficiency. Accordingly, brains from severe homozygous Gars C201R/C201R mice showed a 60% decrease in aminoacylation despite an 8.2-fold increase in GlyRS. Further supporting a GlyRS C201R loss-of-function, wild-type GARS1 overexpression in sub-viable homozygous Gars C201R/C201R animals can restore post-natal viability (Motley et al., 2011). Similar to Gars C201R/+ brains, GlyRS levels were reported to be higher in Gars Nmf249/+ cerebellum, although this was not quantified (Stum et al., 2011). It is, therefore, possible that GlyRS levels are elevated in CMT2D tissues as a compensatory response to diminished aminoacylation.
To test this hypothesis in sensory tissue, we analyzed the GlyRS protein in CMT2D DRG (Figure 5). Coinciding with the perturbation of sensory neuron fate, we observed enhanced GlyRS levels in lumbar, but not cervical, ganglia of mutant Gars mice. Furthermore, the increase was not observed in all lumbar sensory neurons, but preferentially in a portion of NF200 + neurons (Figure 6). This argues against GlyRS upregulation being a compensatory response to impaired charging, an alteration in a non-canonical function (e.g. Johanson et al., 2003;Park et al., 2012;Mo et al., 2016), or that mutant GlyRS protein stability is altered, because if any of those scenarios were true, then GlyRS increase would also likely occur in cervical DRG and across all sensory neurons equally. That is unless there is a greater requirement for glycine charging in cell bodies of larger sensory neurons with the longest axons (i.e., those innervating lower, but not upper, limbs). Contradictory to this idea, GlyRS levels were enhanced in only about a third of NF200 + neurons in mutant lumbar DRG, suggesting that a particular subset may be selectively impacted by the disease.
To further tease apart the basis for increased Gars expression, we assessed levels of additional ARS proteins. We found that LysRS remained unchanged, but that there was a small increase in TyrRS in CMT2D DRG (Figure 7). This indicates that there is no global increase in tRNA synthetase in response to GlyRS C201R expression. We, therefore, observe a GlyRS-specific upregulation, preferentially occurring in a subdivision of NF200 + neurons and only in ganglia that display neuropathology. Why might this be the case? Neuropathy-associated GARS1 mutations have been shown to impair GlyRS localization in neuron-like cell lines (Antonellis et al., 2006;Nangle et al., 2007), which could cause build-up in the soma, although, once again, if this were the cause of increased GlyRS levels then it would probably not be so selectively upregulated. The GlyRS elevation is only present in DRG that display a developmental perturbation in sensory neuron fate, suggesting that the two phenotypes may be linked. Perhaps the NF200 + neurons resident in lumbar ganglia are under stresses not experienced by neighboring subtypes. The integrated stress response (ISR), which is linked to amino acid deprivation (Pakos-Zebrucka et al., 2016), maybe especially activated in these cells. Consistent with impaired protein translation reported in CMT2D fly models (Niehues et al., 2015), the ISR causes a global downregulation of cap-dependent translation of mRNAs, except for a select few that possess upstream open reading frames (uORFs) in their 5 -UTRs, which under non-stressed conditions usually restrict translation initiation of the main downstream ORF (Barbosa et al., 2013). Although not classically thought of as an ISR-associated gene, human and mouse GARS1 express two mRNA isoforms, one of which possesses an uORF that may, under conditions of stress, play a role in the observed GlyRS increase (Alexandrova et al., 2015). However, some KARS1 variants also possess an uORF (AceView, NCBI) and LysRS levels remained unchanged. Alternatively, the increase may be an active, compensatory response by a subset of NF200 + cells to combat degeneration. Indeed, the NF200 + class of neurons includes vibration-sensing mechanoreceptors, which are most impacted in CMT2D patients (Sivakumar et al., 2005).
CONCLUSION
Sensory dysfunction of GARS1-neuropathy patients and mouse models of CMT2D is chronically understudied. This is unsurprising given the relative severity of motor symptoms; however, by studying pathology in both types of peripheral nerve and performing comparative anatomical studies on mouse motor and sensory nervous systems, we are much more likely to determine key pathomechanisms causing the selective pathology characteristic of CMT. Here, we have made four key discoveries: (1) sensory pathology is not equal across all CMT2D ganglia, thus anatomical location dictates disease involvement; (2) perturbed sensory neuron fate is not a general feature of different neuromuscular disease models, supporting its specificity to GARS1 neuropathy; (3) signaling endosome trafficking in a sub-population of Gars C201R/+ sensory neurons remains unaffected, indicating that a widespread CMT2D defect in axonal transport is unlikely; and (4) Gars expression is selectively enhanced in NF200 + lumbar DRG neurons and is thus linked to the subtype switch, perhaps in response to active degeneration.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
ETHICS STATEMENT
The animal study was reviewed and approved by the UCL Queen Square Institute of Neurology Genetic Manipulation and Ethics Committees and performed in accordance with the European Community Council Directive of 24 November 1986 (86/609/EEC). Gars C201R/+ (RRID:MGI 3849420) and SOD1 G93A (RRID:IMSR_JAX 002726) mouse handling and experiments were carried out under license from the UK Home Office in accordance with the Animals (Scientific Procedures) Act 1986 and were approved by the UCL Queen Square Institute of Neurology Ethical Review Committee.
AUTHOR CONTRIBUTIONS
JS conceived the experiments, analyzed the data and wrote the manuscript. JS, AM, TA, and YZ performed the research. GS provided expertise and discussion. All authors contributed to the article and approved the submitted version.
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Domain: Psychology Biology
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Arousal and Locomotion Differently Modulate Activity of Somatostatin Neurons across Cortex
Abstract Arousal powerfully influences cortical activity, in part by modulating local inhibitory circuits. Somatostatin (SOM)-expressing inhibitory interneurons are particularly well situated to shape local population activity in response to shifts in arousal, yet the relationship between arousal state and SOM activity has not been characterized outside of sensory cortex. To determine whether SOM activity is similarly modulated by behavioral state across different levels of the cortical processing hierarchy, we compared the behavioral modulation of SOM-expressing neurons in auditory cortex (AC), a primary sensory region, and posterior parietal cortex (PPC), an association-level region of cortex, in mice. Behavioral state modulated activity differently in AC and PPC. In PPC, transitions to high arousal were accompanied by large increases in activity across the full PPC neural population, especially in SOM neurons. In AC, arousal transitions led to more subtle changes in overall activity, as individual SOM and Non-SOM neurons could be either positively or negatively modulated during transitions to high arousal states. The coding of sensory information in population activity was enhanced during periods of high arousal in AC, but not in PPC. Our findings suggest unique relationships between activity in local circuits and arousal across cortex, which may be tailored to the roles of specific cortical regions in sensory processing or the control of behavior.
The impacts of arousal on sensory perception, and on coding in sensory cortex, have been well studied (Fu et al., 2014;Zhou et al., 2014;McGinley et al., 2015;Vinck et al., 2015;Pakan et al., 2016). It is unclear though how specialized or generalized the relationship between arousal and local inhibitory circuit function is across the cortical hierarchy. For example, while arousal-mediated and attention-mediated reductions in shared variability seem to improve sensory coding (Cohen and Maunsell, 2009;Goard and Dan, 2009), similar effects could be detrimental to the readout of perceptual decisions to control behavior in higher cortex (Runyan et al., 2017;Valente et al., 2021). The basic structure of local circuits is highly conserved across cortical regions, yet differences in neuromodulatory receptor expression, the density of specific cell types, or in the specifics of local connectivity can alter the influence of neuromodulatory input on the pattern of neural population activity. Indeed, the density of SOM neurons is increased relative to PV neurons in association cortex (Kim et al., 2017;Dienel et al., 2021), suggesting that the population-level computations that SOM neurons participate in, and thus the relationship between arousal state and local network state, may differ across the cortical processing hierarchy.
Here, we hypothesized that arousal-related modulation of SOM neurons, and of local neural activity, would be specialized across cortical regions to match different arousal-related demands on local computation. We examined the effects of arousal state on activity in SOM and Non-SOM neurons in the primary auditory cortex (AC), and in the posterior parietal cortex (PPC). AC is a primary sensory region of the cortex, where the relationship between arousal and neural activity has been well studied (Schneider et al., 2014;Zhou et al., 2014;McGinley et al., 2015;Bigelow et al., 2019;Yavorska and Wehr, 2021). PPC is an association-level region that participates in flexible sensorimotor transformations (Fitzgerald et al., 2011;Harvey et al., 2012;Morcos and Harvey, 2016;Licata et al., 2017;Tseng et al., 2022). In PPC, task engagement is known to impact the structure of local population activity (Runyan et al., 2017;Pho et al., 2018;Valente et al., 2021), though relatively little is known about the specific contribution of generalized increases in arousal to the activity of PPC. Outside of a task context, firing rates of neurons in PPC are positively correlated with arousal level (Stitt et al., 2018), but the effects of arousal on specific inhibitory neuron types within PPC are not known. In the current study, we have revealed different relationships among arousal state, the structure of local population activity, and information coding in AC and PPC, suggesting that the effects of arousal on local processing are specialized across the cortical hierarchy.
Experimental design and statistical analysis
All pairwise comparisons were done with two-sided paired or unpaired permutation (i.e., randomization) tests with 10,000 iterations as indicated, where p , 0.0001 indicates the highest significance achievable given the number of iterations performed. Given that the exact p value is unknown in these cases, p values of the highest significance are reported as such rather than as an exact value. All permutation tests were performed for differences in means. For statistical comparisons involving more than two groups, we used Kruskal-Wallis (nonparametric ANOVA) and used unpaired permutation tests post hoc to determine which groups differed from each other. Data fell into natural groupings by (1) brain area (AC or PPC) and by (2) cell-type (SOM or Non-SOM), as indicated by expression of the red fluorophore, tdTomato. All bar plots show the mean and bootstrapped 95% confidence intervals using 1000 iterations unless otherwise indicated. When multiple comparisons were made between groups, significance thresholds were Bonferroni corrected. Sample sizes were chosen based on previous studies comparing population activity dynamics across brain areas or cell types (Runyan et al., 2017;Khan et al., 2018).
Animals
All procedures were approved by the University of Pittsburgh Institutional Animal Care and Use Committee. Homozygous SOM-Cre mice (Sst-IRES-Cre; stock #013044) were crossed with homozygous Ai14 mice (RCL-tdT-D; stock #007914) obtained from The Jackson Laboratory, and all experiments were performed in the F1 generation, which expressed tdTomato in SOM 1 neurons. Mice were group housed in cages with between two and four mice. Adult (8-24 weeks) male and female mice were used for experiments (four male, two female). Mice were housed on a reversed 12 h light/dark cycle, and all experiments were performed in the dark (active) phase.
Mice were anesthetized with isoflurane (4% for induction, and 1-2% maintenance during surgery), and mounted on a stereotaxic frame (David Kopf Instruments). Ophthalmic ointment was applied to cover the eyes (Henry Schein Medical). Dexamethasone was injected 12-24 h before surgery, and carprofen and dexamethasone (Covetrus) were injected subcutaneously immediately before surgery for pain management and to reduce the inflammatory response. Two circular craniotomies, each of 2 mm diameter, were made over left AC and PPC (centered at 2 mm posterior and 1.75 mm lateral to bregma). For AC, the craniotomy was centered on the temporal ridge, and the posterior edge was aligned with the lambdoid suture. Two millimeter biopsy punches were used to outline the circumference of the window before drilling.
One to four evenly spaced ;60 nl injections of the AAV1synapsin-GCaMP6f (stock #100837, Addgene) that had been diluted to a titer of ;1 Â 10 12 viral genomes/ml using sterile PBS were made in each cranial window, centered in each craniotomy. A micromanipulator (QUAD) was used to target injections ;250 mm under the dura at each site, where ;60 nl of virus was pressure injected over 5-10 min. Pipettes were not removed until 5 min postinjection to prevent backflow. Dental cement (Parkell) sealed a glass coverslip (3 mm) over a drop of Kwik Sil (World Precision Instruments) over the craniotomy. Using dental cement, a one-sided titanium headplate was attached to the right hemisphere of the skull. After mice had recovered from the anesthesia, they were returned to their home cages, and received oral carprofen tablets (Bio-Serv) for 3 d postsurgery.
Experimental setup Two-photon microscope
Images were acquired using a resonant scanning twophoton microscope (Ultima Investigator) at a 30 Hz frame rate and 512 Â 512 pixel resolution through a 16Â water-immersion lens (16Â/0.8 numerical aperture; model CF175, Nikon). On separate days, either AC or PPC was imaged at a depth between 150 and 300 mm, corresponding to layers 2/3 of cortex. For AC imaging, the objective was rotated 35-45°from vertical, and for PPC imaging, it was rotated to 5-15°from vertical, matching the angle of the cranial window implant. Fields of view were 500 mm 2 and contained 187 6 95 neurons, 20 6 10 (mean 6 SD) of which were classified as SOM neurons. Excitation light was provided by a femtosecond infrared (IR) laser (Insight X3, Spectra-Physics) tuned to 920 nm. Green and red wavelengths were separated through a 565 nm low-pass filter before passing through bandpass filters (catalog #ET525/70 and #ET595/50, Chroma). PrairieView software (version 5.5; Bruker) was used to control the microscope.
Behavioral monitoring
Running velocity was monitored on pitch and roll axes using two optical sensors (model ADNS-98 000, Tindie) held adjacent to the spherical treadmill. A microcontroller (Teensy 3.1, Adafruit) communicated with the sensors, demixing their inputs to produce one output channel per rotational axis using custom code. Outputs controlling the galvanometers were synchronized with running velocity using a digital oscilloscope [WaveSurfer, Janelia Research Campus, Howard Hughes Medical Institute (HHMI)].
Pupil images were acquired at 1280 Â 1024 pixels, at 10 Hz from an IR camera focused on one eye [Flea3 FL3-U3-13Y3M-C one-half inch Monochrome USB 3.0 Camera, with 1.0Â SilverTL Telecentric Lens (field of view, 6.74 Â 5.39 mm), Edmund Optics]. The pupil was illuminated by the IR light emitted by the two-photon laser and required no additional IR illumination. Movies were acquired with the MATLAB Image Acquisition Toolbox (MathWorks). Pupil area was determined in each pupil movie frame post hoc using custom MATLAB code (MathWorks). The pupil was constricted by controlling ambient illumination with an array of LCD screens (LP097QX1, LG Display) to maintain a moderate pupil area baseline from which increases and decreases in area could be measured.
Experimental protocol
Imaging began 3-5 weeks postsurgery once robust expression of the GCaMP6f virus was observed. In each imaging session, GCaMP6f fluorescence changes were imaged in SOM (tdTomato 1 ) and Non-SOM neurons, while mice ran freely on a spherical treadmill. In the spontaneous context, no sensory stimuli were delivered, while in the passivelistening context, location-varying sound stimuli were presented (see Materials and Methods, subsection Sound stimuli). Spontaneous and passive listening contexts lasted ;25-50 min each. Imaging alternated between AC and PPC across days. Multiple imaging sessions were performed in each cranial window, focusing at slightly different depths and lateral/posterior locations within the imaging windows across sessions. AC and PPC were each imaged in six mice (biological replicates). Each cranial window was imaged up to 11 times (technical replicates). Imaging from a given cranial window was suspended when we observed nuclear inclusion in two or more cells in the field of view, which indicates an overexpression of GCaMP6f.
Sound stimuli
Four magnetic speakers were positioned in a semicircular array (model MF1-S, Tucker-Davis), centered on the mouse's head. The speakers were positioned at À90°, À30°, 130°, and 190°from the midline in azimuth and driven by MATLAB through a digital/analog converter (National Instruments). Speakers were calibrated to deliver similar sound levels (;70 dB) in a sound isolation chamber using a random incidence microphone (model 4939,Brüel & Kjaer). During passive listening, 1 or 2 s dynamic ripples (broadband stimuli created in MATLAB by summing 32 tones spaced across 2-32 kHz, which fluctuated at 10-20 Hz; Elhilali et al., 2004) were presented from one of eight locations. Four of the sound locations corresponded to the locations of the four speakers (À90°, À30°, 130°, 190°), while the other four sound locations (À60°, À15°, 115°, 160°) were simulated using vectorbased intensity panning, where the same sound stimulus was delivered to two neighboring speakers simultaneously, scaled by a gain factor (Runyan et al., 2017). Dynamic ripples were chosen to optimally drive populations of neurons in auditory cortex with diverse frequency tuning preferences. Each sound repeated three times at one location before switching to another. Each ripple played from each of the eight locations in randomized order, with a 240 ms gap between each sound. Output controlling the audio speakers was recorded along with two-photon imaging galvo and running velocity using WaveSurfer (Janelia Research Campus, HHMI), and these signals were aligned offline.
Data processing
Imaging datasets from 24 AC fields of view and 20 PPC fields of view were included from six mice. We excluded any datasets with significant photobleaching or more than two filled cells. We also excluded any AC or PPC dataset from analysis if fewer than one-third of neurons that were significantly responsive (according to our definition in subsection Sound responsiveness) to at least one sound location, as we were interested in the effect of arousal on both spontaneous and sound-evoked responses. For AC datasets, we analyzed single-cell responses to pure tones on a subset of fields of view from each mouse, and then anatomically aligned all fields of view from datasets collected from each window, to ensure each field of view lay in a region representing tone frequencies in the sonic range of the tonotopic axis of primary auditory cortex. We eliminated any datasets where .50% of tone-responsive neurons had a preferred frequency that was in the ultrasonic range (.20 kHz), as well as any fields of view that were aligned anterior to a field of view where this was observed, to assure that we were seeing sound responses in the range of frequencies primarily represented by our dynamic ripples (described in subsection Sound stimuli). We collected wide-field fluorescence responses to pure tones in all AC cranial windows and observed pure-tone responses in the sonic range for all AC windows; however, the extent of the viral expression within windows was too spatially limited to allow for mapping of specific regions.
Image processing
For each field of view, the raw calcium movies collected during the spontaneous activity and passive listening contexts were concatenated before motion correction, cell body identification, and fluorescence and neuropil extraction. These processing steps were performed using Suite2p 0.9.3 in Python (Pachitariu et al., 2017). Suite2p first registered images to eliminate brain motion, and clustered neighboring pixels with similar time courses into regions of interest (ROIs). ROIs were manually curated using the Suite2p graphical user interface (GUI), to ensure that only cell bodies, as opposed to dendritic processes, were included in analysis, based on morphology. Cells expressing tdTomato (SOM cells) were identified using a threshold applied in the Suite2p GUI based on mean fluorescence in the red channel after bleed-through correction applied by the Suite2p cell detection algorithm, along with manual correction. For each ROI, Suite2p returned a raw fluorescence time series, as well as an estimate of neuropil fluorescence that could contaminate the signal. For each cell, we scaled the neuropil fluorescence by a factor by 0.7 and subtracted this time series from the raw fluorescence time series of the ROI to obtain a neuropil-corrected fluorescence signal for each selected cell.
DF/F and deconvolution
Once the neuropil corrected fluorescence was obtained for each neuron, we calculated DF/F for each cell in each frame by calculating (F -F baseline )/F baseline for each frame, where F is the fluorescence of a given cell at that frame and F baseline was the eighth percentile of the fluorescence of that cell spanning 450 frames before and after (;15 s each way, 30 s total). DF/F time series were then deconvolved to estimate the relative spike rate in each imaging frame using the OASIS toolbox (Friedrich et al., 2017). We used the AR1 FOOPSI algorithm and allowed the toolbox to optimize the convolution kernel, baseline fluorescence, and noise distribution. A threshold of 0.05 a.u. was applied to remove all events with low magnitude from deconvolved activity time series. All analyses were performed with both DF/F and deconvolved activity, and showed the same trends. Outside of Figure 1F and 1H, only results using deconvolved activity are shown.
Single-cell modulation by sound stimuli, running behavior, and pupil size Sound responsiveness The deconvolved activity of each neuron was z scored across its entire time series and trial averaged. For each sound location, we then calculated the sound-evoked response as the difference between the mean activity during the sound presentation and the mean activity in the 240 ms before sound onset. We then compared the evoked sound responses to shuffled distributions, where the activity of each cell was shifted randomly by at least 5 s in time relative to sound location time series, and the sound-evoked response was recalculated. This was repeated 1000 times. A neuron was considered to be sound responsive if it had a soundevoked response in at least one sound location that was greater than the 97.5 percentile of the shuffled distribution.
Running bouts and modulation
Running bout onsets were defined as transitions in speed from ,10 to .10 cm/s, and required that the mean Research Article: New Research Figure 1. Imaging spike-related activity in SOM and Non-SOM neurons during behavioral state transitions. A, Viral injections and cranial windows were made over AC and PPC in each SOM-tdTomato mouse. B, In imaging sessions, mice were headfixed over a spherical treadmill and allowed to run voluntarily. Four speakers arranged around the head presented sound stimuli. An infrared camera was used to image the pupil, and a rotating two-photon microscope was focused on either AC or PPC on a given imaging day. C, Each imaging session included spontaneous and passive listening contexts, without or with randomly presented sound stimuli from each of eight locations, respectively. D, Pupil area was monitored via the pupil camera; the scale bar in the bottom image applies to top (constricted pupil) and bottom (dilated pupil). E, Example field of view from auditory cortex, with intermingled tdTomato 1 /SOM 1 (magenta) and tdTomato -/SOMneurons, coexpressing GCaMP6f (green). F, Example aligned behavioral and neural signals collected during the imaging session in E, including running speed (in cm/s), normalized pupil area, dF/F from a Non-SOM (red) and SOM neuron (orange), each overlaid with the deconvolved estimated spike rates of the neuron. G, As in E, for an example posterior parietal cortex field of view. H, As in F, for the PPC field of view in G. I, Proportions of Non-SOM and SOM neurons in AC (left) and PPC (right) with significant positive (white), negative (black), or no modulation (gray) by the of the mouse running. AC Non-SOM, N = 2645; AC SOM, N = 359; PPC Non-SOM, N = 4719; PPC SOM, N = 525. J, As in I, for pupil dilation. K, Proportion of Non-SOM and SOM neurons in AC and PPC that were significantly sound responsive to at least one location (white) or not significantly sound responsive (gray). running speed in the 1 s following the transition was three times greater than the 1 s before running bout onset, and that the mouse maintained a minimum speed of 15 cm/s for the following 2 s.
Running modulation was calculated as the difference in mean activity of a cell in the 1 s before running bout onset and the mean activity of a cell in the 3 s window following running bout onset. A shuffling procedure was applied to determine which cells were positively, negatively, and not modulated by running. The activity of each cell was shifted randomly by at least 5 s in time relative to running speed time series, and for 1000 time-shifted iterations, running modulation was recalculated. Positively modulated neurons had positive running modulation values higher than the 97.5 percentile of the shuffled distribution of that cell. Negatively modulated cells had negative running modulation values lower than the 2.5 percentile of the shuffled distribution of that cell. All other cells were considered to not be modulated by running speed increases.
Pupil dilation events and modulation
Pupil area was normalized to its maximum across the imaging session. To identify pupil dilation events, we first identified all local maxima of the pupil area. We then found the point before this where the derivative of pupil area was zero. We included events where the time from the inflection point to the local maximum was at least a 40% increase in pupil area and that the change from the inflection point to the local maximum was ,1 s, and that the local maximum was at least 50% of the maximum total area by the pupil during that imaging session. We considered each inflection point to be the onset of dilation events.
To capture all pupil dilation-related activity, which had a slower time course than running (see Fig. 4), we calculated pupil modulation for each neuron as the difference between the mean activity in the 1 s time window before dilation event onset and the mean activity in the 5 s time window after dilation event onset. We applied the same shuffling procedure as described for running modulation (subsection Running bouts and modulation) to determine which neurons were positively, negatively, and not modulated by pupil dilation events.
Arousal states
Defining low and high arousal states based on pupil area K-means clustering was applied to the full pupil area time series, which included both spontaneous activity and passive listening contexts, to classify each pupil area measurement as low, transitional, or high arousal. Each pupil area time series was the mean normalized using the following equation: xÀ x x: The Manhattan (called City Blocks) distance metric was applied to define two centroid clusters that served as the high arousal and low arousal groups. Transition periods included timepoints when the absolute difference in distance to the high arousal and low arousal centroids was ,0.05.
Arousal modulation index
The arousal modulation index (AMI) was calculated for each neuron using the following equation: where FR hi is the mean response of the neuron in the high arousal state and FR lo is the mean response of the neuron in the low arousal state. We first maximum normalized the deconvolved activity trace of each neuron across the entire time series. To calculate FR hi (or FR lo ), we summed the activity from the high (or low) arousal state and divided by the total time spent in the high (or low) arousal state in the spontaneous context. This index could vary continuously between À1 and 11, where negative values indicate higher activity in the low arousal state, and positive numbers indicate higher activity during the high arousal state.
Encoding models
We used an encoding model to disentangle the contributions of pupil size and running speed to the activity of neurons in AC and PPC. In the generalized linear model (GLM), the time-dependent effects of all measured external variables on the activity of each neuron were estimated (Pillow et al., 2008;Runyan et al., 2017). The following three classes of predictors were used in different combinations to quantify their contributions to neuronal activity: running, pupil size, and sound stimulus predictors. We used a Bernoulli-based GLM to weight various combinations of predictors based on these variables to predict the binarized activity of each neuron (time series of relative spike rates were thresholded at 0.05). The encoding model is fully described in our previous work (Runyan et al., 2017).
Pupil size and running predictors
Running velocity was measured at a higher time resolution than imaging and was binned to match the sampling rate of two-photon images (30 Hz). We included the velocity along the pitch and roll axes of the treadmill (relative to the mouse body axis). Running velocity measurements were separated into the following four channels: (1) forward, (2) reverse, (3) left, and (4) right directions based on rotation along these axes. Running velocity changes could both precede or follow the activity of individual neurons, so time series of running velocity were convolved with four evenly spaced Gaussian basis functions (240 ms half-width at half-height) extending 1 s both forward and backward in time (eight basis functions total for each running direction: forward, reverse, left, and right). Changes in pupil area were modeled similarly. Because pupil area changes on a slower timescale, the pupil area trace was convolved with 16 evenly spaced Gaussian basis functions 4 s forward and backward in time to allow for either prediction or response to pupil area changes.
Sound stimulus predictors
Sound stimuli were delivered from specific sound locations in the passive listening context. For sound stimulus onsets at each of the possible sound locations, 12 evenly spaced Gaussian basis functions (170 ms half-width at half-height) extended 2 s forward in time from each sound onset. First, second, and third repeats were represented separately because of potential adaptation-related effects. This resulted in 12 basis functions per repeat per sound location  three repeats  eight locations for 288 sound predictors.
GLM fitting and cross-validation procedures
All predictors were maximum normalized before the fitting procedure. The b -coefficients for the predictors were fitted to the activity of each neuron individually, using the glmnet package in R (Friedman et al., 2010) with elasticnet regularization, which smoothly interpolated between L 1 and L 2 type regularization according to the value of an interpolation parameter a, such that a = 0 corresponded to L 2 and a = 1 corresponded to L 1 . We selected a = 0.25.
Trials were randomly split into training (70% of trials) and testing (remaining 30% of trials) sets, while balancing the distribution of sound locations. Fitting was performed on the training set, and within each training dataset, crossvalidation folds (3Â) were also preselected so that sound locations were evenly represented. Model performance (see below, subsection GLM model performance) was assessed on the test set. Each model was thus fitted and tested on entirely separate data to prevent overfitting from affecting results. This train/test procedure was repeated 10 times, with random subsamples of the data included in train and test segments. The overall performance of each model was assessed as its mean across all 10 iterations.
GLM model performance
Each model's performance for each cell was defined as the fraction of explained deviance of each model (compared with the null model). In the null model, only a constant (single parameter) was used to fit the neuron's activity and no time-varying predictors were included. First, we calculated the deviance of the null and behavior model variants (see Materials and Methods, subsection Running and pupil contribution). For each model, the fraction of null model deviance explained by the model (d) was then calculated [(null deviancemodel deviance)/null deviance]. Deviance calculations were performed on a test dataset (30% of the data), which had not been included in the fitting procedure, and this train/test procedure was repeated 10 times on randomly subsampled segments of the data.
Running and pupil contribution
To identify the unique and separable contributions of running and pupil area to activity of SOM and Non-SOM neurons, we fit the following three separate models: (1) full behavior model, (2) "no-pupil" model, and (3) "no-running" model. In the full behavior model, all running, pupil, and sound predictors were included to predict the activity of each neuron. The no-pupil model did not include the pupil predictors, and the no-running model did not include the running predictors. Importantly, this analysis captures only the unique ways that pupil size and running can explain neural activity, where one cannot compensate for the contribution of the other.
We estimated the contribution of pupil or running to the activity of a neuron that could not be compensated for by the other variables, by comparing the model performance (fraction deviance explained, see Materials and Methods, subsection GLM model performance) in the full behavior versus no-pupil or no-running models. The "running contribution" was calculated as the difference in fraction deviance explained of the full model and fraction deviance explained of the no-running model d fbd nr , where d fb is the full behavior deviance and d nr is the no-running deviance. The "pupil contribution" was calculated as the difference in fraction deviance explained of the full model and fraction deviance explained of the no-pupil model d fbd np , where d np is the no-pupil deviance.
Decoding
We used a population decoder to compare the sound location information contained in AC and PPC population activity, and its modulation with arousal state. The details of the decoder that we built to estimate the information about sound stimulus location have been previously described (Runyan et al., 2017). Briefly, for each trial we decoded sound stimulus location from single-trial population activity by computing the probability of external variables (sound location left/right category) given population activity. We used Bayes' theorem, relying on population response probabilities estimated through the full behavior GLM and its predictors in that trial, to compute the posterior probability of each possible sound location stimulus. The decoder was "cumulative" in time, as for each time point t, it was based on all imaging frames from the initiation of the trial through time t. The decoded stimulus location at each time t was defined as the stimulus location with the maximum posterior probability, based on individual neurons or on a population of simultaneously imaged neurons. The population could include SOM, Non-SOM, or the "best" neurons. Non-SOM neurons were randomly subsampled 10 times, matching the sample size of SOM neurons in each iteration. The best neurons were selected as the n individual neurons with the best decoding performance, where n is the number of SOM neurons simultaneously imaged. Decoder performance was calculated as the fraction of correctly classified trials at each time point.
To compare decoder performance in low and high arousal states, trials were classified as "low" or "high" arousal based on normalized pupil area. Only the first sound repetition of each trial was used. Trials were randomly subsampled in the test set to ensure an even distribution of low and high arousal trials, and sound locations. This random subsample was repeated 10 times.
Sound location sensitivity
To assess the location sensitivity of sound-related activity in SOM and Non-SOM neurons in AC and PPC, trialaveraged responses were used to calculate the "location sensitivity index" (LSI), based on vector averaging in the preferred sound direction, as follows: where R is the average response during the sound location presentation, and H is the sound location from À90°t o 190°, indexed by i = 1 to n (eight possible locations). LSI can vary continuously from 0 (unselective) to 1 (selectively responding to only one sound location). Datasets that did not have a minimum of 20 trials for each of the eight sound locations were excluded. Importantly, sounds were played from free-field speakers located at different angles relative to the interaural axis. A high LSI could correspond to true sound location selectivity based on interaural level differences, or to the specific intensity tuning of a neuron, because of different sound intensities impinging on the two ears.
Population-level analyses
Defining population activity axes related to sound location and arousal To determine to what degree sound stimuli and arousal were driving population activity independently of each other in AC and PPC, we computed two axes for each imaging session: a sound location axis and a pupil axis. The sound location axis was defined as the axis in population activity space that connects the mean response on À90°and 190°trials during the passive listening behavioral context, while the pupil axis was defined as the axis that connects the mean population activity during "low arousal" and "high arousal" periods during the spontaneous behavioral context, as defined by our pupil-clustering algorithm. These definitions are analogous to how the "attention axis" is computed in primates (Cohen and Maunsell, 2010;Mayo et al., 2015;Cowley et al., 2020). We then measured the angle between the two vectors with a value from 0°to 90°, where 0°indicates linear dependence between the two subspaces and 90°indicates orthogonality.
Computing signal and noise correlations
We calculated noise correlations as fluctuations around mean sound responses; therefore, we only included neurons that had significant sound responses ( Fig. 1K; see also Materials and Methods, subsection Sound Responsiveness). Because it was rare for the pupil of a mouse to remain either in the high or low arousal cluster for the entire duration of a trial including all three sound repeats, we focused our analysis instead on the first repeat of a trial. We binned the zscored, deconvolved activity of each neuron into 15-frame (;500 ms) bins following sound onset. For each trial classified as high or low arousal, the mean sound response of each cell to all matching sound location presentations (including low, high, and unclassified trials) was subtracted and trials were concatenated. We computed partial Pearson correlations, discounting the effect of running speed (MATLAB function partialcorr), on these traces. Because the ratio of low to high trials was variable across imaging sessions, we subsampled 10 times to balance for matching numbers of high and low trials at each sound location. We only considered imaging sessions in which there were at least 50 matched trials in low and high arousal clusters.
Histology
After all imaging sessions had been acquired, each mouse was transcardially perfused with saline and then 4% paraformaldehyde. The brain was extracted, cryoprotected, embedded, frozen, and sliced. Once slide mounted, we stained brains with DAPI to be able to identify structure. We used anatomic structure to verify the locations of our injections in AC and PPC.
Results
To determine whether the effects of arousal on local activity are conserved across sensory and association cortices, we compared spontaneous and sensory-evoked activity in AC and PPC in six mice of both sexes, during spontaneous shifts in the arousal state of the animals. We used two-photon calcium imaging in superficial cortex to measure the spike-related activity of neurons positive and negative for the red fluorophore tdTomato, which was expressed transgenically in SOM-positive neurons (Madisen et al., 2010;Taniguchi et al., 2011). We virally expressed the genetically encoded calcium indicator GCaMP6f in all layer 2/3 neurons of AC and PPC in each mouse (Chen et al., 2013).
Neural activity was modulated by sound stimuli and by behavioral correlates of arousal
During each imaging session, we focused the microscope on either AC or PPC and imaged neural activity in two contexts. During the "spontaneous" context, the mouse ran freely on the spherical treadmill in the absence of sensory stimulation. During passive listening, sounds were presented from each of eight possible locations (Fig. 1B,C). In both contexts, mice were head fixed and allowed to run voluntarily on a spherical treadmill. To track the behavioral state, running velocity and pupil area of the mouse were recorded throughout imaging sessions (Fig. 1A-H).
We first examined how changes in pupil area and running speed corresponded with changes in the activity of individual neurons during the spontaneous context. Throughout imaging sessions, mice transitioned between behavioral states that reflect different arousal states: stillness and running, and pupil constriction and dilation (Fig. 1F,H). To quantify the effect of behavioral state transitions on the activity of individual neurons, we identified timepoints during the spontaneous context when either running speed or pupil area increased (Materials and Methods, subsections Running bouts and modulation and Pupil dilation events and modulation). The frequency of running bouts and dilation events was similar during PPC and AC imaging sessions (p = 0.59 and p = 0.65, respectively, permutation test, here and throughout unless otherwise noted; see Materials and Methods, subsection Experimental design and statistical analysis). Mice initiated running bouts at a mean rate of 0.915 bouts/min (bootstrapped 95% confidence interval of the mean, 0.749-1.11, here and throughout, unless otherwise indicated), and pupil dilations at 0.981 dilations/min (bootstrapped 95% confidence interval, 0.857-1.12). We observed no difference between AC (N = 24) and PPC (N = 20) imaging sessions when considering pupil area and running speed during passive listening or spontaneous contexts (passive running speed, p = 0.20; spontaneous running speed, p = 0.52; passive pupil area, p = 0.43; spontaneous pupil area, p = 0.73). However, pupil area and running speed tended to be higher in general during the passive listening context (pupil, p = 0.0020; running, p , 0.0001; paired permutation test, N = 44 datasets; Extended Data Fig. 1-1).
To determine how behavioral state affected neuronal activity, we next examined the activity of individual neurons during transitions from stationary to running, in the "spontaneous context," when no sound stimuli were presented. We compared mean activity of each neuron (deconvolved estimated spike rates, here and throughout) in the 3 s time window following running bout onset to the 1 s time window before running bout onset ( Fig. 1I; see Materials and Methods, subsection Running bouts and modulation). Pupil size modulation was calculated similarly, based on transitions in pupil area from constricted to dilated (Fig. 1D,J; Materials and Methods, subsection Pupil dilation events and modulation). We compared the running and pupil modulation of each neuron to shuffled distributions, where activity and behavioral data were time shifted by random intervals, and classified each neuron as positively, negatively, or not modulated by behavioral state transition. Larger proportions of the SOM and Non-SOM populations were positively modulated by both pupil dilations and running bout onsets in PPC than in AC (Fig. 1I,J), suggesting that the effects of arousal on spontaneous activity are not uniform across areas. Among the groups considered, the PPC SOM population had the greatest proportion of neurons modulated by running speed and pupil dilation (Fig. 1I,J).
In the passive listening context, sound stimuli were presented from each of eight locations, centered on the head of the mouse (Fig. 1B,C; see Materials and Methods, subsection Sound stimuli). We chose to manipulate sound location because of the role of PPC in spatial auditory processing during active behaviors (Nakamura, 1999). To determine whether each neuron was generally sound responsive, we computed the mean difference in activity during sound presentations and the prestimulus periods and compared with a shuffled distribution (Materials and Methods, subsection Sound responsiveness). We defined neurons as sound responsive if they responded to at least one sound location, more than would be expected from a random distribution obtained by shuffling. As expected, a greater proportion of AC SOM and Non-SOM neurons was sound responsive compared with PPC (39% of AC SOM and 40% of AC Non-SOM; 29% of PPC SOM and 28% of PPC Non-SOM neurons; Fig. 1K). The fraction of sound responsive neurons in AC is similar to the sparse sound encoding population described by others in layer 2/3 of AC (Hromádka et al., 2008). Furthermore, sound-evoked responses in AC Non-SOM neurons were lower when mice were running than when they were stationary (p , 0.05, Extended Data Fig. 1-2), as has been reported on extensively by others (Schneider et al., 2014;Bigelow et al., 2019;Yavorska and Wehr, 2021). In PPC, soundevoked responses were not affected by running behavior.
Sound location coding in AC and PPC
To characterize the sound location sensitivity of responses in AC and PPC during the passive listening context, we computed the sound Location Sensitivity Index (LSI) of each neuron ( Fig. 2A-D; Materials and Methods, subsection Sound location sensitivity). It is important to note that in the free-field sound stimulation configuration, we cannot distinguish true sound location selectivity from differences in sound intensity tuning. In both AC and PPC, Non-SOM neurons were more sensitive to sound location than SOM neurons (p , 0.0001; Fig. 2B,C). PPC neurons overall were less sensitive than AC neurons (Fig. 2D), and PPC sound responses were less reliable than AC sound responses (p , 0.001; Fig. 2E). Interestingly, among neurons with sound location preferences, PPC sound responses were biased toward the lateral locations at 190°(contralateral) and À90°(ipsilateral), while the distribution of sound location preferences in AC neurons was more uniform (Fig. 2F,G). Based on these differences in sound location sensitivity and response reliability, we expected population activity to encode sound location more accurately in AC than PPC.
To examine sound location coding at the level of neural populations, we constructed population decoders to predict the most likely sound stimulus location (left vs right) using the activity of different subsets of neurons. Each decoder was based on a Bayesian inversion of an encoding model that related the activity of each neuron to sound location and timing, running behavior, and pupil size ( Fig. 3A; Materials and Methods, subsection Decoding; Runyan et al., 2017). The posterior probability of each stimulus location was computed cumulatively at each time point using population activity from all previous timepoints in the trial. Decoder performance was quantified as the fraction of trials where the stimulus with the maximal posterior probability matched the actual presented stimulus.
First, to compare the overall ability of AC and PPC to represent sound location, we used activity of only the best cells (i.e., individual neurons whose activity best decoded sound location) regardless of cell type in the population decoder. The number of best cells used for each dataset was chosen to match the number of SOM neurons imaged during that session. The performance of the "best cell" decoder was above chance when using activity from both AC and PPC (Fig. 3C,D; Materials and Methods, subsection Decoding). However, as expected based on the sound location sensitivity of individual neurons (Fig. 2), AC decoding accuracy was higher than that of PPC (p , 0.0001). Next, we compared the SOM and Non-SOM population decoders from each dataset. Within both areas, sound location decoding was similar when using the activity of either SOM or Non-SOM populations (AC, p = 0.82; PPC, p = 0.56; unpaired permutation tests; Fig. 3C), and both cell type populations from AC outperformed decoding based on PPC populations (p , 0.0001, unpaired permutation test; Fig. 3C,D). All cell type-specific, subsampled population decoders performed worse than the decoders based on the n-matched population of best neurons (p , 0.0001, paired permutation test; Fig. 3D; see Materials and Methods, subsection Decoding). To summarize, sound location was more accurately decoded from AC population activity than PPC, and SOM and random n-matched Non-SOM subpopulations in both areas were similarly informative about sound location. A sparse code for sound location was especially evident in AC, as small numbers of highly tuned best neurons more accurately encoded sound location than the random subsamples of the population (Table 1, full values and statistics).
Activity of SOM neurons was differently modulated during aroused states in AC and PPC
Next, we more thoroughly characterized the activity of SOM and Non-SOM neurons during arousal state transitions. We focused these analyses on the spontaneous context, to isolate the effects of state transitions from the effects of sound stimulation on activity. We defined behavioral state transitions as the onset times of "running bouts," when the mouse's running speed rapidly increased (Materials and Methods, subsection Running bouts and modulation). We aligned and averaged the pupil area measurements to running bout onsets, observing that pupil area also increased during running bouts, though with a slower time course (Fig. 4A, gray trace; N = 44 imaging sessions). We also aligned and averaged the activity of SOM and Non-SOM populations from AC and PPC to running bout onset (Fig. 4A, colored traces; AC, N = 24 imaging sessions; PPC, N = 20 imaging sessions; from six mice). In both AC and PPC, mean activity of SOM and Non-SOM neurons increased with the onset of running bouts. However, this increase in activity was weak in AC because of the prevalence of both positively and negatively modulated SOM and Non-SOM neurons in the AC population (Figs. 1I, 4B). In PPC, Non-SOM and SOM neurons more uniformly increased activity at running onset (Figs. 1I, 4B).
Because of the slow time course of changes in pupil area compared with the more rapid transitions in locomotion (Fig. 4C,D), we also characterized single-neuron activity during periods of sustained pupil constriction and dilation. We classified pupil measurements as corresponding to low, transitional, or high arousal states ( Fig. 4E; Materials and Methods, subsection Defining low and high arousal states based on pupil area), and focused analyses on the low and high arousal states. As expected from the relationship between running speed and pupil area, running speed was higher during the pupil-defined high arousal states than low arousal states (p , 0.0001). Mice ran at a mean 6 SEM of Fig. 4E-H). To compare arousal modulation of activity in each population of neurons, we next computed an AMI (Materials and Methods, subsection Arousal modulation index), which could vary from À1 to 11, with À1 indicating greater mean activity in the low arousal period, and 11 indicating greater activity in the high arousal period. The AMI was higher in PPC than AC neurons (p , 0.0001; Table 2, full values and statistics). Interestingly, the AMI differed by cell type in PPC but not AC. In AC, the AMI was similar in SOM and Non-SOM neurons (p = 0.22), while in PPC, the AMI was higher in SOM than Non-SOM neurons (p , 0.0001; Fig. 4I). Finally, to consider the full-time-varying relationship between ongoing neuronal activity and behavioral correlates of arousal, we correlated the activity of each neuron to pupil area and running speed, across the entire spontaneous behavioral context (Fig. 4J,K). Consistent with the above analyses based on activity aligned on state transitions, SOM and Non-SOM activity in PPC was more strongly correlated with both running and pupil size than in AC (p , 0.0001). Within AC, SOM, and Non-SOM activity was similarly correlated with the two behavioral measures (pupil area, p = 0.063; running speed, p = 0.146), while within PPC the activity of SOM neurons was again more strongly correlated with behavior than was the activity of Non-SOM neurons (p , 0.001 for both running and pupil; Fig. 4J,K, Tables 3, 4, 5).
Together, our results so far indicate that in the absence of sound stimulation, SOM and Non-SOM neurons have heterogeneous activity relationships with arousal state in AC, whether defined by running speed or pupil area. In PPC, neuronal activity was positively modulated with heightened arousal, with a stronger modulation of SOM neurons than Non-SOM neurons.
An encoding model revealed different contributions of running speed and pupil size to single cell activity in AC and PPC Running speed and pupil area are strongly correlated signals but vary on different timescales (Fig. 4C,D) and have separable effects on neuronal activity (Vinck et al., 2015). Our analyses of ongoing spontaneous activity in AC and PPC also hint at the possibility of separable effects of pupil area and running speed, as the activity of AC Non-SOM neurons was more highly correlated with pupil area than with running speed (p , 0.0001, paired permutation test).
To disentangle the relationships among neuronal activity, running velocity, and pupil area, we used an encoding model approach (Pillow et al., 2008;Park et al., 2014;Runyan et al., 2017). We constructed a GLM that used ,0.0001 Unpaired permutation 3,4 ,0.0001 ,0.0001 Unpaired permutation 3,4 ,0.0001 sound stimulus timing and location, pupil area, and running velocity to predict the responses of individual SOM and Non-SOM neurons in AC and PPC, in the passive listening context (Fig. 5A). We note that in the above analyses (Figs. 1, 4), we considered only the speed at which the mouse was running in any direction, as increased running speed is correlated with heightened arousal (Fu et al., 2014;Zhou et al., 2014;Vinck et al., 2015;Mineault et al., 2016;Shimaoka et al., 2018). Because the activity of PPC neurons can be selective for running direction (Nitz, 2006;Whitlock et al., 2012;Runyan et al., 2017;Minderer et al., 2019) in the GLM, we used running velocity rather than speed to obtain more accurate predictions of the activity of each neuron (Materials and Methods, subsection Encoding models; Fig. 5).
To determine the relative contributions of pupil area and running velocity to the activity of neurons, we separately removed these predictors from the model and measured the decrement in the prediction performance of the model. For example, we calculated the pupil size contribution to the activity of a given neuron as the difference between the prediction performance of the model with and without the pupil size predictors. We considered this decrement in model performance as the contribution of pupil size to the activity of the neuron that is not redundant with running speed (Materials and Methods, subsection Running and pupil contribution). If running velocity and pupil area did not make unique contributions to neuronal activity, running predictors would be able to account for the missing pupil area predictors and vice versa, and the performance of the model would not be degraded compared with the full model, which includes both pupil area and running velocity predictors. The model comparison revealed single-neuron activity that could be explained distinctly by running and by pupil in both AC and PPC [ Fig. 5B-G (note neurons along the pupil and running contribution axes in D-G)]. While a Kruskal-Wallis test indicated that the contribution of pupil size differed among the four cell type/area combinations, post hoc tests returned p . 0.05 for all pairwise comparisons (Table 6, full values and statistics). In contrast, running contributions were overall stronger in PPC neurons than in AC neurons (p , 0.0001) and within PPC, running contributions were stronger to SOM than Non-SOM activity (p = 0.0002; Fig. ,0.0001 Unpaired permutation 3,4 ,0.0001 ,0.0001 Unpaired permutation 3,4 ,0.0001 5C,F,G). Within AC, running contributions did not depend on cell type (p = 0.44; Fig. 5C,D,E; Table 6, full values and statistics). Thus, running contributions, but not pupil contributions, reflect the observed differences in the arousal dependence of activity in AC and PPC.
Sound location coding was enhanced with arousal in AC, but not PPC Given the activity changes with arousal in AC and PPC, we hypothesized that arousal would affect sensory information coding in both regions. To test this possibility, we trained and tested the sound location decoder (Fig. 3) in low and high arousal periods (Fig. 6A-E). Sound stimulus trials were classified as occurring during low or high arousal states based on pupil size (Fig. 4E), and low and high arousal trials were evenly balanced to train and test the decoders. Decoding performance for left versus right sound locations using AC populations was modestly improved during heightened arousal (p = 0.0003, paired permutation test), but decoding performance was similarly poor in low and high arousal trials using PPC population activity ( Fig. 6C-E; p = 0.06, paired permutation test). Thus, while sensory coding in AC was slightly improved with heightened arousal, coding within PPC populations was unaffected by arousal state (Table 1, full values and statistics).
To better understand the basis for the improvement in sound location coding with arousal in AC, we related the sound coding of individual neurons (z-scored decoding performance) to the contribution of pupil size to their activity (Fig. 5, encoding model). In AC, sound coding had a weakly negative relationship with pupil modulation (Pearson's correlation, À0.03), where the neurons with the strongest sound coding tended to have the weakest pupil modulation. In PPC, on the other hand, sound coding and pupil modulation were positively correlated (Pearson's correlation, 0.17), as the same neurons can be modulated by both sound and arousal state in PPC. We then examined the modulation of neural population activity by sounds and by arousal state, in population activity space, where each dimension (axis) is the activity of one neuron and a population of n neurons has n dimensions (axes; Cunningham and Yu, 2014). We found the direction of the "sound location axis," along which left versus right sound locations were best distinguished in this n-dimensional space, and the direction of the "pupil axis," along which pupil size best explained population activity, in each AC and PPC population (see Materials and Methods, subsection Defining population activity axes related to sound location and arousal). We then measured the angle between these axes. In AC, these angles were more orthogonal to each other than in PPC (p = 0.0006).
The correlation structure of population activity influences the amount of information that can be encoded across neurons (Averbeck et al., 2006;Panzeri et al., 2022). Specifically, the slope of the relationship between signal and noise correlations determines whether noise correlations limit the information that can be encoded by the population. In other words, greater shared variability among neurons with similar tuning preferences reduces the information that the population can encode. As a consequence, reductions in noise correlations among neuron pairs with similar tuning preferences or increases in noise correlations among neuron pairs with opposing tuning preferences would both theoretically increase the encoding capacity of a neural population (Averbeck et al., 2006;Panzeri et al., 2022). We then wondered whether transitions between arousal states also induced different changes in the correlation structure in AC and PPC, contributing to the improvements in sound location coding in AC. We compared pairwise noise correlations in high and low arousal conditions for both regions, and sorted these by the signal correlations between neuron pairs. Pairwise noise correlations among neurons with similar sound location preferences (high signal correlations) were reduced in the high arousal state in AC (p = 0.002). In PPC, on the other hand, these similarly tuned neurons instead became more correlated with arousal (p , 0.0001). In neuron pairs with negative signal correlations (different tuning), noise correlations were enhanced in AC (p = 0.0015), but did not change with arousal in PPC (p = 0.235). Together, the arousal-induced changes in the correlation structure of population activity suggest that the improvement in sound location coding in AC could result from a reduction in shared variability across neurons with similar sound preferences, in addition to the generalized suppression in sound-evoked responses during locomotion (Schneider et al., 2014;Bigelow et al., 2019;Yavorska and Wehr, 2021) that could sharpen response selectivity.
Discussion
Our goal in this study was to determine whether the activity of inhibitory interneurons is differently modulated by behavioral state across the cortical processing hierarchy. We have measured spontaneous and sound-driven activity in populations of SOM-expressing inhibitory interneurons and Non-SOM neurons in layer 2/3 of AC and in PPC, while mice transitioned between arousal states.
In the absence of sound stimulation, the effects of arousal state on spontaneous activity in AC were complex. Although heightened arousal had a slight positive effect on activity in AC at the population level, activity in individual AC neurons could be positively or negatively modulated, as has been described previously by others (Bigelow et al., 2019;Yavorska and Wehr, 2021). Short spurts of running, as we observed in our imaging sessions (Extended Data Fig. 1-1D), are associated with a net depolarization in AC (Shimaoka et al., 2018), which likely contributes to the net positive relationship between spontaneous AC activity and running observed here. In contrast, the spontaneous activity of most PPC neurons increased during these heightened arousal states, and SOM neurons were even more strongly and uniformly modulated than Non-SOM neurons.
The behavioral state transitions in our study involved increases both in pupil size and in locomotion (Fig. 4A). It is thus crucial to emphasize that the effects of heightened arousal state on neural activity described here include a mixture of motor-related and "true" Figure 6. Arousal impacts the structure of population activity, improving information coding in AC. A, The encoding model was trained on all trials that included all arousal levels, and inverted using Bayes' rule to compute the posterior probability of auditory stimuli given the activity of the neural population in AC and PPC. B, Schematic of the discrimination being performed by the decoder, classifying sound stimuli as occurring from the left or right of the mouse. C-D, Mean fraction correct of cumulative sound location (left sound locations vs right sound locations) decoding in AC (C) and PPC (D) populations in high (dark) and low (light) arousal conditions. Subsampling to match arousal and sound locations was repeated 10 times. E, Cumulative fraction correct of decoding for left versus right sound location at the end of the trial in AC and PPC in high and low arousal. F, For each AC neuron, the sound location decoding performance based on the activity of that neuron is plotted against the pupil modulation of that neuron, as quantified by the encoding model in Figure 5. Neurons are color coded from black to red to indicate their z-scored sound location decoding performance (red neurons . 0 z-scored performance). G, As in F, for PPC. H, Schematic to explain the definitions of the sound location and pupil axes in the high-dimensional population activity space in I. I, Angle in degrees between the pupil and sound location axes in each AC and PPC dataset. The angle between the pupil and sound axes is significantly smaller in PPC than AC. J, Mean pairwise noise correlation among sound-responsive AC neurons during high arousal (gray) and low arousal (black) trials, binned by their pairwise signal correlation. K, As in J, but for PPC. L, Schematic demonstrating the impacts of arousal on sound coding in AC population activity. The shape of the population activity subspace responding to "sound 1" and "sound 2" changes between low arousal (dashed outlines) and high arousal (gray ovals) states, improving their discriminability. This shape change occurs because the shared variability is reduced among neurons coding for similar locations and is enhanced among neurons coding for different locations (see J). M, As in L, for PPC. In PPC, the shape of the area of population activity space encoding sound 1 or sound 2 does not change with arousal, as shared variability is more generally increased across neuron pairs (see K). Across panels: **p , 0.01, ***p , 0.001, ****p , 0.0001. n.s., Not significant. AC Non-SOM, N = 24 datasets; AC SOM, N = 24 datasets; PPC Non-SOM, N = 20 datasets; PPC SOM, N = 20 datasets; throughout figure panels. arousal-related effects, such as those resulting from increases in norepinephrine and acetylcholine release within AC and PPC. While locomotion, whisking, and pupil dilations have all been considered as behavioral correlates of the arousal state of an animal, motor-related feedback acts on specialized circuits within visual, somatosensory, and auditory cortices, determining whether motor behavior positively or negatively influences neural activity Fu et al., 2014;Schneider et al., 2014;Pakan et al., 2016;Bigelow et al., 2019;Yavorska and Wehr, 2021). This motor-related feedback acts in concert with neuromodulatory inputs more directly related to the arousal state of the animal, leading to state-dependent network changes that are specialized across cortical regions. It is unlikely that any aroused state is without a related change in fidgeting, facial movements, or other motor outputs (Musall et al., 2019;Stringer et al., 2019), and so the relative contributions of motor-related versus neuromodulatory inputs to neural activity must be defined for each brain region for a full consideration of state-dependent processing across the cortex. Here, we were able to disentangle the unique contributions of running velocity and pupil size to the activity of individual neurons in AC and PPC using an encoding model (Fig. 5). In both regions, pupil size and running velocity had distinguishable contributions to the activity of SOM and Non-SOM neurons. The magnitude of unique pupil contributions was similar across all groups, but running contributions to activity were significantly stronger in PPC, particularly among SOM neurons.
In AC, the effects of arousal and locomotion can oppose each other, as motor-related feedback activates PV neurons, reducing sound-evoked responses during locomotion (Schneider et al., 2014;Bigelow et al., 2019;Yavorska and Wehr, 2021). We observed a small number of Non-SOM neurons in AC that were strongly and positively modulated with locomotion, which may include the PV neurons mediating the locomotion-related reduction of activity in other neurons (Schneider et al., 2014). In PPC, motor-related effects were largely positive and were particularly strong among SOM neurons, suggesting that, unlike in AC (Schneider et al., 2014), motor feedback does not target PV neurons in PPC. Instead, SOM neurons in PPC may inhibit PV neurons during locomotion, disinhibiting excitatory neurons and further enhancing the arousalrelated increase in activity in the local population. It will be interesting to determine whether this is the case in future studies. Recently, we also discovered that activity within the SOM population is highly coordinated, especially in PPC (Khoury et al., 2022). As a result, the transitions from low to high arousal states would trigger highly coordinated SOM population events, which could strongly impact the local network activity state (Chen et al., 2015;Veit et al., 2017;Wang and Yang, 2018). In the future, causal manipulations of SOM neurons, mimicking their activity during arousal transitions, will help reveal the impact of these coordinated SOM activity events on the activity and coding in the local population.
The effects of arousal on stimulus coding have been examined in depth across primary sensory cortices (Zhou et al., 2014;McGinley et al., 2015;Vinck et al., 2015;Shimaoka et al., 2018;Lin et al., 2019). Previous studies revealed that moderate levels of arousal optimally impact sensory coding (McGinley et al., 2015;Lin et al., 2019), aligning well with the Yerkes-Dodson (inverted-U) relationship between arousal and perceptual task performance (Waschke et al., 2019). Unlike these studies, we did not observe a decrement in sound location coding in the highest arousal state, instead measuring a modest improvement in decoding accuracy in the high arousal state. Mice in our experiments were most likely to visit two separable behavioral states (stationary/unaroused or running/aroused), without the gradation of different levels of arousal observed by others. As a result, the high arousal state described here likely includes a mixture of the moderate and high arousal states defined by others (McGinley et al., 2015). Because sensory coding in PPC depends on the behavioral relevance of stimuli (Fitzgerald et al., 2011;Pho et al., 2018), we expected that sound coding would also improve in PPC with heightened arousal, when mice might be more aware of the sound stimuli. Surprisingly, despite the more pronounced positive modulation of activity in PPC that accompanied increases in arousal and locomotion (Fig. 4), the behavioral state did not affect sensory encoding in PPC (Fig. 6). Our results imply that arousal alone is not sufficient to improve sensory coding in PPC outside of a task context, supporting the idea that task engagement and arousal modulate sensory responses through separate pathways (Saderi et al., 2021).
Finally, to better understand the basis of the improvement of sound coding with arousal in AC (and lack thereof in PPC), we related sound coding and pupil-related effects on population activity and its correlation structure. In AC, sound coding and pupil modulation were strongest in distinct sets of neurons (Fig. 6F), and, with heightened arousal, shared variability was reduced among neurons with similar tuning (Fig. 6J). The net result was an improvement in our ability to decode sound information from AC population activity in the heightened arousal state, as the responses of the population to different stimuli were more separable (Fig. 6L, scheme). In PPC, on the other hand, sound coding and pupil modulation were intermixed within the same neurons (Fig. 6G), and shared variability increased among neurons with similar tuning (Fig. 6K). As a consequence, although activity was strongly modulated in PPC with arousal, the separability of population responses to different sounds was not affected (Fig. 6M). As has been recently reviewed, positive noise correlations among neurons with similar tuning limit the information that a population can encode (Averbeck et al., 2006;Panzeri et al., 2022), which is consistent with our results. To summarize, arousal had different effects on the correlation structure of population activity in AC and PPC. In AC, the result was better separability of the population responses to different sounds, while in PPC the effects of arousal on population activity were information limiting (Averbeck et al., 2006;Panzeri et al., 2022).
To conclude, we have characterized the effects of the global arousal state on population activity in sensory and association cortices by measuring neuronal activity during fluctuations in arousal and locomotion. In AC, but not PPC, sensory representations were enhanced with arousal, even when not behaviorally relevant. An important future direction will be to determine whether global shifts in arousal affect the coding of behaviorally relevant information in PPC, and whether local inhibitory circuits can provide a gating mechanism to enhance the encoding of behaviorally relevant sensory information of PPC.
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Domain: Psychology Biology
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Serotonin-manipulated juvenile green sea turtles Chelonia mydas exhibit reduced fear-like behaviour
: Animals display fear-like behaviours before escaping from predators. This response triggers both behavioural and physiological changes in multiple body systems, allowing animals to escape danger and ensure survival. Fear-like behaviour is modulated by the serotonergic sys-tem in the brain of vertebrates, which shapes social behaviour and cooperative behaviours. Using fluoxetine (FLX), a common pharmaceutical that alters the levels of serotonin in the brain, we aimed to clarify whether the same is true in solitary animals like green turtles Chelonia mydas . Green turtles exhibit individual differences in their response to risk. If fear-related behaviours are regulated by the serotonin system in turtles, the fear-like responses of individuals injected with FLX could change. We therefore assessed the effect of FLX injection on the behavioural responses to a fear stimulus in 9 wild juvenile green turtles in an aquarium setting. We inserted a hand net as a stimulus into the aquarium (within a designated inspection zone) to elicit a fear-like behaviour and measured the time that turtles spent in this zone. All turtles exhibited fear-like behaviour and fled from the stimulus prior to any injection treatment. Turtles with control injection (no FLX) also fled and avoided the inspection zone with the fear stimulus. FLX injection appeared to reduce the turtles’ fear of the stimulus: The total time turtles injected with FLX spent in the inspection zone was significantly longer than for turtles that received a control medium injection. Control turtles fled from the stimulus and were initially vigilant and avoided the area with the stimulus, but then moved throughout the aquarium, including the inspection zone. These data suggest that fear-like behaviour is modulated by the serotonin-mediated nerve system in juvenile green turtles.
INTRODUCTION
Animals show fear-like behaviours to avoid or escape from predators. Avoiding predation is a preeminent selective force in nature because failing to do so immediately reduces the individual's future fitness (Werner & Peacor 2003). Fear response triggers both behavioural and physiological changes in multiple body systems to allow individuals to avoid danger (Cezario et al. 2008, Adolphs 2013). For example, sharks are predators of sea turtles, and antipredator responses in turtles have been shown to enable hiding, predator deterrence, flight and vigilance (Heithaus et al. 2008). The time an animal spends forag-
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ing can be affected by the presence of predators; thus, antipredator responses are essential fitness components that can influence an animal's survival (Heithaus et al. 2002, 2005, 2008, Higgins 2006). However, physiological changes in fear responses have not been studied in sea turtles.
Fear-like behaviours are known to be associated with the brain serotonin system in animals (Adolphs 2013, Beis et al. 2015). Serotonin (5-hydroxytryptamine, 5-HT) is a neurotransmitter released in the brains of several vertebrates in response to stress (Puglisi-Allegra & Andolina 2015). Serotonin participates in the modulation of stress and defensive behaviour in fish (Herculano & Maximino 2014, Winberg & Thörnqvist 2016, Soares et al. 2018) and has been implicated in vertebrate social behaviour (Insel & Winslow 1998, Soares et al. 2018) and cooperative behaviours (Paula et al. 2015). These findings indicate that serotonin may be involved in the mediation of cooperative and defensive behaviours in situations when animals are in fear. In fact, when the guppy treated with fluoxetine (FLX), a commonly prescribed antidepressant in the family of selective serotonin reuptake inhibitors that alters the levels of serotonin in the brain, was exposed to the stimulus of predator animation, it was found to spend more time near the predator image and freezing and less time avoiding the predator. This observation suggests the role of serotonin in cooperative behaviours (Pimentel et al. 2019).
Animals that form groups as a type of cooperative behaviour are able to detect predators sooner than solitary individuals in part due to the improved vigilance effect, so predators seem to be less successful when their prey is fully aware of their presence (Caro 2005). However, sea turtles are primarily solitary animals (Thomson et al. 2015), although some adult green turtles in the Florida Keys are found in groups (Bresette et al. 2010). Green turtles detect a danger signal, respond to danger and show vigilance without external help. In addition, turtles respond to a fear stimulus, including non-lethal factors such as humans (Griffin et al. 2017), hand nets (Kudo et al. 2021), shark decoys (Wang et al. 2010) and boats (Heithaus et al. 2002), with fear-like behaviours and flee from predators. In this situation, it is unclear whether fearlike behaviours in green turtles are similar to the serotonin-derived behaviours displayed by animals that form groups in cooperative behaviours.
The green sea turtle, Chelonia mydas, is under threat from human activities, of which fisheries bycatch has the greatest impact (Lewison et al. 2013). The conflict between conservation and hu man activ-ity needs to be addressed. Much research, e.g. the development of turtle excluder devices, has already been conducted to solve this problem, and understanding how individual animals respond to disturbance, for example to determine what triggers an avoidance response in animals, will be useful for commercial fisheries (Higgins 2006, Wang et al. 2010, 2013, Bostwick et al. 2014). If we can understand what incites fear, we can incorporate some of these stimuli into fishing nets to reduce sea turtle by-catch.
After several years in the pelagic zone juvenile turtles return to coastal waters and occupy various habitats during their development until they reach sexual maturity (Musick & Limpus 1997). Green turtles in tropical and sub-tropical foraging areas establish narrow home ranges during foraging periods in Florida (Makowski et al. 2006), Hawaii (Brill et al. 1995), Mexico (Lamont et al. 2015) and Japan (Kameda et al. 2013). Wild green sea turtles suffer high mortality in their natural habitat for several reasons (e.g.fishing nets, boats, predators); thus, various circumstances can teach them fear (Ishihara et al. 2014). In a study of stimulus presentation experiments, green turtles from the coast of Japan exhibited fear-like responses to hand nets in an aquarium setting: turtles confronted with a net fled immediately and avoided the area with the net for longer than in a control setting (i.e.without a net) (Kudo et al. 2021). However, it is unclear whether these behaviours correspond to the serotonin-derived behaviours in social interaction.
Therefore, this study aimed to clarify whether fearlike behaviour is modulated by serotonin in juvenile green turtles. Green turtles, known to be solitary, exhibit individual differences in their responses to a fear stimulus. If fear-like behaviours are induced by the serotonin system in turtles, the fleeing responses of individuals whose serotonin levels in the brain have been increased by FLX, i.e. pharmaceuticals which affect the serotonergic systems, are likely to change.
Study site and sea turtle collection
The present study was conducted from June to November 2019 on the Hazako coast (32°56' N, 132°2' E) in Oita Prefecture, Japan. Nine wild juvenile green turtles (Chelonia mydas) of standard carapace length, 353−425 mm and body mass of 7.3− 11.0 kg were ob-tained opportunistically from accidental catches in coastal set nets of commercial fisheries and marked with a plastic tag (obtained from the Oita environmental conservation forum) attached to the left rear limb after it had been pierced. The turtles were kept for 2−4 wk in a circular holding tank (diameter: 260 cm, height: 94 cm). Ambient seawater was continuously circulated in the aquarium, and water temperature was maintained at 21−25°C. The holding tank was illuminated by natural light at 80−120 lux. The turtles were provided with defrosted fish daily but did not feed initially after transfer to the holding tank. When they started to feed, they were considered acclimatised to the holding tank and ready for the experiments. At this point, each individual was fed 3−5 thawed silver-stripe round herring every morning.
Experimental setup and design
All tests were conducted in an experimental aquarium (300 × 200 × 100 cm; Fig. 1). A video camera (VCC-H 154 equipped with 540 HF 3.5 M -2 lens, Digimo Innovations) was installed 230 cm above the water surface and the centre of the experimental aquarium to film turtle behaviour during the experiments. Images were recorded on a computer using the LRH 1540 v movie Image recording control system software. A black mesh covered the frame on which the camera was installed (see Fig. 1) to avoid direct sunlight as the reflection in sunlight would have made it impossible to see the image of the turtle. The inspection zone (200 × 100 cm) was designated as anywhere within 1 m of the wall on which the stimulus was presented (Fig. 1).
Trials were conducted over 4 d, with behavioural trials on Days 1 and 2 (Trials 1 and 2, respectively) and FLX trials on Days 3 and 4 (Trials 3 and 4, respectively; Fig. 2). All animal experimentation protocols were approved by the Institutional Animal Care and Use Committee of the University of Tokyo (permit no. P18-15). The study was carried out in accordance with the National Institutes of Health guide for the care and use of laboratory animals (NIH Publications No. 8023, revised 1978) and in com pliance with the ARRIVE guidelines ( [URL] guide lines.org/ arriveguidelines).aquarium without stimulus. Then we threw a hand net tied to a rope into the centre of the experimental tank and pulled the rope towards the wall to place the hand net within the inspection zone. The stimulus was left in place while the turtle was filmed for 20 min before being returned to the holding tank. The same turtle was tested the next day using the same procedure. The order of the turtles being tested was decided haphazardly for Trial 1, and the same order was used the next day for Trial 2. The time each turtle spent in the inspection zone during each trial was measured based on the recording of the behavioural tests.
FLX tests: Trials 3 and 4
On Day 3, the day after the 2nd behavioural trial, the turtles were haphazardly assigned to either the control or the FLX treatment group and injected intraperitoneally with 0.5 ml per kilogram body weight (kg BW −1 ) control medium (1% by vol of Tween ® 80, 1% by weight of methylcellulose and H 2 O; Inagaki et al. 2005) or control medium + FLX (Tokyo Chemical Industry; 20 mg FLX per ml control medium, equivalent to 10 mg FLX kg BW −1 ), respectively. We chose this dose of FLX, because it was the lowest does that elicited a fear-like stimulus response in all 9 animals in preliminary tests (data not shown).
In those tests, we started with injections of 60 mg kg BW −1 based on Deckel (1996). However, at this concentration, movements of treated turtles were getting sluggish, and they did not respond to the fear stimulus. Each turtle was moved to the experimental aquarium 15 min after injection and underwent the same procedure as during the behavioural trials. The FLX trial was repeated on Day 4, with each turtle being assigned to the other treatment group (i.e.control turtles received the FLX injection and vice versa).
Statistical analysis
All of the data met assumptions of normality and homogeneity of variances (Kolmogorov-Smirnov onesample test, p > 0.05) and homogeneity of variances (Levene's test, p > 0.05). In order to confirm whether turtles would flee from the stimulus and consistently respond to the stimulus over time, we compared the total time turtles spent in the inspection zone between the 2 repeated behavioural trials for each individual using paired t-tests. Next, we used paired t-tests to compare the total time each turtle spent in the inspection zone during the 2 behavioural tests (Trials 1 and 2) in the 20 min before versus in the 20 min after the stimulus was added to the tank. The aim of this analysis was to validate the stimulus that was used in the behavioural tests (i.e.without injections). We also compared the time that turtles spent in the inspection zone between the control and FLX treatment groups using paired t-tests. All statistical analyses were performed using SPSS software v25.0 and a significance level of 0.05.
RESULTS
Generally, the total time turtles spent in the inspection zone was significantly shorter in both behavioural tests (Trials 1 and 2) when the hand net was present compared with when it was absent (paired t-test, Trial 1: t 8 = −3.988,p < 0.05, mean ± SD without stimulus : 207.3 ± 107.9 s, with stimulus: 53.2 ± 36.7 s; Trial 2: t 8 = −3.603,p < 0.05, without stimulus: 228.5 ± 112.9 s, with stimulus: 59.2 ± 50.2 s, n = 9 for both trials, Fig. 3). The hand net was therefore deemed a suitable stimulus for investigating fear-like behaviour in green sea turtles. Comparisons between Trial 1 and Trial 2 showed that test results for each individual were significantly correlated (Pearson's correlation: with stimulus, r = 0.85, p < 0.01, n = 9; without stimulus: r = 0.87, p < 0.01, n = 9), indicating that all individuals consistently showed fear-like behaviour.
In the presence of the hand net, the green turtles injected with FLX remained in the inspection zone for a significantly longer period than the turtles that received a control medium injection (paired t-test: t 8 = −2.805,p < 0.05, mean ± SD for the medium control 53.0 ± 45.0 s, and 96.2 ± 39.9 s for the FLX treatment, n = 9, Fig. 4).
DISCUSSION
In this study, juvenile green sea turtles fled from the fear stimulus, and the stimulus elicited turtle responses that indicated fear-like behaviour. The turtles injected with FLX remained in the inspection zone for a longer time than those injected with the control injection. This result indicates that the response of the turtles injected with control medium was changed by the FLX injection, causing them to be less fearful of the stimulus. These results suggest that regulation by the serotonin-mediated nervous system has effects on the response to a fear stimulus in juvenile green turtles.
Sea turtles are primarily solitary animals (Thomson et al. 2015);however, Bresette et al. (2010) reported that adult (> 90 cm standard carapace length [SCL]) and large subadult green turtles (65−90 cm SCL) observed on their foraging ground were found in small groups of 4 or 5 individuals, while juvenile turtles (< 65 cm SCL) were not seen grouping. Since the green turtles we examined had SCLs of 35.3− 42.5 cm, they were juveniles and probably solitary.
In mammals, such as mice, FLX treatment increases the serotonin level in the amygdala (Mar cinkiewcz et al. 2016). Regulation of the serotonin-mediated nervous system with serotonin and selective serotonin reuptake inhibitors suppresses fear-like behaviour (Salchner & Singewald 2002, Spennato et al. 2008, Ravinder et al. 2011, Burghardt et al. 2013, Deschaux et al. 2013). Fear-like behaviour is also related to the expression of genes associated with serotonin synthe-sis, regulation, uptake and degradation (Thörnqvist et al. 2015). Many previous clinical studies have demonstrated that FLX treatment reduces fear and anxiety in mice, rats and humans (Salchner & Singewald 2002, Spennato et al. 2008, Ravinder et al. 2011, Burghardt et al. 2013, Deschaux et al. 2013). Given that FLX treatment suppressed fear-like behaviour in juvenile green sea turtles in the present study, a similar phenomenon may occur in other solitary animals.
All turtles in the behavioural trials (i.e.without injection) consistently showed fear-like behaviour over time (Trial 1 vs. Trial 2) in our study. The turtles' response was changed by the FLX injection, causing the turtles to be less fearful of the stimulus (i.e.spending more time in the inspection zone, see Fig. 4). Similarly, manipulation of the serotoninmediated system with FLX affected personality traits such as shyness and boldness in sticklebacks: shy sticklebacks under FLX treatment spent more time near the image of a predator (Abbey-Lee et al. 2019). In our study some of the turtles we considered to be shy (i.e.those which had spent less time in the inspection zone after the stimulus was added than other turtles in the control medium injection group) showed a trend towards greater boldness when treated with FLX (i.e.these turtles then spent more time in the inspection zone with the stimulus relative to turtles we considered bold based on their control treatment behaviour); however, this trend could not be analysed statistically due to the small sample size.
Fear-like behaviour is generally associated with the serotonin system coupled with experience and memories of fearful experiences in animals (Zanette et al. 2019). The fear caused by a predator in the past can have long-lasting effects which modify the animal's subsequent reactions to predators (Clinchy et al. 2011, 2013, Manzur et al. 2014, Zanette & Sih 2015, Zanette & Clinchy 2017, Crane et al. 2018). Predator-induced fear can even cause post-traumatic stress disorder-like changes in the brains and behaviours of wild animals (Zanette et al. 2019). There are many occasions on which wild green sea turtles along the Pacific coast from northeast to southwest Japan can experience fear on their natural foraging ground, because they are frequently captured in the coastal set nets of commercial fisheries (Ishihara et al. 2014, Narazaki et al. 2015). The density and methods of fisheries vary among locations in Japan, with turtle mortality rates of 0.6−40% as a result of bycatch in set nets (Ishihara et al. 2014, Narazaki et al. 2015). Thus, solitary sea turtles may respond to fear stimuli on the basis of past traumatic experiences, which could reflect the level of pressure from fish- Fig. 1. Experimental setup for the behaviour test
Fig. 3 .
Fig. 3. Comparison of times spent in the inspection zone with (+) and without (−) each stimulus. Black bars: average value; error bars: 95% CIs; grey dots: measured values; grey lines connect the same individuals; *p < 0.05
Fig. 4 .
Fig. 4. Comparison of times spent in the inspection zone with stimulus control injection and fluoxetine (FLX) injection. Black bars: average value; error bars: 95% CIs; grey dots: measured values; grey lines connect the same individuals
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Domain: Psychology Biology
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Synthesis Biological Evaluation of Quinazoline-4-thiones
Several 2,2-dimethyl-3-phenyl-1,2-dihydroquinazoline-4(3H)-thiones and 2-methyl-3-phenylquinazoline-4(3H)-thiones were synthesized and tested for their antimycobacterial, photosynthesis-inhibiting, and antialgal activity. Antimycobacterially active compounds were found among the 6-chloro substituted compounds. 6-Chloro-3-(4-isopropylphenyl)-2-methylquinazoline-4(3H)-thione exhibited higher activity than the isoniazid standard against Mycobacterium avium and M. kansasii. Most of the compounds possessed photosynthesis-inhibiting activity. 6-Chloro-2,2-dimethyl-3-phenyl-1,2-dihydro-quinazoline-4(3H)-thione and its 3´-chloro- and 3´,4´-dichloro analogs were most effective in the inhibition of oxygen evolution rate in spinach chloroplasts. Of compounds selected for toxicological screening, 6-chloro-3-(4-isopropylphenyl)-2-methyl-quinazoline-4(3H)-thione was the only one active in the brine shrimp bioassay.
Introduction
Tuberculosis continues to be a devastating disease worldwide and is believed to be present in about one third of the world's population [1]. The increasing incidence of multi-drug-resistant tuberculosis is emerging as a major infectious disease problem throughout the world [2]. Mycobacterial diseases caused by the Mycobacterium avium -M. intracellulare complex show a rising occurrence among children, the elderly, and HIV-infected patients, and they are frequently fatal [3]. The search for potential antimycobacterial drugs is consequently one of the primary tasks of present-day medicinal chemistry.
Based on the results of the biological tests, four compounds, 1h, 1i, 2b, and 2f, were selected for a toxicological screening bioassay and tested using brine shrimp larvae (Artemia salina L.) as the sensitive organism [36].
2,2-Dimethyl-3-phenyl-1,2-dihydroquinazoline-4(3H)-thiones (1a-k) were synthesized by condensation of the corresponding 2-amino-N-phenylthiobenzamides with acetone under the catalysis by silica gel. The reaction mixtures were allowed to stand at room temperature for 24 h, then concentrated in vacuo, and the products 1 were isolated by column chromatography on silica gel using petroleum ether with acetone as the mobile phase. The starting 2-amino-N-phenylthiobenzamides were prepared by a two-step process from 2-amino-N-phenylbenzamides. Treatment of 2-amino-Nphenylbenzamide with phosphorus decasulfide in pyridine afforded the corresponding pyridinium salt. Hydrolysis of the pyridinium salt in a toluene-water system gave 2-amino-N-phenylthiobenzamide [42]. 2-Methyl-3-phenylquinazoline-4(3H)-thiones (2a-g) were prepared by thionation of the corresponding 2-methyl-3-phenylquinazolin-4(3H)-ones with phosphorus decasulfide in pyridine. The syntheses are outlined in Scheme 1. The characteristic data of compounds 1a-k and 2a-g are given in Tables 1 and 2. Characteristic data of the intermediates were [43] or will be published elsewhere.
Antimycobacterial activity
Antimycobacterial activity of the compounds was tested in vitro against Mycobacterium tuberculosis, M. avium, and M. kansasii, obtained from the Czech National Collection of Type Cultures (CNCTC), and a clinical isolate of M. kansasii, using the micromethod for the determination of the minimum inhibitory concentration (MIC). The MIC values of the compounds are given in Table 3. Antimycobacterially active compounds were found only among the 6-chloro derivatives (X = Cl). Derivatives 1h, 2d, 2f, and 2g demonstrated moderate activity against mycobacteria. The activity of derivative 2f (X = Cl, R = 3-isopropyl), the most active compound, against M. avium and M. kansasii is worth mentioning. In some cases, the antimycobacterial activity observed after 14 days weared off after 21 days of incubation (1c, 1j, 1k). The other compounds showed no activity in the range of concentrations tested (data not given).
Photosynthesis-inhibiting activity in spinach chloroplasts
Most of the tested compounds inhibited the photosynthetic electron transport in spinach chloroplasts. The photosynthesis-inhibiting activity of the compounds was investigated as inhibition of oxygen evolution rate (OER) in spinach chloroplasts. IC 50 values are given in Table 4. The 6-chloro analog 1g was the most effective inhibitor of OER. Its IC 50 value was comparable to that of the standard diuron (DCMU). 6-Unsubstituted compound 1a was 60-fold less potent than 1g. Substitution on the phenyl ring was unfavourable. Whereas mono-and dichloro derivatives 1h and 1i were approximately twice less potent than compound 1g, alkyl derivatives 1j and 1k were more than 100fold less potent. The relatively low photosyntesis-inhibiting activity of compounds 2 is probably a consequence of their low aqueous solubility, and hence their restricted passage through the hydrophilic regions of thylakoid membranes. A comparison of compounds 2a and 2b with their analogs 1g and 1h indicates 75-to 100-fold decrease in activity. Photosynthesis-inhibiting activity of compounds 1d, 1e, and 1f could not be determined due to their incomplete solubility. . IC 50 value for the standard, a selective herbicide 1,1-dimethyl-3-(3,4-dichlorophenyl)urea (Diuron), was 7.3 µmol dm -3 . The other compounds were inactive (less than 5% reduction) or weakly active (27% (1h), 22% (1k), and 15% (2b) reduction of chlorophyll content) in the concentration range from 0.83 to 99.0 umol dm -3 . This could be due to their too low aqueous solubility.
Toxicological screening bioassay
Four compounds, 1h, 1i, 2b, and 2f, were selected according to their biological activity in antifungal [19], antimycobacterial, photosynthesis-inhibiting, and antialgal tests for toxicological screening bioassay using brine shrimp larvae (Artemia salina L.) as the sensitive organism. Only compound 2f was found toxic. Its value of EC 50 was 155.20 µmol dm -3 (EC 50 of MnCl 2 was 41.44 mmol dm -3 ). Other compounds tested demonstrated no significant toxicity in the range of used concentrations.
General
The melting points were determined on a Kofler block and are uncorrected. The samples for elemental analyses and biological tests were dried over P 4 O 10 at 61 °C and 66 Pa for 24 h. Elemental analyses were performed on a C,H,N,S analyzer (FISONS AE 1110, Milano, Italy). The purity of the compounds was checked by TLC using petroleum ether-ethyl acetate (9:1) and petroleum etheracetone (7:3) as the mobile phases. Column chromatography was performed on Silica gel Merck 60 with petroleum ether-acetone (9:1) or toluene. 1 H-and 13 C-NMR spectra were recorded for DMSO-d 6 solutions at ambient temperature on a Varian Mercury-Vx BB 300 spectrometer (operating at 300 and 75 MHz, respectively). Chemical shifts were recorded as δ values in parts per million (ppm), and were indirectly referenced to tetramethylsilane via the solvent signal (2.49 for 1 H and 39.7 for 13 C). Multiplicities are given together with coupling constants (J, in Hz).
2-Amino-N-phenylthiobenzamides
A 100-mL flask was charged with the appropriate 2-amino-N-phenylbenzamide (0.05 mol), tetraphosphorus decasulfide (0.05 mol), and pyridine (35 mL). Reaction mixture was refluxed for 4-6 h and after cooling poured into ice water (250 mL). The obtained precipitate was placed in a 500-mL flask, toluene (150 mL), water (150 mL), and conc. hydrochloric acid (5 mL) were added and the mixture was refluxed for 8-18 h. After cooling to room temperature, the toluene layer was separated and the solvent evaporated in vacuo. The residue was chromatographed on silica gel (toluene) and the product recrystallized from aqueous ethanol (yield 25-45 %).
2,2-Dimethyl-3-phenyl-1,2-dihydroquinazoline-4(3H)-thiones 1a-k
2-Amino-N-phenylthiobenzamide (0.01 mol) was dissolved in acetone (50 mL) at room temperature and silica gel (4 g) was added to the solution under stirring. The reaction mixture was stirred for 24 h at room temperature and then concentrated in vacuo. The residue was chromatographed on silica gel using petroleum ether-acetone (9:1) as the mobile phase. The product was recrystallized from ethanol. The yields, melting points, 1 H-and 13 C-NMR spectral data as well as elemental analyses are summarized in Tables 1 and 2.
Photosynthesis-inhibiting activity in spinach chloroplasts
Spinach chloroplasts were prepared by the procedure described by Walker [45]. The effect of the compounds studied on oxygen evolution rate (OER) in spinach chloroplasts was investigated spectrophotometrically in the presence of the electron acceptor 2,6-dichlorophenol-indophenol (DCPIP) according to Kráľova et al. [46]. The rate of photosynthetic electron transport was monitored as a photoreduction of DCPIP. The chlorophyll (Chl) content was 30 mg dm -3 . Samples were irradiated from the distance of 1 dm with a halogen lamp (250 W) through a 4-cm water filter to prevent overheating of the samples. The activity of compounds 1 and 2 was expressed as IC 50 values, i. e. molar concentration causing a 50% decrease of OER with respect to the untreated control. For low solubility of the studied compounds in water, these were dissolved in DMSO. The applied solvent content (up to 4 v/v %) did not affect the photochemical activity in spinach chloroplasts. Diuron was used as the standard. The value could not be determined.
Reduction of chlorophyll content in the green algae Chlorella vulgaris Beij.
The green algae Chlorella vulgaris Beij. were cultivated statically at room temperature according to Kráľová et al. [47] (photoperiod 16 h light/8 h dark; irradiation: 90 µmol m -2 s -1 PAR; pH 7.2). The effect of the compounds on algal chlorophyll (Chl) content was determined after 7-day cultivation in the presence of the compounds tested. The Chl content in the algal suspension was determined spectrophotometrically after extraction into methanol according to Wellburn [48]. The Chl content in the suspensions at the beginning of the cultivation was 0.1 mg dm -3 . Because of their low water solubility, the tested compounds were dissolved in dimethyl sulfoxide (DMSO). DMSO concentration in the algal suspensions did not exceed 0.25 v/v % and the control samples contained the same DMSO amount as the suspensions treated with the tested compounds. The antialgal activity of compounds was expressed as IC 50 (the concentration of the inhibitor causing a 50% decrease in content of chlorophyll as compared with the control sample) or by the percentage of reduction in the investigated concentration range (0.89 -99.0 µmol dm -3 ). Diuron was used as the standard.
Artemia screeing bioassay
Artemia salina L. eggs were obtained from JBL NovoTermia (Germany). The method of Eppley [49] was applied for A. salina larvae hatching. The test was arranged according to Kiviranta et al. [50]. 24-h old larvae were pipetted into 96-well plates (15-20 larvae per a well). The microcrystalline suspensions of tested substances were prepared by sonication for 1 h in an ultrasonic bath. The solvent was 1% DMSO in artificial seawater (pH 8.0 ± 0.1). The substances were tested in 11 concentrations with 8 repetitions. The final volume was always 150 µL per well. Every experiment was repeated twice at least. The negative control was 1% DMSO solution. The sensitivity of the organism was specified by a solution of MnCl 2 . The mortality was determined after 24 h.
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Domain: Biology Chemistry Medicine
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Novel CD8+ T Cell Antagonists Based on β2-Microglobulin*
The CD8 coreceptor of cytotoxic T lymphocytes binds to a conserved region of major histocompatibility complex class I molecules during recognition of peptide-major histocompatibility complex (MHC) class I antigens on the surface of target cells. This event is central to the activation of cytotoxic T lymphocyte (CTL) effector functions. The contribution of the MHC complex class I light chain, β2-microglobulin, to CD8αα binding is relatively small and is mediated mainly through the lysine residue at position 58. Despite this, using molecular modeling, we predict that its mutation should have a dramatic effect on CD8αα binding. The predictions are confirmed using surface plasmon resonance binding studies and human CTL activation assays. Surprisingly, the charge-reversing mutation, Lys58 → Glu, enhances β2m-MHC class I heavy chain interactions. This mutation also significantly reduces CD8αα binding and is a potent antagonist of CTL activation. These results suggest a novel approach to CTL-specific therapeutic immunosuppression.
The CD8 coreceptor of cytotoxic T lymphocytes binds to a conserved region of major histocompatibility complex class I molecules during recognition of peptidemajor histocompatibility complex (MHC) class I antigens on the surface of target cells. This event is central to the activation of cytotoxic T lymphocyte (CTL) effector functions. The contribution of the MHC complex class I light chain,  2 -microglobulin, to CD8␣␣ binding is relatively small and is mediated mainly through the lysine residue at position 58. Despite this, using molecular modeling, we predict that its mutation should have a dramatic effect on CD8␣␣ binding. The predictions are confirmed using surface plasmon resonance binding studies and human CTL activation assays. Surprisingly, the charge-reversing mutation, Lys 58 3 Glu, enhances  2 m-MHC class I heavy chain interactions. This mutation also significantly reduces CD8␣␣ binding and is a potent antagonist of CTL activation. These results suggest a novel approach to CTL-specific therapeutic immunosuppression.
The peptide-MHC 1 class I complex (pMHC) on a target cell (antigen presenting cell) is recognized by a specific T cell receptor on the surface of CD8 ϩ cytotoxic T lymphocytes (CTL). The pMHC consists of a heavy chain, which is attached to the cell membrane and contains the peptide binding site, and a light chain,  2 -microglobulin ( 2 m). The CD8 molecule is a cell-surface glycoprotein present on CTL, which acts as a "coreceptor"; it is not peptide-specific, but binds to a conserved site on the pMHC molecule, which comprises several regions on the heavy chain and the small DE loop of  2 m consisting of residues 58 -60 (Lys 58 -Asp 59 -Trp 60 ) (1).
After CTL engage pMHC, the earliest intracellular events induce specific phosphorylation of tyrosine residues in the immunoreceptor tyrosine activation motifs within the cytoplasmic tails of the TCR-associated CD3 complex. The cytoplasmic tail of the CD8 ␣-chain is associated with the protein tyrosine kinase p56 lck . Active p56 lck initiates TCR signal transduction by phosphorylating the immunoreceptor tyrosine activation motifs within the CD3 complex. Inhibition of CD8 binding to pMHC therefore inhibits T cell activation (2). Exogenous soluble  2 m can exchange with cell surface-associated  2 m complexed to pMHC (3). Therefore, by mutating the CD8 contact site on  2 m, and exchanging the mutant  2 m into the native MHC, it should be possible to inhibit CTL activation.
Molecular Dynamics and Free Energy
Perturbations-Initial coordinates were taken from the crystal structure of the complex between human MHC class I HLA-A2 and the T cell coreceptor CD8␣␣ solved at 2.65-Å resolution and deposited in the Protein Data Bank (4) under the name 1akj (1). Molecular dynamics and free energy perturbations were performed using CHARMM (version 27) (5) and the standard all-atom parameter set (6). Hydrogens were added using the HBUILD module in CHARMM. Water molecules were added to the complex by superimposing a 16-Å sphere of TIP3P water molecules centered at the  2 m Lys 58 N atom.
The solvent atoms were minimized by 500 steps of steepest descents followed by 1000 steps of conjugate gradient. At the next step, the entire system was relaxed with 500 steps of steepest descents that were switched to conjugate gradient until convergence criteria of r.m.s. gradient of the potential energy lower than 0.3 Kcal/mol⅐Å has been achieved. A 14-Å nonbonded cutoff was employed. The dielectric constant was unity. The system was simulated using a stochastic boundary molecular dynamics (7). The reference point for partitioning the system was the  2 m Lys 58 N atom. The system was divided into a 12-Å reaction region, a 4-Å buffer region, and a reservoir. The frictional coefficients for water oxygen and heavy atoms in the protein were 62 and 200 ps Ϫ1 , respectively (8). The relaxed system was equilibrated at 300 K for 150 ps with a time step of 1 fs followed by 1 ns performed for data collection with coordinates and energies saved to a disc every 1 ps. The  2 m Lys 58 was mutated using the Biopolymer and Homology modules in the MSI software package. For each mutation the procedure described above has been repeated. Relative binding Helmholtz free energies were calculated by the perturbation method (9) as follows: ⌬G 3 ϭ HLA-A2/ 2 m (native) complex 3 HLA-A2/  2 m (Lys3 Glu) complex and ⌬G 4 ϭ HLA-A2/ 2 m (native) /CD8␣␣ complex 3 HLA-A2/ 2 m (Lys3 Glu) /CD8␣␣ complex.
Since free energy is a state function, it is path-independent, and the free energy difference: ⌬G 4 -⌬G 3 is equal to the difference ⌬G 2 -⌬G 1 (see "Results"). Each perturbation was performed in two steps using a total number of 26 windows. At the first 16 windows the lysine H␦1, H⑀1, H⑀2, N, H1, H2, and H3 atoms (including their charges) were deleted. C␦ atom type was modified to sp 2 carbonyl carbon. H␦2 and C⑀ atom types were modified to carboxylate oxygens. At the last 10 windows, the charges of the remaining side chain were adjusted to asp side chain. Trajectories were produced by MD simulations at the same conditions to these described above with 150 ps of equilibration and 100 ps of data collection at every window.
Soluble TCR Preparation-The TCR used for the SPR experiments derives from the JM22 T cell clone (10,11). It is specific for an HLA-A2-restricted peptide (GILGFVFTL) from the influenza matrix protein (58 -66) and uses gene segments TCRAV10S2J9S11C1 and TCRBV17S1J2S7C2. The two fragments of soluble JM22-TCR␣ (residues 1-204 for the ␣-chain and 1-245 for the -chain) were expressed in E. coli, refolded and purified as described previously (12). The TCR concentration was determined from the extinction coefficient (105,500 M Ϫ1 x cm Ϫ1 , determined by amino acid analysis), assuming 100% activity.
Soluble HLA-A2/ 2 m Complex Preparation-Soluble influenza peptide-HLA-A2/ 2 m complexes were prepared by refolding HLA-A2 heavy chain carrying the biotin tag with  2 m wildtype or mutant (both expressed in E. coli) and the synthetic peptide corresponding to influenza matrix protein 58 -66 GILGFVFTL (Genosys, Woodlands, TX) as described in Garboczi et al. (13). The refolded complexes were purified by both anion exchange and gel filtration before being used in SPR experiments. HLA-A2 heavy chain was enzymatically biotinylated as described (14) using N-hydroxysuccinimidobiotin (Sigma) and BirA enzyme. Tetrameric pMHC I complexes were produced by conjugation with phycoerythrin-labeled extravidin as described previously (14). DNA constructs encoding the  2 m mutants (Lys 58 to Arg, Asp, Glu, Ser, Val, Tyr, Cys, SES, Trp, and GRG) were produced using the Quick-Change Site-Directed Mutagenesis Kit (Stratagene) and checked by DNA sequencing of the entire coding fragment. The mutant proteins were expressed and the inclusion bodies purified as for the wild type.
Surface Plasmon Resonance Experiments-SPR binding studies were performed at 25°C using a BIAcore TM 3000 (BIAcore AB, St. Albans, UK) in HBS (BIAcore AB). HBS contains 10 mM HEPES, pH 7.4, 150 mM NaCl, 3.4 mM EDTA, and 0.005% Surfactant P20. Peptide-HLA class I complex was bound to the BIAcore chip by producing recombinant soluble pMHC fused to a biotinylation tag, which was specifically biotinylated in vitro (14) and flowed over a streptavidin-coated chip surface. Streptavidin (Sigma) was covalently coupled to Research Grade CM5 sensor chip (BIAcore) via primary amines using the Amine Coupling kit (BIAcore). For coupling, the streptavidin was dissolved in 10 mM sodium acetate, pH 5.5, and injected at 0.2 mg ml Ϫ1 . Immobilization level was 6000 response units on average. Biotinylated HLA-A2/ 2 m (wild type and mutants) complexes were immobilized at 12,000 -12,500 Response units by injection of 5-35 l at 40-100 g ml Ϫ1 , at a flow rate of 5 l min Ϫ1 . The injections of the different CD8␣␣ (sCD8) and JM22-TCR␣ (sTCR) solutions were performed at a flow rate of 5 l min Ϫ1 . K d values were obtained either by Scatchard plots or by nonlinear fitting of the Langmuir binding isotherm (where B is sCD8 (or sTCR) concentration and AB max is maximum sCD8 (or sTCR) binding) to the data using the Levenberg-Marquardt algorithm as implemented in the Windows 98 application Origin (version 6.1; Microcal Software, Northampton, MA).
Cell Culture and CTL Activation Assays-pBMC were isolated from fresh blood by Ficoll-Hypaque density gradient centrifugation. CD8 ϩ CTL clones were generated and maintained as described previously (15). Target cells in cytotoxicity assays were HLA-matched immortalized T cell hybridomas or Epstein-Barr virus transformed B-lymphoblastoid cell lines (B-LCL) incubated with peptide as shown, or infected with recombinant vaccinia virus (rVV), and labeled with 51 Cr (Amersham Biosciences, Amersham, UK). Peptides were synthesized using standard Fmoc (N-(9-fluorenyl)methoxycarbonyl) chemistry and were Ͼ90% pure as determined by high performance liquid chromatography (Research Genetics, Huntsville, AL). Vaccinia infections were effected at 3-5 plaque-forming units/cell for 1 h and followed by a 6-h incubation period to allow expression prior to 51 Cr labeling. The rVV expressing HIV-1 Nef was constructed from a full-length proviral clone isolated from donor SC1 according to standard protocols; wild type WR rVV was used to infect control target cells (16). Lysis assays were performed in low percentage fetal calf or human serum using standard 51 Cr release methodology (17). Tetrameric pMHC I complex activation assays were performed by measurement of extracellular RANTES release after 30min exposure as described previously (18). All data points for both lysis and RANTES release assays represent the mean of triplicate readings; y axis error bars show the S. D. in each case.
Flow Cytometric Analysis-Cells were stained with phycoerythrinlabeled tetrameric pMHC I complexes containing wildtype or Lys 58 3 Glu forms of  2 m as described previously (19). Stained cells were analyzed using a Becton Dickinson Calibur flow cytometer with CellQuest software. does not interact with the CD8␣-1. In the Lys 58 3 Arg mutant (side chain in purple) the H-bond to CD8␣-1 Asp 75 is preserved. b, in the Lys 58 3 Ser and Lys 58 3 Cys mutants the interaction with the CD8␣␣ is mediated through a water molecule: the side chain donates its hydroxyl or thiol hydrogen to a water molecule, which donates its hydrogen to CD8␣␣ Val 24 carbonyl oxygen. c, the native structure  2 m Lys 58 and CD8␣-1 Asp 75 (blue) compared with steric hindrance via a bulky side chain mutants: Lys 58 3 Tyr (side chain in green) and Lys 58 3 Trp (side chain in purple). The bulky side chain fills the cavity between the HLA-A2/ 2 m CD8␣␣, and the interaction with the CD8␣␣ is poor. d, comparison between the native  2 m DE loop consisting of residues 58 -60 (Lys 58 -Asp 59 -Trp 60 ), shown in blue, to insertions: Lys 58 3 SES (green) and Lys 58 3 GRG (purple).  2 m residues Leu 40 -Ala 79 are shown in azure. e, comparison between the native structure to a Lys 58 3 Val mutation (blue). The hydrophobic valine side chain repels CD8␣-1 Asp 75 carboxylate. f, mutations to a negatively charged side chain: Lys 58 3 Asp (yellow), Lys 58 3 Glu (purple) compared with the native lysine (gray). In both Lys 58 3 Asp and Lys 58 3 Glu mutations the positive charge on HLA-A2 Arg 6 heavy chain attracts the mutant's carboxylate. Only Lys 58 3 Glu carboxylate is in close proximity to CD8␣-1 Asp 75 O␦2. mutations to short polar side chains (Lys 58 3 Ser, Lys 58 3 C), mutations introducing steric hindrances via bulky side chains (Lys 58 3 Tyr, Lys 58 3 Trp) or insertions (Lys 58 3 SES, Lys 58 3 GRG), a mutation to a medium length hydrophobic side chain (Lys 58 3 Val), mutations to negatively charged side chains (Lys 58 3 Asp, Lys 58 3 Glu).
We studied the hydrogen bond network formed between the wild type Lys 58 , or mutations of this residue, to CD8␣-1 as shown in Table I. The perturbation to the conformation of the contact residues (CD8␣-1:Arg 4 , Asp 75 , Val 24 -Val 26 ,  2 m:Trp 60 , Lys 58 ) (1) caused by various Lys 58 mutations is presented as the root mean square (r.m.s.) deviation of these residues to that observed in the crystal structure.
Neutralizing the Lys 58 positive charge in silico leads to the loss of two H-bonds to CD8␣-1 Asp 75 ⌷␦2 and Val 24 O atoms, as illustrated in Fig 1a and Table I, and formation of alternative hydrogen bonds with water molecules in the cavity of the HLA-A2/CD8␣␣/ 2 m complex. A mutation that preserves the positive charge (Lys 58 3 Arg) has a less dramatic effect: the H-bond to CD8␣-1 Asp 75 ⌷␦2 is preserved, and the r.m.s. value is similar to that of the wild type structure and smaller than that of the neutral Lys 58 side chain. Therefore, eliminating the positive charge on the side chain is likely to reduce the binding affinity to the CD8␣␣.
In short polar side chain mutants, Lys 58 3 Ser and Lys 58 3 Cys, the interaction with CD8␣␣ is mediated through a water molecule as shown in Fig. 1b and Table I. The side chain donates its hydroxyl or thiol hydrogen to a water molecule, which donates its hydrogen to CD8␣␣ Val 24 O. Unlike Lys 58 3 Ser, where the H-bond network is stable, the H-bond between the thiol moiety and the water molecule in the Lys 58 3 Cys mutant fluctuates during the simulation. This has an impact on the r.m.s. values shown in Table I, which increase from 0.70 for Lys 58 3 Ser to 0.93 for Lys 58 3 Cys. These results suggest that removing the H-donor group from the side chain at position 58 should reduce the binding affinity to CD8␣␣.
Mutations introducing steric hindrance via a bulky side chain, Lys 58 3 Tyr and Lys 58 3 Trp, are illustrated in Fig. 1c. The bulky side chain fills the cavity between HLA-A2/ 2 m and CD8␣␣, and the interaction with CD8␣␣ is impaired by the lack of H-bonding, resulting in high r.m.s. values (Table I). Lys 58 3 GRG and Lys58 3 SES insertions (Fig. 1d) perturb the tertiary structure and lead to increased r.m.s. values of 1.21 and 1.78, respectively. The higher r.m.s. value of the Lys 58 3 SES insertion is due to reversal of the positive charge and the fact that serine has a side chain that contributes to the overall steric hindrance.
The Lys 58 3 Val mutation yielded a higher r.m.s. value than all other single substitution mutations. In bulkier mutations, such as Lys 58 3 Trp, the side chain can orient itself toward the HLA-A2/ 2 m-CD8␣␣ cavity, whereas the valine side chain is not long enough to have this effect. As a result, the hydrophobic side chain repels the polar CD8␣-1 Asp 75 (Fig. 1e). Fig. 1f shows mutations to a negatively charged side chain (Lys 58 3 Asp, Lys 58 3 Glu). Repulsion between the carboxylates leads to an average distance of 6.27 Å between the CD8␣-1 Asp 75 O␦2 and Lys 58 3 Asp O␦1 oxygens. Strikingly, the distance between CD8␣-1 Asp 75 O␦2 and Glu 58 O⑀1 oxygens in the Lys 58 3 Glu is only 4.09 Å. Both Lys 58 3 Asp and Lys 58 3 Glu are attracted to the positive charge on HLA-A2 Arg 6 heavy chain: in 52.9% of the frames taken from the MD simulation, the distance between the mutated Glu 58 O⑀2 and HLA-A2 Arg 6 N1 atoms was lower than 5 Å, fluctuating to a minimum of 3.18 Å. This interaction stabilizes the HLA-A2/  2 m complex and orients the Lys 58 3 Glu side chain toward CD8␣-1 Asp 75 . HLA-A2 Arg 6 heavy chain attracts the Lys 58 3 Asp carboxylate as well; however since Lys 58 3 Asp side chain is shorter, it is distant from Asp 75 O␦2.
The theoretical results show that, although the contribution of the  2 m Lys 58 to CD8␣␣ binding is relatively small (1), its mutation should have a marked effect upon CD8␣␣ binding.
To test these predictions, we used SPR to measure the binding of soluble CD8␣␣ (sCD8) and soluble JM22 T cell receptor (sTCR) to HLA-A2-influenza matrix peptide complex, containing wild type or mutant  2 m (Fig. 2). The affinity of sTCR for pMHC was similar for all of the complexes studied (Table II), indicating that the active material on the chip surface was correctly folded. However, the absolute measurements of response varied markedly between the different complexes, indicating varying proportions of active material on the chip surface. This implies that some mutant  2 m complexes are more stable than others. In particular, HLA complexes containing Lys 58 to Tyr, Trp, SES, and GRG mutations show reduced stability (data not shown). These observations correlate with refolding efficiency, with yields of Lys 58 3 Arg and Lys 58 3 Glu complexes being several times higher than those for Lys 58 3 SES and Lys 58 3 GRG complexes (data not shown). This difference agrees with the MD simulations, which predict that the mutant Lys 58 3 Arg complex structure is closest to that of the wild type (Table I), and that the complex containing the  2 m Lys 58 3 Glu mutant is stabilized by the interaction with HLA-A2 heavy chain R 6 . There is a strong correlation between the measured sCD8 binding (Table II) (Table I). The wild type structure forms two hydrogen bonds and shows the highest affinity for sCD8. Complexes containing  2 m mutants Lys 58 3 Arg, Ser, or Cys are predicted to form only a single H-bond (mediated through a water molecule for Lys 58 3 Ser and Lys 58 3 Cys) and show decreased binding affinity for sCD8 (Fig. 2, Table II). In complexes containing other  2 m mutants (Lys 58 mutations to Val, Asp, Tyr, Trp, GRG, SES, and Glu), where no H-bond was predicted, the sCD8 binding was negligible (K d Ͼ 1 mM). Similarly, mutations of Asp 59 and Trp 60 also greatly reduced sCD8 binding (data not shown). The BIAcore binding data showed that sCD8 binding is undetectable in HLA complexes containing  2 m in which Lys 58 is mutated to Glu (Fig. 2, Table II), yet this complex behaves identically to complexes containing wildtype  2 m in terms of sTCR binding, refolding yield, and stability. (2). Tetramer containing wild type  2 m folded around the cytomegalovirus-derived HLA-A2-restricted peptide NLVPMVATV was included as a control for nonspecific binding. b, tetramer-induced activation of 003 CTL. Tetramers folded around SLYNTVATL containing either wild type or Lys 58 3 Glu  2 m were incubated in 96-well plates with 10 4 CTL per well at the concentrations shown. Release of RANTES into the culture supernatant was measured after 30 min by enzyme-linked immunosorbent assay. The tetramer containing Lys 58 3 Glu  2 m induces less activation compared with the tetramer containing wildtype  2 m at low concentrations. The control tetramer containing wild type  2 m folded around the NLVPMVATV peptide failed to activate CTL at 10 Ϫ7 g/ml. c, antagonism of CTL activation by soluble Lys 58 3 Glu  2 m. Target T2 hybridomas were plated at 5 ϫ 10 3 per well in 96-well round-bottomed plates with soluble proteins at 60 g/ml as indicated and cognate peptide at 10 Ϫ6 M. After incubation for 20 min at room temperature, CTL were added at an effector:target (E:T) ratio of 2:1. Effector CTL (clone 4D5) were specific for the HLA-A2-restricted MAGE-3 tumor antigen epitope FLWGPRALV (17). The assay was harvested after 6 h. Similar results were obtained with the HLA-A2-restricted SLYNTVATL-specific CTL clone 5D8 (data not shown). d, inhibition of direct ex vivo fresh PBMC response to endogenously processed antigen. Target cells were autologous SC21 B-LCL infected with either Nef or control WR rVV as described and incubated to allow expression in the presence of 50 g/ml protein as indicated for 6 h. Proteins were present at this concentration throughout the 51 Cr labeling process and for the entire duration of the assay. The assay was harvested after 12 h. Vaccinial expression of HLA class I-restricted antigens obviates the problems associated with potential effects of  2 m or mutants thereof on peptide exchange at the cell surface when external loading protocols are followed. The inhibitory effect of the mutant Lys 58 3 Glu protein is controlled by the addition of equimolar concentrations of wild type  2 m. Similar results were seen in direct ex vivo assays with RPMTYKGAL-pulsed SC21 B-LCL (data not shown). The Lys 58 3 Glu mutant also inhibited a primary CTL line (data not shown) specific for the HLA B35-restricted parvovirus B19-derived epitope QPTRVDQKM (NS1, residues 391-399) (22).
The strong correlation between the predictions of the molecular dynamics simulations, free energy calculations, and biophysical measurements of sCD8 binding led us to conclude that the  2 m mutant containing a glutamate residue at position 58 (Lys 58 3 Glu) was the most promising candidate for further investigation.
The impact of mutating  2 m on the interaction between soluble antigen and TCR expressed on the cell surface was investigated using tetrameric pMHC I complexes. Fluorochrome-labeled tetramers containing either wild type or Lys 58 3 Glu forms of  2 m-stained CD8 ϩ CTL at equivalent levels across a range of concentrations (Fig. 3a). However, tetrameric complexes containing the Lys 58 3 Glu form of  2 m were substantially impaired in their ability to activate CTL. This effect was titratable and most apparent at lower concentrations (Fig. 3b). These data indicate that, at similar levels of interaction with cell surface TCR, the impaired ability of complexes containing the Lys 58 3 Glu form of  2 m to engage the CD8 coreceptor translates into a biologically significant effect. This is consistent with previous work demonstrating that the major role of the CD8 coreceptor is the provision of signaling moieties that allow an enhanced sensitivity to antigen (2).
The effects of soluble exogenous wild type and mutant  2 m proteins on CTL interactions with target cells expressing pMHC I antigenic complexes were assessed using standard 51 Cr release cytolytic assays. At concentrations of pMHC on the target cell surface insufficient to saturate the maximal lytic capacity of the CTL, exogenous wild type  2 m, and those mutants that efficiently refold with soluble MHC class I heavy chain to form stable complexes, consistently enhanced CTLmediated lysis and interferon-␥ production (data not shown). This effect likely relates to the ability of these proteins to stabilize pMHC class I on the target cell surface in a TCRcognate form and is consistent with previous work (3,20). At higher concentrations of pMHC class I on the target cell surface, exogenous mutant forms of  2 m that impair CD8 binding to pMHC class I were found to inhibit CTL-mediated lysis (Fig. 3c). In all experiments with CD8-dependent CTL, the Lys 58 3 Glu mutant was the most potent inhibitor of CTL activation. These results indicate that mutant forms of  2 m can, through inhibition of CD8 binding to complexed pMHC I, antagonize CTL activation.
The potential use of such reagents in a therapeutic setting requires an effect on whole blood antigen-specific responses mounted by polyclonal unmanipulated CTL. We examined this using peripheral blood mononuclear cells (PBMC) isolated from donor SC21, an HIV-1-infected individual with a potent CTL response to the viral Nef protein that maps predominantly to the HLA B7-restricted epitope RPMTYKGAL (residues 75-83).
The Lys 58 3 Glu mutant was found to inhibit this fresh PBMC lytic response to endogenously presented pMHC class I antigen in comparison to equimolar levels of wild type  2 m (Fig. 3d).
In conclusion, we have developed stable  2 m mutants that inhibit CD8 coreceptor binding to pMHC I and exert an inhibitory effect on CTL activation. These data suggest that such mutant forms of  2 m could be used to selectively modulate the CD8 ϩ cellular immune response, a principle that could be applied therapeutically (21).
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Domain: Biology Chemistry Medicine
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Convolutional Neural Network Transformer (CNNT) for Fluorescence Microscopy image Denoising with Improved Generalization and Fast Adaptation
Deep neural networks have been applied to improve the image quality of fluorescence microscopy imaging. Previous methods are based on convolutional neural networks (CNNs) which generally require more time-consuming training of separate models for each new imaging experiment, impairing the applicability and generalization. Once the model is trained (typically with tens to hundreds of image pairs) it can then be used to enhance new images that are like the training data. In this study, we proposed a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), to outperform the CNN networks for image denoising. In our scheme we have trained a single CNNT based “backbone model” from pairwise high-low SNR images for one type of fluorescence microscope (instance structured illumination, iSim). Fast adaption to new applications was achieved by fine-tuning the backbone on only 5–10 sample pairs per new experiment. Results show the CNNT backbone and fine-tuning scheme significantly reduces the training time and improves the image quality, outperformed training separate models using CNN approaches such as - RCAN and Noise2Fast. Here we show three examples of the efficacy of this approach on denoising wide-field, two-photon and confocal fluorescence data. In the confocal experiment, which is a 5×5 tiled acquisition, the fine-tuned CNNT model reduces the scan time form one hour to eight minutes, with improved quality.
Introduction
The field of microscopy is changing rapidly to include both hardware and software-based advancements that are expanding the biological imaging frontier. The ongoing advancements in fluorescence microscopy have allowed biologists to image nearly at the molecular level 1 .
Similarly, developments in light-sheet microscopy have allowed for faster and more gentle imaging than ever before 2 . These transformative breakthroughs are happening alongside constant improvements in fluorescent dyes as well as the hardware and software underlying traditional imaging techniques like confocal and two-photon microscopy. Despite these remarkable progresses there remains the compromise where the temporal and spatial resolution limits the adequate collection of photons. Importantly, lower fluorescence excitation power is often necessary to avoid photobleaching or cellular phototoxicity 3 . This last point is importance for imaging live samples where fast, low light level setup is required to avoid motion artifacts while maintaining sample health.
Deep learning algorithms are currently being used for many image processing tasks including segmentation 4 , super-resolution 5 , and contrast generation in the label free imaging 6 .
There has been a recent spate of deep learning to restore or enhance fluorescence imaging where signal-to-noise (SNR) has been degraded due to the need to optimize imaging parameters for speed or lower dosage along the lines listed above [7][8][9][10] . These algorithms use pairs of matching high and low SNR image samples to build a model to restore (denoise or enhance) low SNR images (supervised training) or use information in the low SNR images themselves (unsupervised training) for the same purpose. In most cases the best results in restoration are achieved in supervised training (Fig. 1A). For instance, using a U-net architecture, content-aware image restoration (CARE) 11 networks enhanced image resolution, denoise, and remove resolution anisotropy. The three-dimensional residual channel attention networks (3D-RCAN) 12 have provided further gains in image enhancement for volumetric time-lapse imaging (confocal and super-resolution) and in expansion microscopy.
Although CNN models like CARE and 3D-RCAN are very effective, they require considerable time and data to train. The main limitation is the lack of fast adaptation, where new models are trained from the scratch for new experiments with lengthy computing time and a dedicated training dataset. The model trained under a combination of cell types, hardware and imaging protocol is not robustly transferrable to other experiments 12 . This limits the adaptability to new experiments for which they have not been trained. In contrast, blind zero-shot denoisers including Noise2Void and Noise2Noise and more recently Noise2Fast, have been shown to denoise images with a per-sample training [13][14][15][16] , where a full training is conducted for the very low SNR image to be processed, without requiring a pre-acquired training dataset. Limitations of these methods include the slow inference due to per-sample training and inferior performance compared to supervised training 15 . The high-quality data can be degraded with realistic and diverse noise distribution 17 to mitigate the requirement of acquiring corresponding low quality data.
The transformer architecture is capable of learning long-range signal coherence and is scalable to large-scale datasets 18 . It is the foundation for the very successful large language model, such as the ChatGPT 19 . For the domain of imaging, the transformer based models show improved adaptation than CNNs as the attention mechanism, the key component in transformer, computes the data specific coefficients to transform inputs into outputs, while the CNNs applied the fixed parameter sets after training to all input data 20 . This adaptation ability is further enhanced by the multi-head soft attention heads in the transformer 18 .
To improve the model adaptation for microscopy denoising experiments, we propose a novel transformer architecture which we term convolutional neural net transformer (CNNT) to effectively process large microscopy images Instead of training individual models for each experiment, we train a generalized backbone model using diverse datasets followed by fine tuning this generalized backbone for each new experiment (Fig. 1B). We show the CNNT finetuning for each new experiment requires only few numbers of image pairs (e.g.5-10) which greatly reduces the time necessary to train (<10mins) and offers better performance than CNN models as well as training from the scratch. The per-trained model is generalized across different imaging modalities (e.g. from iSim to two-photon), cell types and imaging protocols. We first trained the backbone model on U2OS cells image data acquired with only an iSim scanner. The trained backbone was fine-tuned on data from different sample types imaged on iSim, wide-field, confocal and two-photon scanners. We compared the CNNT performance with RCAN3D and Noise2Fast for both image quality and training speed. Further, the live zebrafish was images in an 5x5 tile setup for large field-of-view. Low SNR images from fast imaging due to the need to freeze sample motion was significantly improved after model inference, leading to a seven-fold speedup.
CNNT: a novel imaging transformer model
CNNs excel at processing imagery data due to their nature of spatially invariant inductive bias. However, CNNs struggle to capture long range signal correlation along the time or Z dimension without significant computation overhead 21 . The standard transformer model performs very well on time series data due to the attention mechanism that is able to combine all time points to compute the output. However, it struggles with images due to the heavy computational costs especially when working with 2D+T or 3D images. The standard transformer works with linear attention mechanism that maps the input to key, query, value (k,q,v) matrices and then to compute the attention score. The inputs are mapped to the k,q,v matrices with linear layers, which allows for expressive learning of representations but also incurs a heavy quadratic cost with respect to the input length 22 . We introduce a novel transformer architecture that can work efficiently with 2D+T and 3D images. In our approach we opt to use convolution layers instead of linear layers to map the inputs. Not only does this reduce the complexity from quadratic to linear number of operations, but also reintroduces the spatial invariant of convolutions to the transformer architecture. This novel architecture, coined Convolution Neural Net Transformer (CNNT), allows for several enhancements from both CNNs and transformers alone.
As shown in Fig. 2, with the ability to work with flexible dimension sizes as input and output, the CNNT becomes a plug and play module which we use to create complete architectures. As a baseline we create a standalone module of CNNT cell which consists of an input projection that expands or contracts the channel dimension, followed by a CNNT attention module that enriches the input with convolved attention, followed by a standard CNN mixer that helps in sharing information across feature channels. The modular structure of CNNT cell becomes the building block for a complete model. We chose U-net as our base architecture as it is effective to combine information from different resolution levels and computational more efficient. This CNNT U-net has the first two levels downscaling the image spatially and increasing the feature dimension. Last two levels scale up the image spatial dimensions back to the original size and reduce the feature dimension correspondingly. Before each upscale level, we concatenate the input with the output from the corresponding down scale level. Each level of the U-net is made of four CNNT cells stacked on top of each other. We refer to the complete architecture as CNNT U-net.
Backbone training and finetuning with CNNT U-net
The Training was split into two stages for backbone learning and fine-tuning. Instead of training a model for every experiment, we first train a general backbone with data from several organelles pooled together. For fast adaptation to a new experiment, a finetuning step is performed, requiring only a small dataset and only a few minutes of extra train time. We found backbone trained with one microscopy type (iSim 23 in this study) is fine-tuned very well on other scope types (e.g.widefield, confocal, two-photon). The backbone training does take longer to complete, but once trained, it can be shared for many new experiments. The finetuning takes only a few minutes as the model only needs to adapt to the small new data, not learn from scratch.
We defined a backbone dataset created with a combination of 7 different organelles and 4 finetuning datasets created of 4 different organelles. The 7 different organelles used in backbone training were all under an iSim scanner with a total of 154 z-stack images for 7 different organelles. Finetuning experiments were from three different microscopies. None of the finetuning data was present in the backbone training. In each case we see a significant boost in SNR compared to the noisy raw image. In addition, CNNT also outperforms the current state-ofthe-art. For each downstream task, 3 different models were finetuned using either 5, 10 or 20 samples.
CNN models for comparison
We compare our results with RCAN3D which is considered state-of-the-art supervised machine learning model, and Noise2Fast which is one of the best self-supervised machine learning model.to-noise ratio image pairs of T cells were acquired as follows: i) 25 ms exposure, 10% illumination intensity, 5% laser power; ii) 25 ms exposure, 30% illumination intensity, 5% laser power; iii) 100 ms exposure, 100% illumination intensity, 10% laser power.
Two-photon imaging of zebrafish embryos
All zebrafish experiments were performed in compliance with the National Institutes of Health guidelines for animal handling and research using an Animal Care and Use Committee (ACUC) approved protocol H-0252(R5). Zebrafish were raised and maintained at the temperature of 28.5C. Zebrafish handling, breeding and staging were performed as previously described The scanners allow us to perform repeated imaging and average them to reduce the noise.
We exploit this to create a series of images with consistently increasing SNR by taking 64 repetitions of the same field-of-view and then averaging the first n time points to get the n th image in the series. We selected time average 1-63 as the noisy image and average 64 as the ground truth. We test the finetuned model on all time point averages from 1 to 64 to evaluate model behaviors for inputs with different SNR levels.
Confocal Imaging of Mouse Lung Tissue
Lungs isolated from C57BL/6 mice were inflated with 4% paraformaldehyde/PBS and immersed in 4% paraformaldehyde/PBS at 4°C overnight. After fixation, the lungs were immersed in 30% sucrose/PBS at 4°C overnight and then embedded in OCT compound. Cryosectioning of the lung tissues was performed at a thickness of 50 µm and mounted on Superfrost Plus Gold microscope slides (Fisher). Immunostaining was performed with the following primary antibodies: rat anti-CD45 antibody (1:500, eBioscience, 14-0451-85), mouse anti-αSMA-Cy3 (1:500, Millipore Sigma, C6198), armenian hamster anti-PECAM- Line-average varied with "ground truth" images collected with a line average of 32 while faster "noisy" test images were collected with a line average set to be 4. The ground-truth images were acquired in ~1hour, but noisy data was acquired in ~8mins.
Results
Instead of training new models for every experiment from scratch, a general backbone was trained and then finetuned with a few new samples to quickly adapt to a new experiment. In each downstream task we finetuned the backbone with 5, 10 or 20 samples and compared the CNNT results with RCAN3D and Noise2Fast. In all tasks the CNNT considerably improves the SNR and outputs consistently good results no matter the number of samples used to finetune.
Models show robust adaptation across scanner types, samples imaged and acquisition condition.
The finetuning time for CNNT is less than 10 minutes for 10 samples, as seen in Fig 3-6.
The inference time is a minute on big images of size of 100x1200x1200. RCAN has a slower train time of 2 hours using the shared codebase and configuration of large model. Noise2Fast has to learn noises for every data, leading to every long inference time.
Widefield
The MEF cells are imaged with a widefield camera. For this modality 20 images were collected and 10 were separated for testing.5 and 10 samples were individually used to finetune the CNNT. RCAN was trained on 10 samples as well.
As shown in Fig. 3, CNNT performs well under this scenario as well and removes noise uniformly throughout the field-of-view. RCAN performs well in the center regions but does not remove noise uniformly throughout the image. Noise2Fast struggles to output sharp and clean images. The CNNT results have the higher PSNR and SSIM3D metrics, over the RCAN and Noise2Fast. The fine-tuning time is ~13 minutes for CNNT with 10 samples, while the RCAN training time is over 2 hours. Supplement Video1 shows the MEF cell images before and after CNNT model finetuned with 10 samples, and the ground-truth data for the reference.
Two-photon
For this experiment we curated 24 train images focusing on zebrafish liver and pancreas.6 separate images of full scans were collected as test set. Out of 24 train images, CNNT was trained on either 5, 10 or 20 samples. RCAN was trained on all 24. Resulting models may benefit both imaging and downstream analysis of light microscopy images.
Conclusion
In Again, the timesaving of CNNT finetuning is prominent, with superior or similar image quality.
RCAN3D was trained from scratch following the publication[ref]. Noise2Fast learns noise prediction for each example it sees.//github.com/AiviaCommunity/3D-RCAN). Following the recommendation in its paper, all data available for each downstream task was used to train RCAN3D models. The evaluation was done on the same test images as the CNNT. The training and evaluation wall-clock time duration was recorded. Noise2Fast 15 Noise2Fast implementation was from its official repository ( [URL] does not require paired samples; instead, a model is trained from scratch for every noisy image it sees. The published training parameters were used in all experiments Downstream finetuning tasks Wide-Field Imaging of MEF Cells Non-muscle myosin 2A-GFP mouse embryonic fibroblast (MEF) cells were maintained in DMEM media (Gibco, Cat# 11965-092) containing 5% FBS (Gibco, Cat# 16000-044). Primary mouse non-muscle myosin 2A-GFP T cells were isolated with EasySep™ Mouse T Cell Isolation Kit, following manufacturer's instructions (Stemcell, Cat# 19851). Mouse T cells were maintained in RPMI 1640 media (Gibco, Cat# 11875093) supplemented with 10% fetal bovine serum (Gibco, Cat#16000-044) and interleukin-2 (Stemcell, Cat# 78081.1). For this experiment, cells were cultured in glass-bottom dishes (MatTek, Cat# P35G-1.5-20-C). For immunostaining, we used β-actin (A5441, Sigma), NM2A (909801, BioLegend), Alexa Fluor 568 goat anti-mouse (ThermoFischer Scientific, Cat# A-11004), and Alexa Fluor 488 goat anti-rabbit (ThermoFischer Scientific, Cat# A78953) antibodies. For immunostaining, MEF cells were fixed and permeabilized with 4% paraformaldehyde (Sigma-Aldrich, Cat# 158127) and 0.05% Triton X-100 (Sigma-Aldrich, Cat# T8787) in PBS (KDMedical, Cat# RGF-3210) for 15 min at room temperature. To block nonspecific binding, samples were washed with 1x Blocker BSA in PBS twice, blocked for 1 hour at room temperature, and subsequently stained with 1:500 dilution of primary antibodies in 1x Blocker BSA (Thermo Scientific, Cat# 37525) in PBS for overnight at 4°C. Samples were washed three times (5 min) with 1x Blocker BSA in PBS, and stained with 1:500 secondary antibodies in 1x Blocker BSA in PBS for 2 hours at RT. Samples were washed three times (5 min) with 1x Blocker BSA in PBS. The stained samples were mounted and imaged using 90% Glycerol (Sigma-Aldrich, Cat# G2025) in PBS. Live cell fluorescence imaging was performed using a Leica DMi8 microscope equipped with 100x/1.4NA oil immersion objective lens and Okolab stage top incubator with CO2, temperature, and humidity control. For widefield fluorescence microscopy, low and high signal-
(
Kimmel et al., 1995. Developmental Dynamics 203:253-310; The Zebrafish Book: A Guide for the Laboratory Use of Zebrafish Danio (" Brachydanio Rerio"), M Westerfield -2007 -University of Oregon). To prevent pigmentation, the embryos used for confocal analysis were cultured in fish water containing 0.003% 1-phenyl-2-thiouera (PTU, Sigma-Aldrich, P7629) from 24 hpf. The following strain was used: Tg(ins:dsRed) m1081 ;Tg(fabp10:dsRed;ela3l:GFP) gz12 ; Tg(ptf1a:EGFP) jh1 transgenic line (Anderson et al., "Loss of Dnmt1 catalytic activity reveals multiple roles for DNA methylation during pancreas development and regeneration", Developmental Biology, Volume 334, Issue 1, 1 October 2009, Pages 213-223). Originally, the line was obtained crossing the Tg(ins:dsRed) m1081 ;Tg(fabp10:dsRed;ela3l:GFP) gz12 line, also known as 2-Color Liver Insulinacinar Pancreas (2CLIP) with the Tg(ptf1a:EGFP) jh1 line.:dsRed) m1081 ;Tg(fabp10:dsRed;ela3l:GFP) gz12 ; Tg(ptf1a:EGFP) jh1 transgenic line at room temperature using a LEICA SP8 confocal microscope and a 25x (0.95 NA) water dipping lens (Leica HC FLUOTAR L VISIR) with a dual beam Insight (Ti:sapphire) laser (Newport/Spectra-Physics, Irvine, CA) . The fish express dsRED fluorescent protein in the islets of Langerhans in the endocrine pancreas, driven by the insulin (ins) promoter and in the liver hepatocytes, driven by the fatty acid binding protein 10a (fabp10a gene). Heterozygous parents were crossed and then the collected embryos were selected at 3 days post fertilization (dpf) using a fluorescent SteREO Discovery. V12 stereomicroscope (Zeiss). At 5 dpf, zebrafish embryos were anesthetized using a buffered tricaine methanesulfonate (MS-222, Sigma-Aldrich, E10521) solution in 0.003% PTU solution in E3 medium. The anesthetized embryos were then included in a 1% solution of low melting agarose dissolved in 0.003% PTU solution in E3 medium on a glass coverslip (Warner Instruments, CS-40R15) and carefully oriented in a lateral position. During the whole imaging embryos were kept in a solution of MS-222 and 0.003% PTU in E3 medium. Pairs of low-noise "ground-truth" and fast "noisy" image stacks of DsRed and GFP were acquired at a scan rate of 8000 Hz using a resonant scanner with a format of 512 x 512 pixels, 0.2 x 0.2 micron pixel sizes, and excitation at 1045nm (DsRed) and 920nm (GFP), with emission bandwidths of 650-700nm(DsRed) and 500-552nm (GFP), and an interslice distance of 0.5 microns. A line average of 8 was used for the ground truth images whereas no line averages were used for the noisy image stacks resulting in a decreased time of imaging of more than 6 folds.
Fig. 4 shows
Fig. 4 shows CNNT outputs clean images with rich details, even surpass the quality of the ground truth. This may indicate extra quality gain can be achieved with pre-training. For different number of fine-tuned samples, CNNT image quality remain steady, better than RCAN3D and Noise2Fast. Due to the ground-truth images are noisy, we notice CNNT does not give the highest PSNR. The fine-tuning time is ~6mins for 10 samples, much faster than training from the scratch. Supplement Video 2 shows zebra fish pancreas results, against the groundtruth.
Fig. 5
Fig.5gives model outputs for averages 1, 4, 8, 16, 32, and 64. We see that CNNT U-net is robust against this wide range of input SNRs. For Avg 1 where the input signal is weak, the model recovers the detailed structures. With improved input SNR for more averages, the model behaved robustly, giving consistently good quality.
this paper we introduced a novel deep learning architecture: Convolutional Neural Net Transformer, a hybrid architecture of CNNs and transformers and used it to denoise low SNR microscopy images. CNNT improves over standard CNNs due to the ability to work on arbitrary time or depth dimension using the attention mechanism. We further improved model adaptation by utilizing a backbone and finetuning scheme. With backbone model trained on iSim datasets, the CNNT is successfully finetuned on widefield, two-photon and confocal microscopies, with improved image quality and much faster training time.
Fig. 1 |
Fig. 1 | Backbone and finetuning to train the light microscopy image enhancement model. A) A separate model is trained for every imaging experiment. This training from the scratch method proves to be effective but need more samples and longer training time to reach good performance. Furthermore, since every training is independent, the model under training cannot utilize the curated datasets to help current imaging experiment. B) We propose first to train a backbone model from the large, diverse and previously curated datasets. The trained backbone model is further finetuned for every new experiment, with much smaller amount of new data. Given an effective backbone model architecture, this method will be much faster in training, and also allow the current training to reuse information acquired in previous experiments. Inspired by the success of transformer model in language pre-training, we proposed a novel imaging transformer architecture, CNNT, to serve as an effective backbone.
Fig. 2 |
Fig. 2 | The CNNT-Unet architecture. A) The whole model consists of a pre and post convolution layers and the backbone. The input tensor has the size of [B, C, Z, H, W] for batch, channel, depth, height and weight. The C input channel are first uplifted to 32 input channels into the backbone. The post-conv layer will convert the output tensor from the backbone to C channel. There is a long-term skip connection over the backbone. B) The backbone has a Unet structure, consisting of two downsample blocks and to upsample blocks. Every downsample CNNT block will double the number of channels but reduce the spatial size by x2. Every upsample block will reduce the number of channels and expand the spatial size. C) The CNNT block includes only the CNNT cell. Every cell contains CNN attention, instance norm and CNN mixer. This design mimics the standard transformer cell design but replacing the linear attention and mixer to be the CNN attention and CNN mixer, to reduce computational cost for high resolution images. D) The CNN attention is the key part of imaging transformer cell. Unlike the linear layers in the standard transformer, the key, value and query tensors are computed with convolution layers, which
Fig. 3 |
Fig. 3 | The Widefield experiment to image the MEF cells. The pre-trained CNNT backbone was finetuned on 5 and 10 widefield image samples. The resulting model was compared to RCAN3D and Noise2Fast for image quality and computing time. A) The low-quality noisy image as the input to the models. B-C) The CNNT results after finetuning for 30 epochs on 5 and 10 samples. The quality improvement is noticeable. E) The RCAN3D model trained from the scratch for 300 epochs gave good improvement. F) The Noise2Fast result is subpar. G) the high-quality ground-truth for SSIM3D and PSNR computation and for reference. The CNNT finetuning is much faster than RCAN3D training and Noise2Fast and offer better quality measurements.
Fig. 4 |
Fig. 4 | The two-photon experiments for the pancreas of a zebra fish. A) The low-quality image does to provide enough SNR and contrast to delineate features like islets. B, C, D) The CNNT greatly improved the image quality for 5, 10 and 20 samples. The model is robust for even 5 samples, leading to a very fast ~3.5mins finetuning time. E and F) The RCAN3D and Noise2Fast training are much longer with suboptimal quality recovery. G) The ground-truth in this experiment bears a still lower SNR. The models achieved better quality than the ground-truth images.
Fig. 5 |
Fig. 5 | The multi-average tests for the zebra fish imaging. The imaging was repeated for N=64 times. CNNT models were tested for robustness for different level of input quality with increasing number of averages. The Avg 1, 4, 8, 16, 32 and 64 were shown here. The model was found to be robust against the lower input SNR, giving consistently boost of image quality. The model also robustly recovered finer features. No sign of hallucination was found.
Fig. 6 |
Fig. 6 | The confocal imaging for the mouse lung tissue. A) The low-quality image was acquired with very low photon counts. B, C, D) CNNT finetuning with 5, 10, 20 samples shows recovered tissue structures and removal of background random noise. E) The RCAN3D model also gave good improvement in quality. F) The Noise2Fast had more signal fluctuation, compared to supervised models. G) The high-quality ground-truth reveals the tissue anatomical structure.
Fig.
Fig. 1 | Backbone and finetuning to train the light microscopy image enhancement model. A) A separate model is trained for every imaging experiment. This training from the scratch method proves to be effective but need more samples and longer training time to reach good performance. Furthermore, since every training is independent, the model under training cannot utilize the curated datasets to help current imaging experiment. B) We propose first to train a backbone model from the large, diverse and previously curated datasets. The trained backbone model is further finetuned for every new experiment, with much smaller amount of new data. Given an effective backbone model architecture, this method will be much faster in training, and also allow the current training to reuse information acquired in previous experiments. Inspired by the success of transformer model in language pre-training, we proposed a novel imaging transformer architecture, CNNT, to serve as an effective backbone.
Fig. 3 |Fig. 4 |
Fig. 3 | The Widefield experiment to image the MEF cells. The pre-trained CNNT backbone was finetuned on 5 and 10 widefield image samples. The resulting model was compared to RCAN3D and Noise2Fast for image quality and computing time. A) The low quality noisy image as the input to the models. B-C) The CNNT results after finetuning for 30 epochs on 5 and 10 samples. The quality improvement is noticeable. E) The RCAN3D model trained from the scratch for 300 epochs gave good improvement. F) The Noise2Fast result is subpar. G) the high quality ground-truth for SSIM3D and PSNR computation and for reference. The CNNT finetuning is much faster than RCAN3D training and Noise2Fast and offer better quality measurements.
Fig. 5 |Fig. 6 |
Fig. 5 | The multi-average tests for the zebra fish imaging. The imaging was repeated for N=64 times. CNNT models were tested for robustness for different level of input quality with increasing number of averages. The Avg 1, 4, 8, 16, 32 and 64 were shown here. The model was found to be robust against the lower input SNR, giving consistently boost of image quality. The model also robustly recovered finer features. No sign of hallucination was found.
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Domain: Biology Computer Science
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Study of the limits of resistance of endophytic fungi Fusarium equiseti and Cylindrocarpon magnusianum to the action of copper and chromium (VI)
. Endophytic fungi are promising subjects for study as agents for increasing plant resistance. They have a wide distribution area, diverse morphology and are able to adapt to stressful environmental conditions. We have carried out studies on the effect of different concentrations of copper and chromium on the growth of endophytic fungi Fusarium equiseti and Cylindrocarpon magnusianum. The results showed that these micromycetes are able to adapt to a high content of heavy metals in the environment and, therefore, can be used in the development of technologies for increasing plant resistance.
Introduction
Recently, great interest of the scientific community is directed to the study of consortium relations of higher plants with endophytic microorganisms, including micromycetes. Microscopic fungi inhabiting plant roots increase the efficiency of the assimilation of mineral elements by plants [1,5]. Besides, root endophytic fungi help plants to adapt and show resistance to environmental stress factors: high temperature, drought, soils salinization, including heavy metals (HM), and action of pathogenic organisms [3,6,7].
However, a difficult moment in the study and practical application of the most studied in this area arbuscular-mycorrhizal fungi that form endomycorrhiza is their obligate symbiotrophism and, as a consequence, the complexity of their cultivation in the mycorrhization technology development. In this regard, endophytic micromycetes are of greater interest for study [8]. Endophytic fungi have a wide distribution area, they are morphologically diverse in nature and are able to adapt to stressful environmental conditions [9]. Moreover, fungal cultures isolated from contaminated soils have wider tolerance limits to the action of pollutants, including salts of heavy metals [10,11,9].
Fusarium equiseti and Cylindrocarpon magnusianum are representatives of root endophytes, which are widely found in nature [13]. Fusarium equiseti was previously considered a pathogen [14,15], however, in recent years in research, the fungus has shown the ability to fight root pathogens [13]. Cylindrocarpon magnusianum is found in places of soil pollution with oil, which can be in demand in the restoration of oil-contaminated lands [16]. In addition, in our earlier studies, both fungi showed high resistance to the content of sodium chloride in the substrate [2]. Based on this, we set a goal to study the resistance limits of Fusarium equiseti and Cylindrocarpon magnusianum to different concentrations of heavy metal salts, in particular, copper and chromium, and evaluate the possibility of using them to develop a technology for increasing plant resistance to HM salts.
Methods
Fusarium equiseti and Cylindrocarpon magnusianum cultures, which were used in our studies, were isolated from the root system of plants growing in contaminated soils in the city of Izhevsk (Udmurt Republic). The mushrooms were cultivated on an agar medium with the addition of different concentrations of copper and chromium salts (biogenic and non-biogenic chemical elements): Cu -50; 100; 150 mg/l; Cr -2.5; 5; 10 mg/l. There was also a test case. During the experiment, the diameter of the mycelium of fungi and their growth rate were measured. The peculiarities of the response of fungi to stress conditions were assessed by the content of malondialdehyde (MDA), which is a product of lipid oxidation. It should be noted that a number of studies confirm the dependence of the MDA concentration in the fungal mycelium on the HM content in the substrate; therefore, this indicator can serve as a biomarker of stress conditions in which endophytes find themselves [4,12]. The content of MDA in mushrooms was estimated by the degree of accumulation of the product of its reaction with thiobarbituric acid (TBA), determining the optical density of the solution on a spectrophotometer at a wavelength of 532 nm. For this, 2 ml of distilled water and 3 ml of 10% TCA were added to a test tube with mushroom biomass. A 2 ml sample was taken from the resulting homogenate and 0.5% TBA was added.
Analysis of the effect of different concentrations of copper and chromium on the growth of Fusarium equiseti and Cylindrocarpon magnusianum
The results of measuring the mycelium diameter and the growth rate of Fusarium equiseti are shown in Figures 1-2 The experimental results showed that the chromium content in the medium influenced the growth of F. equiseti. The diameter and growth rate of the fungal mycelium at all metal concentrations in the substrate were significantly lower than in the control. At the same time, there were no significant differences between the variants themselves in the size of the diameter.
F. equiseti showed great resistance to copper. At concentrations of 50 and 100 mg/l, the diameter of the fungal mycelium at the beginning of the experiment exceeded the control values. The growth rate of mycelium also at the beginning of the experiment was significantly higher than the control at concentrations of 50 and 100 mg/l. The chromium content in the medium had no inhibitory effect on C. magnusianum. The diameter of the fungus mycelium at all metal concentrations in the medium did not differ significantly from the control. The growth rate of the fungal mycelium did not differ from the control in all variants of the experiment.
Copper, on the other hand, had an inhibitory effect on the growth of C. magnusianum. With an increase in the concentration of copper in the substrate, a decrease in the diameter of the mycelium was observed. The growth rate of mycelium at the beginning of the experiment was significantly lower than in the control.
The results of determining the MDA content in the mycelium of fungi are presented in Table 1. The content of MDA in the mycelium of F. equiseti and C. magnusianum grown on a substrate containing chromium in different concentrations exceeded the control values. Only at the highest concentration (10 mg/l), the MDA content in the mycelium did not differ significantly from the control. At the same time, the growth rate of F. equiseti mycelium at this concentration during the entire experiment was the lowest in comparison with other experimental variants, although there was no significant difference with the control. In the mycelium of C. magnusianum at the highest chromium concentration, the MDA content was significantly lower than in other variants, but remained higher than in the control.
From here, we can conclude that the chromium content in the substrate caused a stress reaction in F. equiseti, but the organism, having overcome the adaptation period, resumed growth processes.
In the experiment with copper, the MDA content in the mycelium of F. equiseti and C. magnusianum at the lowest metal concentration in the substrate (50 mg/l) did not differ significantly from the control. As the copper concentration in the substrate increased, the MDA content in the fungal mycelium increased and reached its maximum values at a concentration of 150 mg/l. Since the growth of mycelium continued at a given copper concentration in the substrate, it can be concluded that MDA synthesis played a role in the system of adaptive responses of the fungus.
Conclusions
1. Endophytic fungi are promising objects for study as agents for increasing plant resistance. They have a wide distribution area, diverse morphology and are able to adapt to stressful environmental conditions [9]. At the same time, the habitat of micromycetes plays an important role in their resistance to pollutants. Fungal cultures isolated from contaminated soils have wider tolerance limits to the action of the pollutant [10,11,12]. 2. Fusarium equiseti and Cylindrocarpon magnusianum showed high metal resistance to chromium and copper. Herewith C. magnusianum showed great resistance to chromium, while F. equiseti -to copper. 3. The results of the analysis of the MDA content in the mycelium of fungi suggest that the synthesis of MDA and an increase in its concentration in the mycelium is a reaction to an increase in the concentration of HM in the substrate. Thus, F. equiseti and C. magnusianum exhibit an adaptive response to the content of copper and chromium in the substrate and can be used in the development of the technology of increasing plant resistance to heavy metal salts.
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Domain: Engineering Biology
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Hydrocarbon Biodegrading Potentials of a Proteus vulgaris Strain Isolated from Fish Samples
A Proteus vulgaris bacterium SR-1 was isolated from a freshly killed fish sample collected close to the point of crude oil spill in the Niger Delta region, Nigeria. Problem statement: The application of native bacterial species in bioremed iation processes has long been desired, because the y would be cost effective and efficient in terms of a cclimation time. The ability to isolate high number s of certain oil-degrading microorganisms from oil-po lluted environment is evidence that these microorganisms are the active degraders of that env ironment. In this study, we reported the potential of a candidate bacteriumProteus vulgaris SR-1 in the biodegradation of Bonny light crude oi l, d esel and kerosene. Approach: To screen for oil degrading capability, the bacter ium was cultivated in Minimal Salts Medium (MSM) supplemented with 1% (v/ v) sterile Bonny Light Crude Oil (BLCO). Oil degradation was monitored by measurement of tur bidity using a spectrophotometer and the pH, total viable counts of the culture fluids were dete rmined at time intervals as biodegradation indices. The ability of strain to degrade diesel and kerosene oils was also studie d while the level of used hydrocarbon degradation was determined using the gr avimetric analysis. The bacterium was screened for presence of Plasmid DNA and implication of plasmid in hydrocarb on degradation was investigated. Results: (1) The bacterium utilize hydrocarbons as sole sou rce of carbon and it biodegraded Bonny light crude oil, kerosene and die sel media by as much as 78, 79 and 73.8% respectively, in the presence of 1.0% NaCl (w/v) af ter 96 h. The total viable count after 96, 120 and 168 h of biodegradation of the test hydrocarbons ra ge between 6.2 and 9.1 log 10 c.f.u mL , (2) The results showed that increasing NaCl concentration i n water had decreasing effect on hydrocarbon degradation. (3) pH of media decreased from 7.0 to between 3.29 and 5.02 during the reaction period while growth increases. (4) Plasmid analysis revealed the presence of a plasmid of approximately 9.1 kb in the bacterial isolate. Conclusion/Recommendations: The results of this study showed that Proteus vulgaris SR-1 is a highly adapted bacterium with great poten tial to biodegrade hydrocarbons and the genes responsible f or hydrocarbons biodegradation could be located on the (9.1 kb) plasmid it harbors.
INTRODUCTION
In Nigeria, the scourge of environmental pollution has reached a frightening scale in recent years especially in the Niger-Delta region, the largest delta in Africa and the third largest in the world where most of the crude oil in the country is found (HRW, 1999). This region encompasses an area of approximately 70,000 km 2 accounting for about 7.5% of the country's total land mass, covering a coastline of 560 km, about two-third of the country's entire coastline. In the Niger Delta, increasing petroleum exploration and transportation has introduced large amounts of hydrocarbons into the area (Odokuma and Dickson, 2003a;2003b).
Accidental and deliberate crude oil spills have been and still continue to be, a significant source of environmental pollution and poses a serious environmental problem, due to the possibility of air, water and soil contamination (Trindade et al., 2005). According to Fasasi (2006), oil spill also destroys the biodiversity of the delicate ecosystem of the Niger Delta. The processes leading to the eventual removal of hydrocarbon pollutants from the environment has been extensively documented and involves the trio of physical, chemical and biological alternatives (Okoh, 2006).
Many bacteria and fungi have demonstrated potentials in the biodegradation of hydrocarbon pollution and these organisms are widely distributed in marine, freshwater and soil habitats (Head and Swannell, 1999). The biodegradation of crude oil by microorganisms is one of the primary ways for eliminating crude oil from contaminated sites and appears to be the most environmentally friendly method of removal of oil pollutant (Barathi and Vasudevan, 2001;Balba et al., 2002;Urum et al., 2003). The ability to isolate high numbers of certain oil degrading microorganisms from oil-polluted environment is commonly taken as evidence that these microorganisms are the active degraders of that environment (Okerentugba and Ezeronye, 2003). The relevance of bacterial isolates in bioremediation of hydrocarbon contaminated systems especially in Nigeria is continually been investigated with the hope of stocking organisms that are useful for the bioremediation of crude oil polluted environments (Okoh et al., 2001;Okoh, 2003;Ogbeifun et al., 2004;Lu et al., 2000;Holt et al., 1994;Colle et al., 1996). In this study, we report the potential of a candidate bacterium-Proteus vulgaris SR-1in the biodegradation of Bonny light crude oil, diesel and kerosene.
Microorganism, identification and maintenance:
The hydrocarbon degrading bacterium strain used in this study were isolated from our previous study (Olajide et al., 2009) from newly killed fish samples collected close to the point of spill in the Niger Delta region in Nigeria. The purified bacterium strain was characterized for Gram reaction, cell morphology (Olajide et al., 2009) and biochemical/enzymatic analysis. The bacterium strain was grown in nutrient broth culture medium with a 2% (v/v) inoculum and incubated 37°C with shaking at 125 rpm (Lab-line No 3590) and regenerated twice before use in the manipulations. The regenerated strain was maintained on nutrient Broth agar (Merck) slants at 4°C and subcultured every three months or when necessary.
Inoculum was prepared by growing cells at 37°C for 24 h in Nutrient Broth and stirred in a rotary shaker at 120 rpm. The composition of the mineral salt medium used in this study was described by Tuleva et al. (2002).
Chemical reagent and hydrocarbon substrates:
The Bonny light crude oil, diesel and kerosene were obtained from the Nigerian National Petroleum Corporation (NNPC) Warri, Delta State, Nigeria and all other chemicals used for this study are of analytical grade from BDH Chemicals Ltd. Poole, England except otherwise stated.
Screening of hydrocarbon degrading bacterium:
Fifty milliliter of the mineral salt medium was distributed in a sterile conical flask, 1% of each hydrocarbon was aseptically added and controls also set up. After sterilization, the bacteria isolate Proteus vulgaris was inoculated into the flasks. The flasks were incubated at 37°C on a rotary shaker (Labline No 3590), with shaking at 120 rpm for one week. Oil degradation was monitored by measurement of turbidity at a wavelength of 540 nm using a spectrophotometer (NOVASPEC II, Pharmacia Biotech). The measurement was taken for one week period to allow appreciable monitoring of bacteria growth under experimental conditions alongside that of sterile control. The pH, Total Viable Counts (TVC) of the culture fluids were determined at time intervals as biodegradation indices (Rahman et al., 2002;Emtiazi and Shakarami, 2004).
Biodegradation of hydrocarbons by pure culture:
A 50 mL of cooled sterilized mineral salts medium was dispensed into several sterilized 100 mL conical flasks and 0.5 mL of each sterilized hydrocarbon added to make the usual 1% w/v oil. To each of the flasks, a pure culture of isolate Proteus vulgaris already grown in nutrient broth and standardized to constant mass was added at about 1.0 mL of culture to a flask to maintain uniformity as much as possible. The flasks were incubated on rotatory shaker with shaking at 120 rpm for one week at room temperature. After incubation, the level of used hydrocarbon degradation was determined using the gravimetric analysis (Chang, 1998;Marquez-Rocha et al., 2001). The percentage of hydrocarbon remaining was calculated compared to the control.
The effect of hydrocarbon degradation on pH:
To study the effect of pH on hydrocarbon degradation, the pH of the medium was adjusted to the desired pH by adding either 0.1 M HCl or 0.2 M NaOH using glass electrodes. Following inoculation, the flasks were incubated on shaker at 150 rpm for 3 days. The emulsification capacity of the culture broth free of cells was also determined (Yakimov et al., 1995).
Investigation of different salt concentrations effect on hydrocarbon degradation:
The effect of salinity on hydrocarbon degradation was determined by adding different concentrations (0.0-2%) of NaCl to the minimal salts medium. The mixtures were incubated at 37°C on shaker at 150 rpm for 3 days (Prommachan et al., 2001). The emulsification capacity of the culture broth free of cells was also determined.
Plasmid DNA detection procedure: The presence of plasmid DNA in Proteus vulgaris SR-1 was done in accordance with the description of Kado and Liu (1981). Cells were grown in 3 mL of LB broth overnight at 37°C to an optical density at 600 nm of 0.8 and pelleted by centrifugation (5,700 rpm, 4°C, for 7 min). The cell pellet was thoroughly resuspended in 1 mL of TE buffer (40 mM Tris-acetate and 2 mM sodium EDTA). The Tris was adjusted to pH 7.9 with glacial acetic acid. The cells were lysed by adding 2 mL of lysing solution (3% SDS and 50 mM Tris (pH 12.6). The solution was adjusted to pH 12.6 by adding 1.6 mL of 2 N NaOH, which was mixed by brief agitation. The solution was heated at 50-65°C for 20 min in a water bath and 2 volumes of phenol-chloroform solution (1:1 vol/vol) were added. The solution was emulsified by shaking briefly and the emulsion was broken by centrifugation (6,000 rpm, 15 min, 4°C). Avoiding the precipitate at the interface, the upper aqueous phase was transferred to Eppendorf micro centrifuge tubes. Samples were withdrawn directly for electrophoresis immediately or stored at 4°C until tested. As plasmid DNA samples contain RNA, 1 µL of RNase was added to each 35 µL of sample, mixed and incubated for 5 min at 37°C prior to gel loading. This was followed by addition of 7 µL of loading dye. Plasmid DNA was analyzed by 1.0% agarose gel electrophoresis in TAE buffer pH 8.5 for 30 min. Lambda HindIII DNA molecular mass marker (23130-564 bp) was used as a standard for molecular mass determination. The gel was stained with ethidium bromide (0.5 mg mL −1 ) and observed under UV transilluminator for the presence of plasmid band. above, were digested an hour at 37 °C with ten units of various restriction enzymes and the fragments thus generated were fractionated by electrophoresis on 1.0 % agarose gel as described.
Isolation and identification of bacterial strains:
The test bacteria is a small, motile, Gram-negative, single rods, indole positive, oxidase negative, facultative anaerobe, while the results of biochemical and enzymatic assay correct identification rate without additional tests assay was 88.9% at the species level and 98.4% at the genus level. The overall rate of correct species identification was about 90% consistency index of Proteus vulgaris.
Hydrocarbon biodegradation:
The utilization of hydrocarbons (Bonny light crude oil, Diesel and kerosene) as a substrates by isolate Proteus vulgaris SR-1 is evident by the increase in cell density. The results showed maximal increase in optical densities and total viable count concomitant with decrease in pH on fourth, fifth and eight day of reactions (Fig. 2-4). The maximum cell densities were 6.2 log 10 c.f.u mL −1 and over a 96-120 h of growth in 1% (v/v) Bonny light crude oil and diesel respectively. The maximum cell density of 9.1 log 10 c.f.u mL −1 was obtained on 168 h during growth in the presence of 1% (v/v) kerosene (Fig. 4).
Effect of Hydrocarbon Degradation on pH:
The initial pH of medium was 7.0 in this experiment. Generally, a decreasing trend of pH was observed in the experimental flasks within the incubation period as growth increases in the presence of the three different hydrocarbons investigated (Fig. 1-3).
Effect of salinity on hydrocarbon degradation:
The effect of different NaCl concentrations (0-2%) on hydrocarbons degradation from the crude oilcontaminated water was studied. The results showed that increasing NaCl concentration in water had decreasing effect on hydrocarbon degradation. The amount of oil degraded by Proteus vulgaris SR-1 strain increased initially to a maximum level at 1.0% w/v NaCl, but thereafter decreased with increasing salt concentration and the patterns were similar for the three different test hydrocarbon substrates (Fig. 4-6). In all the various concentrations of NaCl used, salt concentrations did not affect the viable cell count during biodegradation. The results obtained were almost the same for the different hydrocarbons, though the number of total viable counts varied in each oil as shown in Fig. 4-6. Gravimetric analysis of oil degraded: The NaCl concentration for optimal growth was 1.0% (w/v) as observed in Table 1 The biodegradation of hydrocarbons were higher in 1.0% NaCl and was 78.1, 79.9 and 73.8% for Bonny light crude oil, kerosene and diesel respectively.
Evidence of plasmid DNA in isolate:
Plasmid analysis revealed the presence of a plasmid of approximately 9.1 kb in the bacterial isolate Proteus vulgaris SR-1 (Fig. 7).
DISCUSSION
The isolate, Proteus vulgaris SR-1 was able to grow on crude petroleum as the sole source of carbon and energy when screened for hydrocarbon utilization. Interestingly, this same organism has been implicated in hydrocarbon degradation (Kayode-Isola et al., 2008).
The growth profiles of the bacterium were monitored by the optical densities, total viable count and the pH of the culture media. The results were shown in Fig. 1-3 and reflect that the isolates grew maximally on the three different hydrocarbon substrates when supplied as the sole source of carbon and energy. This technique was used in several studies to show the ability of bacteria utilizing crude oil (Emtiazi and Shakarami, 2004). In a similar investigation by Rahman et al. (2002) the total viable count method was used to confirm the potential of different kind of bacteria utilizing hydrocarbon. Thus bacterium growth reached the stationary phase and moved into the death phase in almost all the cases with the exception of kerosene. This is probably due to the chemistry of the hydrocarbon and the order of hydrocarbon degradation was; Kerosene>Bonny light crude oil>diesel.
The utilization of the petroleum hydrocarbons as sole carbon and energy source by the isolate resulted in the growth with a resultant production of acid. This is probably as a result of chemical change of the crude oil hydrocarbons and production of by products and ability of isolated Proteus vulgaris to use crude oil and generate organic acids. Thus reducing the pH as has been reported elsewhere (Matthew, 2006). The initial pH of the medium was 7.0 and this decreased steadily as growth increases in the presence of the three different hydrocarbons as presented in Fig. 4-6. This finding is in agreement with the study of Sepahi et al. (2008) who reported that microbial degradation of hydrocarbons often leads to production of organic acids, thus the organic acids probably caused the reduction in pH.
It is evident from this study that isolated Proteus vulgaris did not exhibit any lag phase in the culture media ( Fig. 1-3). The result can be attributed to genetic makeup due to the constitutive expression of hydrocarbon catalyzing enzymes. This finding is in agreement with the study of Okerentugba and Ezeronye (2003) who reported that microorganisms growing on crude oil hydrocarbon did not exhibit any lag phase.
The tolerance of bacteria to salinity gradients could play a major role in its preferential use in the degradation of oil in marine environment. In this study, the amount of oil recovered from the sterile oil controls increased with increasing concentration of NaCl. The quantity of oil degraded increased with increasing NaCl concentration in the experimental. The results obtained in (Table 1) showed that Proteus vulgaris did not tolerate high concentrations of NaCl. It would therefore be unexpected that the isolate would thrive in marine system. Hence, it will be necessary to determine optimum salinity for every studied system.
The implication of plasmids in the degradation of petroleum hydrocarbons has also been a subject of investigation. Several methods have been reported for the isolation of plasmid DNA from Gram-negative bacteria (Davies and Normark, 1980;Anderson and McKay, 1983;Owen and Hernandez, 1990;Mottaleb et al., 2003). The application of these methods failed to demonstrate plasmids in Proteus vulgaris. The most satisfactory being the procedures described by Kado and Liu (1981). By this procedure, it was possible to identify one plasmid of molecular weight 9.1 kb from Proteus vulgaris (Fig. 7). SDS-curing of the isolates leads to complete loss of plasmid and hydrocarbon degradation activity.
CONCLUSION
The results of this study showed that Proteus vulgaris is a highly adapted bacterium with great potential to biodegrade hydrocarbons and the genes responsible for hydrocarbons biodegradation could be located on the (9.1 kb) plasmid it harbors.
ACKNOWLEDGEMENT
I am grateful to Tel Aviv University and Prof. David Gutnick, of the Department of Molecular Microbiology and Biotechnology, Tel Aviv University, Israel, for the use of their equipment in carrying out the molecular aspect of this study.
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Domain: Engineering Biology
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A Novel Implementation of Nature-inspired Optimization for Civil Engineering : A Comparative Study of Symbiotic Organisms Search
The increasing numbers of design variables and constraints have made many civil engineering problems significantly more complex and difficult for engineers to resolve in a timely manner. Various optimization models have been developed to address this problem. The present paper introduces Symbiotic Organisms Search (SOS), a new nature-inspired algorithm for solving civil engineering problems. SOS simulates mutualism, commensalism, and parasitism, which are the symbiotic interaction mechanisms that organisms often adopt for survival in the ecosystem. The proposed algorithm is compared with other algorithms recently developed with regard to their respective effectiveness in solving benchmark problems and three civil engineering problems. Simulation results demonstrate that the proposed SOS algorithm is significantly more effective and efficient than the other algorithms tested. The proposed model is a promising tool for assisting civil engineers to make decisions to minimize the expenditure of material and financial resources.
Introduction
In recent decades, design optimization has become a critical and challenging activity that has gained in importance in the field of civil engineering. A goal of designers is to obtain optimal solutions in order to reduce construction project costs. Optimization allows designers to create better designs that reduce expenditures of material and financial resources as well as time. However, modern engineering design problems have increased tremendously in complexity and now frequently address complicated objective functions with large numbers of design variables and constraints [1]. This complexity has inspired numerous studies worldwide with the shared goal of developing a model that effectively optimizes current civil engineering problems.
Many optimization methods have been introduced over the past four decades. Gradient-based methods were the first of these methods to be widely used in solving decision-making problems in civil engineering [2].
These methods are often inadequate in dealing with the complexities inherent in many of today's optimization problems due to poor handling of largescale variables and constraints. Additionally, these methods also use analyses that require gradient information to improve initial solutions. However, the designers usually have insufficient knowledge to locate the initial solutions, as they have no way to identify the most promising area for the global optimum of the current problem. Therefore, these gradient-based search methods frequently fail to converge on global optimum because of failed guesswork in defining the area of the global optimum. The above concerns have encouraged researchers to work to develop better optimization models.
The field of nature-inspired algorithms has been studied extensively with regard to its potential to solve optimization problems due to its superior performance in handling models that are highly nonlinear and complex. One of the most significant advantages of nature-inspired algorithms is that these algorithms do not use gradients to explore and exploit the problem search space. Instead, they combine natural pattern rules and randomness to identify near-optimum solutions efficiently [3]. Examples of nature-inspired algorithms include: Genetic Algorithm (GA) [4], Particle Swarm Optimization (PSO) [5], Differential Evolution (DE) [6], and Artificial Bee Colony (ABC) [7].
In recent years, numerous studies have proposed nature-inspired approaches to solve civil engineering problems. In construction management, natureinspired algorithms have been used to solve problems such as project site layout [8], time-cost trade-off [9], and resource leveling [10]. In structural engineering, examples of nature-inspired applications include: truss design [11,12] and frame design [13]. Nature-inspired algorithms have also been used in dealing with geotechnical problems [14], pavement engineering [15], and concrete mix design [16,17]. As civil engineering problems become more complex, new nature-inspired algorithms will continue to emerge.
A new nature-inspired algorithm called Symbiotic Organisms Search (SOS) has been developed by Cheng and Prayogo [18]. The SOS algorithm mimics the interactive behavior between living organisms in ecosystem. In the previous study, the performance of SOS has been compared with other nature-inspired techniques in numerous mathematical test functions and engineering problems. The comparison results indicate that SOS was able to achieve a better performance in terms of effectiveness and efficiency [18]. As a new nature-inspired algorithm, it is worthwhile to explore and investigate the SOS algorithm in seeking the global solution. This paper studies the effectiveness of Symbiotic Organisms Search (SOS) in solving various civil engineering optimization. SOS is first validated on benchmark functions and then tested on three practical civil engineering problems. The obtained results are then compared with well-known optimization techniques.
The Symbiotic Organisms Search (SOS) Symbiotic Organisms Search (SOS) Algorithm
SOS is a new nature-inspired algorithm inspired by the natural phenomena of symbiotic interactions proposed by Cheng and Prayogo [18]. Over the past years, SOS has been proven to successfully solve various problems in different fields of research [19][20][21][22]. In surviving environmental change, the living organisms often develop symbiotic interactions among themselves. The most common examples of symbiotic interactions found in nature may be divided into three main categories: 1. Mutualism: This category describes the interactive behavior between two different living organisms that gain advantage mutually from that interaction. An example of mutualism is the relationships between oxpecker and zebra. Oxpecker lands on zebra, eating all the parasites. This activity benefits both zebra and oxpecker, since oxpecker collects foods and zebra gains pest control. Another example of mutualism is the relationship between bee and flower. In SOS algorithm, three phases of the search are performed mimicking the three symbiotic interactions namely mutualism, commensalism, and parasitism phase. By performing these three phases, SOS attempts to move a population (ecosystem) of possible solutions to a better region in the search space during the searching process for the optimal solution. In SOS, each solution in the population is known as an organism. Every organism is associated with its fitness value, which represents the survival advantage within the current environment. Through successive iterations, the fitness values of the organisms are improved by simulating the symbiotic interactions. The process of generating solutions through three phases is repeated until stopping criteria are satisfied. The source code for a MATLAB implementation of SOS is publicly available at [URL] next section provides further details on the three phases.
Mutualism Phase
The mutualism phase simulates the mutualism between two living organisms, ecoi and ecoj. The mechanism of mutualism is modeled in Equations ( 1) -( 5).
where ecoi is the i-th organism of the ecosystem, ecoj is the j-th organism of the ecosystem where j ≠ i, BF1 is the benefit factor matched to ecoi, BF2 is the benefit factor matched to ecoj, ecobest represents the best organism in the current iteration, ecomutual represents the relationship characteristic between organism ecoi and ecoj, ecoi new and ecoj new represent candidate solutions for ecoi and ecoj after their mutualistic interaction, respectively.ecobest is the target point for every organism to increase its fitness during its interaction with another organism. Organisms ecoi and ecoj are updated only if their new (ecoi new and ecoj new) fitness is better than their old fitness (ecoi and ecoj).
Commensalism Phase
The commensalism phase simulates the commensallism between two living organisms, ecoi and ecoj with ecoi gains advantage and ecoj is unaffected. The mechanism of commensalism is modeled in Equation (6).
where ecoi is the i-th organism of the ecosystem, ecoj is the j-th organism of the ecosystem where j ≠ i, ecoi new represents candidate solutions for ecoi after their interaction, respectively.
Organism ecoi is updated only if its new fitness is better than its old fitness.
Parasitism Phase
The parasitism phase simulates the parasitism between two living organisms, ecoi and ecoj with ecoi gains advantage and ecoj is harmed. Organism ecoi serves a role similar to the anopheles mosquito and, thus, create an artificial parasite called ecoparasite.
Generally speaking, ecoparasite is a clone of organism ecoi. To differentiate the ecoparasite from ecoi, some random decision variables from the initial ecoparasite will be modified randomly. The location of the modified decision variables is determined randomly using a random method. For each dimension, a uniform random number is generated. If the random number is less than 0.5, the variable will be modified by a random value generated by uniform distribution; otherwise, it will stay the same.
Organism ecoj serves as a host to the ecoparasite. If ecoparasite has a better fitness value, it kills organism ecoj and replaces its position in the ecosystem. If the fitness value of ecoj is better, ecoj survives and the ecoparasite can no longer exist in the ecosystem.
The Framework of the SOS Algorithm for the Design Optimization in Civil Engineering
Design objectives in design problems also have various other constraints including deflection, stress, material dimensions, pressure, and temperature. Many civil engineering problems may be expressed as constrained optimization problems. This paper handles the constraints using Deb's feasibility rules [23]. The use of SOS in constrained optimization problems that incorporate Deb's rules is summarized as follows.
Initialize Ecosystem
The SOS establishes an initial ecosystem by generating a matrix that contains uniform random numbers that exist within the given boundaries.
After the initialization is complete, the initial best solution is calculated. The ecosystem is expressed as follows: In this step, the initial ecobest is determined by choosing the fittest organism in the initial ecosystem.
Simulate Interaction between Organisms through the Mutualism Phase
After the ecosystem initialization, each organism in the ecosystem will go through three phases, mutualism, commensalism, and parasitism. In the mutualism phase, ecoj is picked randomly from the ecosystem that is designated to interact with ecoi where i is start from 1, 2, 3, … to ecosize, j is a random number which ≠ i. New candidate solutions ecoi new and ecoj new are calculated using Equations ( 2) and (3), in which ecomutual is determined using Equation (1) and Benefit Factors (BF1 and BF2) are determined using Equations ( 4) and (5). New candidate solutions ecoi new and ecoj new are compared to the old ecoi and ecoj. Deb's rules are implemented to retain the fittest solutions in the search space for the next iteration.
Simulate Interaction between Organisms through the Commensalism Phase
In the commensalism phase, another organism, ecoj, is picked randomly from the ecosystem to interact with ecoi. The new candidate solution ecoi new is calculated using Equation ( 6) and compared to the older ecoi. Deb's feasibility rules are applied to identify the fittest organism as the solution to be carried forward into the next iteration.
Simulate Interaction between Organisms through the Parasitism Phase
In the parasitism phase, another organism, ecoj, is picked randomly from the ecosystem to be a host organism.ecoparasite is created by mutating the parent organism ecoi in random dimensions using distributed random numbers that are limited within a specific range. Deb's rules are then used to compare this vector to host organism ecoj. If the host organism is fitter than ecoparasite, the host organism will survive to the next iteration and ecoparasite will be eliminated. Conversely, a fitter ecoparasite will lead to its retention into the next iteration and elimination of ecoj.
Updating the Best Organism
When the fitness of the organism ecoi is better than the fitness of the ecobest, the ecobest is updated with ecoi.
Termination
If the current ecoi is not the last member of the ecosystem, the SOS will automatically select the next organism to simulate the mutualism, commensalism, and parasitism, and update the ecobest. After all members of the ecosystem finish the whole process, SOS will check the termination criteria. The common termination criteria used in the literature are the maximum number of iterations and the maximum number of function evaluations. SOS will stop if one of the termination criteria is reached; otherwise, SOS will start the new iteration.
Practical Examples on Civil Engineering Problems
This section uses three widely used civil engineering problems to assess SOS performance. Obtained SOS optimization results are then compared to data published in the literature. These problems are: (1) reinforced concrete beam design minimization, (2) 25-bar transmission tower truss weight minimization, and (3) site layout optimization for caisson structure fabrication.
Reinforced Concrete Beam Design Minimization
This case study is a cost minimization problem of the reinforced concrete beam as illustrated in Figure 1. This was first presented by Amir and Hasegawa [24]. The beam is assumed simply supported with a 9.144-m (30-ft) span and subject to a live load of 1 ton (2.0 klbf) and a dead load of 0.5 ton (1.0 klbf) accounting for the beam weight. Concrete compressive strength (c) and reinforcing steel yield stress (y) is 34.474MPa (5 ksi) and 344.74 MPa (50 ksi), respectively. The unit cost of steel and concrete are $472.4/m 2 / linear m ($1.0/in 2 /linear ft) and $9.449/m 2 /linear m ($0.02/in 2 /linear ft), respectively. The cross sectional area of reinforcing (As), beam width (b), and beam depth (h) are selected as the decision variables.
The structure should be designed to meet the minimal strength required under ACI 318-77 building code: (7) where Mu, Md, and Ml, respectively, are the flexural strength, dead load, and live load moments of the beam. In this case, Md = 152.53kNm (1350 in kip) and Ml = 305.06kNm (2700 in kip). Beam depth ratio is restricted to be less than or equal to 4. The optimization problem may be stated as: Minimize: Subject to: Table 1 presents the optimum designs of this problem and the parameters used, including several comparisons with prior research on SD-RC [24], GA and FLC-AHGA [25], CS [26], FA [27]. In this case study, SOS found the same optimum solution identified by FA in 1/10th the time required by FA using 15 organisms.
Loads are shown in
The result for the SOS was found after 30 independent runs. The results for the other algorithms were referenced from Degertekin and Hayalioglu [35]. It is apparent that the design solution obtained by HS [31] is theoretically infeasible because these solutions violate the design constraint stated in [35].
The results produced by the SOS algorithm were competitive with those produced by TLBO [35] and SAHS [34] and superior to those of HPSO [32], and BB-BC [33]. Furthermore, the SOS algorithm delivered a better average solution, and lower standard deviation compared to the TLBO algorithm, supporting that the SOS algorithm is a better optimization method than TLBO in terms of consistency.
Site Layout Optimization for Caisson Structure Fabrication
The performance of SOS was validated for solving construction site-level facility layout, a function within the field of construction management. Next, a real-life site-level layout problem previously posited by Kim et al. [36] was investigated. The aim of this case study was to design the site layout for caisson structure fabrication. The site layout considered nine facilities including: (1) steel plate storage, (2) concrete mold storage, (3) steel rod storage, (4) concrete curing place, (5) fabrication factory of caisson wall, (6) prefabrication factory of base plate, (7) steel rod factory, (8) crane 1, and (9) crane 2.
These nine predetermined facilities must be properly assigned to nine predetermined locations scattered over the site. The goal of this case study is to obtain the optimum layout which has the shortest total traveling distance between facilities. The total traveling distance (TD) minimization problem is stated as: Minimize: (11) Subject to: where n is the number of facility locations; is the permutation matrix variable such that when facility x is assigned to location I, is the traveling frequency of the construction crew between facilities x and I and is the distance between location i and j. The traveling frequency and distance table are shown in Table 5 and Table 6, respectively.
In this experiment, we compared SOS with PSO and DE. Because the site-level facility layout is a permutation problem, we modified the continuous-based initial solution vector into the permutation vector using the indices that would sort the corresponding initial solution vector. The experiment setup was as follows: All the algorithms used the same common control parameters with a population size of 50 and a total of 20,000 function evaluations. The crossover rate (CR) and the scaling factor (F) for DE were chosen as 0.9 and 0.5, respectively. The cognitive and social factors (c1 and c2) were set to 1.8 and the inertia weight (w) was set to 0.6 for PSO. Table 7 summarizes the results obtained by the SOS algorithm and by the other algorithms over 100 independent runs. The best-known answer for this case study is [9 1 8 7 6 5 3 2 4] with a total travel distance of 7727 meters. SOS algorithm delivered the best average solution, worst solution, and lower standard deviation in comparison with DE and PSO. Furthermore, SOS achieved the highest success rate in finding the best solution over 100 runs.
Conclusion
This paper introduced the use of a new optimization algorithm called Symbiotic Organisms Search (SOS) in civil engineering applications. SOS is a population based nature-inspired algorithm that mimics the interactive behavior between organisms in an ecosystem. The three phases of mutualism, commensalism, and parasitism inspire SOS to find the optimal solution for a given objective. Incorporating the characteristic of natural organism interactions into the search strategy supported the superior performance of the SOS algorithm.
In this paper, we first validate the performance of SOS against different optimization methods in constrained benchmark problems and then test the performance of SOS in numerous practical civil engineering problems. SOS precisely identified all optimum solutions in every run with significantly fewer function evaluations than algorithms tested in previous works. The novel SOS algorithm presented in this paper is adequately robust to solve various civil engineering problems. The proposed model may be an effective new tool to guide and support the decision-making process of practitioners.
Table 2 .
Load Case for the 25-bar Spatial Truss
Table 2 .
There are two types of given variables for this problem. The first version uses discrete variables, while the second version uses continuous variables.
Table 1 .
Results of the Reinforced Concrete Beam Example
Table 5 .
Traveling Frequencies between Two Locations
Table 6 .
Distance between Two Locations (m)
Table 7 .
Result of Site-level Facility Layout for Caisson Structure
Table 4 .
Optimum Design Comparison for the Continuous 25-bar Spatial Truss Structure
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Domain: Engineering Environmental Science Biology
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Energy Management of Mature Mammalian Spermatozoa
The ultimate goal of a mammalian sperm is the transmission of paternal genome to the next generation. To achieve this goal, mammalian spermatozoa are very specialized cells, which have a precise cellular design in dependence on the evolutionary reproductive strategy chosen by each species. This specificity is a very important point, since aspects such as the exact point of ejaculate deposition, oestrous time-lapse, number of males mated with a sin‐ gle female and number and sequence of oocytes released in a single ovulation will be key factors in the modulation of sperm function in order to yield optimal “in vivo” fertility rates. A practical consequence of this specificity is that the functional features that distinguish sperm from one species cannot be extrapoled to other species, hindering thus the assump‐ tion of an overall picture to explain mammalian sperm function. Furthermore, the extraordi‐ nary complexity of the molecular mechanisms implied in the control and modulation of mammalian mature sperm functions makes impossible to a complete description of these mechanisms in the limited space of this chapter. In this way, this chapter will be devoted to a succinct overview of the mechanisms by which mature mammalian sperm manage their energy levels, with a special emphasis in the observed differences among species and also during their entire life span of sperm from ejaculation. For this purpose, this chapter is cen‐ tered in the description of specific, very important and punctual aspects of sperm energy metabolism. The first aspect is the type of energy sources, both external and internal, that mammalian sperm can utilize to obtain energy. The second aspect is centered in the main metabolic pathways that mammalian sperm utilize to obtain energy, as well as in the basic control mechanisms that modulate these pathways. The third point will involve the precise role that mitochondria play in the control of the overall mammalian sperm function. Finally, the fourth and last point will be focused in the existence of separate metabolic mammalian
Introduction
The ultimate goal of a mammalian sperm is the transmission of paternal genome to the next generation. To achieve this goal, mammalian spermatozoa are very specialized cells, which have a precise cellular design in dependence on the evolutionary reproductive strategy chosen by each species. This specificity is a very important point, since aspects such as the exact point of ejaculate deposition, oestrous time-lapse, number of males mated with a single female and number and sequence of oocytes released in a single ovulation will be key factors in the modulation of sperm function in order to yield optimal "in vivo" fertility rates. A practical consequence of this specificity is that the functional features that distinguish sperm from one species cannot be extrapoled to other species, hindering thus the assumption of an overall picture to explain mammalian sperm function. Furthermore, the extraordinary complexity of the molecular mechanisms implied in the control and modulation of mammalian mature sperm functions makes impossible to a complete description of these mechanisms in the limited space of this chapter. In this way, this chapter will be devoted to a succinct overview of the mechanisms by which mature mammalian sperm manage their energy levels, with a special emphasis in the observed differences among species and also during their entire life span of sperm from ejaculation. For this purpose, this chapter is centered in the description of specific, very important and punctual aspects of sperm energy metabolism. The first aspect is the type of energy sources, both external and internal, that mammalian sperm can utilize to obtain energy. The second aspect is centered in the main metabolic pathways that mammalian sperm utilize to obtain energy, as well as in the basic control mechanisms that modulate these pathways. The third point will involve the precise role that mitochondria play in the control of the overall mammalian sperm function. Finally, the fourth and last point will be focused in the existence of separate metabolic mammalian sperm phenotypes as the result of the precise evolutionary strategy launched by each species to optimize fertility.
Mature mammalian spermatozoon: a dynamic cell with changing energy necessities during its lifetime.
A common characteristic of mature mammalian sperm among species is that these cells are dynamic structures, which must underlie dramatic functional changes during their entire life span, from ejaculation to syngamia. These functional changes, in turn, will imply equally dramatic changes in all aspects of sperm energy management, from external energy sources to energy-consuming functions such as specific motion patterns or capacitation-linked cellular and membrane changes. Thus, a succinct description of the most important changes of mammalian sperm function from ejaculation is needed to a better understanding of the observed changes in sperm energy management during their life span.
Ejaculation implies the launching of a rapid succession of events that completely changes sperm physiology. Thus, ejaculated spermatozoa acquire a fast motion pattern; which is accompanied with several changes in cell membrane composition. The main responsible for these changes are seminal plasma, which contacts with spermatozoa during ejaculation. Composition of seminal plasma is complex. Even worse, seminal plasma has a totally different composition when comparing among different species. An example of this is the monosaccharide composition of seminal plasma. We can detect a wide variety of different sugars, such as glucose, fructose and sorbitol. Moreover, the concentration of these sugars is completely different. Thus, whereas fructose is the main sugar in species like human, glucose is present in significant amount in species like boar [48]. The main monosaccharide present in horse seminal plasma is, at the contrary, sorbitol [42], whereas species like dog has not any monosaccharide in significant concentrations [44,48]. A similar pattern can be found when analyzing seminal plasma proteins. Thus, whereas dog has practically only one protein, which is characterized by its arginine esterase activity [10], other species such as boar and ram has a wide variety of proteins, including a membrane-protective protein family [8,56].
What could be the main reason for the enormous differences in composition among seminal plasma from separate species?Investigators can only speculate regarding this point. However, it would be reasonable to suppose that the main reason for these differences is the very specific evolutionary reproductive strategies developed in each species to optimize their fertilizing abilities. In this way, it is logical to suppose that seminal plasma of one long-lived species like dog, would have completely separate characteristics to the seminal plasma of other shorter-lived species, like bull. It is noteworthy that dog spermatozoa have to survive for relatively long periods inside the bitch genital tract and, moreover, must be prepared to compete against spermatozoa from other individuals. On the contrary, bull spermatozoa is adapted to a shorter life-span, since the time lapse between ejaculation and cow ovulation is very short indeed. In any case, seminal plasma has some common features among species intended to solve common problems that spermatozoa find after ejaculation. Thus, seminal plasma must contain components that activate sperm motility. This is absolutely essential in species in which ejaculation is carried out either at the vaginal vestibulum or at cervix. In these placements, the female genital tract presents a very active immunological system, which is further activated during oestrus [23]. This very active system will eliminate all spermatozoa that would not be enough fast or enough fortunate to leave the area and, in this way, sperm motility must be activated immediately after ejaculation. A wide array of seminal plasma components have been identified as motility activators. From these, probably the most known are prostaglandins, which have been found as a common seminal plasma component in several species like human and bovine [25,52], although there are other components that plays a role as motility activators. Regarding prostaglandins, it has been described that their motility activation role is not mediated by receptors [49]. The activation of this non-receptor pathway would evolve the activation of specific energy-consuming pathways, pointing thus the importance of a fine regulation of the sperm energy metabolism in order to optimize sperm function.
Notwithstanding, seminal plasma must contain other components than those merely acting as motility activators. In this way, functions such as protection against immunological system of female genital tract and signaling to achieve total "in vivo" capacitation into the oviduct are also very important roles associated with seminal plasma. As in case of motility activation, each species will contain separate compounds in their seminal plasma in order to achieve these roles and, at this moment, this is a poorly understood investigation field. Another possible role for seminal plasma is as energy source for the first steps of spermatozoa after ejaculation. Thus, plasma seminal sugars could be a feasible energy source. However, it is difficult to understand why seminal plasma in all species does not contain glucose as their main energy source, since glucose is the most important energy-producing monosaccharide for all of mammalian tissues. Instead of this, seminal plasma contains other sugars, specially fructose, but also sorbitol and other [6,28,32,33,36,42,45], which are not as efficient as glucose as primary energy sources (see as an example in boar sperm [36]. Again, investigators can only speculate on this point. However, recent data from our laboratory seem to indicate that sugars could play a role of specific sperm function modulator besides their energy-fuelling role. This point would be developed in a more in depth manner when discussing external energy sources of sperm, although the possibility that seminal plasma monosaccharides play another role than that of energy sources can be seriously considered. In any case, after ejaculation only a small percentage of ejaculated spermatozoa are able to leave the ejaculation placement and subsequently they reach oviduct after their uterine transit. Of course, energy requirements of sperm that are in course to the oviduct through uterus are totally different to those immediately after ejaculation. Freshly ejaculated spermatozoa require an energy metabolism in which energy was rapidly generated, in order to support the great amount of energy required by spermatozoa to activate for leaving the ejaculation point. In contrast, spermatozoa that have reached uterus do not require this fast and great energy consumption. In this way, their energy requirements would be much less great and imperative. This is especially important in those species, like pig [31], in which transport through uterus is mainly carried out by uterine peristaltic contractions rather by the sperm motion by itself. This drop in energy requirements will be surely linked to a change in energy-fuelling pathways, although at this moment the changes suffered by energy metabolism during this step are not well known. However, this is not the last change that mammalian sperm metabolism has to suffer in their life time. Once sperm reach oviduct, they rest in the oviductal crypts until their re-activation, following ovulation. This resting step is of the utmost importance, since at this point sperm, in tightly contact with oviductal cells, reach full capacitated status [14,55]. Capacitation implies a myriad of functional and structural changes, like loss of cell membrane cholesterol, increase in tyrosine, serine and threonine phosphorylation levels of a wide array of separate proteins and intracellular calcium mobilization, whose full description is not possible in this chapter (see [52,53] as reviews). Capacitation, however, has a great interest in the sense that its full achievement again implies new energy requirements to carry out processes like the increase in tyrosine phosphorylation of specific sperm proteins, such as pro-acrosin [13,19]. This new requirements will imply again new changes in energy metabolism, which will be closely linked to the progressive changes that sperm function must suffer during this period.
Finally, remnant sperm will be loaded from the oviductal crypts in order to undergo oocyte penetration. In this period, capacitated sperm adopt a totally specific motility pattern known as hyperactivated motility, with separate characteristics depending on the studied species [51]. Once reached the oocyte, sperm have to penetrate it, launching a series of energy-consuming processes like adherence to oocyte zona pellucida and subsequent acrosome exocytosis [29]. Again, energy requirements will change when comparing with other sperm life-span steps. In this sense, acrosome exocytosis will need a fast and intense energy burst and, in fact, it has been described that progesterone-induced acrosome exocytosis in boar sperm that were previously subjected to "in vitro" capacitation is simultaneous to an intense and transitory increase in O 2 consumption, which would correspond to transitory mitochondria activation [43].
What are the main conclusions that can be yielded?The basic conclusion from all of this information would be that the dramatic changes that undergo mammalian spermatozoa from ejaculation to oocyte penetration must be accompanied by concomitant, dramatic changes in their energy regulating mechanisms. Little is known regarding how mammalian sperm modulate these changes, and this is one of the most challenging investigation fields that is currently open in the study of mammalian sperm function.
Energy sources for mammalian spermatozoa
Energy production requires easy availability of energy substrates. In a general sense, any eukaryotic cell can obtain energy from either external or internal sources. External sources can be very different, from monosaccharides to lipids, whereas internal sources are mainly polysaccharides such as glycogen and lipids, although other internal sources can be aminoacids and other. Regarding mammalian sperm, separate external and also internal energy sources have been described, offering thus a view not radically different to that observed in other eukaryotic cells. Notwithstanding, there are several characteristics that differentiates mammalian sperm to other eukaryotic cells in this point. Thus, the sequence of rapid location changes that underwent spermatozoa after ejaculation implies that they are synchronically placed in very separate locations inside female genital tract. This phenomenon will imply separate availability of external energy substrates, depending on the exact placement of spermatozoa. Moreover, the majority of cells integrated in a mammalian body will obtain their external energy sources directly from blood. This is not the case of spermatozoa, which are not keeping in direct contact with blood during their entire life. In this way, external energy sources might came from secretions of cells from the female genital tract or from seminal plasma. This implies that sperm must have very efficient mechanisms to uptake external energy resources, which will not be directly directed towards them. Moreover, and centering on seminal plasma as energy source, the time lapse that ejaculated sperm are in close contact with seminal plasma is, in fact, short. In fact, in species in which the ejaculated volume is short and rapidly placed inside a female genital tract of large size, like cow, contact between sperm and seminal plasma is very short, indeed. In any case, after ejaculation, both sperm and seminal plasma will immediately contact with secretions from the female genital tract. In this manner, sperm would be able to simultaneously find energy sources from both seminal plasma and the female genital tract. Only in species in which the volume of the ejaculate was very voluminous, like porcine, sperm will contact seminal plasma during a significant time lapse. Taking into account all of this information, it seems obvious that the adequate sperm external energy sources intake will depend of two main factors. The first factor will be the efficiency of external energy sources uptake mechanisms that sperm have developed. The second factor will be the specific mixture of external energy sources that sperm find in their journey inside the female genital tract. This mixture will came from both seminal plasma and female genital tract secretions, with predominance from one or the other source depending on the species and the exact location inside the genital tract.
All of this digression has been only centered in the origin of sperm external energy sources. However, what are exactly these energy sources?There is a general consensus in pointing at monosaccharides as the main energy sources for mammalian sperm [46]. Notwithstanding, sperm can utilize other substances than monosaccharides. Thus, boar sperm is able to utilize a wide range of substances, such as glycerol, lactate, pyruvate and citrate [26,27,37]. Other species are able to utilize also non-monosaccharide substrates as energy sources, although more information is needed to clarify this point (see as examples [22,50]). The utilization of non-monosaccharide substrates as energy sources raises the question of the usefulness of these substrates. It has been suggested that these substrates could be an alternative in circumstances in which monosaccharide availability was limited [27,37]. However, a thorough study of the energy substrates content that is present in each segment of the female genital tract is lacking, even in the best studied species. This impedes the complete elucidation of this suggestion. Despite this, the role of non-monosaccharide substrates as external energy sources for mammalian spermatozoa deserves a more in-depth study in order to clarify its biological importance and role. Turning on monosaccharides, one of the most intriguing questions is the ability of sperm to utilize a variety of sugars that are present, at east in seminal plasma. As indicated above, this is a very difficult question to study, since the sugars composition of seminal plasma is very different among species. In this sense, there are a significant number of species in which fructose is the main sugar, like human or mice [32]. However, in other species, like boar, fructose is not predominant [6,32]. The main sugar in horse is not fructose or glucose, but sorbitol [42]. This sorbitol is further converted in fructose through the action of the enzyme sorbitol dehydrogenase, despite of old works indicating that horse sperm were not able to metabolize sorbitol [42]. Finally, there are also species, like dog, which lacks any monosaccharide in its seminal plasma [44,48]. Another intriguing question is the mere presence of sugars, like fructose and sorbitol that are not typical in any other animal tissue. In fact, both fructose and sorbitol are typical vegetal sugars, without any significant presence in animals, excepting in mammalian seminal plasma. It is noteworthy that the utilization of sugars like fructose, sorbitol and mannose by sperm of species like boar and dog of is in fact less effective in order to obtain energy than that of glucose [36,45]. The greater effectiveness of glucose is mainly linked to a greater sensitivity to the hexokinase system, which phosphorylates sugars as a first step in their metabolization pathway [36,45]. Taking into account this lower efficiency, it is difficult to understand the biological logics to utilize sugars like fructose or sorbitol as mere energy sources for sperm. Another explanation for this apparent contradiction would be that non-glucose monosaccharides could exert other roles than that of mere energy sources. In this sense, incubation of dog sperm with fructose induced a specific decrease of serine phosphorylation levels of several proteins with key roles in the regulation of sperm cell function, such as protein kinases Akt, PI3 kinase, ERK-1 and protein kinase C ( [17] and Figure 1). Strikingly, the incubation with glucose induced a specific increase in the serine phosphorylation levels of other key regulatory proteins, like c-kit, Raf-1, tyrosine kinase and several protein phosphatases ([17] and Figure 1) These effects might induce sugar-specific changes in the overall dog sperm function. On the contrary, the incubation of boar sperm with either glucose or fructose did not induce any of the specific actions observed in dog sperm ( [17] and Figure 1). All of these results clearly indicate that sugars can have a sugar-and species-specific action as signaling compounds, modulating thus sperm function in a closely-linked manner, depending on the moment in which sperm and sugar were kept in contact. In this way, the idea of seminal plasma sugars as mere energy sources would be dismissed and being substituted by another idea; sugars as both energy sources and direct sperm function modulators in a simultaneous and coordinated form.
External sources, however, are not the only possibility found by mammalian sperm to obtain energy. Sperm can obtain energy also from endogenous sources. One of the most studied sources is glycogen. The presence of a functional glycogen metabolism has been demonstrated in several species, such as dog, boar, horse, ram and bonnet monkey [5], although no glycogen was found in other species like mice and rat [3]. The primary role of these internal energy sources would be, logically, the maintenance of a limited energy reservoir, although this could not be a universal function. In fact, dog sperm glycogen plays a role in the achievement of "in vitro" capacitation in a medium without glucose by being a key intermediate metabolite in the obtainment of energy through gluconeogenesis, which was essential to the achievement of the capacitated status [1,2]. Thus, in dog sperm, glycogen plays an important role as capacitation regulator, besides its energy reservoir role. In this manner, a more in-depth sturdy of the exact role of glycogen should be needed to obtain a clearer picture of the utilization of endogenous substrates as energy sources in sperm.
Main metabolic pathways to obtain energy and control mechanisms involved in the coordination of energy management of mammalian sperm
If monosaccharides are considered as the most important exogenous energy source for mammalian sperm, the question of the precise metabolic pathways by which mature mammalian sperm obtain energy is relatively straightforward. Thus, the main metabolic pathway is glycolysis. The preeminence of glycolysis in freshly ejaculated sperm has been demonstrated in species like bull, mice and boar [21,33,39]. In fact, in species like boar, at least the 95% of the energy obtained from glucose is obtained through the glycolytic pathway in freshly obtained ejaculates [33]. The explanation of the glycolysis preeminence in these species is easy to understand. Glycolysis would reach high velocity rates taking from a very high velocity of sugars intake and phosphorylation. This will be due to the very high sensi-tivity to sugars of both GLUTs monosaccharide transporters and overall hexokinase activity. In fact, glycolytic rate is so high in species like bull that it rarely achieve the theoretical stoichiometric ATP yield of the glycolytic pathway, living thus to the establishment of an active substrate cycling, important to the maintenance of motility [21]. In fact, the ability of generate energy of a specific sugar would depend on the ability of each sugar to be uptaken and subsequent phosphorylated. In this way, it is important to remind that this sensitivity changes upon a sugar-and species-specific basis. Thus, as described above and taking porcine sperm as a basis, it is important to remind that the velocity by which glucose is phosphorylated and then incorporated to the glycolytic flux is greater than that observed by other sugars like fructose, sorbitol and mannose [36]. The ability of each species to utilize each separate sugar will be then different, depending on the specific machinery that sperm have in order to uptake and further phosphorylate monosaccharides. In fact, this machinery can be more different among species than that previously thought. As an example, dog sperm have two separate hexokinase activities. The first has a very high sensitivity for sugars as glucose, with a Km of about 0.1 mM. The second hexokinase activity has much lover glucose sensitivity, with kinetic properties very similar to those described for hepatic glucokinase [16]. This sophisticated machinery makes dog sperm able to develop a dual reactivity to react against very separate glucose concentrations, specifically changing sperm function in contact with environments with these separate characteristics. Remarkably, sperm from other species such as boar have not any glucokinase-like activity [16], reflecting thus a species-specific reactivity against glucose that is inititated at the very start of the glucose utilization pathway.
The final sugar utilization step is the entry of pyruvate obtained at the end of glycolysis into mitochondria to be subsequent degraded into the mitochondrial respiration system. There is not a universal agreement regarding the importance of mitochondria-based energy obtainment. In this way, an optimal mitochondrial function has been related not only with sperm motility in bull [18], horse [20], ram [34] and mouse [39] but also with fertilization ability in human (30). However, gene knock-out of the glycolytic enzyme glyceraldehyde-phosphate dehydrogenase (GAPDH) in transgenic mice caused the appearance of non-motile sperm and a significant reduction of the ATP content (10% of the total) despite having no deficiency in oxygen consumption [38]. This seems to imply that although a correct mitochondrial function is needed to the maintenance of an optimal sperm function, mitochondrial respiration would not be the most important role of mitochondria to exert their activity. Another explanation would be that mitochondrial respiration would not be important in the maintenance of the overall energy status of sperm, although it would be of the utmost importance in the maintenance of punctual aspects of sperm function. In this sense, progesterone-induced acrosome exocytosis of boar sperm subjected to a previous "in vitro" capacitation is concomitant with a rapid, intense and transitory burst of oxygen consumption [42]. Moreover, unpublished results from our laboratory show that the inhibition of this oxygen consumption burst is concomitant with an almost complete lack of progesterone-induced acrosome exocytosis (Figure 2 and data not shown). These results are concomitant with overall low levels of oxygen consumption, which in fact indicate that the majority of ATPs obtained by boar sperm do not come from mitochondrial respiration [33]. However, the re-sults seem to indicate that the minority mitochondrial respiration is essential to obtain a feasible progesterone-induced acrosome exocytosis.
Figure 2. Rhythm of O 2 consumption of boar sperm subjected to "in vitro" capacitation and subsequent "in vitro" acrosome reaction in the presence or absence of olygomycin A or in Ca 2+ -depleted capacitation medium. Boar sperm were incubated for 4h and then were added with 10 µg/mL progesterone and subjected to a further incubation for 60 min. A): Sperm cells incubated in a standard capacitation medium or in media added with 2.4 µM olygomycin A. : Control cells.: Spermatozoa incubated in capacitation medium added with 2.4 µM olygomycin A from the beginning of the incubation.▲: Spermatozoa incubated in a standard capacitation medium for 4h and subsequent added with 10 µg/mL progesterone and 2.4 µM olygomycin A together. B): Sperm cells incubated in a standard capacitation medium or in Ca 2+ -depleted media.○: Spermatozoa incubated in capacitation medium without Ca 2+ and added with 2 mM EGTA from the beginning of the experiments.: Spermatozoa incubated in a standard capacitation medium for 4h and subsequent added with 10 µg/mL progesterone and 2 mM EGTA together Results are expressed as means ±S. E. M. for 7 separate experiments. Asterisks indicate significant (P<0.05)differences when compared with the respective Control values. Results excerpted from [43]) and unpublished data from our laboratory.
However, as exposed above, monosaccharides are not the only energy source that sperm can utilize. Other substrates, such as citrate and lactate, can be utilized to obtain energy, at least in several mammalian species. The ways by which mammalian sperm utilize these non-monosaccharide substrates have not been as thoroughly studied as those linked to monosaccharide metabolization. In this way, boar sperm have been one of the most studied species. In this species, extracellular citrate and lactate are utilized after their intake by metabolization through the Krebs cycle [37]. This metabolization is the same than that detailed for many other cellular types. However, sperm utilization of citrate and lactate has several specific features. Thus, a sperm-specific lactate dehydrogenase (LDH) isozyme has been described in several species [12,27,37,40,41]. This specific isozyme, named LDH-X is the most important LDH form in sperm in which it has been described, such as boar [27], whereas its activity presents several differentiate features. In this sense, the LDH-X is distributed in both soluble and non-soluble fractions of sperm extracts obtained through sonication [37], indicating thus the existence of a specific distribution pattern of this LDH-X in sperm. Moreover, the kinetic characteristics of the LDH are different, depending on the location of the enzyme, either in the soluble or the non-soluble sperm extract fraction [37]. In fact, immunocytochemistry of boar sperm has shown that the LDH-X is mainly located at the midpiece and principal area of the tail, linking thus its activity to the neighboring of mitochondrialocated Krebs cycle activity [37]. All of these information clearly indicate that the regulation of sperm LDH activity, and hence lactate metabolism, is regulated in a very complex manner, with mechanisms depending on factors such as the precise location of the key regulatory enzymes. Another interesting feature of both sperm lactate and citrate metabolism is that lactate enters the Krebs cycle through a direct pathway, which does not need its previous conversion to pyruvate [27,37]. This direct pathway is important, since it not only produces energy, but also relevant levels of reductive potential, allowing sperm to regenerate significant amounts of NAD + . Regarding citrate, sperm can metabolize it through two simultaneous pathways. The first pathway is through direct utilization by Krebs cycle, yielding CO 2 and ATP. The second pathway is indirect, by following two sequential steps. A first step in which citrate enters into the Krebs cycle. In the second step the metabolites derived from citrate after its pass through the Krebs cycle are directed to the pyruvate carboxylase step, which converted these metabolites in lactate, which, in turn, will be sent to the extracellular medium and again re-entered into the Krebs cycle through the LDH-X step. At first glance, the biological meaning of this second, convoluted pathway is not immediately understood. However, if the maintenance of a correct NAD + /NADH equilibrium is considered as basic to maintain a proper sperm function, the main objective of this second, indirect pathway would be not the obtainment of energy, but of reductive potential. In this way, citrate and lactate can have a paramount role not as energy producers, but as reductive potential metabolites.
Roles of mitochondria in the control of the overall mature boar sperm function
As previously indicated, the main energy source for mature mammalian sperm are ATPs obtained either through the glycolytic pathway or mitochondrial oxidative pathways. The precise equilibrium between both energy-obtaining pathways will be different among species and in cases like boar and mice, this equilibrium is greatly unbalanced towards glycolysis, which is the overly majoritary energy-obtaining pathway in the presence of sugars like glucose [33,39)]. This pre-eminence of glycolysis in species like boar and mouse arises to an important question, if sperm mitochondria seem no have a predominant role in these species in obtaining energy, what are their main role?Investigators can only speculate on this point, although there are several data regqarding mainly boar sperm that can aid to obtain a better vision of this issue. The first data correspond to the observation of boar sperm mitochondria ultra-structure (Figure 3). Electron microscope images of boar sperm mitochondria show an organella with very few prominent inner membrane crests. Instead of this, the inner mitochondrial space is mainly occupied by thin and short crests and with an amorphous and homogeneous matrix. This is very different to the classical image for mitochondria, which, like those form hepatocytes, show an inner structure crowded with prominent inner crests. Taking into account that the most important steps of the electronic transport system and subsequent ATP synthesis are structurally linked to inner mitochondrial crests, it is easy to assume that boar sperm mitochondria would be not be very efficient as energy suppliers. In fact, the oxygen consumption rate of boar sperm, which is a direct measure of mitochondrial ability to generate energy, is about 2 magnitude orders lower than that measured in pig hepatocytes [4,43]. However, this does not preclude that mitochondria-originated energy would not be important for sperm function in species in which glycolysis is the most important energy-synthesizing pathway. Regarding this point, our laboratory has shown that the achievement of a feasible, progesterone-induced "in vitro" acrosome reaction is concomitant with a sudden and intense peak of O 2 consumption rate and also of intracellular ATP levels ( [43] and Figures 2,4). Furthermore, unpublished data from our laboratory clearly shows that this peak is not present in conditions in which progesterone-induced acrosome reaction is prevented. These results strongly suggest the existence of a close relationship between mitochondria-generated energy and the achievement of the acrosome reaction, despite of the low energy-efficiency of these organelles. Intracellular ATP levels of boar sperm subjected to "in vitro" capacitation and subsequent "in vitro" acrosome reaction in the presence or absence of olygomycin A or in Ca 2+ -depleted capacitation medium. Boar sperm were incubated for 4h and then were added with 10 µg/mL progesterone and subjected to a further incubation for 60 min. A): Sperm cells incubated in a standard capacitation medium or in media added with 2.4 µM olygomycin A. : Control cells.: Spermatozoa incubated in capacitation medium added with 2.4 µM olygomycin A from the beginning of the incubation.▲: Spermatozoa incubated in a standard capacitation medium for 4h and subsequent added with 10 µg/mL progesterone and 2.4 µM olygomycin A together. B): Sperm cells incubated in a standard capacitation medium or in Ca 2+ -depleted media.○: Spermatozoa incubated in capacitation medium without Ca 2+ and added with 2 mM EGTA from the beginning of the experiments.: Spermatozoa incubated in a standard capacitation medium for 4h and subsequent added with 10 µg/mL progesterone and 2 mM EGTA together Results are expressed as means±S. E. M. for 7 separate experiments. Asterisks indicate significant (P<0.05)differences when compared with the respective Control values. Results excerpted from unpublished data from our laboratory.
Figure 5.
Percentages of total motility of boar sperm subjected to "in vitro" capacitation and subsequent "in vitro" acrosome reaction in the presence or absence of olygomycin A or in Ca 2+ -depleted capacitation medium. Boar sperm were incubated for 4h and then were added with 10 µg/mL progesterone and subjected to a further incubation for 60 min. Total motility has been defined as the percentage of spermatozoa with a curvilinear velocity (VCL) higher than 20 µm/sec. A): Sperm cells incubated in a standard capacitation medium or in media added with 2.4 µM olygomycin A. : Control cells.: Spermatozoa incubated in capacitation medium added with 2.4 µM olygomycin A from the beginning of the incubation.▲: Spermatozoa incubated in a standard capacitation medium for 4h and subsequent added with 10 µg/mL progesterone and 2.4 µM olygomycin A together. B): Sperm cells incubated in a standard capacitation medium or in Ca 2+ -depleted media.○: Spermatozoa incubated in capacitation medium without Ca 2+ and added with 2 mM EGTA from the beginning of the experiments.: Spermatozoa incubated in a standard capacitation medium for 4h and subsequent added with 10 µg/mL progesterone and 2 mM EGTA together Results are expressed as means±S. E. M. for 7 separate experiments. Asterisks indicate significant (P<0.05)differences when compared with the respective Control values. Results excerpted from 42) and unpublished data from our laboratory However, the fact that Krebs cycle seems to be important only in punctual moments of the boar sperm life-time does not necessarily indicate that boar sperm mitochondria are only important in this point. It is noteworthy that mitochondria have much more roles than purely being a mere energy-producing factory. Mitochondria also play a key role in the control of other very important aspects of eukaryotic cells function, like modulation of apoptosis and the control of calcium metabolism. Thus, it is very probable that mitochondria from sperm of species like boar or mouse exert their most important functions on other cellular functional points than energy management. Unpublished results from our laboratory are strongly pointing out this supposition. Thus, the incubation of boar sperm in a capacitation medium in the presence of olygomycin A, a specific inhibitor of the electronic chain and the chemiosmosis steps [11], immobilizes boar sperm and prevent them to achieve "in vitro" capacitation. However, this effect was accomplished without any significant changes in the rhythm of O 2 production and the intracellular ATP levels (Figures 2, 4, 5 and data not shown from our labvoratory). In contrast, the incubation of boar sperm in a capacitation medium without calcium induces an increase in the velocity parameters of these cells, although the achievement of capacitation is also prevented (data not shown). The effect linked to the lack of extracellular calcium however, is again concomitant with no changes in both the rhythm of O 2 production and the intracellular ATP levels (Figures 2, 4 and data not shown). The conclusion from these results is that boar (and probably mice) sperm mitochondria play an important regulatory role in the control of functional aspects such as motility patterns and the achievement of "in vitro" capacitation by ways that are not directly linked to energy production. This opens a new perspective in the manner in which investigators would have to approximate to the understanding of the mitochondria role in the control of sperm function. However, much more work is needed in order to achieve a complete view of this complex phenomenon.
Metabolic phenotypes: a result of the separate evolutionary strategies developed by mammals to optimize reproductive indexes
All of data showed above highlight a phenomenon that has not been much explained, although it is well known by all of investigators in this field. This phenomenon is the strong species-specificity that energy obtainment mechanisms show when comparing separate mammals. Differences are so intense that several metabolic phenotypes can be defined, depending on the metabolic characteristics showed by each species. In this manner, there are at least two separate metabolic phenotypes regarding mammalian spermatozoa. The first phenotype will be composed by species in which energy substrates, mainly monosaccharides, will be directed to the practically immediate utilization of all of the assimilated sugars through the appropriate catabolic pathways, especially glycolysis. This specific metabolic phenotype is very common in mammalian sperm, especially in those species, which do not require a long, sperm-survival time-lapse inside the female genital tract such as pig and bull [24,47]. However, a second phenotype is evident in species where sperm survival inside the female genital tract must be relatively long, such as the dog [15]. In these species, an energy strategy based upon an entirely catabolic metabolism would not be efficient. The optimization of energy Management in relatively long-living sperm like dog would be optimized with the presence of alternative anabolic pathways, such as glycogen synthesis, which allows for the maintenance of a significant mid-to-long intracellular energy reserve. This re-serve would play an important role in the maintenance of "in vivo" sperm survival. In fact, as discussed above, the existence of a fully functional glycogen metabolism has been demonstrated in sperm from species like dog, boar, horse and ram [5]. Remarkably, dog sperm shows the most active glycogen metabolism of all of the studied species, in this way accumulating the maximal recorded intracellular levels [5]. As described above, this glycogen plays an important role in the achievement of feasible "in vitro" capacitation [1,2], reinforcing thus the importance of this anabolic pathway in dog. The importance of glycogen synthesis in dog would be surely linked to another important feature, also described above. It is worth noting that dog sperm is the only studied species so far that shows the presence of two separate hexokinase activities. The first of them is similar to hexokinase-I, which is present in all of the studied mammalian sperm. The second, however, is similar in kinetic and immunologic properties to the hepatic and pancreatic isoform glucokinase [16]. The presence of a glucokinase-like activity in dog sperm but not in other species like boar acquires utmost importance when the precise role that hepatic and pancreatic glucokinase plays is studied. Thus, it is well known that hepatic glucokinase acts as a "metabolistate" that diverts hexoses metabolism to either anabolic or catabolic pathways, depending on factors such as the precise physiologic cell status and sugar extracellular levels [9]. If a similar role for the dog sperm glucokinase-like activity is assumed, the inference that this protein also regulates the entry of energy metabolites in either anabolic or catabolic pathways can be also yielded. These assumptions, notwithstanding will depend on both the precise energy necessities and the extracellular concentrations of sugars inside the female genital tract. Moreover, this "metabolistate" seems to be in the basis of above described, observed differential effects of fructose and glucose in the serine phosphorylation levels of dog sperm proteins like protein kinase C [17]. Thus, dog sperm reaches an even more fine regulation of not only their intracellular energy levels, but also their overall functional status. This very fine regulation would surely increase survival ability of these cells. These two separate metabolic phenotypes would not surely be the only present among mammalian species. Much more work is needed in order to describe and analyze this phenomenon. In any case, the existence of these metabolic phenotypes would be of the greatest importance. These phenotypes, in fact, will be the reflection of the sperm specialization due to the adoption of separate reproductive strategies among mammals. Thus, these separate evolutive, reproductive strategies will cause the existence of great differences among sperm of separate species not only under a morphological, but also under a metabolic point of view. These differences among species would be, in turn, at the basis of the described differences in vital aspects of sperm function, such as motility patterns and capacitation mechanisms. Finally, these physiological differences would also be reflected in changes in the specific strategy developed to store a particular semen sample from a precise species in optimal conditions.
Modulation of energy metabolism as a tool to improve IA results
It seems obvious that a good regulation of the energy regulation mechanisms would be of the utmost importance in order to optimize sperm storage and, hence AI results. Surprisingly, very few investigations have been conducted on this specific point. This could be due to the historical misinterpretation of sperm energy regulatory mechanisms. Historically, these mechanisms has be considered as being simple and linear and, hence of little practical importance [32]. In this way, the majority of semen extenders contain inordinate concentrations of various sugars, like glucose and fructose. The basis for this addition is the thinking that sperm will utilize separate sugars in a similar manner and by linear, concentration-dependent mechanisms. This strategy has at least three weak points. The first point is the fact that the optimal utilization of sugars by sperm is reached to determined concentrations of this sugars. For instance, the optimal utilization rate of glucose by sperm from dog and boar is reached to very low concentrations of the sugar, at about 0.1 mM [16,36,37,45]. This indicates that the addition of low concentrations of sugar to the extenders would be enough to maintain sperm energy levels. This is not followed by the majority of extenders, in which sugars are added to concentrations above 50 mM. At these concentrations, the sperm energy machinery is overrated and non-optimal, despite the fact that cells are stored to low temperatures. The second weak point is the fact that, as described above, sugars could have other effects that being mere energy supplies (see [17]). In this case, the election of either glucose or fructose in a species can influence their ability of survival by modifying specific aspects of sperm functionality. The third weak point is that mammalian sperm are abler to utilize nonglucidic substrates as energy sources. Nonglucidic substrates like lactate and citrate are frequently added to semen extenders in order to play roles that are not related to the maintenance of sperm energy levels. Some of these roles are, for instance, maintenance of osmolarity and pH. However, sperm cells can consume these substances and, in this way, the extender design would lose its conservative properties, since some protective functions (maintenance of osmolarity, pH, etc.) could be impaired when these substances are metabolized by sperm. Following this rationale, the exact proportion of glucose and nonglucidic substrates like citrate and lactate greatly affects several parameters of boar-semen quality analysis during storage at 15ºC-17ºC. Some of the parameters affected by the exact sugar/ non-sugar composition of extenders were the membrane integrity, the response to functional tests like the osmotic resistance test and the overall mid-term survival at 15ºC-17ºC [35]. These results strongly suggest that the exact proportion of these substrates, more than their final concentration, is of the greatest importance to optimize the maintenance of sperm function during sperm storage in refrigerated conditions. As a conclusion, the lack of a proper knowledge of the mechanisms linked to the control of mammalian sperm energy management is hampered a further optimization of the semen extenders utilized in the different species. This would have a detrimental effect in the subsequent AI results obtained with semen stored in sub-optimally designed extenders. This highlights the great interest in more investigations in order to elucidate the exact mechanisms of energy management in all of the domestic mammalian species.
Conclusion
Energy management of mature mammalian spermatozoa is a much complex question than that usually devised. This complexity is due to a combination of factors, such as the existence of rapid and profound environmental changes during the entire life of sperm postejaculation, as well as the development of many different evolutionary reproductive strategies among mammalian species, which lead sperm to develop specific energetic strategies. In this sense, factors like the time that sperm have to spend inside the female genital tract or the existence of competence among sperm from separate males inside the female will play important roles in the design of an optimal energy management strategy in each mammalian species.
Figure 1 .
Figure 1. Mini-array analysis of the tyrosine, serine and threonine phosphorylation status of several proteins involved in the regulation of cell cycle and overall cell function in dog and boar spermatozoa after incubation with glucose or fructose. Dog and boar spermatozoa were incubated for 5 min in the absence (C-) or presence of either 10mM fructose (10mM F) or 10mM glucose (10mM G). The tyrosine-(Phos-Tyr), serine-(Phos-Ser) and threonine-phosphorylation (Phos-Thre) levels of each spot in the mini-arrays were then analysed. The figure shows a representative image for five separate experiments. Figure excerpted from [17]).
Figure 4 .
Figure 4. Intracellular ATP levels of boar sperm subjected to "in vitro" capacitation and subsequent "in vitro" acrosome reaction in the presence or absence of olygomycin A or in Ca 2+ -depleted capacitation medium. Boar sperm were incubated for 4h and then were added with 10 µg/mL progesterone and subjected to a further incubation for 60 min. A): Sperm cells incubated in a standard capacitation medium or in media added with 2.4 µM olygomycin A. : Control cells.: Spermatozoa incubated in capacitation medium added with 2.4 µM olygomycin A from the beginning of the incubation.▲: Spermatozoa incubated in a standard capacitation medium for 4h and subsequent added with 10 µg/mL progesterone and 2.4 µM olygomycin A together. B): Sperm cells incubated in a standard capacitation medium or in Ca 2+ -depleted media.○: Spermatozoa incubated in capacitation medium without Ca 2+ and added with 2 mM EGTA from the beginning of the experiments.: Spermatozoa incubated in a standard capacitation medium for 4h and subsequent added with 10 µg/mL progesterone and 2 mM EGTA together Results are expressed as means±S. E. M. for 7 separate experiments. Asterisks indicate significant (P<0.05)differences when compared with the respective Control values. Results excerpted from unpublished data from our laboratory.
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Domain: Engineering Environmental Science Biology
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Impact of Aquatic Salinity on Mangrove Seedlings: A Case Study on Heritiera fomes (Common Name: Sundari)
Mangroves are the characteristic littoral plant formation of tropical and subtropical sheltered coastlines [1]. Being on the land sea interface, they are always associated with and subjected to saline seawater. However saline condition is not a prerequisite for their development, rather mangroves choose saline condition to avoid the competition with the more vigorous terrestrial plants. Based on the physiological studies, Bowman [2] and Davis [3] concluded that mangroves are not salt lovers, rather salt tolerants. But excessive saline conditions retard seed germination, impede growth and development of mangroves. Indian Sundarbans, the famous mangrove chunk of the tropics is gradually losing Heritiera fomes (commonly known as Sundari) owing to increase of salinity in the central sector of the delta complex around the Matla River. Reports of alteration of growth in mangroves due to difference in salinity between western and central sectors of Indian Sundarbans are available [4]. However no study has yet been carried on the effect of salinity fluctuation on the photosynthetic pigments and carotenoid level of mangroves under culture conditions from this part of the Indian sub-continent. The effects of salinity on mangroves have been studied in relation to antioxidative enzymes [5,6], leaf structure, rates of transpiration, stomatal conductance and rates of photosynthesis [7,8] and changes in chloroplast structure and function [5,9]. Reported that Na+/H+ antiport catalyzed exchange of Na+ for H+ across the vacuolar membrane of the cells of Bruguiera sexangula offered tolerance to ionic stress imposed by NaCl and this mechanism was important for cellular salinity adjustments. Also, the mechanism of acclimation to salt in mangroves was suggested to be linked to the changes in the vacuolar size in B. sexangula [10]. Further, one of the biochemical mechanisms by which mangroves counter the high osmolarity of salt was accumulation of compatible solutes [5].
Introduction
Mangroves are the characteristic littoral plant formation of tropical and subtropical sheltered coastlines [1]. Being on the land sea interface, they are always associated with and subjected to saline seawater. However saline condition is not a prerequisite for their development, rather mangroves choose saline condition to avoid the competition with the more vigorous terrestrial plants.
Based on the physiological studies, Bowman [2] and Davis [3] concluded that mangroves are not salt lovers, rather salt tolerants. But excessive saline conditions retard seed germination, impede growth and development of mangroves. Indian Sundarbans, the famous mangrove chunk of the tropics is gradually losing Heritiera fomes (commonly known as Sundari) owing to increase of salinity in the central sector of the delta complex around the Matla River. Reports of alteration of growth in mangroves due to difference in salinity between western and central sectors of Indian Sundarbans are available [4]. However no study has yet been carried on the effect of salinity fluctuation on the photosynthetic pigments and carotenoid level of mangroves under culture conditions from this part of the Indian sub-continent. The effects of salinity on mangroves have been studied in relation to antioxidative enzymes [5,6], leaf structure, rates of transpiration, stomatal conductance and rates of photosynthesis [7,8] and changes in chloroplast structure and function [5,9]. Reported that Na + /H + antiport catalyzed exchange of Na + for H + across the vacuolar membrane of the cells of Bruguiera sexangula offered tolerance to ionic stress imposed by NaCl and this mechanism was important for cellular salinity adjustments. Also, the mechanism of acclimation to salt in mangroves was suggested to be linked to the changes in the vacuolar size in B. sexangula [10]. Further, one of the biochemical mechanisms by which mangroves counter the high osmolarity of salt was accumulation of compatible solutes [5].
In this paper, we present the effect of salinity on pigments in Heritiera fomes under hydroponic culture with an aim to obtain insights into the changes in chlorophyll and carotenoid level with salt acclimation. Such study is important from the point of sea level rise and subsequent saline water intrusion into the islands of Indian Sundarbans as the lower Gangetic delta complex is extremely vulnerable to climate change related effects owing to its location below the mean sea level and experiencing a sea level rise of 3.14 mm/yr. Moreover unlike other mangrove species Heritiera fomes prefer extremely low saline condition and hence can act as biological signal of climate change related to sea level rise.
Plant materials and culture conditions
Seeds of Heritiera fomes were collected from Sundarbans mangrove system of India ( Figure 1). Seedlings were raised in the laboratory condition by diluting the water collected from high saline zones of Sundarbans (salinity = 30 psu) under photosynthetically active radiation (PAR) of 1220-1236 µmol m -2 s -1 during January 2017. Two-month-old healthy seedlings were selected for hydroponic culture in Hoagland's nutrient medium (pH = 5.8-6.0). The preliminary experiments were carried out in the selected species at five different salinities (2, 5, 10, 15 and 20 psu) in order to determine the optimum range of salinities in context to photosynthetic pigments and carotenoids. The cultures were aerated continuously with an air bubbler. The hydroponic cultures were maintained in a culture room under a 14 h photoperiod at PAR of 300 µmol m -2 s -1 , 26 ± 30C, and 80% RH. The culture medium was changed every 7 d. Leaves were harvested at 7, 14, 21 and 30 d intervals to measure the pigment concentrations.
Extraction and estimation of pigments
Leaves (0.5 g) were homogenized in chilled N, N-dimethylformamide (DMF) in a mortar and pestle in the dark at 4 0 C and the homogenates were centrifuged at 8800×g for 10 min. The supernatants were collected and absorption spectra at 663.8 and 646.8 nm were recorded using Jasco V-530 UV-vis spectrophotometer for estimation of Chl a, Chl b and total Chl following the procedure of Porra et al. [11]. For the estimation of total carotenoids, leaf tissues (0.5 g) were homogenized in chilled 80% (v/v) acetone; the homogenates were centrifuged at 8800 × g for 10 min at 4 0 C in the dark. The absorbance of the acetone extracts was measured at 663, 645 and 470 nm. Total carotenoids were calculated according to Arnon [12].
Statistical analysis
Statistical analysis of the results was carried out according to Duncan's multiple range tests. Data were also subjected to analysis of correlation coefficient (r) in order to evaluate the interrelationship between salinity and selected pigments [13].
Results and Discussion
We observed that the collected seedlings of Heritiera fomes could tolerate maximum salinity up to 15 psu and could be maintained for more than 30 d. On increasing the salinity to 20 psu the leaves began to fall off after the second week, and thus all the experiments were done up to 30 d in the salinity level 2, 5, 10 and 15 psu treated plants. The unhealthy conditions of the experimental seedlings of Heritiera fomes at 20 psu may be attributed to their ambient salinity in the western sector of deltaic Sundarbans region from where they were collected which usually ranges between 2 psu to 10 psu [14]. Such low salinity in the western part of the study area is due to freshwater discharge from the Ganga-Bhagirathi-Hooghly River that has its origin in the Gangotri Glacier of the Himalayas.
The concentrations of chlorophyll and carotenoid pigments decreased significantly with the increase in salinity (Table 1, Figures 2 & 3). The total chlorophyll decreased by 38.54%, 44.00%, 63.85% and 63.89% at 7, 14, 21 and 30 d intervals respectively due to change of salinity from 2 psu to 15 psu. The Chl a:b ratio in the plant, however, remained almost constant for the species and varied only marginally during the period under observation. In our experiments with differential salinity exposure the Chl a:b ratio yielded a value between 3.00 to 3.41 (Table 1). It appears from the results that high salinity did not affect Chl a:b ratio even though the total chlorophyll content decreased at high salt concentration. A similar trend in carotenoid content, expressed in fresh wt. basis, was observed ( Table 1). The pigment decreased by 26.32% at the end of 7 d, 19.04% at the end of 14 d, 33.33% at the end of 21 d and 27.78% at the end of 30 d. The decrease of the selected pigments with aquatic salinity is statistically significant ( Table 2). The decrease in chlorophyll content at higher salinity might possibly be due to changes in the lipid protein ratio of pigment-protein complexes or increased chlorophyllase activity [15]. Our results agree with several reports of decrease content of chlorophyll and carotenoids by salinity as reported in a number of glycophytes [16,17]. As the Chl a:b ratio remained unaffected at high saline condition in the selected species, it appears that the light harvesting complex (LHCs) of thylakoid membranes are little altered by salt exposure. Units of all pigments are mg.gm -1 fresh weight; Different letters besides figures indicate statistically different means as at p ≤0.01. The adverse impact of salinity on leaf chlorophyll of Heritiera fomes may significantly affect the rate of photosynthesis as this pigment is an indispensable raw material for running the process. Till date there have been few studies on the effect of salinity on photosynthetic gas exchange in mangroves. Clough [18] stated in his communication that the rate of light saturated photosynthesis decreases with increasing salinity of ambient media, attributing this to co-limitation of assimilation rate by stomatal conductance and photosynthetic capacity in response to differences in water status induced by the various salinity treatments. Thus, on the evidences available so far it is most likely that salinity exerts its effect on photosynthesis mainly through changes in leaf water status. The present study reveals that the photosynthetic process may be affected at high saline condition due to decrease in Chl a and b concentrations in Heritiera fomes. This present study is different from several previous works as the salinity of water has been altered naturally (through rain water dilution) keeping all the constituent salts of brackish water constant unlike several previous studies where the plants were exposed to different NaCl concentrations [19,20] that are not the real image of ambient seawater. Various studies have shown that a number of mangrove species grow best at salinities between 4 psu and 15 psu [18,[21][22][23][24] and for Heritiera fomes, the preferred salinity range is much lower [25]. Our results show that Heritiera fomes of Indian Sundarbans region can flourish luxuriantly under low salinity conditions. At 15 psu, the plants become acclimated to salt after one to two weeks of exposure, but at 20 psu the seedlings could hardly adapt.
Indian Sundarbans and its adjacent estuaries at the apex of the Bay of Bengal are one of the less studied regions of the world ocean in context to impact of rising salinity fluctuation on mangrove floral community, although the region sustains the 5 th largest mangrove chunk in the world (2120 km 2 in the Indian part and 4500 km 2 in the Bangladesh part). The present study is extremely important from the point of view of rising salinity in the central sector of Indian Sundarbans over a period of 2 decades [26] due to complete obstruction of the freshwater supply of Ganga-Bhagirathi-Hooghly River as a result of heavy siltation since the late 15 th century [25] and rising sea level [27] at the rate of 3.14 mm/yr, which is higher than the global average sea level rise of 2.12 mm/yr and 2.50 mm/ yr along the Indian coastline [28]. The pigments, being the key machinery in regulating the growth and survival of the mangroves require an optimum salinity range between 4 to 15 psu [22,23] for proper functioning.
Heritiera fomes, the freshwater loving mangrove species prefers an optimum salinity between 2 to 5 psu [29]. It appears from our results that the growth of the species would be better if freshwater of the western sector of Indian Sundarbans is channelized to the central sector through capital dredging (initially) and periodic dredging (yearly). The processes will not only help to ecorestore the system through recruitment of freshwater loving mangrove species (like Heritiera fomes, Nypa fruticans, Bruguiera gymnorhiza etc.), but may also help to combat the intrusion of seawater from the southern part of Sundarbans mangrove ecosystem due to expansion of the Bay of Bengal water on account of warming [29,30].
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Domain: Environmental Science Biology
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T HE R OSENBLUTH SAMPLING C ALCULATION OF H YDROPHOBIC -P OLAR M ODEL
Lattice proteins are models resembling real proteins. They comprise an energy function and a set of conditions specifying the interaction between elements occupying adjacent lattice sites. In this paper we present an approach examining the behavior of chains of a large number of molecules. We investigate this by solving a restricted random walk problem on a cubic lattice and square lattice. More specifically, we apply the hydrophobic-polar model to examine the spatial characteristics of protein folds using the Monte Carlo method. This technique is the so-called Rosenbluth sampling method for solving restricted random walk problems. Specifically, by solving such walks we obtain plausible folds. In addition, this method can be extended to solve the hydrophobic-polar model. In this paper, we describe this method as an algorithm that calculates the energy spectrum for the hydrophobic-polar model, and the related formula for estimating the number of folds. Moreover, we estimate the number of folds for each sequence using hydrophobic-polar model energy estimation. On test sequences the predicted protein folds were obtained with a mismatch of one unit according to the energy. We also observe that the estimated number of folds depends only on the length and not on the type of sequence. This promising strategy can be extended to quantify other proteins in nature.
INTRODUCTION
The search for a more efficient algorithm of protein folding in the hydrophobic-polar (HP) model is an important aspiration in many disciplines (Sali et al. (1994), Pande (2010)). Knowing how proteins fold can help elucidate their three-dimensional structure-function relationship, which is crucial to the understanding of enzymes and to the treatment of misfolded-protein diseases such as Alzheimer's, Huntington's, and Parkinson's disease. The numerical simulation focused on those proteins is particularly useful for drug design, as it allows to test different physical characteristics using models of various complexities. Indeed, if high-resolution chemical structure is used, leading to precise molecule representations, dynamical simulation showing atomic interactions can be reached. This might ultimately provide more effective and personalized drugs.
It has been shown that the HP protein folding model is NP-Hard (Berger & Leighton (1998)), which means it is difficult to solve efficiently for longer protein sequences. In order to overcome this obstacle, many heuristic algorithms have been proposed (Jiang et al. (2003), Yanev et al. (2017)). Besides heuristics mostly based on optimization, other approaches are based on the idea that cooperativity of folding occurs, as local conformational choices which constraints the optimization space in which solutions are searched. Those assumption-based methods include hydrophobic zipper method Dill et al. (1993), which assumes that once a hydrophobic contact is created it cannot be broken. And the core-directed chain growth method Beutler & Dill (1996) which constrains the optmization search within the space of solutions having a hydrophobic core with a square (in 2D) or a cube (in 3D).
In this context, there is theoretical and experimental evidence of the advantage of solving a restricted random walk problem (RRW) on cubic and square lattices. One of the earliest proposed numerical algorithms which apply the RRW paradigm is the one designed by M. Rosenbluth and A. Rosenbluth (Rosenbluth & Rosenbluth (1955)). In this report, we present a benchmark implementation of Rosenbluth methods for the HP model with an additional extension to estimate the number of possible sequence configurations.
HYDROPHOBIC-POLAR MODEL
In the hydrophobic-polar model, the set of twenty standard amino acids is reduced to two: H (hydrophobic amino acid) and P (hydrophilic amino acid). More formally, the model relies on embedding a given finite polypeptide sequence s = (s 1 , . . ., s i , . . ., s k ) where s i ∈ {H, S}, into a given infinite graph G. In this article, the graph G will primarily be the three-dimensional cubic lattice G = Z 3 and square cubic lattice G = Z 2 over integer numbers Z. A fold of length k for s in G is an injective mapping f : [1, . . ., k] → G such that adjacent integers map to adjacent points of G.
The set of all folds of length k is denoted as Z k . In addition, each point f (i) is assigned one letter from the polypeptide sequence s i . Such neighboring points form a bond. Each point of Z 3 has six neighbors (x ± 1, y ± 1, z ± 1). The energy of the fold of s is expressed as (1) ∆(p, q) = 1 if p and q are adjacent but do not connect amino acids, 0 otherwise with energy equation: The above equation for calculating the energy of fold s in G can also be expressed as negation of the number of H − H bonds in the fold, where a bond is a pair of symbols corresponding to adjacent points, except for those H's which are adjacent to pairs of sequences s. The goal of the HP model is to minimize the energy min Note that for a given number of adjacent points k in the fold, any configuration consisting of k adjacent points laid out joined in succession on a cubic lattice Z 3 is considered. The method proposed by Rosenbluth & Rosenbluth (1955) involves drawing successive steps of a random walk only from among acceptable points, which are points previously not visited. In this section, we describe the random procedure in more detail. We will focus on the 3D case, i.e G = Z 3 , but the method is easily transferable to the 2D case.
Regarded as a random walk problem, for any walk consisting of m adjacent points and ending at position (x, y, z) m , all six positions are a priori equally likely at iteration m. The excluded volume effect is simulated by the requirement that a fold is not allowed to cross itself or back up on itself at any iteration. Consequently, at any iteration, there are at most five possible positions to move to. For simplicity, we assume that the first link originates from (0, 0, 0). Any satisfactory set of m adjacent points start from the origin (x, y, z) m is associated with a weighting function w m of possible positions calculated at each step according to the procedure described below. At any iteration m where the most recent link terminates at (x, y, z) m and 5 potential positions (x ± 1, y ± 1, z ± 1) m must be considered, while position (x, y, z) m−1 is ruled out immediately. All five remaining potential positions at m + 1 may be associated with values (x, y, z) i for i = m − p where p is an odd number greater than 1. If the comparison reveals this to be the case, a modification of the weight W m must be made, obtaining W m+1 .
Below we present all possible cases at iteration m: 1. All six new position (x ± 1, y ± 1, z ± 1) m are occupied. The process is then terminated with weight W m = 0.
2. Only w m new positions are unoccupied, with 0 < w m ≤ 5. Then During this process, an embedding is generated. If the embedding is equal to the length of the sequence, we can calculate energy according to the presented formula.
ESTIMATION OF FOLD NUMBERS
In this section, we present a mathematical justification for the estimation of folds.
Let us assume in general terms that when constructing fold f i of length k with partial weights 0 < w i ≤ 5: It is not excluded that at a certain step we may have no further possibilities for continuation, i.e., w m+1 = 0. We then say that a non-extendable fold of length m has been formed. Let Y denote the set of all folds of length m = k and non-extendable folds of length m < k. Recall that set of all folds of length m = k is denoted as The probability of picking a random fold f i ∈ Y of length m is equal to: with a weight function for the specific fold One can interpret W (f i ) as the weight of fold f i . Let us now repeat the draw using the growth method n times. There are n random folds f 1 , f 2 , . . ., f n from set Y .
Let n s denote the number of drawn elements f i for which W (f i ) = s and the set of these elements Then, based on the large numbers law: Therefore, the above expression can be written as the average weight of the drawn folds. We note that: Finally, the expression for W can be written as: We introduce the following notation for fold estimators of length k: To validate this fold estimator we test sequences of different length and type and the results are reported in the following section.
ESTIMATION STANDARD ERROR FOR Ẑk
For estimation of standard error for Ẑk , we used the batch means method. We will briefly describe how this method was applied in this setup.
Let us divide the sequence of weights: of length n into j "blocks" of length l each (so n = jl): Let us denote by μb the mean calculated from the block The estimator for the variance is defined as: Then the standard error is the root of the estimator for the variance: This method significantly speeds up the variance calculation by the standard method of generating estimators and calculating the standard deviation.
RESULTS
The experiments were run on Google's Colab platform on Intel(R) Xeon(R) CPU @ 2.20GHz with 13GB RAM. We investigated 2 different datasets one 2D and one 3D. The algorithm code, written in Python, can be found at the following website: Wie (2022). The method was run for n = 10 5 of suitable configurations folds with a specific sequence s of length k. We used j = 10 3 blocks in the batch means method for estimation standard error.
BENCHMARK FOR 2D
Using the method proposed above, we calculate Ẑk with a statistical error. The algorithm was initially tested for several sequences in dimension 2 (for Z 2 from site LABORATORY (2011).(2011). We can conclude that our energy is minimally different. Having computed an estimation for all folds Z 2 for each sequence, we can conclude that the number of folds Ẑk does not depend on the tested sequence s. We can observe that the estimated number of folds depends only on length k and not on the form of s.
ESTIMATION FOR 3D
The experiments were performed using n = 10 5 and j = 10 3 blocks in batch means method. The code can be found in Wie (2022). Estimated energy is equal to 0 for all k. This is because it is difficult to fold for Z 3 so that the number of H − H bonds is minimised. The second experiment lasted 4 minutes. Results for this dataset are reported on Referring to experiment 1 we see that there is a significant increase in the number Ẑk of these folds for each sequence. For values of k = 24, 25 we observe particularly large differences.
The experiment itself shows how difficult it is to wrap sequences in 3 dimensions. Estimations for sequences 24 and 25 alone show that the number of folds is on the order of 10 16 and 10 17 , as shown in Table 1 and 2. However, the energy of the fold is still zero. Therefore, the 3D model is significantly more difficult than the 2D model. For the initial cases k = 1, 2, the results are obvious. If the length of the sequence is 1 (k = 1), then we have only 6 possible points. For k = 2 we have 36 possible point assignments, 6 of which are forbidden. We have also prepared special sequences for cases k ∈ {1, 2, 3, 4} that will always have zero energy. It is not possible to wrap a sequence in such a way that points with H − H labels touch each other.
DISCUSSION
Correctly predicting protein conformations based on the amino acid sequence is of pivotal importance for drug design and other relevant computational chemistry tasks. In this paper, we report our computational experiments, where we use HP sequences corresponding to published benchmarks LABORATORY (2011) with a 2D lattice in the HP model. Our model successfully estimates the number of folds for a particular sequence; regardless of the type of sequence but only on its length. For small sequences, the method accurately estimates the number of folds. Our experiments show that for sequences of size k = 24, 25 the 3D model becomes significantly more complex than the 2D model. It has been observed that adding one dimension significantly affects the solution base. In 2D, the energies are −6 and −7, respectively, while in 3D the energy is zero for both cases. There are too many degrees of freedom to draw consecutive points. Therefore, it is difficult to find a wrap that has non-zero energy even for shorter sequences. However, the Rosenbluth sampling method can be successfully used to estimate the number of all folds, especially those with energy 0. This can help design heuristic algorithms based on this hindsight. The estimation itself, according to our mathematical justification, increases in accuracy as we increase the number of iterative executions of the method. The described approach is effective for identifying and sampling configurations on a lattice geometry. This kind of representation can be useful in the context of ab initio protein structure prediction Rashid et al. (2016). Expansions as implementations on quantum devices have been proposed, but those have been limited to the 2D case so far Micheletti et al. (2021). Conversion of the proposed tool into quadratic unconstrained binary optimization (QUBO) Kochenberger et al. (2014) using 3D lattices on quantum devices will be investigated in future work.
Figure 1 :
Figure 1: In each figure we embed a given finite polypeptide sequence in the square lattice G = Z 2 . The energy in the presented diagrams can be easily deduced. Each red line indicates a bond. The number of these edges corresponds to energy Êmin .
Figure 2: In the graph above, we can observe that the fold did not wrap in such a way that the two H − Hs are next to each other. Therefore, energy is equal to 0. This is because we have significantly more degrees of freedom in 3D space.
Table 2 :
Table 2, with 2 examples of resulting predicted folding depicted in Figure 2. In the accompanying table we count the fold estimation values for dimension 3 for Z 3 .
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Domain: Environmental Science Biology
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Southward surface flow in the central South Pacific
A large-scale surface flow with a southward component is proposed for the central South Pacific Ocean based on an interpretation of existing closely spaced and accurately measured temperatures and salinities along two latitudes in two different southern hemisphere winters: 28 ̊S (Scorpio) and five degrees south of that (WOCE). Such a southward flow is not predicted from theory nor is it shown on current charts and globes. The observed longitudinal maximum in surface temperature along 28 ̊S is centered around 130 ̊W and has an amplitude of at least 5 ̊C and an east/west range of about 60 ̊ of longitude. This striking feature is most easily explained by horizontal transport from latitudes closer to the equator. Since temperature atlases show that equatorial surface temperatures are always highest in the west, the origin of the warm water probably is toward the western side of the ocean as well. Thus the surface flow surrounding the longitudinal temperature maximum should be directed to the southeast. Where the surface temperatures are maximum the mixed layer depths are relatively large in a convex downward lens with maximum depths of 100 m; a correlation that is consistent with warm water moving south and being cooled from above. Salinities are maximum near the temperature maximum, also suggesting that the source of the surface flow is at low latitudes, where evaporation is usually expected to exceed precipitation. It is conjectured that the large-scale southeastward flow of the South Pacific is the analogue of the northeastward wide warm current off California documented over 30 years ago.
INTRODUCTION
A large-scale southward surface flow in the middle of the South Pacific is unexpected. For example, no globes or atlases show such a feature. It does not fit in with the customary pattern of gyres, the length of ocean basins. Also the classical and currently popular wind-driven circulation theory does not predict it, given the present knowledge of the mean winds in the area. In fact, this classical theory forecasts equatorward rather than poleward flow, vertically integrated, in the center of the South Pacific. Therefore, a graduate student in any oceanographic institution will not at present be told of such a southward flow nor will he be led to try to find it in this part of the ocean.
Nevertheless, an interpretation of solid evidence, consisting mainly of a coast to coast east/west line of accurately measured and closely spaced sea surface temperatures along 28˚S from the Scorpio Expedition is presented and discussed here from which the above conclusion is hard to escape [1]. Well defined is a large-scale maximum in surface temperature in the east/central part of the basin (Figure 1). Most of the highest temperatures in the whole section are contained within this maximum. To the east, west and underneath the maximum, below about 100 m, the temperatures are colder, and the air above is colder too because the measurements took place in the southern hemisphere winter. One very reasonable conclusion as to how that maximum was created involves horizontal advection from lower latitudes. Though reasonably strong already, this hypothesis will gain further backing in what follows.
Other data, principally salinity, taken simultaneously, lend support to the same interpretation that the temperatures of Figure 1 suggest, as pointed out later. These data from Scorpio are not new, having been obtained on an oceanographic cruise in 1967, but they come from high quality hydrographic instruments, and the main features they show can be trusted to be real. For example, the amplitude of the large-scale temperature maximum of interest here is at least 5˚C whereas temperature was measured to the nearest 0.01˚C, which is overkill for the study below.
Since the postulated southward flow comes mainly from temperature and salinity data, there can be no definite estimate of the current's speed at this time. However, the location of the flow, as well as its width, depth and general direction are pretty well delineated from only two east/west hydrographic lines: the one from Scorpio and the other from a later expedition about five degrees south of the Scorpio section, as will become clear.
In the North Pacific a large-scale (4000 km) northeastward flow of warm surface water has been described; it occurs off the coast of California [2]. The concept of a very wide northeastward drift was originally founded on millions of ship-injection temperatures averaged over five degree squares and one month intervals, covering a large part of the North Pacific during a thirty year period. What the ship-injection temperatures lack in accuracy, being individually only good to one degree Fahrenheit, they make up for in quantity. These ship-injection temperatures exhibited a large-scale maximum in surface temperature in the eastern North Pacific along 35˚N and 40˚N in every month of all 30 years, establishing the permanence of the maximum [2]. A coast to coast hydrographic section through the maximum along 35˚N in March and April, 1976, confirmed its reality and shallowness (depths less than or equal to about 100 m from the surface). After seven years of taking and analyzing data and considering all the possibilities for explanations, I finally felt able to say that the longitudinal temperature maximum is the surface signature of a very wide warm surface and near surface flow heading northeast off California.
Because of the previous studies of the mid-latitude poleward surface flow in the eastern North Pacific a search was recently made for something similar in the South Pacific. What follows is the result of that search. Why it took so long to look for the possibility of an analogous South Pacific flow is not so easily explained. Actually, a small start toward the present interpretation of the Scorpio surface and near surface data along the eastern half of the 28˚S section occurred earlier [3].
SURFACE TEMPERATURES ALONG 28˚S
During the midst of the southern hemisphere winter of 1967, in June and July, an east/west line of surface temperatures (Figure 1) was taken as part of the Scorpio II Expedition along 28˚15'S. Stretching from South America to Australia the measurement stations were closely spaced, the average separation between them being about 1.5 degrees of longitude. This turns out to be more than adequate for defining the main temperature maximum.
Jumping boldly out of the middle of the graph of Figure 1 is the primary feature to note: the large-scale longitudinal maximum in surface temperature. In fact, the greatest number of the highest temperature values in the whole section is contained within this broad maximum, which is centered slightly east of the middle of the basin. There can be no doubt about the reality of the maximum. What cannot be determined from a single section, of course, is any estimate of its permanence. Also if the maximum has seasonal variations in amplitude and location, by analogy with a similar feature in the North Pacific, these cannot be determined from one cruise. Two cruises would not suffice, no matter how accurate or closely spaced the measurements are.
Non-seasonal variations are a possibility too. One that comes to mind in the South Pacific is the El Nino, a phenomenon in which warm water temporarily replaces the normally cold water along the coast of South America with devastating consequences for the biology of the region.
Even if the large-scale temperature maximum of Figure 1 only existed for a few months, how is it to be explained?Logically the source of the warm water should be at or near the equator. Thus, Figure 1 can essentially stand alone, without requiring a detailed discussion, but other ancillary data will be brought in to help in understanding the remarkable temperature distribution, and more explaining will be coming.
Considering just the surface temperatures by themselves, how did all that warm water surrounding the temperature maximum get to the middle of the ocean during the height of the cooling season?There appears to be no obvious way to create the temperature maximum by in situ heating and/or cooling coupled with vertical wind mixing and stirring, but with no advection, without straining the imagination. Therefore, the warm water almost certainly must have been transported horizontally into that location from a place that is even warmer, i.e. a latitude lower than 28˚S, since some heat undoubtedly escaped into the atmosphere during transportation. A current with a southward component surrounding the maximum in Figure 1 is thereby inferred from the surface temperatures alone.
Some theoreticians may prefer to think that the massive amount of warm water, in a longitude band of about 60 degrees and from the surface down to about 100 m (see the discussion of Figure 4 in the next section), was somehow bodily trucked into place by a planetary wave mechanism, but that is their privilege. For the North Pacific temperature maximum a wave phenomenon was never considered to be a plausible explanation. Equally farfetched to me is the notion that there is some kind of vertical physical connection between the surface layer, that includes the temperature maximum, and variations of the topography of the ocean's bottom directly underneath. If the surface temperature maximum occurs over a ridge, as it does in the WOCE line discussed below, that is probably just a coincidence. The location of the surface flow undoubtedly has more to do with the horizontal driving forces at and near the surface as well as the Coriolis force that tries to shift the current sideways.
It is well known from the temperature atlases that the warmest surface temperatures at any time of the year, and in both North and South Pacific, are in the western equatorial waters, and the coolest are in the eastern tropical Pacific [4]. Consequently, the source of the warm water in Figure 1 might be not only at a lower latitude but also toward the western side of the ocean. In other words, the mean direction of flow at the temperature maximum should be toward the southeast.
For completeness, in the western half of Figure 1 is a temperature variation smaller in amplitude (about 1˚C) and much smaller in longitude (only about 5 degrees) than the large-scale one in the east discussed above. This small-scale variation may be real also and probably deserves to be studied separately. Notice that embedded within the large-scale maximum in the east there are apparently some smaller scales, but they are not very remarkable and do not attract attention away from the coherence of the main maximum.
SURFACE SALINITY, DENSITY, MIXED LAYER DEPTH AT 28˚S
Surface salinities along 28˚S (Figure 2) have a largescale maximum with the highest values anywhere in the section located in the region of the large-scale lon- gitudinal temperature maximum (The amplitude of this feature is 0.4 parts per thousand whereas salinity was measured to 0.001 of a part per thousand). This is at least consistent with the warm water coming from lower latitudes where it is generally considered that evaporation will exceed precipitation most of the time in the open ocean.
From Scorpio surface temperatures and surface salinities the surface densities were calculated and presented in the data report. Surface densities (Figure 3) are the lowest anywhere in the section around the temperature maximum, which agrees with the density being dominated by temperature, as is normally the case far from continental influences. Longitudes and variable values graphed in Figures 1-3 were taken directly from the published data report; mixed layer depths (Figure 4) involve two intermediate steps.
Calculations of the thickness of the mixed layer were carried out as follows. At a given station and starting at the surface the vertical temperature gradient was computed between all consecutive pairs of temperatures, taken at a discrete set of depths (that varied from station to station), until the first significantly larger value was found. Then the depth of the mixed layer was taken to be the depth of the upper value of that pair of temperatures. In several cases the depth separation between pairs of temperatures was not optimum for making this calculation, causing the mixed layer depth to sometimes be noisy from one station to the next. A 10 point running mean was then applied to the mixed layer depth values, which makes the curve of mixed layer depth vs. longitude smoother without compromising the integrity of the underlying physics, hopefully. Another way to obtain a smoother mixed layer depth curve would be to start with a different set of data, continuous in the vertical (i.e. from XBTs or STDs), as was done for the 35˚N section [3]. This is a possible project for another time.
Below the large-scale longitudinal surface temperature maximum of the central/eastern part of the section the mixed layer depth also has a broad maximum, the greatest depths bottoming out at about 100 m (Figure 4). Therefore, where the surface temperature is relatively high, in the middle of the basin, the mixed layer depth is relatively large. That overall correlation is consistent with warm surface water moving southward and being cooled from above. Alternative explanations for the correlation may occur to the reader, but no credible ones have emerged from the present study. Exactly the same correlation was found for the wide warm current of the North Pacific: high surface temperature equals deep mixed layer, in general.
As Figures 3 and 4 indicate the warm current is floating on top of denser water.
Comparing Figures 1 and 4 it can be seen that the highest temperatures of the longitudinal maximum are significantly west of the center of the region of greatest mixed layer depths. This same asymmetry, surprising at first, has already been found for the large-scale temperature maximum of the eastern North Pacific, an explanation of which has been offered [5].
Figures 2-4 confirm that the east/west scale to be associated with the temperature maximum of Figure 1 is at least 60 degrees of longitude, which makes this South Pacific feature twice as big longitudinally as is the comparable temperature maximum in the North Pacific. One reason for why this is so may be that at 28˚S the flow has a more easterly component, in relation to its southward component, compared to that of the northeastward flow at 35˚N. Verification of the reasoning awaits the accumulation of further data.
In Figure 4 the maximum depths of the mixed layer in the eastern part of the section are 100 m below the surface temperature maximum; this is the same as that under the temperature maximum of the North Pacific. An apparent upper limit of 100 m for the mixed layer depth could be an indicator of the greatest depth of the southward horizontal flow being in the neighborhood of 100 m also, but it may prove difficult to obtain a definitive check on that idea in the South Pacific. However, in the North Pacific the distinctive shallow salinity minimum lies parallel to and just below the mixed layer along 35˚N providing a signal that the northward flow has reversed direction there, now coming from higher latitudes where precipitation exceeds evaporation [2].
One very large mixed layer depth of relatively small horizontal scale occurs in the far western side of Figure 4. Four very large mixed layer depths of small scale were found on the western side of 35˚N section [3]. These interesting features remain unexplained largely because they are incompletely defined by measurements along a single east/west line.
OTHER LATITUDES
Scorpio I took place along 43˚S. There is no surface temperature maximum similar to that in Figure 1 along that line. Rather the surface temperature steadily increases, on the large scale, eastward from about 125˚W to the coast of South America, so the maximum in the eastern Pacific is at the coast. One might be inclined to guess that only the left limb of the maximum in Figure 1 remains after the warm current has run into the continent at latitudes between 28˚S and 43˚S. More data between these two latitudes would be needed before further discussions of this point can be continued. Some years after the Scorpio Expedition, and at the same time of year, a second east/west hydrographic section was made in the South Pacific at about five degrees south of 28˚15'S as part of the WOCE program (World Ocean Circulation Experiment). Data from this WOCE line (P06), as well as all the others, and color coded vertical section displays of temperature, salinity, etc. can be found on the web: [URL] feature very similar to that shown in Figure 1 can be seen in these data as well. From the coast of South America the surface temperature gradually increases westward to a maximum and then decreases again. The fact that the surface temperature maximum is a real feature that shows up in the data from two different cruises, made fairly close to the same east/west line in different years, but at the same time of year, provides some evidence that the maximum may be a permanent feature. If the longitudinal surface temperature maximum along 28˚S is the analogue of the similar feature in the North Pacific, as is proposed here, then it can be considered permanent by extrapolation, assuming the 30 years of ship-injection temperature data in the North Pacific are sufficient to prove it.
One difference between the two South Pacific sections is that the longitude of the surface temperature maximum in the WOCE line is closer to 120˚W than to 130˚W as it is in Figure 1. In moving south about five degrees of latitude the maximum has shifted eastward by about 10˚ of longitude. This eastward shift of the longitudinal maximum with increasing latitude in the South Pacific is to be anticipated as explained next based on what has already been discovered in the North Pacific. It is also in qualitative agreement with a mean direction of flow being southeast.
Between 35˚N and 40˚N the longitudinal maximum in surface temperature in the eastern North Pacific shifts eastward by a mean value of about 7.5 degrees of longitude, which is based on ship-injection surface temperature data, the mean being taken over all twelve months and all thirty years [3]. Thus the approximate 10 degree eastward shift over about five degrees of latitude for the South Pacific maximum between two separate cruises is in accord with the mean eastward shift of 7.5 degrees in the surface temperature maximum of the North Pacific between 35˚N and 40˚N Pacific documented before.
Only one other east/west line (P21) of surface temperatures (and other hydrographic properties) was made in the South Pacific as part of WOCE, along about 18˚S, but it exhibits no large-scale temperature maximum in the central or eastern part of the basin.
DISCUSSION
The world's largest ocean is also the one most barren of data that are applicable to the present project. For example, if a data base of ship-injection temperatures exists for the South Pacific, I am not aware of it. Very likely the number of merchant ships crossing back and forth between South America and Australia is considerably smaller than what has been happening in the North Pacific between California and Japan for a long time. However, two transoceanic hydrographic sections are enough to uncover the southeast surface flow of warm water and to suggest that this current is the analogue of the wide northeast flow of warm water off California.
More data in the South Pacific would of course be desirable for exploring in greater detail the characteristics of the longitudinal temperature maximum. Not very likely is the probability that further hydrographic cruises will come along anytime soon. They are very costly. Monitoring of just the surface temperature along one or more east/west lines could be done in the future in a more inexpensive way, using less accurate thermometers, such as XBTs, more widely spaced. Also useful information could be obtained along lines that are long but do not extend all the way from South America to Australia, perhaps about half way across.
Although fairly strong evidence has been accumulated for a broad and shallow southeast flow in the middle of the South Pacific, based on temperatures and salinities along two east/west lines across the ocean, what is the most likely range of speeds of the flow?That question remains open, but the average speed is probably slow, in the range of 1 -10 cm/sec, if the flow is the similar to the one in the North Pacific. A "drift" may be a more realistic description than a "current".
Just because a current is slow does not mean it is unimportant. Poleward transport of excess heat from the tropics, over 90% of which is received within the top 100 m of the ocean, due to the constant absorption, per square meter and per second, of solar radiation, is one important job this surface current, which has a suggested depthscale also of about 100 m, carries out every minute of every day. That is one of the jobs that the wide warm surface current of the North Pacific has been proposed to do too [2]. Such (thermohaline) currents are not driven by the winds but by a horizontal pressure force related to the unstable equator to pole temperature gradient at and near the surface. That thermohaline and wind-driven currents can exist side by side at the surface of the ocean has apparently not been fully recognized before.
Openly accessible at
Sitting directly on top of the surface temperature maximum at mid-latitudes in the eastern North Pacific is the center of the North Pacific High pressure cell at sea level, at least in the thirty year mean [5]. Establishing a similar ocean/atmosphere connection for the South Pacific does not appear to be possible at the present time for lack of observations.
CONCLUSION
A very wide warm surface flow, with a southward component, is postulated for the central South Pacific based on an interpretation of closely spaced and accurately measured temperatures and salinities taken along two coast to coast transects between South America and Australia in two different southern hemisphere winters. Maximum depths of the current are estimated to be about 100 m; speeds are not known but presumed to lie in the range: 1 -10 cm/sec. The most striking feature of the data along 28˚S (Figure 1) is a longitudinal temperature maximum at about 130˚W; it has a temperature amplitude of at least 5˚C and an east/west range of around 60˚ of longitude. This temperature maximum is hypothesized to be the surface signature of the southward warm drift, which is not predicted by any theory and does not appear on current charts or globes. An analogous wide northward warm flow in the North Pacific off California was documented in greater detail in the 1970s.
Figure 1 .
Figure 1. Surface temperature as a function of longitude at 28˚15'S between Australia on the left and South America on the right.
Figure 2 .
Figure 2. Surface salinity as a function of longitude at 28˚ -15' S between Australia (left) and South America (right).
Figure 3 .
Figure 3. Surface density as a function of longitude at 28˚15'S between Australia (left) and South America (right).
Figure 4 .
Figure 4. Mixed layer depth as a function of longitude at 28˚15'S between Australia (left) and South America (right). A 10 point running mean has been applied to the calculated values.
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Domain: Environmental Science Biology
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Mobile epifaunal assemblages associated with Cystoseira beds : comparison between areas invaded and not invaded by Lophocladia lallemandii
The study compared the structure of mobile epifaunal assemblages associated with Mediterranean Cystoseira beds between areas invaded and not invaded by Lophocladia lallemandii. A total of 150 taxa were identified: 42 Polychaeta, 78 Arthropoda, 26 Mollusca and 4 Echinodermata. Epifaunal assemblages differed between areas invaded and not invaded by Lophocladia lallemandii when the invasive species reached maximum values of cover and biomass, while differences between conditions were not significant in other periods of the year. The main differences were the greater abundance of amphipods, isopods and polychaetes in invaded areas and the greater abundance of molluscs and decapods in non-invaded areas, while richness and total abundance of organisms were not significantly different between conditions. The effects of Lophocladia lallemandii invasion on Cystoseira-associated assemblages seem to be limited to the period of vegetative growth of the alga and reversible during the period of its vegetative rest.
INTRODUCTION
Introduced seaweeds are responsible for severe worldwide biological invasions, with important effects on native macroalgal and animal assemblages (Piazzi et al. 2001, Buschbaum et al. 2006, Schaffelke and Hewitt 2007, McKinnon et al. 2009, Byers et al. 2010, Pacciardi et al. 2011). Effects of invasion may be particularly serious when habitat-forming species are involved, as each change in population of these organisms may have severe effects on associated assemblages (Gribben et al. 2009). Macroalgae are important habitat-forming organisms in temperate coastal systems, providing a suitable habitat for many epiphytes and mobile invertebrates (Edgar and Moore 1986, Taylor and Cole 1994, Cacabelos et al. 2010) and influencing the structure and the biodiversity of coastal systems (Tanaka and Leite 2003, Bates and Dewreede 2007, Wikström and Kautsky 2007).
In the Mediterranean Sea, species of genus Cystoseira are the most important habitat-forming species in shallow rocky bottoms (Ballesteros 1990a, b), where they play a key role in determining patterns of diversity (Sales and Ballesteros 2009). The erect structure of Cystoseira thalli, like those of other canopy species, can limit the spread of most invasive seaweeds, constituting a mechanical barrier against the invasion (Bulleri et al. 2010). However, invaders such as the Rhodophyta Lophocladia lallemandii (Montagne) F. Schmitz (Bedini et al. 2011) seem to be facilitated by the presence of Cystoseira beds. This species is widespread in tropical and subtropical waters and was probably introduced into the Mediterranean Sea through the Suez Canal (Boudouresque and Verlaque 2002). Until now, in the Mediterranean Sea, invasive events by L. lallemandii have only been described in the Balearic Islands (Patzner, 1998, Cebrian and Ballesteros 2010, Marbà et al. 2014) and in the Tuscan Archipelago (Bedini et al. 2011). In both areas, the alga is able to reach high values of percentage cover and biomass (Bedini et al. 2011) on rocky bottoms and in seagrass meadows (Ballesteros et al. 2007, Sureda et al. 2008, Marbà et al. 2014). Cystoseira beds are particularly subjected to invasion (Cebrian andBallesteros 2007, Bedini et al. 2011), as thalli of these algae may offer a valuable substrate for L. lallemandii anchoring (Bedini et al. 2011). Negative effects of L. lallemandii invasion have been described for sessile invertebrates in meadows of the seagrass Posidonia oceanica (L.) Delile (Cabanellas-Reboredo et al. 2010, Deudero et al. 2010), while no information is available about effects of invasion on mobile macro-invertebrates.
The present study aimed to compare the structure of mobile epifaunal assemblages associated with Cystoseira beds between areas invaded and not invaded by Lophocladia lallemandii. The following hypotheses were tested: i) epifaunal assemblages associated with Cystoseira beds invaded by L. lallemandii differ in species composition and abundance from those colonizing non-invaded beds, ii) temporal patterns of assemblages vary between conditions, iii) differences between conditions are greater during the period of maximum vegetative growth of L. lallemandii.
MATERIALS AND METHODS
The study was carried out around the Island of Pianosa, in the Tuscan Archipelago National Park (northwestern Mediterranean Sea) at 5 m depth (Fig. 1). Lophocladia lallemandii started to spread around the island in 2008, and in 2010 it colonized with variable coverage a stretch of about 10 km between 2 and 10 m depth (Bedini et al. 2011). The alga begins to grow in July, reaches its maximum abundance in November and completely disappears between January and June (Bedini et al. 2011). All around the island, the rocky bottom between 4 m and 8 m of depth is colonized by Cystoseira spp.assemblages (mostly C. crinita Duby and C. brachycarpa var.balearica (Savageau) Giaccone). In invaded C. crinita beds, the biomass of L. lallemandii in November was about 0.2 kg dw m -2 (Bedini et al. 2011).
Four areas of about 100 m 2 were selected in C. crinita beds along the southern coast of the island, two of them invaded by L. lallemandii and two non-invaded; areas were randomly chosen among those available for each condition (Fig. 1). On four dates during a one-year period (May 2010, August 2010, November 2010, May 2011), three samples were collected in each area. Samples were constituted by all organisms present within an area of 400 cm 2 . All mobile macro-invertebrates present in each sample were identified and the abundance of each species was expressed as number of individuals m -2 . Epifaunal assemblages were analyzed by Permutational Analysis of Variances (PERMANOVA, Anderson 2001). A three-way model was used with Condition (Invaded vs. Non-invaded) as a fixed factor, Date (4 levels) as a random factor crossed with Condition and Area (2 levels) as a random factor nested in Condition. Data were log(x+1) transformed before calculation of the Bray-Curtis index of dissimilarity. The Monte-Carlo procedure was used when the number of permutations was low. A two-dimensional multidimensional scaling (n-MDS) was used for a graphical representation of results. The SIMPER routine was performed to establish which taxa most contributed to the dissimilarity between conditions and dates.
The number of taxa per sample and the abundance of organisms were detected by analyses of variance (ANOVA), with the same factors and levels used for multivariate analyses; Cochran's C-test was utilised before each analysis to check for homogeneity of variance and data were transformed when necessary (Underwood 1997).
ANOVA analyses detected a significant difference among dates for the abundance of organisms (F=80.7,p=0.003) and the mean number of taxa per sample (F=20.6,p=0.001), while differences between conditions were not significant (F=2.6,p=0.120 and F=44.6, p=0.071 respectively). Both variables were higher on spring dates than on the others (Fig. 2).
PERMANOVA detected a significant interaction between Date and Condition (Table 2). The pairwise test showed that differences between conditions were significant in November 2010 but not on the other sampling dates (Table 2). In invaded condition, May 2010 and May 2011 differed from August and November 2010; in non-invaded condition, November 2010 differed from the other dates. MDS showed a greater disjunction between invaded and non-invaded assemblages in November 2010 than in the other sampling dates (Fig. 3).
The main differences between spring sampling dates (May 2010 and May 2011) and autumn ones (November 2010) were a higher abundance of organisms in spring, especially the molluscs Barleeia unifasciata and Bittium latreillii and the amphipods Hyale schmidti, Ampithoe ramondi, Phtisica marina and Caprella spp.; only a few taxa were more abundant in autumn, including the decapods Cestopagurus timidus and Calcinus tubularis (Table 3).
DISCUSSION
Results of the study described the structure of epifaunal assemblages associated with Cystoseira crinita beds and highlighted differences between areas invaded by Lophocladia lalemandii and non-invaded areas related to the vegetative cycle of Rhodophyta.
Epifaunal assemblages associated with C. crinita were characterized by high abundance and diversity, compared with those described for other seaweed habitats (Gestoso et al. 2012, Janiak et al. 2012, Engelen et al. 2013). Macroalgal assemblages associated with Mediterranean Cystoseira beds are well known (Boudouresque 1972, Sales and Ballesteros 2010), while epifaunal assemblages have been less investigated and knowledge is limited to particular taxa (Arrontes and Anadon 1990, Chemello and Milazzo 2002, Fraschetti et al. 2002). The present study, analysing the whole epifaunal assemblages, confirms the important ecological role of Cystoseira beds in Mediterranean coastal systems. Cystoseira thalli, like those of other structurally complex algae (Tanaka andLeite 2003, Wikström andKautsky 2007), may create a high number of microhabitats, hosting organisms with different requirements (Russo 1990, Gee and Warwick 1994, Taylor 1998). Moreover, Cystoseira beds may offer an effective refuge from predators and abundant food availability (Buschmann 1990, Holmlund et al. 1990, Martin-Smith 1993).
The sampling design of the study was not suitable for assessing the temporal dynamics of the assemblages. However, in non-invaded areas, epifaunal assemblages associated with C. crinita showed great differences between sampling dates, suggesting the occurrence of seasonal patterns which should be investigated through further studies. Seasonal variations in epifaunal assemblages associated with Cystoseira spp. as a consequence of taxa life cycles and modifications in seaweed structure have already been described (Fraschetti et al. 2002, Gozler et al. 2010). In fact, Cystoseira are perennial species with seasonal cycles of vegetative growth: they reach their maximum size in spring-summer period, while in autumn they lose secondary branches, changing their habitus (Sales and Ballesteros 2012). Temporal changes of epifaunal associated with Cystoseira spp.can also be caused by changes of macroalgal epiphyte assemblages. In fact, Cystoseira species host an abundant assemblage of macroalgae, mostly constituted by seasonal filamentous species (Ballesteros et al. 2009, Sales andBallesteros 2010), which may change greatly throughout the year following the growth cycles of the main taxa.
The seasonal development of L. lallemandii overlaps this scenario. In fact, the study results showed that epifaunal assemblages associated with Cystoseira crinita beds differed between areas invaded and not invaded by Lophocladia lallemandii in November, when the invasive species reached maximum values of cover and biomass (Bedini et al. 2011), while assemblages showed no differences between conditions in other periods of the year.
The main effects of the presence of L. lallemandii were an increase in amphipods and polychaetes and a decrease in decapods and molluscs. Species more linked to the architecture of Cystoseira thalli may be damaged by substrate modification; in fact, many epifaunal organisms are related to particular macroalgae and may be strongly influenced by the presence of invasive species (Viejo 1999, Gestoso et al. 2010). On the other hand, polychaetes are not specifically facilitated by the morphology of canopy seaweeds, and food preference and/or different substrate requirements may well cause their increase in invaded areas; in fact, several polychaete species may be facilitated by turfs created by L. lallemandii, where sediment and organic matter could be trapped. Caprellid amphipods need cylindrical substrates with a small diameter to be encircled by pereopods in order to avoid being dislodged by water movements (Aoki and Kikuchi 1990), and the presence of L. lallemandii can increase the substrate available for anchoring. Moreover, herbivore amphipods, ampithoids in particular, may also be influenced by the increase in food availability in invaded areas (Duffy 1990, Duffy and Hay 2000, Poore et al. 2008).
The results show that the effects of L. lallemandii colonization on mobile organisms are related more to changes in species composition than to changes in patterns of diversity. This finding agrees with those described for other introduced seaweeds, suggesting that, while macroalgal invasions strongly affect diversity of sessile assemblages (Ribera and Boudouresque 1995, Piazzi et al. 2001, Schaffelke and Hewitt 2007, Baldacconi and Corriero 2009, Zuljevic and Nikolic 2008), the effects of invasions on mobile organisms are more related to changes in the structure of assemblages (Vázquez-Luis et al. 2009, Gestoso et al. 2012, Janiak et al. 2012, Pacciardi et al. 2011, Engelen et al. 2013).
Differences between invaded and non-invaded beds were not evident five months after the disappearance of L. lallemandii. The effects of invasion on Cystoseiraassociated assemblages seem to be limited to the period of vegetative growth of the alga and reversible during the period of its vegetative rest. Recovery of assemblages could be related both to the intrinsic characteristics of organisms and to the lack of damage to C. crinita thalli. Macro-invertebrate assemblages are able to respond rapidly to various kinds of impacts (Teixeira et al. 2009), but they are also able to recover their original structure quickly after disturbance because of the short life cycles of the organisms (Lu andShio-Sun Wu 2007, Pacciardi et al. 2011). Moreover, recovery followed the return of the habitat to its original structure. In fact, until now, no evidence of Cystoseira regression has been observed in invaded areas of Pianosa Island (Bedini et al. 2011). Although L. lallemandii completely cover Cystoseira thalli during the period of spread, several months without the invasive alga seem to be enough to avoid severe damage to Cystoseira beds.
The effects of L. lallemandii invasion at Pianosa Island seem to be less severe than those described in the Balearic Islands. However, the colonization of L. lallemandii in the Tuscan Archipelago has recently started and more severe effects could be hypothesized after longer periods of colonization on both Cystoseira beds and its associated assemblages, indicating the importance of monitoring the spread of the invasive alga.
Fig. 1 .
Fig. 1. -Map of the study site. Black lines indicate zones colonized by Lophocladia lallemandii. White stars, invaded areas; black stars, non-invaded areas.
Table 2 .
-Results of PERMANOVA analysis on epifaunal assemblages. Significant effects are in bold. MC, Monte-Carlo procedure.
Table 3 .
-Results of SIMPER test showing the contribution of taxa to determining differences in assemblages between invaded and non-invaded areas in November 2010 and between May and November in non-invaded areas
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Domain: Environmental Science Biology
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Quantifying the consequence of applying conservative assumptions in the assessment of oil spill effects on polar cod (Boreogadus saida) populations
In order to assess the potential impact from oil spills and decide the optimal response actions, prediction of population level effects of key resources is crucial. These assessments are usually based on acute toxicity data combined with precautionary assumptions because chronic data are often lacking. To better understand the consequences of applying precautionary approaches, two approaches for assessing population level effects on the Arctic keystone species polar cod (Boreogadus saida) were compared: a precautionary approach, where all exposed individuals die when exposed above a defined threshold concentration, and a refined (full-dose-response) approach. A matrix model was used to assess the population recovery duration of scenarios with various but constant exposure concentrations, durations and temperatures. The difference between the two approaches was largest for exposures with relatively low concentrations and short durations. Here, the recovery duration for the refined approach was less than eight times that found for the precautionary approach. Quantifying these differences helps to understand the consequences of precautionary assumptions applied to environmental risk assessment used in oil spill response decision making and it can feed into the discussion about the need for more chronic toxicity testing. An elasticity analysis of our model identified embryo and larval survival as crucial processes in the life cycle of polar cod and the impact assessment of oil spills on its population.
Introduction
The exploration, production and transport of oil and gas in the Arctic region involves challenges related to environmental, socio-economic and cultural issues as well as good governance. High quality knowledge about the potential impact of exploration, production and transport activities and adequate use of this knowledge is a prerequisite to make informed decisions in case of an oil spill. An important instrument within this context is the net environmental benefit analysis (NEBA). This is a procedure for assessing the environmental consequences of oil spill response options. It enables decision-makers to select appropriate responses minimizing potential impact (Wenning et al. 2018). NEBA relies on sound risk assessments using oil toxicity data that assessed potential oil impacts on relevant biological resources preferably at the population level. A worst-case (precautionary) assumption is often applied within a NEBA using fixed threshold values. These threshold values cover both short and long term effects of oil exposure (e.g. Coelho et al. 2015), although in practice acute data usually form the main basis for these threshold values. The current study aims to explore the consequences of using these threshold values as cut off points by assessing population level impacts of oil exposure for polar cod (Boreogadus saida) in the Arctic. Polar cod is the most widespread and abundant fish of the Arctic Ocean (Lowry and Frost 1981;Parker-Stetter et al. 2011) and it plays a key role in the Arctic marine ecosystem (e.g. Mueter et al. 2016) connecting zooplankton with marine vertebrate predators, such as marine mammals and birds (Welch et al. 1992). An oil spill effect on B. saida populations will therefore potentially cascade through the Arctic food web, as demonstrated for sea ice effects where modelling results suggested negative top-down effects by polar cod on amphipods (Stige et al. 2019).
Polar cod is well-adapted to large annual fluctuations in physical (ice cover) and biological (primary productivity) characteristics, which probably explains its overwhelming success in Arctic waters (Lowry and Frost 1981). Due to its ecological role and abundance, it is a good model species to study.
In the current study, the toxic effect of oil was investigated with two different approaches: a precautionary approach, in which all exposed individuals survive when exposed to concentrations below a threshold value and die when exposed above this threshold value, and a refined (fulldose-response) approach, in which an assumed relationship results in an effect per toxic unit (TU). An age-based matrix model was developed to simulate the population development of polar cod. A benefit of matrix models is that they are analytically tractable and their behaviour is well analysed (Caswell 2001). They are often used in ecotoxicology and risk assessment approaches (e.g. Caswell 1996; Klok and De Roos 1996;Smit et al. 2006;Klok 2008;Bergek et al. 2012;De Vries et al. 2018).
With a daily-based simulation of the matrix model we investigated the impact of selecting either the precautionary approach using a single threshold value or a refined approach using a full dose-response relationship on the population dynamics of polar cod. A similar exercise, published by De Vries et al. (2018), was performed for another well-adapted, keystone organism in the Arctic Ocean, namely for herbivorous copepods of the genus Calanus (Welch et al. 1992;Falk-Petersen et al. 2007).
Overview of steps
To investigate population level effects of oil spills on polar cod in the Arctic, an age-structured matrix population model was developed. In this model, the life-history parameters survival and reproduction were parameterized with values and relations taken from literature. As an important step in the modelling process we performed an elasticity analysis for the underlying parameters to assess the effect of a relative change in each of the life-history parameters on the relative change of the population growth factor. To incorporate toxic effects of oil into the population model, mortality and/or reproductive output endpoints are required. Because the relationship between exposure and the effect on the life history parameters of polar cod was not available in literature, we used hypothetical effect levels based on an acute to chronic ratio (ACR) (for fish) in this study to scale up to the population level (May et al. 2016).
The (scarcely) available literature on toxic effects of oil on polar cod vital rates was only used to assess their relation to our hypothetical effect levels, by calculating field relevant toxic units based on these data.
Life cycle and model approach
For this study we used a matrix population model as these models: (1) have a direct relation to empirical age-structured data, (2) the resulting population growth factor is clearly linked to life-history parameters such as reproduction and survival, and (3) a relatively low number of input data is required (Beissinger and Westphal 1998).
First, literature was surveyed to establish the life cycle of B. saida (reported in detail in Online Resource 1). The life strategy of polar cod consists of early maturity, relatively rapid growth in comparison to other gadoids at cold temperatures (Laurel et al. 2016), small body size, and a relative short life span that rarely exceeds 5 years with a maximum of seven (Craig et al. 1982;Cohen et al. 1990;Jensen et al. 1991). It can be found in demersal, pelagic, and sympagic (in association with sea-ice) habitats, depending on the life stage (Cohen et al. 1990). Aggregation of individuals (schools) is known (Welch et al. 1992;Crawford and Jorgenson 1996). Figure 1 represents the life cycle on which the model was based. It includes the assumption that the male to female ratio equals 1:1 and only the female part of the population was considered (by basing the model on female vital rates), thereby discarding the female-skewed sex ratio in later years. Also a region with optimal conditions was assumed enabling polar cod to reach maturity in 3 years and a maximum age of approximately 7 years. For simplicity, constant yearly spawning was assigned to take place in a single day for fish in their fourth to seventh year.
This results in a post-spawning population model with a generation time of 4 years (x 4 (t) is the class that represents this population part). The four classes (x 1 (t), x 2 (t), x 3 (t), x 4 (t))) T represented newly hatched polar cod (0+), 1 year old, 2 years old and older than 3 years. The Leslie matrix M for this system was given in Eq. 1. Time t was measured in years. Further details are presented in Online Resource 2 ( Fig. S2.2) such as the structure of the year and how this is implemented in the model, the derivation of the matrix elements from daily based survival probabilities, and what these elements look like in terms of daily survival and fecundity (m). The yearly survival probability in class x i (i = 1, 2, 3, 4) is y i and y i = s 365 i with s i the daily survival probability. Daily embryo and larval survival was denoted as s 0 ; b denotes the hatching duration of the embryos in days.
Parameterisation
We based model parameter values (Table 1) on information obtained from literature making several assumptions. The temperature tolerance of polar cod adults is much higher than larvae. Adults are found in waters with temperatures ranging from −2 to 13.5 °C . Egg and larval development of polar cod require temperatures below 3 °C for normal development of eggs and larvae (Aronovich et al. 1975;Sakurai et al. 1998;Dahlke et al. 2018) and is under pressure from global warming (Dahlke et al. 2018). Nahrgang et al. (2015) suggested that polar cods are total spawners (i.e. release one batch of eggs per spawning season) with a group-synchronous development. The duration of the egg laying period is therefore assumed to be 1 day (see section 'Reproduction' in Online Resource 1). The number of eggs produced per female increases with age and totals 75,000 eggs per female over their entire life span . Most females generally mature at age 3 (Craig et al. 1982;Cohen et al. 1990). For the model, the egg production was assumed to be constant over the years and reproduction took place from the fourth until the seventh year of life. This resulted in an egg production of 18,750 eggs per female per year. The daily embryo and larval survival was based on data from Sakurai et al. (1998) and Nahrgang et al. (2016), and was on average 1% (see also Table 2). Hatching duration (b) also depends on water temperature. Data from Sakurai et al. (1998) were used to describe the temperature dependent hatching duration (see Online Resource 2). The daily survival in the juvenile (including older larvae), sub-adult and adult stages of polar cod were calculated using the relationship between body weight and natural mortality in juvenile and adult fish (Lorenzen 1996) (see section 'Mortality' in Online Resource 1). After assigning the parameters a value based on the literature an elasticity analysis Aronovich et al. (1975) and Melnikova and Chernova (2013) b Based on data from Sakurai et al. (1998) c Based on data from Nahrgang et al. (2016) d Based on the weight dependent mortality relationship (Lorenzen 1996, Online Resource 1, Eq. S1.1) d Based on Hop et al. (1995) and Hop and Gjøsaeter (2013) The number of eggs laid in one day per female 9 × 10 3 to 6 × 10 6d,e 18,750 e No was performed to determine the relative sensitivity of the model to changes in parameter values (Online Resource 2).
Toxicity
Toxic units (TU) were used in this study to express oil exposure (Von der Ohe and De Zwart 2013). To derive exposure values x (in TUs, see details in Online Resource 3) from a (measured) oil compound concentration c was scaled with its inherent effect concentration (e.g., acute 50% lethal concentration, LC50 4 , for adult fish (= class 4)): x = c∕LC50 4 . Both the No Observed Effect Concentrations (NOEC) and 50% lethal concentrations (LC50 i ) are also scaled with the LC50 4 (for later use in Eqs. 2 and 3): n = NOEC/LC50 4 = 1∕ACR , L i = LC50 i ∕LC50 4 for (i = 0, 1, 2, 3, 4, where 0 represents embryo and young larvae up to the time that all embryos are hatched). Thus L i is the LC50 in stage i relative to the LC50 in the adult stage. A theoretical relation based on the acute-to-chronic ratio (ACR) for fish (May et al. 2016) was used to parametrize the concentration-time-response-relationship on the population growth factor. These TUs were used to express the exposure to both single oil components and to mixtures of oil components.
Although oil toxicity could affect several of the life history parameters we focused on toxic effects on survival. Because limited toxicity data were available, a theoretical approach was used to describe the relation between exposure and effect. We used the same approach as described by De Vries et al. (2018) assuming that the hazard rate (h(t), the probability per unit of time to die at time t), is affected by an exposure x (in TU) above the No Observable Effect Concentration (NOEC) of n TU: with the h 0 (t) baseline or natural hazard rate (including predation, see Lorenzen 1996). The relationship is assumed to be multiplicative, and the magnitude of the effect is expressed as exp(β i ) per toxic unit above the NOEC. Following De Vries et al. (2018), the effect magnitude is expressed as (see details in Online Resource 4): A difference with the approach of De Vries et al. (2018) is that the LC50 (L i , expressed as toxic units) was assumed not to be equal in each life stage. Ratios of effect concentrations between fish life stages as reported by Hutchinson et al. (1998) were used for this purpose (Table 3). An ACR value of 12 was used for fish (as derived by May et al. 2016) and an acute exposure time t a of 4 days (as per OECD standards for fish tests (OECD 1992)).
Crude oil is a complex mixture of both hydrocarbons, such as alkanes, cycloalkanes and aromatic hydrocarbons, and non-hydrocarbon compounds, with varying effects on exposed biota. Toxicological risks of oil mixtures are primarily determined by its water soluble components (e.g. French McCay 2002; Olsen et al. 2013). Information on the impact of oil-components on life-history characteristics in different developmental stages are relatively scarce (Olsen et al. 2013). Some effect values on vital rates are available for polar cod exposed to specific crude oils and were only used in the discussion to place the modelling results into context of field relevant concentrations (reviewed by De Hoop et al. 2011;Gardiner et al. 2013;Nahrgang et al. 2016) (see Online Resource 3, Table S3.1).
Oil spill scenarios
For the model we consider a population of polar cod to be all individuals that are present in a specific region during the simulation/observation time. This population is considered to be a closed system. The model simulated the polar cod population on a day to day basis (Online Resource 2) exposed to a range of TUs for a range of exposure durations (2, 4, 8 and Total length (L in mm) of 1 to 4 year old polar cod in the Arctic region (mainly North-eastern part of Svalbard) , calculated wet weight (W, using Online Resource 1 Eq. S1.2), calculated mortality (using Online Resource 1 Eq. S1.1) and calculated daily survival of (sub)adults (based on the average mortality of 1 and 2 year olds (sub-adults) and the average mortality of 3 to 7 year olds (adults)) 16 days) and a range of exposed fractions of the polar cod population. The exposure concentration varied between 0.9/ ACR (i.e., 90% of the NOEC) and 1.1 TU (i.e., 10% above the LC50) in 16 equidistant exponential steps and the exposed fraction of the population varied between 1% and 99% in nine logarithmic steps. The affected fraction indicated the fraction of the population that is being exposed to oil. Only this fraction was affected, following one of two different approaches evaluated. The approaches were: (1) precautionary: all individuals exposed above the NOEC die instantaneously; (2) refined (fulldose-response): individuals die as the result of an increased hazard rate, which depends on the exposure concentration and duration (as described above). Because the model is not spatially explicit, no exchange of individuals between the exposed and the unexposed fraction of the population was modelled. Recovery time was evaluated for the entire adult population consisting of the unexposed individuals and those that survived exposure.
The number of adult individuals peaked each year at the census moment (after hatching). The recovery time was defined as the time it takes to reach at least the number of adults of an unexposed population measured at the census moment after an exposure. Because this definition is linked to the census moment, which takes place just after hatching, the recovery time was always expressed in number of full years starting from year one of the simulation. Obviously, in reality, recovery can take less than a year.
For both the precautionary and the refined approach, results were obtained through simulation runs with the matrix model as given in Online Resource 2 (and Eq. 1). For this purpose, a period of 20 years was simulated, with oil exposure taking place during the breeding season at the end of the second simulated year. We report about the simulation where all life stages (including the embryo-stage) were affected by the exposure. We also considered a case where the first class (newly hatched polar cod 0+) was the only year class exposed to the oil spill and a case where all but the reproducing adults were exposed to the oil spill (the results of these last two cases can be found in Online Resource 5).
For each combination of exposed fraction, exposure concentration, and exposure duration, the resulting recovery times were compared for both approaches (precautionary and refined) and expressed as the ratio R of recovery time for the precautionary approach 1 (T r1 ) to the recovery time for the refined approach 2 (T r2 ):
Elasticities
The population growth factor based on the parameter values as used in the model ranged from 1.92 to 2.12 with temperatures varying between −2 and 3 °C (Fig. 2a). Obviously, the growth factor depends on the temperature because hatching time depends on temperature (see Fig. S2.1). The elasticities (see Fig. 2b, c, d and Online Resource 2) showed that the model, in terms of the population growth factor, was most sensitive to the daily survival of the embryos and larvae, and less to the yearly survival in the fourth year class, whereas the variation in length of the hatching period b had only a minor impact on the population growth. The production of embryos and the survival in the first years of life had the same elasticities (Online Resource 2). This suggests that the embryo and larval survival are crucial factors to consider in oil toxicity experiments. Figure 3 shows the ratio of recovery times of the precautionary and the refined approach. A higher ratio R represents a larger difference between the precautionary approach and the refined approach with a more optimistic estimate for the refined approach as R>1. Figure 3 furthermore shows that temperature has only a minor effect on this relative difference. At most there is 1 unit difference in R between the simulated temperatures (ranging from −2 up to 3 °C).
Precautionary versus refined approach
We assumed that no effects occurred below the chronic NOEC for both the precautionary and the refined approach, explaining why the ratio of recovery times is by definition 1 for exposures below the NOEC in Fig. 3. For low concentrations (just above the NOEC of 1/12 TU), the difference between the precautionary approach and the refined approach were the largest (Fig. 3). For higher concentrations, the difference between the model simulations of the refined and precautionary approach decreased (Fig. 3). This was also the case for longer exposure durations (Fig. 3).
When less than half of the population is exposed (<51%), no difference in recovery time between the two approaches was detected with the model used here. When more than half of the population (≥51%) is exposed, the precautionary approach resulted in a longer predicted recovery time. The ratio of recovery times between the precautionary and the refined approach was eight at maximum (i.e. for the 99% exposed). This means that for these cases the recovery time in the precautionary approach was eight times longer compared to the refined approach.
We have also determined what happens if not all year classes are exposed to the oil spill (see Online Resource 5). If only the youngest age class is exposed to the oil spill, then the precautionary and refined approaches give the same recovery durations (so R equals one for all doses and exposure durations). If all age classes except the reproducing class are exposed to the oil spill then we see a similar pattern in the ratio of the recovery durations (R) as in our scenario where all stages are exposed to the oil spill.
Discussion
Our model for polar cod represented a population with an approximate doubling of the population every year at temperatures between −2 and 3 °C. This implies that the population is buffered by loss due to predation from higher trophic levels in the Arctic food web or other factors. The survival of embryos and larvae during hatching appeared to be of crucial importance for the population growth factor, as revealed by the elasticity analysis. With respect to the comparison of the precautionary and refined scenarios this study showed that the refined approach provided shorter recovery durations compared to the precautionary approach after exposure of all year classes to an oil spill. If only the youngest class is exposed then both approaches give a similar result.
Limitations of our study
The ability of polar cod to adapt to local conditions implies that its biological characteristics (biomass, feeding, growth rates, spawning and hatching) vary geographically, seasonally and from year to year (Lowry and Frost 1981;Jensen et al. 1991;Hop et al. 1995;Bouchard and Fortier 2008). The current understanding of polar cod is surprisingly fragmented and inconclusive, leaving major gaps in knowledge that prevent a holistic understanding of the interaction between the species and its environment (Mueter et al. 2016).
Our literature study (see Online Resource 1) revealed that (1) the spawning strategy of polar cod has yet to be fully understood (Nahrgang et al. 2015), (2) the mortality of embryos and larvae reported in literature (Sakurai et al. 1998;Fortier et al. 2006;Nahrgang et al. 2016) vary considerably, (3) the natural mortality of (sub)-adult polar cod is not measured, and (4) there is insufficient experimental information on the relation between chronic oil exposure levels and effects on survival in polar cod.
Based on earlier findings (Nahrgang et al. 2015) we modelled the yearly spawning (point (1) above) as occurring on one day, namely releasing one batch of eggs per spawning season with a group-synchronous development. To assess the effect of this assumption we have simulated also a population with spawning occurring over 10 days. This did not Fig. 2 The temperaturedependent model results. The model gives as a consequence of temperature dependent hatching period, at different ambient temperatures of the sea water differences in a the dominant eigenvalue, b the elasticity of hatching period b, c the elasticity of the daily egg and larval survival ( s 0 ), and d the elasticity of the yearly adult survival (note that from the formulas S2.9, S2.11-2.13 in Online Resource 2 it is clear that e(y 1 ) = e(y 2 ) = e(y 3 ) = e(m)) affect our results in a qualitative way, although the maximum of the ratio R was a bit higher (11 instead of 8; results not shown).
To overcome points (2) and (3) we used the weight dependent mortality relationship for fish (Lorenzen 1996;Peterson and Wroblewski 1984) to calculate yearly survival. In Online resource 1 we compared this value to other estimates for this species and similar species.
With respect to point (4), the lack of experimental data on mortality due to oil (specifically LC50 values), we extrapolated LC50 values from experimental NOEC values in polar cod using the acute-to-chronic ratio (ACR) of 12.0 (May et al. 2016) (see Online Resource 3 Table S3.1). Effects of oil components on polar cod are available (see Online Resource 1) but these have limited value for use in our population model, as most studies used biomarkers (genes, enzymes, metabolites, DNA damage) as endpoints of which the quantitative relation to the vital rate survival is unknown (Nahrgang et al. 2009;2010a, b, c;Fig. 3 The relation between exposure concentration and the ratio between recovery times of the two considered scenarios. The ratio R (Eq. 4) equals the recovery time of the precautionary approach (=worst-case scenario) divided by the recovery time of the refined approach. It is shown as dependent on the exposure concentration for three water temperatures: −2 °C (a), 0.5 °C (b) and 3 °C (c). In nine panels the percentage of the population that is exposed is increased logarithmically from 1 to 99%, see header of each panel. The ratio R is also dependent on the duration of the exposure (denoted with lines with different markers). The left dashed vertical line resembles the NOEC (n = 1/ACR in TU) and the right dashed vertical line the LC50 for adult fish (L 4 = 1 in TU). The minimum recovery duration is one year. The ratio R is equal to 1 when the precautionary approach and the refined approach result in the same recovery duration, when R is greater than 1, recovery times calculated with the precautionary approach are longer than those calculated with the refined approach. The curves are not smooth, because the recovery durations (used to calculate R) are expressed as full years. At lower concentrations, lines corresponding to different exposure durations overlap, and only the line corresponding with the longest exposure duration is visible Christiansen et al. 2010;Geraudie et al. 2014;Bender 2015;Bender et al. 2016).
Obviously the extrapolated LC50 data we use is less informative than experimental species derived LC50 data. Such more realistic LC50 data, however, is not expected to affect our model results in a qualitative way, since the highest sensitivity of the population growth factor on daily survival of embryos and larvae is independent of toxicity data and both the precautionary and the refined approach are based on the same extrapolated LC50 data.
Given that the population growth factor was most sensitive to the daily survival of the embryos and larvae, we recommend to invest in relevant experiments in early live stages of polar cod. These experiments should focus on chronic exposure (duration at least 4 days, to allow steady state in toxicokinetics), and report the number of survivors over time as well as the LC50.
The lack of polar cod specific chronic LC50 data, combined with information on the number of survivors over time, deters a full toxicokinetics toxicodynamic (TKTD) approach such as the Dynamic Energy Budget approach exemplified in Atlantic cod Gadus morhua (Klok et al. 2014). As noted by Klok et al. (2014) the mechanistic DEB approach can help to reduce experimental effort, since DEB allows extrapolation of toxicity parameters out of the range of tested exposure durations, and allows extrapolation from one tested petroleum substance to others with the same mode of action (e.g. non polar narcotic). Moreover, DEB models have the added value that sublethal toxic effects such as reduction in growth of individuals can be extrapolated to the population level (Kooijman 2010;Klok and De Roos 1996). This seems particularly relevant for polar cod since available laboratory data already show reduction in length (which implies reduced growth over time) of larvae at NOEC concentrations ; Table S3.1).
Given limitation in available toxicant-induced mortality data, our current approach, using the ACR for fish, to parameterise the concentration-time-response-relationship and express exposure in TU (von der Ohe and De Zwart 2013), proved useful. A similar approach was also used by De Vries et al. (2018) to investigate the consequences of assumptions in oil spill assessment on population impact of Calanus species. For fish, the LC50 value, based on parent naphthalene concentration, was 30 µg L −1 for juvenile polar cod exposed to the water accommodated fraction of oil (Gardiner et al. 2013). Comparing this LC50 value with NOEC levels on larval survival (37 days exposure to a crude oil water-soluble fraction with a sum of 26 polycyclic aromatic hydrocarbons 2.18 μg L −1 )), would result in a ratio of 13.8. Because this ACR is close to the applied generic ACR of 12, our theoretical approach is supported. In addition, ratios of effect concentrations between fish life stages (Hutchinson et al. 1998) were considered acceptable for refinement of our hypothetical effect levels, although these ratios do not specifically apply to oil components.
It should also be noted that we did not account for the longer lifespan of females in our female-based model which can be inferred from the sex ratio of polar cod that approaches 100% females in cohorts with higher ages. This was not possible because the generic relation between weight and natural mortality (Online Resource 1, Lorenzen 1996) does not consider intraspecific and interspecific variation, and thus no differences between sexes.
An example of differences between sexes is that female fish have generally a lower ethoxyresorufin-O-deethylase (EROD) activity than male fish during spawning (Larsen et al. 1992;Arukwe and Goksøyr 1997). Because EROD activity points to potentially metabolizing toxic compounds (Sarkar et al. 2006), female fish might be more sensitive to contaminants than males. Luckily, the elasticity of the survival parameters of juveniles and adults is low in our model, but we might have underestimated the recovery times in the refined approach due to this. Although a linear model as we now have developed is not accounting for density dependence, the most obvious effect of compensatory growth in young fish is that they might grow faster and become mature earlier. If it was included in the model it would have occurred under both considered scenarios and the ratio R of the return times would not have been affected.
Reality check
The TU range applied in this study increases up to 1.1, which corresponded to an exposure concentration slightly above the LC50. Exposure above LC50 values can be realistic in field situations, especially directly after and/ or near the source of a spill (Table 4). TUs in field situations based on concentrations from actual, experimental and modelled spills (see Online Resource 3 for derivation of TU) ranged between <0.00003 and 19.11, with most values below 0.1 TU. Whereas the concentrations in the field usually form a gradient, here we only simulated a constant exposure concentration for each scenario. According to our modelling results this meant that for field situations, the predicted impact based on either approach (precautionary versus refined) would in most cases lead to comparable results in terms of recovery times. However, the precautionary approach results in impact estimates that are up to eight times as severe as those determined with the refined approach. This is especially the case when a larger part (more than half) of the population is exposed and when temperatures are relatively low. Hu and Wroblewski (2009) developed an age-structured Leslie matrix model of the larger Atlantic cod (Gadus morhua) and used three different values (0.2, 0.3, and 0.4 year −1 ) for natural mortality resulting in age-specific probabilities of survival for cod (ages ≥1 year) of 0.819, 0.741, and 0.670, respectively. Indeed, these yearly survival probabilities are much higher than those assumed for polar cod (and used within this study). Hu and Wroblewski (2009) acknowledged that, based on weight dependent mortality, theoretically, the natural mortality for northern Atlantic cod older than 1 year should be approximately 0.2 year −1 . This value is similar to an estimated mortality based on the longevity relationship (following Hoenig 1983, natural mortality of G. morhua is 1.83 year −1 , see Online Resource 1). The mean annual natural mortality rate estimated by Aanes et al. (2007) using an age-structured model based on catch-at-age data and abundance indices, ranged from <0.2 to >0.7 with a mean value of 0.36. This modelled value is higher than estimated using the longevity relationship. Aanes et al. (2007) report that the estimates are rather imprecise. The component accounting for the majority of the fluctuations in natural mortality was caused by year-to-year variability (Aanes et al. 2007). Bogstad et al. (1994) studied cannibalism using stomach content data and virtual population analysis for Atlantic cod (G. morhua). They found an average yearly mortality due to cannibalism and competition of 0.32-0.64 year −1 averaged over 2.33 years. Hu and Wroblewski (2009) also included higher rates to investigate the effect of high natural mortality (possibly due to predation) on cod population dynamics and found that the population growth factor for G. morhua is more affected by survival in all ages than by the fertility values. The analysis of the current model study showed that the same applies for polar cod, with the highest elasticity for the survival of embryos and larvae.
Comparison with model approaches for Atlantic cod
Of course commercial fishing also affects the population growth factor and therewith the population's recovery time after an oil spill (Reed et al. 1984). Therefore, effects of fishing and potential oil spills should preferably be evaluated jointly using the same basic principles (Carroll et al. 2018). Carroll et al (2018) simulated oil spills in the Atlantic cod (G. morhua) spawning grounds to assess potential impacts of oil spills on the cod fishery. Reductions in survival for pelagic stages of cod were up to a maximum of 43%, resulting in a decrease in adult cod biomass up to a maximum of 12%. The study suggests that the diverse age distribution helps protect the adult cod population (Carroll et al. 2018).
For polar cod, commercial fishing has been reported to peak in 1971 at 348 thousand tonnes, but after 1984 world catches declined steadily (FAO 2017). The total stock size of polar cod in that period is unknown, but it has been measured acoustically since 1986 and has fluctuated between 0.1 and 1.9 million tonnes from 1986(ICES 2014. In that period the greatest increase in abundance has been observed between 2003 (0.3 million tonnes) and 2004 (1.1 million tonnes), and it further increased up to 1.9 million tonnes in (McBride et al. 2016. The FAO reports that polar cod populations are little affected by fishing because r-selected species can support high levels of fishing mortality and have a short recovery time (FAO 2017).
Recovery times
The recovery times in our model were based on population growth rates under ideal environmental and ecological conditions. In reality, actual recovery times could be longer, e.g. due to higher actual mortality (such as high predation, fishing or extreme environmental conditions). Our results included the effect of temperature on the population recovery time by addressing the temperature dependence in embryo and larval development. A relatively high temperature corresponds to a relatively short hatching duration (i.e. larval development is temperature dependent (Sakurai et al. 1998)), which, in our model, resulted in a high hatching success due to the cumulative daily survival probability and consequent increase in population growth factor. These direct effects are relevant when considering short term, local variance of environmental conditions, but when discussing the effects of temperature in relation to climate change there are other aspects to consider. A warmer climate will lead to changes in phenotypic traits including earlier maturation, smaller size, increased investment in reproduction at early age and in sum a reduced fecundity , and to changes in the ecosystem (species presence, interactions, habitat and timing and quality of seasonal cycles). It could be argued that a short-term temperature increase up to 3 ˚C would enhance polar cod population growth (as indicated in this study based on the temperature-dependent hatching time) whereas prolonged exposure to elevated temperatures (i.e. global warming) will ultimately reduce population growth due to ecological cascading effects (as suggested by Nahrgang et al. 2014).
Consequences for oil spill response
Differences between the precautionary and refined approach for polar cod were not as large as found in an earlier study with Calanus (De Vries et al. 2018). Because not all exposed individuals die in the refined approach the recovery times are smaller than in the precautionary approach. That is the reason why the difference between the two approaches is largest for exposures of short duration and low toxic concentrations. The fact that the refined and precautionary approach give the same recovery times for cases where less than half of the population was exposed show that the precautionary approach is a good starting point for handling oil spills that do not potentially affect a large part of a population. Using either the precautionary or refined approach has consequences for assessing oil spill response options (i.e., NEBA). When considering multiple mitigating measures for oil spill response, multiple environmental compartments are assessed (water column, water surface and shore). Comparing results for these different compartments can only be sensible when similar approaches have been used. Only using the same level of conservatism for each compartment leads to results that can be compared. From a precautionary and pragmatic point of view, it is best to apply the precautionary approach, as it offers sufficient conservatism and requires less data. However, when weighing spill response options, a more realistic impact assessment can be advised.
For modelling a realistic impact assessment more information is required on temperature-dependent reproduction and survival of differently aged specimens of polar cod. With respect to toxicity tests in the laboratory additional knowledge is needed on how different life stages and sexes are affected by crude oil mixtures and different chemical substances. It is also important to be able to link external and internal concentrations in experimental settings. That way oil spill scenarios can focus on external concentrations.
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Domain: Environmental Science Biology
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Direct and indirect effects of climate change on soil microbial and soil microbial-plant interactions: What lies ahead?
. Global change is altering species distributions and thus interactions among organisms. Organisms live in concert with thousands of other species, some beneficial, some pathogenic, some which have little to no effect in complex communities. Since natural communities are composed of organisms with very different life history traits and dispersal ability it is unlikely they will all respond to climatic change in a similar way. Disjuncts in plant-pollinator and plant-herbivore interactions under global change have been relatively well described, but plant-soil microorganism and soil microbe-microbe relationships have received less attention. Since soil microorganisms regulate nutrient transformations, provide plants with nutrients, allow co-existence among neighbors, and control plant populations, changes in soil microorganism-plant interactions could have significant ramifications for plant community composition and ecosystem function. In this paper we explore how climatic change affects soil microbes and soil microbe-plant interactions directly and indirectly, discuss what we see as emerging and exciting questions and areas for future research, and discuss what ramifications changes in these interactions may have on the composition and function of ecosystems.
INTRODUCTION
Climatic change is altering species distributions and simultaneously impacting interactions among organisms (Wookey et al. 2009, van der Putten 2012). Natural communities are complex and composed of organisms with very different v www.esajournals.org life history traits, thermal tolerances, and dispersal ability. Further, interactions among community members can be beneficial, pathogenic, or have little to no functional impact and these interactions may change with environmental stress (Vandenkoornhuyse et al. 2015). Numerous studies show that shifts in species interactions in response to climate change cascade to alter biodiversity and the function of terrestrial ecosystems (Walther et al. 2002, Gottfried et al. 2012, Langley and Hungate 2014, but fewer studies focus on soil communities (Schimel et al. 2007, de Vries et al. 2012. Soil organisms interact with one another as well as with plants in a myriad of ways that shape and maintain ecosystem properties. In fact, soil microbial interactions, with each other as well as with plants, can shape landscape patterns of plant and animal abundance, diversity, and composition (Berg et al. 2010, van der Putten et al. 2013. Plant-microbial interactions are considered negative when the net effects of all soil organisms-including pathogens, symbiotic mutualists, and decomposersreduce plant performance, while interactions are considered positive when the benefits brought about by the soil community enhance plant performance such as biomass production and survival. Therefore, given their importance in defining ecosystem properties, understanding how soil microbe-microbe and soil microbe-plant interactions respond to climate change is a research priority that will shed light on important ecosystem functions such as soil carbon storage and net primary productivity (Ostle et al. 2009, Berg et al. 2010, Fischer et al. 2014).
The ;120 Gt yearly flux of carbon into and out of terrestrial ecosystems far exceeds the amount of carbon that is being produced by the combustion of fossil fuels (IPCC 2013). Thus, a small change in the amount of carbon an ecosystem exchanges with the atmosphere could have a large impact on future concentrations of atmospheric carbon. Ecosystem models, to date, have considerable uncertainty surrounding carbon feedbacks to the atmosphere from terrestrial ecosystems (Todd-Brown et al. 2012). Much experimental research has, therefore, focused on how to generate more reliable predictions of carbon fluxes with the goal of estimating how much carbon can be stored in terrestrial ecosystems. Soils, in combination with plant biomass, hold ;2.53 more carbon than the atmosphere (Singh et al. 2010). Soils have the capacity to retain large amounts of carbon and their ability to sequester carbon has helped to mitigate rising atmospheric [CO 2 ]. Several factors regulate the amount of carbon soils can sequester including climate, the parent material, the age and texture of the soil, the topography, the vegetation type, and the composition of the soil community (Jenny 1941). However, microbial decomposers ultimately regulate the rate limiting steps in the decomposition process and thus the influence of abiotic factors on decomposition. Yet, how microbial activity will influence carbon feedbacks among plants, soil, and the atmosphere is uncertain (Todd-Brown et al. 2012, Treseder et al. 2012, Verheijen et al. 2015. If the activity of the soil community, such as the decomposition rate, increases relative to inputs coming from plants and animals, then the amount of carbon in soil will decrease as carbon enters the atmosphere, which can amplify carbon-climate feedbacks (Zhou et al. 2009, Wieder et al. 2013. In addition to the direct control over the decomposition process, microbial communities can influence important plant properties such as productivity and litter quality (Harris et al. 1985, van der Heijden et al. 1998, properties that regulate fluxes in the carbon cycle. Clearly, microbial activity plays a large role in future terrestrial carbon feedbacks, however our current understanding of climate effects on microbemicrobe or plant-microbe interactions remains uncertain.
Here, we explore how climatic change affects soil microbe-microbe and plant-microbe interactions directly and indirectly as well as some of the ramifications shifts in these interactions may have for the composition and function of ecosystems (Figs. 1 and 2) We also explore some of the key questions that remain unanswered on this topic (Fig. 3). While the direct impacts of climatic change on microbial function have been well reviewed (Blankinship et al. 2011, Henry 2012, Manzoni et al. 2012, A'Bear et al. 2014, we argue that while the indirect effects via shifts in plant-soil microbe and soil microbe-microbe interactions are less acknowledged they have the potential to mediate important processes such as plant chemistry, plant community composition, and mineraliza-tion rates much like shifts in other ecological interactions alter important functions (Figs. 1 and 2) (Gilman et al. 2010, Adler et al. 2012, Steinauer et al. 2015.
DIRECT IMPACTS OF CLIMATIC CHANGE ON SOIL COMMUNITIES AND PLANTS
Climatic change alters the relative abundance and function of soil communities because soil community members differ in their physiology, temperature sensitivity, and growth rates (Castro et al. 2010, Gray et al. 2011, Briones et al. 2014, Delgado-Baquerizo et al. 2014, Whitaker et al. 2014. The direct effects of climatic change on microbial composition and function have been reviewed extensively (Blankinship et al. 2011, Henry 2012, Manzoni et al. 2012, A'Bear et al. 2014. Warming by 58C in a temperate forest, for example, altered the relative abundances of soil bacteria and increased the bacterial to fungal ratio of the community (DeAngelis et al. 2015). Microbial communities respond to warming and other perturbations through resistance, enabled by microbial trait plasticity, or resilience as the community returns to an initial composition after the stress has passed (Allison and Martiny 2008). Shifts in microbial community composition are likely to lead to changes in ecosystem function when soil organisms differ in their functional traits or control a rate-limiting or fate-controlling step (Schimel and Schaeffer 2012). For instance, specific microbial groups regulate ecosystem functions such as nitrogen fixation, nitrification (Isobe et al. 2011), denitrification (Bakken et al. 2012, Salles et al. 2012, and methanogenesis (Bodelier et al. 2000). Change in the relative abundance of organisms who regulate specific processes can have a direct impact on the rate of . The ecosystem-scale responses to the indirect effects of global change on community interactions (e.g., via changes in species distributions and/or traits) may be as large, or even larger, than the direct effects. Combined, the direct and indirect effects of global change on ecosystems may magnify, counterbalance, or reverse ecosystem carbon feedbacks to the atmosphere.
v www.esajournals.org that process. However, some processes that occur at a coarser scale, such as nitrogen mineralization, are more tightly correlated with abiotic factors such as temperature and moisture than microbial community composition because a diversity of organisms drives these processes (Hooper et al. 2005).
Global changes such as warming are directly altering microbial soil respiration rates because soil microorganisms, and the processes they mediate, are temperature sensitive. The role of elevated temperature in microbial metabolism has received considerable recent attention (e.g., Bradford 2013, Frey et al. 2013, Hagerty et al. 2014, Karhu et al. 2014. Given no changes in community composition, the intrinsic temperature sensitivity of microbial activity is defined as the factor by which microbial activity increases with a 108C increase in temperature (Q 10 ). Q 10 is often used in climate change models to account for microbial temperature sensitivity; however, using this relationship masks many of the interactions that influence the temperature sensitivity of microbial processes such as decomposition. Therefore, using only Q 10 to account for temperature sensitivity in models may lead to poor predictions. Further, while decomposition of soil organic matter, soil respiration, and growth of microbial biomass generally increase with temperature, these responses to experimental warming are often short-lived in field studies . The transitory effects of warming on soil communities have been hypothesized to occur as labile soil carbon substrates are depleted by increased microbial activity and because of trade-offs as microbial communities either acclimate, shift in composition, or constrain their biomass to respond to altered conditions and substrate availability Martiny 2008, Bradford 2013). Experimental warming can initially alter the composition of microbial communities, and shift the abundance v www.esajournals.org of gram-positive and gram-negative bacteria (Zogg et al. 1997), or warming effects may take many years before a response is evident within the microbial community (Rinnan et al. 2007(Rinnan et al. , 2013. Interestingly, results from field and lab studies often contradict one another (von Lü tzow and Kö gel-Knabner 2009) and both long-term field experiments (Sistla and Schimel 2013) and short-term laboratory tests ) of thermal compensation by microbial communities can support opposite conclusions. These contrasting results have left the evidence and mechanisms for thermal acclimation debated , Hartley et al. 2009, Bradford 2013. Clearly the direct effects of temperature on microbial physiology are complex and likely mediated by microbial adaptations, evolution, and interactions over time.
Temperature changes are often coupled with changes in soil moisture, which may explain some inconsistent results from experiments exploring how microbial communities respond to climatic change. For example, rates of microbial activity at warmer temperatures can be limited by diffusion and microbial contact with available substrate (Zak et al. 1999). While bacterial communities may respond rapidly to moisture pulses, the slower-growing fungal community may lag in their response (Bell et al. 2008, Cregger et al. 2012, Cregger et al. 2014. Further, drought amplifies the differential temperature sensitivity of fungal and bacterial groups (Briones et al. 2014). Even with small changes in soil moisture availability (,30% reduction in water holding capacity), soil fungal communities may shift from one dominant member to another while bacterial communities remain constant. These patterns indicate greater fungal than bacterial plasticity during non-extreme wet-dry cycles (Kaisermann et al. 2015). Soil communities adapted to low water availability or repeated wet-dry cycles may elicit less of a compositional or functional shift to changing water regimes (Evans et al. 2011). Interactions among microbes v www.esajournals.org and background temperature and moisture regimes in any given location influence microbial composition and function with changing climate. However, it is still unclear (1) how temperature and moisture, and their interaction, affect specific microbial functional groups, such as methanogens, within a community; (2) what effects microbial community changes have on functions like decomposition of new and old soil organic matter; and (3) which mechanisms drive the net ecosystem response of microbial activities to climate change. We recommend exploring these questions using factorial warming and community manipulations along gradients of temperature (such as elevation) or moisture. Similarly, another useful approach to explore these questions would be to use reciprocal transplants of plants and/or soils along environment gradients. This approach would couple changes in temperature and moisture in order to explore shifts in the microbial community from a functional perspective using PLFA methods (although this is a coarse approach, and more refined analysis would be desirable) and from an evolutionary perspective using phylogentic dissimilarity methods (e.g., Fierer et al. 2012). If this type of experimental design were performed in ecosystems where 13 C had been manipulated for several years (e.g., free-air carbon enrichment sites; see Norby and Zak 2011) then the effects on old (experimentally depleted 13 C) and new (higher 13 C of recent root and litter inputs) soil carbon dynamics could be teased apart.
Climate change impacts on plant-microbial interactions
With warming, plant species are migrating to higher elevations and latitudes (Grabherr et al. 1994, Walther et al. 2002, Parmesan and Yohe 2003, are leafing out and flowering earlier in the growing season (e.g., Cleland et al. 2007, Wolkovich et al. 2012, and are altering the expression of functional traits (Walker et al. 2006, Hudson et al. 2011, Verheijen et al. 2015. Scaling up to the community level, warming has resulted in shrubification of the arctic as woody shrubs have replaced grasses and forbs in several regions leading to changes in ecosystem properties and carbon feedbacks in these systems (Sturm et al. 2001, Hinzman et al. 2005, Lawrence and Swenson 2011, Pearson et al. 2013. Soil commu-nities, especially communities that are tightly coupled with plants, may be facilitating or retarding plant community transitions (Figs. 1 and 2). For example, root-associated microbial communities can have a strong influence on plant survival, phenology, and expression of functional traits ( Van der Heijden et al. 1998, Friesen et al. 2011, Wagner et al. 2014)-all traits that are responding to changes in climate. The consequences of interactions between plants, their associated microbial community, and climate change on ecosystem functions are still poorly understood ( Fig. 1; Fischer et al. 2014, Mohan et al. 2014. Shifts in the bulk soil microbial community induced by climate change may have long lasting effects on plant performance and/or establishment and soil carbon balance ( Fig. 1). In fact, the interactions between plants and soil communities, such as plant-soil feedbacks, are some of the most important, yet least understood, controllers of soil nitrogen and carbon dynamics. If climate change alters soil microbial communities and this change determines plant species establishment and growth, then ecosystem responses will be contingent on the interactions between plants and soil communities. Recent studies suggest that rapid responses of the surrounding soil community can buffer plants to drought stress (Lau and Lennon 2012). Mounting evidence suggests that changes in microbial diversity can alter selection on plant functional traits (e.g., Lau and Lennon 2011). The indirect impacts of climate on plants and their associated soil communities can differ significantly from the direct effects of climate on the bulk soil community (Kardol et al. 2010). For example, Kardol et al. (2010) found that changes in precipitation altered the soil community and its function in a TN (USA) oldfield, but the effect of precipitation on soil community composition and function varied by the plant the soil was collected from. Further, soils that were collected across the site and homogenized to assess the impact of climate change on communities and functions showed a relatively muted response. These results suggest that soil ecosystem responses to climate changes could be offset if plant community composition shifts with climate change. Therefore, these community and functional shifts may be underrepresented in most studies because soils are v www.esajournals.org collected across plant species and homogenized together (Kardol et al. 2010). Given the strong interaction between plants and their associated soil communities the effects of these interactions may build up in the soil system and impact ecosystem function (e.g., carbon cycling) and trajectory (e.g., plant establishment) over time; however, experiments need to be conducted to tease these interactions apart (Figs. 2 and 3).
Climate change alters plant and microbial distribution
While plant species migrations in response to climate change are well described (Grabherr et al. 1994, Walther et al. 2002, Parmesan and Yohe 2003 most studies fail to address the ability of associated soil micro-organisms to shift their range to maintain the positive or negative relationship between the plant and the soil community (van der Putten 2012). Soil biota may be poor dispersers, therefore they may respond to climate change at a different rate than plants (see van der Putten 2012). In fact, we know very little about the dispersal ability of microorganisms at the local community level or if shifts in dispersal ability translate into shifts in coarser-scale functions such as decomposition (Fig. 3). Nonetheless, we do know that differential dispersal abilities among plants and microbes can alter plant establishment and plant productivity as well as the interactions among plants in a community (Bever et al. 1997, Nuñez et al. 2009, Bever et al. 2010, for instance, via shifts in plant litter input quality. Some range expanding plant species are better defended against aboveground herbivores and/or develop less pathogenic activity in their soils compared to their related natives in the new range (Engelkes et al. 2008, Morriën et al. 2011). If plants that successfully establish in new ranges have higher induced levels of plant defense compounds such as polyphenols (Engelkes et al. 2008), then litter input quality will decline and the decomposer community will shift in composition or activity. While it is still relatively unknown how the disconnected geographic migration of plants and microbes will impact the adaptation and establishment of plants in new environments (Fig. 3), the plant invasion literature indicates that lack of ectomycorrhizal fungi can stop or slow pine invasions (Nuñez et al. 2009). Similarly, geographic disconnects might influence the composition and functioning of the microbial community, but this is also relatively unexplored (van der Putten 2012). Yet, we stress it is still largely unknown if microbial dispersal limitation could scale to impact coarse-scale processes such as decomposition and nutrient mineralization. The rate at which isolated microbial communities are able to adapt to climate change is mostly an unknown. Therefore, simple questions such as at what scale might microbial dispersal limitation begin to matter for ecosystem function and how quickly will microbes adapt to changing climate, still need to be answered (Fig. 3). Experiments using soil and plant reciprocal transplants across transitional areas such as tree lines or ecotonal boundaries might be one way to tackle these sorts of questions.
Relative to aboveground plant structures, soils are buffered to changes in temperature, precipitation, and possibly to extreme events like frost (Durán et al. 2014). Belowground communities are, therefore, structured by different environmental conditions than aboveground communities (Fierer and Jackson 2006) and are constrained by different life history characteristics. For this reason, the direct environmental pressures plants are experiencing under global climate change may be different from what their associated soil community is experiencing. Microbial communities clearly respond to both biotic and abiotic drivers, but the indirect effects of climate change, mediated by plant community shifts, may counteract or be different than the direct effects (Kardol et al. 2010). Soil communities may respond to climate stress by changing their distribution in the soil profile (Fig. 2). For example, they may move down in the soil profile if temperatures at the surface are outside of their thermal optima range. Re-sorting of soil communities, and thus interactions, in the soil profile may further alter plant-microbe-process interactions. However, to what degree a change in the direct and/or the indirect effects of climate change on microbe-microbe or plant-microbe interactions are relevant for ecosystem functioning is still unknown. Decoupling of plant and microbial community responses to the same environmental drivers using experimental (e.g., reciprocal transplants) and observational (e.g., elevational and environmental gradients) approaches should be a focus of future work (see Marshall et al. 2011, and Farrer et al. 2015 for examples of experiments exploring decoupling).
Climate change alters plant phenology, which alters microbial communities With warming, plant species are leafing out and flowering earlier in the growing season (e.g., Cleland et al. 2007, Wolkovich et al. 2012). However, climate change also impacts root phenology and subsequent plant-rhizosphere interactions, processes less explored in phenology studies (e.g., Iversen et al. 2015). If root growth peaks early in the growing season, aboveground and belowground phenologies can be synchronous (Scagel et al. 2007, Medvigy et al. 2009). However, because root and shoot phenologies are commonly asynchronous (Lahti et al. 2005, Willaume and Pages 2006, Palacio and Montserrat-Marti 2007, Steinaker and Wilson 2008, Abramoff and Finzi 2015, shoot phenology may not always be a reliable predictor of plant response to climate change. Root phenology varies by species and among ecosystems because it is driven by complex interactions between abiotic factors such as temperature and moisture as well as stored plant carbon and nutrients (Abramoff and Finzi 2015). Growing season durations are increasing under climate change (IPCC 2013). Therefore, successive peaks across the growing season in foliar, root, and mycorrhizal biomass may become further asynchronous leading to changes in nutrient and carbon fluxes ( Figs. 1 and 2). Variation in root-shoot phenology will impact rhizosphere interactions and may influence the distinct seasonal assemblages of soil microbial groups (Schadt et al. 2003, Waldrop and Firestone 2006, Dumbrell et al. 2010. If the relationship between photoperiod, temperature, and soil moisture becomes asycnchronous with climate change, then root, shoot, and microbial phenologies may also become asynchronous (Figs. 1 and 2). These interactions may drive plant community shifts and affect ecosystem productivity under climate change.
Climate change impacts on fine-scale plant-microbe interactions may alter plant traits
Bacteria and fungi often have close associations with plant roots (Bais et al. 2006). These associations can alter the expression of plant traits such as leaf area and nutrient content (Harris et al. 1985, Bishop et al. 2011, Friesen et al. 2011. Root symbionts such as rhizobia bacteria (de Bello et al. 2010) and mycorrhizal fungi (Johnson et al. 1997) affect plant productivity by altering plant nutrient status. The impact of specific strains of rhizobia on other plant traits may be equally important under global change. For example, when a common rhizobia strain was present in nitrogen-fixing mutualisms, plant specific leaf area and carbon assimilation rate increased (Harris et al. 1985). Mycorrhizal fungi associate with nearly all landplants (Brundrett 2002) and are important players in carbon and nutrient-cycling processes (van der Heijden et al. 1998, Read andPerez-Moreno 2003). Similar to rhizobia, mycorrhizal fungi exchange nutrients for plant carbon, thus influencing plant carbon to nutrient ratios and subsequently plant productivity (Smith and Read 2008). Consequently, mycorrhizal fungi affect decomposition activity within the soil microbial community by altering plant litter quality as well as carbon inputs (Clemmensen et al. 2013, Moore et al. 2015. Specific mycorrhizal strains can alter plant reproduction (Streitwolf-Engel et al. 2001), tiller production, root biomass production, rooting depth (Ellis et al. 1985), and herbivory rates Bever 2007, Roger et al. 2013). However, the interactions between mycorrhizal strain identity and plant host are not always symbiotic and can change with environmental factors or even plant stress (Johnson et al. 1997, Treseder 2004. Further, mycorrhizal community composition can change with climatic factors such as temperature (Deslippe and Simard 2011). Two current knowledge gaps are: (1) how climate change might alter the direction of plant-root microbe interactions from positive to negative or vice versa (e.g., Soussana and Hartwig 1996); and (2) whether climatic change will alter interactions between plants and their myriad of symbionts in tandem, possibly having additive effects on ecosystem function (e.g., ).
Climate change influence on microbe-microbe interactions?
Microbes form complex networks of interactions that are continually responding to changes in resources. For example, mycorrhizal fungi foraging can alter free-living bacterial communities in ways that vary nitrogen transfer from the mycorrhizae to the plant (Nuccio et al. 2013) as well as decomposition of organic matter (Clemmensen et al. 2013, Leifheit et al. 2015, Moore et al. 2015. Rising temperatures lead to increased carbon allocation to mycorrhizal hyphae, which may swing the mycorrhizal association from symbiotic to parasitic (Hawkes et al. 2008). Shifts in these mycorrhizal-plant interactions can cascade to alter the soil microbial composition (de Boer et al. 2005, Nazir et al. 2010) and activity (Leifheit et al. 2015; J. A. M. Moore et al., unpublished manuscript) in ways that may exacerbate the negative or positive interaction between the plant and its associated community. Other interactions among bacteria and fungi in the freeliving community are likely to modify ecosystem functions and carbon feedbacks, but this has been less explored.
ARE WE ASKING QUESTIONS AT THE CORRECT SPATIAL AND TEMPORAL SCALES?
Current challenges in soil ecology involve how to ask and answer questions at the appropriate temporal and spatial scales (Fig. 3). Microbial organisms and communities live and interact in relatively small soil structures, have short life spans, and the majority of individuals can be dormant at any point in time. Further, it is difficult to determine the abundance of active groups in the community and extrapolation of experimental results has been difficult because experiments are often established only at single sites and experimental designs are generally not replicated across ecosystems that vary in vegetation type, soil type, or background climate. In spite of these concerns, scientists have broadly applied plant-and ecosystem-centric measurements to studies of microbial communities and processes in field based systems. This approach may lead us to under-predict how microbemicrobe and microbe-plant interactions will shape ecosystem response to climate change.
Scaling plant-microbe interactions across space
Soil communities are diverse and perform a diversity of functions Whitman 2005, Fierer et al. 2012). Although changes in microbial diversity at a fine scale may not alter certain ecosystem processes, they may at a coarse scale. Soil microbial communities are primarily composed of fungal and bacterial groups and these groups have different functions in the decomposition process. In general, bacterial decomposition pathways quickly decompose labile substrates, while slower fungal-dominated decomposition pathways target more complex organic materials (de Boer et al. 2005). Soil microbes, particularly fungi, play pivotal roles in altering soil structure (e.g., aggregate formation), which can alter carbon processes (Six et al. 2006, Leifheit et al. 2015. Changes in the composition (and consequently function) of soil bacteria and fungi are thus expected to affect soil carbon storage (Moore et al. 2015).
Microbial communities operate at very small spatial and time scales, where entire communities may be limited to a single soil particle or soil aggregate and can turn over under an hour (Sessitsch et al. 2001, Gonzalez et al. 2012). However, scientists measure microbial communities across meta-communities (a single soil core), at the individual plant-level (comprising an estimated 10 6 -10 8 microbes), or the ecosystem level. Thus, scaling microbial interactions and processes from the cell or soil particle to the ecosystem is a significant challenge. The soil matrix is a highly heterogeneous environment and there can be nutrient and moisture oases that promote hotspots of microbial activity and potentially select for microbes that can take advantage of those resources. Soil aggregates, particles bound together by biological residues, can protect microbes from extreme conditions in the external environment and contain water and nutritional resources (Six et al. 2006, Bach andHofmockel 2014). Further, microbial functions, such as enzyme production and substrate availability, can vary through the soil matrix (Šnajdr et al. 2008). Variation in microbial community composition through the soil profile can affect microbial interactions with plant roots (Hoeksema et al. 2010, Johnson et al. 2015, such as competition for nitrogen (Hodge et al. 2000, v www.esajournals.org Bardgett et al. 2003). Ecosystem processes emerging from interactions between plants and microbial communities differ when plants interact with different soil communities (van der Heijden et al. 1998, 2008, Kardol et al. 2014). Since soil microbial communities vary across small spatial scales, their processes need to be first scaled up to the level of the plant. This is probably a function of soil resource availability, pH, physical soil properties such as texture and bulk density, and salinity/cation exchange capacity. Observational studies across gradients of these factors that document shifts in microbial communities with and without plants are needed to identify important abiotic factors regulating small-scale microbial beta-diversity. Then the whole plantmicrobe interactions need to be scaled up to the level of the ecosystem, which is likely a function of plant diversity and climate. Additionally, because microbial communities influence the expression of plant traits, shifts in plant-associated microbial communities can be scaled to ecosystem properties through the measurement of plant functional traits and the large body of literature linking plant traits to ecosystem function (Chapin et al. 1993, Grime 1998, Díaz and Cabido 2001. We suggest using elevational gradients of climate or plant diversity gradients to test how plant-microbe interactions vary across space and time. Temperature and the composition of plant communities vary systematically along elevational gradients and, when coupled with experimental warming and species manipulation treatments, they are powerful tools to explore how short-and long-term changes in temperature alter biodiversity, species interactions, and the carbon cycle (Sundqvist et al. 2013). Further, elevational gradients often have a large amount of micro-climatic variation making them excellent test systems (Scherrer and Kö rner 2010). While the variation across a landscape is vast, it can represent a short snapshot in time. Understanding how plants and microbes will respond to climate over longer time scales that encompass non-overlapping drivers and exploring if there are mismatches where the response of soil communities and plants diverge still remains challenging. Experiments should, therefore, include both a spatial element (e.g., using an elevational gradient) and temporal element (e.g., sampling over many seasons and years and/or using space for time substitutions).
Scaling plant-microbe interactions through time, from the plant's perspective
Interactions between plant hosts and soil microbial communities shift through time and scaling these interactions to longer time scales remains a challenge from both a plant and a microbe perspective. Plant hosts can live on a landscape from years to centuries while communities living around and in their roots exist anywhere from hours to months (Gonzalez et al. 2012. Thus, the factors shaping the stability of these two communities may differentially shift over time. Most of our understanding on the stability and functioning of plant-rhizosphere interactions comes from rather short-term studies, where 2-3 growing seasons is considered long-term (Bardgett et al. 2005). Yet, from these relatively short studies we learned that plants can select against less beneficial root symbionts (both rhizobia and mycorrhizal fungi ) by altering carbon allocation patterns towards beneficial partners, thus promoting the long-term stability of plant interactions with beneficial strains (Kiers et al. 2003, Bever et al. 2009). These shortterm studies predict specialization of mutualisms over time, in this case the maintenance of only beneficial symbionts. However, natural plant symbiont populations are multi-functional (Smith and Read 2008), and which nutrient acquisition is only a single function occurring with other functions such as protecting the host plant against abiotic (Martínez-García et al. 2015, Zuccarini andSavé 2015) and biotic stressors (Allen et al. 1989, Bennett et al. 2006, Roger et al. 2013. Therefore, overall plant health may increase with a diverse consortium of mycorrhizae or rhizobia strains, even though in the short-term it may be carbon costly to the plant host (van der Heijden et al. 1998, Verbruggen and. Changes in host plant ontogeny and shifts in resource need over a plant lifetime may drive long-term dynamics of plant-mycorrhizal interactions. While less explored in plant-soil interactions, this principle is demonstrated in ant-acacia networks where the continuum from positive to negative interactions shifted over the host-plant lifespan . Mycorrhizal strains have a variety of impacts on their plant-host including increased leaf quality (e.g., Smith and Read 2008) or herbivore defense (Bennett et al. 2006, Bennett and Bever 2007, Gehring and Bennett 2009, Roger et al. 2013. If a plant associates with a mycorrhizal partner that increases leaf quality but not herbivore resistance early in their lifespan, then they could be more susceptible to herbivory later in their lifespan. Therefore, tradeoffs may exist in short-versus long-term benefits of microbial association for the host plant and may explain the diverse assemblages of mycorrhizal fungi observed in natural communities (Dumbrell et al. 2010, Davison et al. 2011, although this remains untested (Fig. 3).
Scaling plant-microbe interactions through time, from the microbe's perspective
Microbial populations can evolve on short time scales, thus shifting plant-microbe interactions quickly and altering how we scale ecosystem processes up to longer time periods (Chave 2013). Microbes can be active, potentially active, and dormant at any point in time and these states can shape the responsiveness of the ecosystem to perturbations (Blagodatskaya and Kuzyakov 2013). Even at the scale of a plant's growing season, microbial communities shift in their composition (e.g., Smit et al. 2001, Cregger et al. 2012. In fact, microbial communities can vary more across seasons than in response to longterm (.10 y) climate manipulations (Yuste et al. 2014). Therefore seasonal variation in microbial communities could be more important than interannual variation for ecosystem carbon fluxes. In a 6-year precipitation manipulation study, background microbial variation was consistently higher than variation due to altered precipitation (Gutknecht et al. 2012). Microbial adaptation and acclimation are often cited as explanations for why climate manipulations have no effect at long time scales . Quick adaptation of microbial communities may allow for survival of plant species to contemporary climate change, as the longer generation times of plant host lag behind the highly dynamic microbial communities (Lau and Lennon 2012). Evolution may swamp long-term effects of climate and background microbial community variation may overwhelm climate treatments on seasonal timescales. Capturing plant-microbe interactions through time will require sampling at the appropriate time scale. A good starting point may be matching sampling time points with generation time of the target microbial groups. Moreover, data are often collected using different methods and at different time steps. Facing these challenges, we should be continuously asking whether we are measuring microbial processes at relevant spatial and temporal scales and identifying and counting the relevant members of the community.
EMERGING TECHNOLOGIES ADVANCE OUR UNDERSTANDING OF PLANT-MICROBE RESPONSES TO CLIMATIC CHANGE
Traditionally, studies aimed at understanding microbial dynamics have used methods such as PLFA (phospholipid fatty acid analysis), TRFLP (terminal restriction fragment length polymorphism), DGGE (denaturing gel gradient electrophoresis), or simply measures of biomass to understand complex community dynamics and function. While these methods have exposed patterns of microbial community composition at a coarse level (e.g., Gray et al. 2011), they do not show individual taxa responses and give limited insight into functional shifts. With the advent of new sequencing technologies and in the wake of the -omics revolution, researchers have begun to explore microbial interactions with hosts at a higher resolution and with more functional significance. By using meta-genomics, -transcriptomics, -proteomics, and -metabolomics, scientists are able to define changes in microbial communities that will result in a better understanding of which microbes are present in an ecosystem and what their potential functions are (e.g., Castro et al. 2012, Muller et al. 2013). Further, with technologies such as stable isotope probing, it is possible to target the active microbial community involved in a myriad of functions (e.g., Mau et al. 2015).
As the use of these techniques is increasing, researchers are left with many questions about which techniques yield the most accurate results, and further, what is the most informative and accurate way to analyze these extremely large datasets. Currently, amplicon sequencing of the 16s rRNA gene has become commonplace to characterize bacterial community composition in v www.esajournals.org ecosystems (Sanschagrin and Yergeau 2014). This yields large amounts of data at a depth that has started saturating species accumulation curves, but gives little to no information on potential functional shifts in communities (Fierer et al. 2012). Because of this, some have started using shotgun metagenomics to understand both microbial community composition and functional potential by assessing the diversity of functional genes within a habitat. Although this method yields data relevant to potential function, it lacks the depth associated with amplicon sequencing, so rare taxa may be overlooked (Shade et al. 2014, Lynch and Neufeld 2015, Zhou et al. 2015. With the rise of numerous new technologies aimed at understanding microbial dynamics in soils, it is important to begin sampling microbial communities at a scale that is relevant to the diversity and or function of these tiny organisms. Due to vast heterogeneity within a soil core, it may be difficult to deduce meaningful diversity patterns about these communities at such a coarse spatial scale (Ranjard et al. 2003). Microorganisms interact at the soil aggregate scale, with considerable variability observed across soil aggregates, or at the scale of the plant root-soil interface (Lombard et al. 2011). To truly begin understanding how microorganism interact with each other and plant hosts, future studies should take the questions they are asking about diversity and or function into account and adequately adjust their sampling scheme.
Beyond issues of what technologies to use to best study microbial communities, is the issue of how to analyze these large datasets (Zhou et al. 2015). An abundance of programs exist today to help with processing and analysis like qiime (Caporaso et al. 2010), mothur (Schloss et al. 2009), or less well known programs like IM-TORNADO (Jeraldo et al. 2014) using a number of diverse taxonomic databases to assign taxa identity. Use of each of these processing pipelines and assigning taxonomy with the different databases may yield different results with any given dataset. Researchers need to begin comparing these methods, and developing a standard protocol, so datasets can be compared across laboratories and research groups. Specifically, researchers need to explore which processing pipeline yields the most relevant results in a timely fashion, which database has the most up-to-date and accurate taxonomic information for the taxa of interest, and how to standardize analyses across research groups to glean the most information out of a given dataset.
Another area where technological advances are increasing our understanding of the molecular basis for plant-microbial interactions are at the plant root-soil interface, where microorganisms are abundant and interact intimately with plant roots (e.g., Hol et al. 2013). Subsets of these soil microbes infiltrate the plant root and colonize the interior spaces; yet, how these microorganisms invade the plant root is difficult to tease apart. What molecular signaling occurs that allows microbes to evade the innate immune system of the plant and actively infiltrate the plant root? By using new sequencing technologies which allow cost efficient, rapid sequencing of full organismal genomes, we are beginning to piece together the molecular basis for these interactions. In the case of the mutualistic relationship between the ectomycorrhizal fungus Laccaria and its plant host, studies have shown that the Laccaria genome harbors unexpected features like effector type small-secreted proteins with unknown functions that are only expressed in symbiotic tissues (Martin et al. 2008). Further, one plant host, Populus, carries whole gene deletions for the D-mannose lectin-like receptor that significantly reduces Laccaria colonization (Labbé et al. 2011). Understanding the molecular basis for these interactions will enable manipulation of the microbial community to enhance plant and ecosystem level functions. It will also allow researchers to begin making predictions about which microbes will be present in the plant root endosphere and start building microbial communities capable of enhancing plant growth, carbon allocation, and carbon storage.
INTERACTIONS THAT INFLUENCE TIPPING POINTS IN SYSTEMS (STABILITY-EXTREME EVENTS)
Major disturbance events, such as heat waves, droughts, frosts, fire, and storms are increasing with global change (Frich et al. 2002). These events can alter large-scale processes such as regional net primary productivity (Ciais et al. 2005, Gu et al. 2008) as well as soil physical and biotic properties (Ajwa et al. 1999, Certini 2005, Yuste et al. 2014. The stability of an ecosystem after disturbance depends on factors such as previous exposure to disturbance as well as community composition and diversity (Banning andMurphy 2008, Wardle andJonsson 2014). Similar to aboveground (Maestre et al. 2012), there is growing evidence that belowground diversity is an important component of ecosystem stability and multifunctionality (Lefcheck et al. 2015;X. Jing et al., unpublished manuscript). For example, belowground arbuscular mycorrhizal fungal diversity can influence aboveground plant diversity, percent plant cover, soil aggregation, soil moisture, and soil carbon and nitrogen sequestration (Van der Heijden et al. 1998, 2008, Wilson et al. 2009, Yang et al. 2014. Further, when under stress diverse bacterial communities have a more stable biomass because they contain a larger number of organisms that are tolerant to stressors (Awasthi et al. 2014). Similarly, when microbial stress was reduced (e.g., higher soil carbon and nitrogen availability) across 58 studies spanning numerous ecosystems, microbial biomass turnover was slower suggesting a stabilizing effect on ecosystem functions over time (Wardle 1998). With anthropogenic induced changes to precipitation regimes, including increased storm severity and intensity influencing species gains and losses, understanding how belowground community stability may influence aboveground community response to disturbance is key for predicting how ecosystems may respond to global change (van der Heijden et al. 1998, Rillig et al. 2002, Brussaard et al. 2007, Wilson et al. 2009). While extreme events may only happen periodically, they will likely have long-lasting impacts on plant-microbe and microbe-microbe interactions and processes.
CONCLUSION
Interactions among plant and soil communities may be unpredictable when observing their responses to natural fluctuations in climate or at a single time point (Fig. 2). Due to the temperature sensitivity of carbon cycling processes, small changes in temperature could result in a large release of soil carbon back to the atmosphere (Fig. 1). Plant-derived carbon inputs strongly mediate the temperature sensitivity of soil carbon decomposition, but the relative importance of direct versus indirect effects of climate change on soil carbon dynamics remains unresolved especially in ecosystems that are in transition from one state to another. We posit that the indirect effects of climate change on microbes mediated through plants may be stronger than direct effects of climate on shaping microbial community composition and function. These effects, however, must be measured at appropriate temporal and spatial scales, ideally in microbe-centric studies in order to complement the existing landscape of plant-centric climate change studies. Novel technological approaches will be pivotal in microbe-centric studies as we aim to reveal those taxa most sensitive to climate and those whose responses lead to shifts in microbial community function. Overall, these advances will be critical for making predictions about ecosystem tipping points, effects of extreme events, and the stability of communities under climate change. In sum, if we are to understand whether climate influenced shifts in microbe-microbe and plant-microbe interactions are equal or greater than the direct effects of climate change on the composition and function of ecosystems, we need to determine the best approaches to observe, quantify, and scale these interactions. Combinations of observations along natural gradients, with manipulations and experimental testing as well as modeling of plant and soil microbial communities and their interactions in response to climate change drivers is necessary to predict future ecosystem function.
ACKNOWLEDGMENTS
This work was supported, in part, by the U. S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Sciences Program under Award Number DE-SC0010562. Many thanks to Charlie Kwit for suggesting we take this on. The authors would like to thank two anonymous reviewers for very helpful comments on a previous version of this manuscript.
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Domain: Environmental Science Biology
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Improved method for triacylglycerol-derived fatty acid profiling by various non-lethal and lethal sampling techniques in Atlantic salmon
The current paper compares the use of adipose fin and muscle biopsies as non-lethal sampling procedures, and the suitability of triacylglycerol (TAG) versus total lipid (TOT) fatty acid composition (fatty acid methyl esters, FAME) to estimate dietary history in farmed and wild maturing Atlantic salmon Salmo salar. TAG FAME gives best resemblance to dietary history. Fatty acid composition differs between tissues, and sample series should preferably be from 1 tissue only. TOT FAME supplies informative data on dietary history in fatty tissues, but differs from TAG FAME when total lipid levels are low. The reason is a larger contribution of phospholipid FAME. In wild maturing salmon, lipid content in adipose fin and muscle is low. TAG provides good data on dietary history, while TOT is less informative. Escapees are well identified analysing 18:2n-6 and 18:3n-3 fatty acids, which are high in commercial feed. For estimating a wider range of dietary history after escape (switch to wild prey), or feed preferences in wild fish, other fatty acids have to be taken into account. This requires the use of multivariate methods, like principal component analysis. Adipose fin and muscle biopsies are good alternatives for non-lethal sampling using the TAG method. The minimum amounts of samples to be used are proposed to be 0.5 to 1 g of adipose fin and 60 to 100 mg of muscle. The method of storage (liquid nitrogen/dry ice) does not affect fatty acid compositions. Other tissues can also be used for fatty acid profiling. Snout is a good alternative, being lipid-rich even in wild fish with low muscle lipid.
INTRODUCTION
With the rapid expansion in farming of Atlantic salmon Salmo salar over the past 50 yr, escapes from cages have become a serious problem, both for the fish-farming industry and for the conservation of wild stocks. The official figures of salmon escaping from fish farms in Norway show a variable trend ranging from 38 000 to 921 000 during the period from 2001 to 2012 (Directorate of Fisheries; www.fiskeridir.no). However, these figures are considered lower than the actual number of fish escaping (Baarøy et al. 2004, Skilbrei & Wennevik 2006). Escapees may spread diseases and parasites (Heuch & Mo 2001, Skilbrei et al. 2010). Some may also enter rivers to spawn (Saegrov et al. 1997). Genetic changes have been demonstrated in wild salmon populations as a result of interbreeding between farmed escaped salmon and wild conspecifics (Crozier 1993, Clifford et al. 1998, Glover et al. 2013).
Fish may escape during all stages of the production cycle, from hatcheries in freshwater and from netpens in seawater (Jensen et al. 2010). The spawning behaviour of salmon that escape at an early life stage is relatively similar to that of wild fish, while being very different from salmon escaping as adults (Fleming et al. 1997). Fish that escape from sea net-pens during their first spring/summer migrate to the open ocean and return to spawn after 1 to 3 yr (Skilbrei 2010a,b). The time of escape therefore influences the risk of introgression of escaped cultured with wild salmon populations.
Knowledge about the escape history of salmon in rivers is important for risk assessments and also to develop efficient strategies to reduce the impact of escapements. Molecular genetic methods have been successfully implemented to identify the farm of origin for farmed Atlantic salmon captured shortly after their escape (Glover et al. 2008, Glover 2010). Furthermore, such methods have been adapted to assist the identification of farmed and wild salmon in the wild (Karlsson et al. 2011) and to reveal the mixture of escapees from multiple farms in the same region (Zhang et al. 2013). However, molecular genetic markers are not able to answer other questions linked to escapees, e.g.how long the escapees have been in the wild and if they have started to feed on wild prey. In order to address these and other ecological issues, other types of markers are required.
One such approach is the analysis of fatty acid profiles. Over the past decade, there has been a general switch towards higher inclusions of terrestrial type feed ingredients, including vegetable oils, in Atlantic salmon farming. There is also increasing interest in other terrestrial lipid sources such as poultry fat. The fatty acid profiles of these diets will differ significantly from the normal diet of wild Atlantic salmon in the marine environment. For example, vegetable lipids used in current salmon feeds are low in the typical marine polyunsaturated fatty acids (PUFA) 20:5n-3 and 22:6n-3 and high in shorter chain PUFAs like 18:2n-6 and 18:3n-3. There are also highly variable amounts of fatty acids like 18:1n-9 and 16:0, depending on type of oils used. As tissue fatty acid compositions in fish are, to a large extent, governed by the content of the diet (Henderson & Tocher 1987, Bell et al. 2001, Olsen et al. 2003, 2004, Torstensen et al. 2005), quantifying tissue lipids can enable the identification of escaped fish and give good estimates of feeding history and how long they have been eating natural prey after their escape.
Fatty acid profiling methods are already in use and have been used, e.g., to distinguish farmed from wild salmon (Molkentin et al. 2007, Axelson et al. 2009, Megdal et al. 2009) and the feeding patterns of horse mackerel Trachurus mediterraneus around sea cages (Fernandez-Jover et al. 2007). The application of the method varies between studies, which may affect interpretation of the data. Most commonly, authors analyse the total lipid (TOT) fatty acid compositions. However, such analysis includes both membrane lipids (phospholipids, PL) and depot lipids (triacylglycerols, TAG), whereby the relative contribution ranges from mostly PL in very lean fish to mostly TAG in very fat fish. The challenge in using TOT analysis is that the metabolic pathways of synthesis differ significantly between the 2 lipid classes. PL synthesis is under strict metabolic control (Henderson & Tocher 1987, Olsen et al. 1991). In wild fish PL fatty acids are dominated by PUFAs (generally > 50%, e.g.eicosapentaenoic acid [EPA] and docosahexaenoic acid [DHA]) in addition to 16:0, 18:0 and 18:1n-9. Other monoenes and PUFAs are generally very low. Changing diets has a limited effect on phospholipid fatty acid composition (Olsen et al. 2003). The fatty acid composition of depot TAG, on the other hand, mirrors the diet (Henderson & Tocher 1987, Olsen et al. 1991, Corraze & Kaushik 1999). Consequently, in lean tissues, applying total fatty acid analysis causes the PL fatty acids to dominate profiles, and does not give a good estimate of dietary history. In fatty tissues on the other hand, TAG contributes > 80% of the fatty acids and may thus give a good estimate of dietary history. However, lipid content in tissues may vary considerably, particularly in maturing Atlantic salmon that are anorectic before spawning and also invest considerable energy into gonad maturation (Aksnes et al. 1986, Kadri et al. 1996). Analysing TAG only may, in these cases, be advisory, and recent studies have shown a very good correlation between dietary fatty acids and muscle TAG composition in Atlantic salmon during simulated escapes from net-pens (Olsen & Skilbrei 2010).
The tissues available for study may also vary depending on availability. In some cases, fish are killed, and it is possible to sample ample amounts of the desired tissue for analysis. For example, salmon may be tagged with a coded wire tag in the snout in re lease experiments and marking programmes (Hand et al. 2010), and heads or snouts of fish are collected from the salmon fishery for tag identification. Furthermore, there is also an increased demand in fish surveys for fish to be kept alive and returned quickly to the river after the sample has been taken. Non-lethal sampling is essential in these cases. Examples of such sampling targets include scales (Grahl-Nielsen & Glover 2010), adipose fin, mucus, or muscle-biopsies. To ensure minimum damage to the fish, sample volumes need to be as small as possible. To address these questions, the current paper aims to (1) compare the ability of tissue TOT and TAG fatty acid profiles to describe diet history and (2) explore the suitability of adipose fin and small muscle biopsies as candidates for fatty acid profiling in maturing Atlantic salmon.
Sampling 1-Farmed salmon
Farmed Atlantic salmon Salmo salar (1.5 kg average body weight) in 5 × 5m sea cages were fed a standard commercial diet (Skretting No. 9) until achieving a weight of 6.6 ± 0.4 kg, at approximately 83.9 ± 2.2 cm fork length, mean ± SEM, n = 15). The samples were collected in November during the spawning season (sexually mature fish), as this would be comparable to newly escaped salmon entering rivers for spawning. The fish were collected from the net-pen at sea and killed by percussion. Biopsy samples were immediately collected on the back side, posterior to the dorsal fin, using Osteo-RAM biopsy needles (7G×100mm; RI. MOS s.r.l). The samples were then released into cryo-tubes and snap-frozen in liquid nitrogen, or put on dry ice. Control muscle samples were directly cut from the same fish and flash-frozen in liquid nitrogen. All samples were then stored at −80°C prior to analysis. The biopsy samples were in the range from 40 to 100 mg, muscle samples were in the range from 400 to 900 mg, and adipose fin samples ranged from 300 to 600 mg. As an initial screening test, we chose 1 fish by random and analysed TAG samples from skin mucus, peritoneal adipose tissue, roe, tongue, muscle, liver and adipose fin.
The samples were then used for (1) comparing the fatty acid profiles of TOT and TAG of muscle and adipose fin and (2) comparing different freezing methods for biopsy samplings in the field. The latter was done to assess the quality of biopsy material obtained using (a) liquid nitrogen (flash frozen) or (b) frozen in dry ice and then compared with large muscle pieces flash-frozen in liquid nitrogen as controls.
Sampling 2-Wild salmon
Nine wild salmon were angled in River Dale (location; 60.653°N, 5.809°E) during autumn 2011 and kept in a 3 m diameter fiberglass tank supplied with running water until 20 November, when they were killed and tissue samples from snout, muscle and adipose fin were taken. Mean fork length (± SEM) was 72 ± 4.0 cm. They were not fed during this period.
Analysis
TOT was extracted according to Folch et al. (1957). The lipid classes were quantified using 10 × 10 cm HPTLC plates (Merck) and the double development system of Olsen & Henderson (1989). TAG was separated using hexane:diethyl ether:acetic acid (80:20:2) as developing solvent (Olsen & Henderson 1989), visualised using 0.2% 2', 7'-dichlorofluorescin in 95% ethanol spray and viewed under ultraviolet-light for detection. The TAG was scraped off the plate and subjected to the sulphuric acid catalysed transesterification of Christie (1982), extracted into hexane and stored at −80°C until analysed. Quantitative analysis of fatty acid methyl esters (FAME) was carried out by gas liquid chromatography using a HP 5890 gas chromatograph, autoinjector and splittless injection. The column used was a J&N Scientific Inc. DB-23 fused silica column (30 m × 0.25 mm i.d.). The oven temperature was programmed to rise from 50 to 170°C at 25°C min −1 , and then to 210°C at 1.5°C min −1 , with a final holding time of 5 min. Separated components were identified with reference to authentic standards.
Statistics
Data were analysed statistically with SPSS Ver.21.0 for personal computers. Prior to statistical testing, all data were checked for homogeneity of variances by the Levene test, and, where necessary, they were subjected to arcsine transformations. Numerical results are expressed as means (± SEM) unless otherwise stated. Data from different tissues were compared using 1-or 2-way ANOVA, where appropriate, and subjected to Tukey's multiple range test. The level of statistical significance was set at p < 0.05. Sirius Version 9.0©, Pattern Recognition Systems (www.prs.no), was used for principal component analysis (PCA).
RESULTS
The lipid content and lipid class composition varied between the farmed (Sampling 1) and wild Salmo salar (Sampling 2) and between the tissue samples (Table 1). Muscle from maturing fish held in a sea cage at Matre was high in lipid (32.8 ± 1.6%). Using biopsies tended to lower the lipid content of the samples somewhat. The adipose fin was lean, with only 1.1 ± 1.6% lipid of wet weight. The lipid class compo-sition in muscle was dominated by TAG, comprising 91.8 ± 0.5% of the lipid with only 4.4 ± 0.7% being phospholipids. The adipose fin had 19.8 ± 5.6% as TAG and 45.2 ± 6.0% as phospholipids, with the remaining lipid being mainly sterol esters/wax esters and cholesterol (data not shown). Maturing wild fish returning to rivers to spawn had low muscle lipid levels (5.0 ± 1.0%). Adipose fin lipid was again < 2%, while snout had a relatively high lipid content (10.3 ± 1.0%). The TAG level was clearly related to tissue lipid and was highest in snout lipid (92.3 ± 4.1% of lipid), lower in muscle (83.8 ± 2.2% of lipid) and lowest in adipose fin (19.9 ± 5.5%). Total polar lipids were 4.4 ± 2.7%, 13.7 ± 2.2% and 45.2 ± 6.0% in the respective tissues.
The FAMEs of snout, muscle and adipose fin of wild maturing Atlantic salmon returning to the Dale River showed the same pattern of change as in the TOT and TAG levels of the farmed fish (Table 3). In particular, in TOT, there was a general increase in 16:0, 18:0, 20:5n-3 and 22:6n-3 compared to TAG and a reduction in 18:1n-9, 20:1n-9 and 22:1n-11. This led to increased levels of saturated fatty acids (SFA), total PUFA and n-3 and reductions in monoenes. When comparing TAG only, muscle deviated most from snout and adipose fin, particularly with lower levels of 16:0 and 20:5n-3 and higher levels of 20:1n-9 and 22:1n-11.
The first 2 PCs of individual fish FAMEs of wild mature Atlantic salmon returning to the Dale River for spawning (Sampling 2), accounted for 90.4% of the variation (Fig. 3). PC1 accounted for 65.7% of the variation, and PC2, for 24.7%. For PC1, TOT always gave a more positive weighting compared to TAG, regardless of the tissue. On a tissue level, adipose fin TOT clustering caused positive weighting and muscle TAG caused a negative weighting. Snout was intermediate between the two. When variables were extracted, it was evident that PC1 was ex plained mainly as a positive contrast between 16:0, 18:0, 20:5n-3 and 22:6n-3 and as a negative contrast to the long-chain monoenes (18:1n-9, 20:1n-9 and 22:1n-9) (Fig. 4). PC2 is mainly explained as the inverse relationship between 18:1n-9 and 20:1n-9/22:1n-11.
The effect of biopsy methods on the TAG fatty acid composition of maturing farmed salmon maintained in cages at Matre Research Station is shown in Table 4. The first column is the dietary fatty acid composition. There were no significant effects of the sampling or freezing methods on fatty acid composition.
To compare adipose fin TOT and TAG of farmed (Sampling 1) and wild salmon (Sampling 2), a PCA plot was produced including these variables (Fig. 5). The first 2 PCs explained 97.1% of the variation. The TAG and TOT samples of the farmed fish data gave 2 small and distinctly separated clusters where TAG had a positive weighting with PC1 (78.7%), while TOT was neutral. Wild fish TAG also tended to have a positive weighting compared to muscle TOT, but data were more scattered. For PC2 (18.4%)TAG FAMEs had a positive weighting compared to TOT for both groups of fish. The variable loading for the extracted components showed a positive weighting in PC1 is mainly due to higher levels of 18:1n-9 and 18:2n-6 in a reverse relationship with 16:0, 18:0, 20:5n-3 and 22:6n-3 (Fig. 6). For PC2, the main positive weighting by 20:1n-9 and 22:1n-11 is reversed by a negative weighting of 16:0, 18:0, 22:6n-3 and 18:2n-6.
DISCUSSION
The lipid content in the tissues varied from around 1% of wet weight in adipose fin to > 30% in farmed maturing Salmo salar. Wild fish had considerably lower lipid content in muscle. This relates to the fact that maturing salmon become anorectic and invest a 18:0 3.0 ± 0.2 a 5.9 ± 0.2 b 2.5 ± 0.3 a 6.1 ± 0.2 b 3.2 ± 0.2 a 8.5 ± 0.5 c *** *** * 18:1n-9 22.5 ± 1.0 ab 18.9 ± 1.1 a 22.9 ± 1.5 ab 17.4 ± 1.4 a 24.9 ± 1.7 b 17.3 ± 0.9 a -*** -18:1n-7 3.6 ± 0.2 b 2.8 ± 0.2 ab 3.2 ± 0.3 ab 2.6 ± 0.2 a 3.1 ± 0.2 ab 2.7 ± 0. 18:3n-3 0.9 ± 0.1 ab 1.5 ± 0.5 b 0.8 ± 0.1 ab 0.9 ± 0.1 ab 0.9 ± 0.1 ab 0.6 ± 0.1 a * -- 20:1n-9 11.4 ± 0.8 b 9.3 ± 0.9 ab 16.5 ± 1.3 c 12.6 ± 0.9 bc 10.7 ± 0.6 b 6.1 ± 0. 1986, Kadri et al. 1996)). The lowest level of lipid that we have recorded in wild maturing salmon is 1.5% muscle lipid (data not shown). It was also interesting to note that snout had high lipid contents even in fish with low muscle lipid. In many release experiments and marking programmes salmon snouts are tagged with a coded wire (Hand et al. 2010). Snouts/heads are then collected from salmon fisheries for analysis. Hence the material available for analysis is much greater than that available from muscle samples. The minimum sample needed to obtain sufficient lipid material will therefore depend on the tissue and life stage of the fish, and the laboratory setup. We usually need 100 ng of injected sample in our gas chromatograph to obtain good fatty acid traces. As we use autoinjectors requiring volumes of 50 µl, the amount of fatty acids required for a good resolution run is around 5 µg. This means that the minimum required lipid is around 10 µg for lipid-rich samples and around twice that amount for lipid-poor tissues. Increasing sensitivity and manual injections could easily lower the required amount further. However, it should be taken into account that samples on occasion may need to be re-prepared and re-analysed. According to these considerations, it should be sufficient to sample 100 mg of adipose fin to obtain 1 mg of lipid, or 500 µg TAG. For muscle tissue, 100 mg would give anything from 5 to 30 mg of lipid dependent on the nutritional status of the fish. This is in the range of material sampled with the biopsy needles (30 to 150 mg). At the lower end, this would provide a minimum of 4 to 5 mg lipid content which is perfectly feasible to analyse for fatty acids. But, as a rule of thumb, we suggest a minimum amount of sample to be in the range of 60 to 100 mg for muscle and 0.5 to 1.0 g for adipose fin.
In the muscle of these fish, TAG would contribute to > 90% of the total fatty acids, and the PL contribution would be less pronounced. This caused less difference between muscle TOT and TAG. This became especially clear in the PCA plots where the driving force separating adipose fin TOT from the other samples was due to elevated levels of the PL fatty acids 20:5n-3, 22:6n-3, 20:4n-6, 16:0 and 18:0 and lowered the content of the remaining fatty acids when compared to TAG. This change clearly moved the fatty acid profile away from the dietary profile. The small but notable inverse relationship between 18:2n-6 and 18:1n-9 in dicates that adipose fin TAG preferentially incorporates 18:1n-9, but also that the level of 18:2n-6 may be a trait difference, as indicated previously for other fatty acids (Schlechtriem et al. 2007).
Analysing wild fish is obviously more challenging than analysing farmed fish as the former will have fed on different preys. Consequently, analysing average values will be of less significance. However, with regard to TOT and TAG comparisons, PCA plots again showed that the leaner the tissue, the more important PL fatty acids (16:0, 18:0, 20:5n-3 and 22:6n-3) become for the variation. Conversely, the more tissue lipid, the more important 18:1n-9, 20:1n-9 and 22:1n-11 become for the variation. These are dietary fatty acids describing a dietary history and are not extensively incorporated into PL. It thus appears, if we accept that TAG represents dietary history, that analysing FAME from TAG rather than TOT will give a better indication of previous feeding history. But, if tissues are lipid rich, both options could be chosen.
One application of fatty acid profiling is to study the foraging behaviour and escape history of escaped farmed fish (Olsen & Skilbrei 2010, Abrantes et al. 2011). Modern farm diets have high inclusion levels of vegetable oils and, in some countries, also terrestrial lipids such as poultry fat. These oils typically have high contents of signature fatty acids like 18:2n-6 and 18:3n-3 which are only found in small amounts in natural diets. Canola oil is also popular to include in diets, as it contains high levels of 18:1n-9 and relatively low levels of plant PUFAs. Wild fish will, on the other hand, have variable contents of other fatty acids giving signatures to prey history. For example, many lower tropic level copepods and krill contain high levels of the long-chain alcohols like 20:1n-9 and 22:1n-11 (Falk-Petersen et al. 2009, Olsen et al. 2010) that are oxidised to fatty acids and deposited as fatty acids in TAG upon digestion. These are not incorporated into PL as readily as long-chain PUFAs. When adipose fins of farmed and wild fish were compared using PCA plots, the main fatty acids explaining the difference were 18:1n-9 and 18:2n-6, both very high in artificial diets. Wild fish were also clearly separated by their high content of 20:1n-9 and 22:1n-11. The distances were not always as clear using TOT FAME, and they were only well separated on PC2. It was also evident that the wild fish had a wider dietary history, leading to higher variation in individual fatty acid compositions.
In conclusion, adipose fin and muscle biopsies are good alternatives for non-lethal sampling of Atlantic salmon in the process of fatty acid profiling. The minimum amounts of samples to ensure sufficient material are suggested to be 0.5 to 1 g of adipose fin and 60 to 100 mg of muscle. The method of storage (liquid nitrogen or dry ice) does not affect fatty acid compositions. TAG fatty acids provide the closest resemblance to dietary history. But tissues do differ in some ways, and sample series should preferably be carried out on one tissue only. TOT FAMEs supply informative data on dietary history in fatty tissues, but will differ from TAG FAMEs when total lipid levels are low; the reason for this lies in a larger contribution of phospholipid FAMEs. In wild maturing salmon, therefore, where muscle lipid is low, TOT profiling will be highly influenced by phospholipids. Data may not provide reliable information with regard to previous feeding history. For fatty acid profiling of escaped salmon previously fed commercial diets, quantifying the main vegetable/terrestrial fatty acids (18:2n-6, 18:3n-3 and 18:1n-9) is sufficient to provide reliable information to identify recently escaped farmed fish (Megdal et al. 2009, Olsen et al. 2010). Although both TOT and TAG FAMEs can be used, we argue in favour of using TAG analysis. Analysing TOT FAMEs may lead to the erroneous conclusion that the fish have switched to wild prey without this actually being the case. For example, if lean tissue is analysed, 18:2n-6 will be lowered in TOT and the level of marine PUFAs will be increased when compared to the real diets. This will not be the case for TAG. We therefore conclude that TAG has a higher accuracy compared with TOT profiling; this level of accuracy is needed in detailed studies on fish of different origins, for example when monitoring the escape histories of farmed fish that may have switched to natural prey. For estimating a wider range of dietary histories, like variations in wild prey preference, multivariate methods are required (e.g. PCA analysis).
Most tissues can be used for TAG fatty acid profiling. We initially examined skin mucus as an alternative non-lethal sampling (data not shown). However, mucus is low in lipids, and sampling may damage skin or scales. In cases of lethal sampling, we also tested livers, roe, peritoneal fat and tongue (data not shown). Livers will change composition postprandially and are not recommended in starved fish. Lipidrich tissues such as peritoneal fat and tongue are well suited.
fin and muscle triacylglycerols (TAG) and total lipids (TOT) of maturing Atlantic salmon fed on a defined diet over 12 mo (percent of fatty acids). Data are means (± SEM) of 14 fish. Potential differences between the tissues were assessed by 2-way ANOVA and Tukey's post hoc test. Main effects are given in the right columns -Tissue: effect of adipose fin and muscle; TAG: effect of TAG or TOT fatty acid methyl ester composition; Tissue × TAG: interaction of the 2; SFA: saturated fatty acids; MUFA: monounsaturated fatty acids; PUFA: polyunsaturated fatty acids. Significance was accepted at p < 0.05. Numbers within each line not sharing common superscript letters are significantly different: ***p < 0.001, **p < 0.01, *p < 0.05
255Fig. 1 .
Fig. 1. Salmo salar. Score plot of principal component (PC) analysis of fatty acid methyl esters (FAME) in the muscle (M) and adipose fin (A) in farmed salmon. Black letters: total lipid FAMEs; red letters: triacylglycerols. Numbers in the alphanumeric codes refer to specific individuals
Fig. 3 .
Fig. 3. Salmo salar. Score plot of principal component (PC) analysis of levels of fatty acid methyl esters (FAME) in total lipids (black) or triacylglycerols (red) in the adipose fin (A), snout (S) and muscle (M) samples of wild salmon. Numbers in the alphanumeric codes refer to specific individuals
Fig. 5 .
Fig. 5. Salmo salar. Score plot of principal component (PC) analysis of levels of fatty acids in total lipids (black) or triacylglycerols (red) in the adipose fin of farmed (F) and wild salmon from the Dale River (D). Numbers in the alphanumeric codes refer to specific individuals
Table 2 .
The data clearly showed
Table 1 .
Salmo salar. Lipid content and lipid class composition of the fish collected in Samplings 1 (farmed salmon) and 2 (wild salmon). Lipid contents are shown as means (±SEM). Potential differences between the tissues were assessed by 1-way ANOVA. Significance was accepted at p < 0.05. Numbers within each line not sharing common superscript letters are significantly different. Lipid class composition is the mean of 5 fish. Farmed fish: maturing fish at Matre Research Station,
Table 2 .
Salmo salar. Fatty acid composition in the diet and in the adipose
Table 3 .
Salmo salar. Fatty acid composition in the snout, muscle and adipose fins triacylglycerols (TAG) and total lipids (TOT) of wild Atlantic salmon caught upon entering the Dale River (percent of fatty acids). Data are means (± SEM) of 9 fish. Potential differences between the tissues were assessed by 2-way ANOVA and Tukey's post hoc test. Main effects are given in right columns-tissue: effect of adipose fin, snout and muscle; TAG: effect of TAG or TOT fatty acid methyl ester composition; Tissue × TAG: interaction of the 2; SFA: saturated fatty acids; MUFA: monounsaturated fatty acids; PUFA: polyunsaturated fatty acids. Significance was accepted at p < 0.05. Numbers within each line not sharing common superscript letters are large part of their energy into gonad maturation and swimming to the spawning grounds (Aksnes et al.
, but to a lesser extent than they affect TAG. Depot lipids are, on the other hand, reserves of energy and essential fatty acids, and, although some modification occurs, the fatty acid composition is generally highly dictated by diets(Henderson & Tocher 1987, Olsen et al.
Table 4 .
Salmo salar. Triacylglycerol fatty acid composition (percent of fatty acids) of diet, control muscle and muscle biopsies (in liquid nitrogen or dry ice) of farmed sexually mature Atlantic salmon. Data are means (± SEM) of 12 fish.
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Domain: Environmental Science Biology
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Long-term trends in summertime habitat suitability for delta smelt, Hypomesus transpacificus. San Francisco Estuary and Watershed Science 6: Issue 1 Article 1
The biological productivity of river-dominated estuaries is affected strongly by variation in freshwater inflow, which affects nursery habitat quality. Previous research has shown this is generally true in the upper San Francisco Estuary, California, USA; however, one endemic species of high management importance, delta smelt (Hypomesus transpacificus), has shown ambiguous population responses to river inflow variation. We hypothesized that populationlevel associations with abiotic habitat metrics have not been apparent because the effects occur seasonally, and at spatial scales smaller than the entire upper San Francisco Estuary. We tested this hypothesis by applying regression techniques and principal components analysis (PCA) to a long-term data-set (1970–2004) of summertime fish catch, and concurrently measured water quality (specific conductance, Secchi disk depth, and water temperature). We found that all three water quality variables predicted delta smelt occurrence, and we identified three distinct geographic regions that had similar long-term trends in delta smelt capture probabilities. The primary habitat region was centered on the confluence of
The biological productivity of river-dominated estuaries is affected strongly by variation in freshwater inflow, which affects nursery habitat quality. Previous research has shown this is generally true in the upper San Francisco Estuary, California, USA; however, one endemic species of high management importance, delta smelt (Hypomesus transpacificus), has shown ambiguous population responses to river inflow variation. We hypothesized that populationlevel associations with abiotic habitat metrics have not been apparent because the effects occur seasonally, and at spatial scales smaller than the entire upper San Francisco Estuary. We tested this hypothesis by applying regression techniques and principal components analysis (PCA) to a long-term data-set of summertime fish catch, and concurrently measured water quality (specific conductance, Secchi disk depth, and water temperature). We found that all three water quality variables predicted delta smelt occurrence, and we identified three distinct geographic regions that had similar long-term trends in delta smelt capture probabilities. The primary habitat region was centered on the confluence of the Sacramento and San Joaquin rivers; delta smelt relative abundance was typically highest in the Confluence region throughout the study period. There were two marginal habitat regions-including one centered on Suisun Bay-where specific conductance was highest and delta smelt relative abundance varied with specific conductance. The second marginal habitat region was centered on the San Joaquin River and southern Sacramento-San Joaquin Delta. The San Joaquin region had the warmest water temperatures and the highest water clarity, which increased strongly in this region during 1970-2004. In the San Joaquin region, where delta smelt relative abundance was correlated with water clarity, catches declined rapidly to zero from 1970-1978 and remained consistently near zero thereafter. However, when we combined these regional results into estuary-wide means, there were no significant relationships between any of the water quality variables and delta smelt relative abundance. Our findings support the hypothesis that basic water quality parameters are predictors of delta smelt relative abundance, but only at regional spatial scales.
iNTRoDuCTioN
In tidal river estuaries, the size and quality of particular habitats can be functions of river inflows (Livingston et al. 1997;Peterson 2003). For instance, most tidal river estuaries have prominent frontal zones (also known as low-salinity zones and entrapment zones) where combinations of hydrodynamics and organism behavior result in conspicuous aggregations of turbidity, plankton, and young fishes. The habitat value of low-salinity zones for young fishes and other planktonic organisms may be enhanced by increased river inflows (e.g., Jassby et al. 1995). Young fishes can actively maintain position within low-salinity zones (Bennett et al. 2002), where their feeding success can be enhanced due to similar aggregations of zooplankton prey (Dodson et al. 1989;Kimmerer et al. 1998). Presumably, the enhanced feeding opportunities lead to comparatively rapid growth, and thus lower cumulative predation mortality during vulnerable early life stages (Houde 1987). The typically turbid conditions in low-salinity zones also may directly reduce predation losses to visual predators (Gregory and Levings 1998).
In the upper San Francisco Estuary (California, USA; Figure 1) (hereafter upper estuary), the linkage between river inflow and the low-salinity zone was formalized into a water quality standard using an integrative parameter called X 2 , which is the distance in kilometers (km) from the mouth of San Francisco Bay at the Golden Gate Bridge to the location of the estuary where mean bottom salinity is 2 practical salinity units (psu) (Jassby et al. 1995).
The abundance or survival of numerous organisms is elevated in years when mean spring and early summer X 2 locations are moved seaward (closer to the Golden Gate) by high river inflows (Jassby et al. 1995;Kimmerer 2002a). Some species' X 2 responses degraded following the introduction of the overbite clam Corbula amurensis in 1986 (Kimmerer 2002a;Sommer et al. 2007); however, most historically flowresponsive taxa continue to have statistically demonstrable linkages between abundance or early life stage survival and X 2 position.
The Delta Smelt
Delta smelt (Hypomesus transpacificus) is an annual fish endemic to the upper San Francisco Estuary lowsalinity zone (Moyle et al. 1992). It is arguably the most imperiled estuarine fish species in the United States (Bennett 2005). Numerous field studies have qualitatively or semi-quantitatively described delta smelt distribution relative to salinity (e.g., Moyle et al. 1992;Bennett 2005), or its covariate, X 2 (e.g., Dege and Brown 2004). A general association with open water and, in particular, large shoal habitats in Suisun Bay and the Sacramento-San Joaquin River confluence has also been reported previously (e.g., Moyle et al. 1992;Bennett et al. 2002). Despite its distribution within the low-salinity zone, delta smelt abundance has not responded predictably to interannual river flow variation (Stevens and Miller 1983;Jassby et al. 1995;Kimmerer 2002a;Bennett 2005). Rather, delta smelt have undergone a long-term abundance decline characterized by an abrupt decline around 1982 (Kimmerer 2002a), and very low abundance in recent years ; Figure 2). The reasons for the persistently low abundance since 1982 are thought to result from multiple interacting factors including larval advection during high flows in winter-spring 1982 and 1983, a drought during 1987-1992, entrainment in water diversions, contaminant exposures, and competition from introduced species (Moyle et al. 1992;Bennett 2005).
Analyses such as the X 2 relationships described above (Jassby et al. 1995;Kimmerer 2002a) implicitly assume that habitat and abundance linkages are 3 detectable at the scale of the entire upper estuary. The success of this approach suggests that it is an appropriate scale for many taxa. However, it is possible that the entire upper estuary may be too broad a spatial scale to understand the degree to which estua-rine habitat conditions affect delta smelt abundance. Because of their limited distribution, we hypothesize that linkages between delta smelt abundance and abiotic habitat suitability exist at smaller spatial scales (i.e., regions of the estuary). If true, this could explain the lack of correspondence between delta smelt abundance and X 2 position in the upper estuary. An alternative hypothesis is that the abiotic components of habitat are never limiting to the delta smelt population: i.e., linkages do not exist at any spatial scale.
Here, we test this hypothesis by examining long-term monitoring data collected on juvenile delta smelt and concurrently measured water-quality variables. Specifically, we addressed three study questions: 1) What is the abiotic habitat of delta smelt during summer?
3) Are delta smelt abundance and water quality correlated regionally and at the scale of the entire upper estuary?
Study Area
The upper San Francisco Estuary is the mixing zone for Pacific Ocean water that enters San Francisco Bay under the Golden Gate Bridge, and freshwater inputs from numerous streams, most notably the Sacramento and San Joaquin Rivers ( Figure 1). The estuary is, in all aspects, a highly altered ecosystem (Nichols et al. 1986;Kimmerer 2002b). For instance, the Sacramento and San Joaquin Rivers drain about 40% of California's surface area, and their inflows strongly affect estuarine salinity (Jassby et al. 1995), but, whenever possible, river inflows are regulated. The Sacramento-San Joaquin Delta is maintained as a permanently freshwater environment (but still under tidal influence) to support regional agriculture and the export of large quantities of freshwater out of the delta for agricultural and municipal users to the south and west (Kimmerer 2002b). The largest export diversions are located in the San Joaquin River basin ( Figure 1), but most of the export is supported by reservoir releases from the Sacramento River basin, which receives considerably more precipitation. During January-June, estuarine salinity standards based on X 2 positions require significant freshwater inputs to the estuarine ecosystem (Kimmerer 2002b). Major changes in the upper estuary during the 1970-2004 study period included: increased rock reinforcement of levees, increased freshwater exports (Kimmerer 2002b), increased autumn salinity intrusion , increased species invasion rates (Cohen and Carlton 1998), decreased pelagic productivity, increased water clarity (Jassby et al. 2002), the proliferation of submerged macrophytes (Brown and Michniuk 2007), and decreased native fish abundance (Matern et al. 2002;Brown and Michniuk 2007).
METHoDS
The California Department of Fish and Game Summer (CDFG) Tow-Net Survey (TNS) was developed to index age-0 striped bass (Morone saxatilis) relative abundance; the data have been used extensively to analyze striped bass population dynamics (Turner and Chadwick 1972;Stevens et al. 1985;Kimmerer et al. 2000). However, the TNS has also always collected delta smelt incidentally because of their similar distribution to young striped bass. Delta smelt collected by the TNS generally range from about 25-50 millimeter (mm) fork length. Delta smelt relative abundance indices based on the TNS have also been developed and used to analyze long-term population trends (Moyle et al. 1992;Kimmerer 2002a;Bennett 2005). The TNS conducts three tows at each of up to 32 stations ( Figure 1) during each of its component surveys using a conical net (1.5-square meter (m 2 ) mouth; 2.5 mm cod-end mesh) towed obliquely through the water column from bottom to surface. A minimum of two of these surveys is conducted each year. The delta smelt relative abundance index is calculated as follows. The sum of catch from the three tows at each station is multiplied by a water-volume estimate to produce station-specific estimates of catch per volume (Chadwick 1964). Next, these volumetric density estimates are summed across all stations, and the average of the summed volumetric density estimates from the first two surveys comprises the summertime abundance index (Moyle et al. 1992).
TNS Data Sampling
The TNS fish sampling began in 1959; concurrent data collection on water temperature (°C), water clarity (Secchi disk depth; cm), and specific conductance (microSiemens per centimeter [µS . cm -1 ]) began in 1970. Note that specific conductance is a surrogate for salinity in the estuary. The Pearson correlation coefficient between the mean April-July X 2 position used by Jassby et al. (1995) and Kimmerer (2002a), and the mean estuary-wide log 10 specific conductance used in the present study, is 0.94. Since the two measurements are closely correlated, and because X 2 is a calculated variable, we chose to use the data actually collected during the TNS. The TNS sampling has had an average survey starting date of 13 July, but surveys have been conducted as early as 4 June and as late as 28 August in some years. To standardize the survey results across years, we used the data for each year's survey that 1) occurred closest to 13 July, and 2) had at least 28 of 32 stations sampled. When at least 28 stations were sampled, the survey grid was sampled with adequate spatial coverage. Typically, when fewer than 32 stations were sampled, the most seaward stations were dropped because they were considered unlikely to yield young-of-the-year striped bass due to high salinity. Water that is too saline for young-of-the-year striped bass is generally also too saline for delta smelt.
Modeling
We used binomial generalized additive modeling (GAM; smoother = cubic spline) to explore relationships between delta smelt occurrence (distribution) and water quality variables. Generalized additive modeling can depict nonlinear responses to environmental gradients (Stoner et al. 2001;Stratoudakis et al. 2003;Feyrer et al. 2007). For this analysis, we converted the raw delta smelt count data into occurrence (binomial or presence/absence) data, which provides a conservative but robust means of constraining the underlying, highly-skewed error distributions that typify raw count data based on trawl sampling. We developed GAMs for each water quality variable individually to estimate their explanatory power, and for all three variables combined. The model including all three variables lowered the null deviance (analogous to variance in parametric statistics) in the data by at least 8% more than any single variable. The P-values for each water quality variable were also always < 0.05 (usually much less) whether considered individually or together. However, this was due, in part, to the very high degrees of freedom in the model. Thus, in addition to P-values, we used findings from laboratory physiology studies (Swanson et al. 2000) and other field studies of delta smelt distribution (Dege and Brown 2004;Nobriga et al. 2005;Feyrer et al. 2007) to guide our interpretation of the relevance of GAM results (see Discussion).
In addition to being statistically robust, we expected delta smelt occurrence to be relatively resistant to declining catches through time, particularly in association with water quality combinations representing the best available habitats. We assumed the population decline would lower catches even in the best available habitats, but that delta smelt would still be present where conditions remained most suitable. Lastly, we assumed that error in the association between delta smelt occurrence and water quality attributable to tidal time-scale variation from taking point measurements of water quality was insignificant for two reasons. First, the large amount of data (> 30 years; n = 954 samples) provided a large buffer against this type of random error. Second, delta smelt move actively in conjunction with tides to maintain position within the low-salinity zone (Bennett et al. 2002), so it is likely they generally remain associated with suitable water quality combinations. The GAM was essentially a test of this assumption.
Testing Abundance Trends
We also tested for associations of water quality and delta smelt relative abundance. We tested for water quality-abundance relationships at two spatial scales, regional and estuary-wide. For the former, we first used principal components analysis (PCA) to group stations with similar time trends in delta smelt capture probabilities based on the GAM. We used this step to provide an objective spatial context to subsequently explore regional relationships between water quality and delta smelt relative abundance. The PCA result indicated that the estuary could be divided into three regions (see Results; Figure 1). Only 21 of the 32 sampling stations were sampled every year, so the PCA was restricted to these 21 stations; however, stations from all but the westernmost part of the sampling array were well represented (Figure 1). To explore regional habitat-abundance associations, we divided the water quality and delta smelt catch data into the PCA-derived regions. Then, for each region, we calculated mean delta smelt catch per tow and means of the water quality parameters. To ensure the regions represented distinct habitats, we tested for differences in means of water quality parameters 6 using one-way ANOVA and Tukey post-hoc multiple comparison tests. We performed a separate ANOVA for each water quality variable. We also tested for long-term regional trends in each water quality variable using linear regression. The water quality data were log 10 -transformed before the ANOVAs to improve their conformance with the assumptions of parametric statistical testing. We evaluated regional time trends in log 10 + 1-transformed delta smelt relative abundance using LOWESS regression to facilitate interpretation of nonlinear trends (Trexler and Travis 1993). Lastly, we used Spearman's rank correlation to test for regional habitat-abundance associations. We used Spearman's correlation because some of the regional habitat-abundance relationships were unidirectional but curvilinear, and, in some cases, variance in the response (delta smelt relative abundance) was a function of the predictor variable; the use of ranked data was therefore appropriate. The significance level chosen for all of the regional analyses was a < 0.05.
We used multiple linear regression to test for habitatabundance relationships at the scale of the entire upper estuary. In this analysis, we regressed the TNS abundance indices shown in Figure 2 on the mean water temperature, water clarity, and specific conductance values shown in Figure 3. All variables were log 10 -transformed prior to the analysis to bring the variance closer to the normal distribution assumed for parametric statistical tests. We used multiple linear regression in this case because scatter-plots did not indicate any obvious habitat-abundance associations, but it was not possible to tell visually whether interactions among the water quality variables were important. The overall model was considered statistically significant if the F-statistic obtained had a probability of < 0.05. Likewise, the contributions by individual water quality variables to the overall model were considered statistically significant at a < 0.05. . Time series of mean (+/-1 SD) July water quality variables-Secchi depth (clarity), specific conductance (salinity) and temperature-based on data from the CDFG Summer TNS.
RESuLTS
All three water quality variables significantly predicted delta smelt occurrence (Table 1), suggesting they all interact to influence delta smelt distribution. Delta smelt capture probabilities were highest at low specific conductance (1,000-5,000 µS ⋅ cm -1 ; approximately 0.6-3.0 psu) and low water clarity (< 40 cm Secchi disk depth) (Figure 4). Water temperature influenced delta smelt occurrence more like a 'switch' than the other two variables; capture probabilities did not have a strong trend at temperatures lower than about 24°C, but capture probabilities decreased abruptly at higher temperatures. Note that the scatter in each panel of Figure 4 depicts the variation caused by the other two water quality variables.
The PCA produced three principal components with eigenvalues > 1.0 that cumulatively explained 80% of the variance in station-specific trends in delta smelt capture probabilities (PC1, eigenvalue = 7.85, variance explained = 37%; PC2, eigenvalue = 5.86, variance explained = 28%; PC3, eigenvalue = 3.02, variance explained = 14%). All stations loaded negatively on PC1, which reflected a long-term trend of declining capture probability at every station. However, scatter-plots (not shown) indicated that the stronger the negative PC1 loading, the more strongly capture probabilities declined at that station through time. Stations numbered in the 800-900s generally had the most strongly negative trends in capture probabilities ( Figure 5). We defined the San Joaquin region (Figure 1; Figure 5) based on a combination of PC1 and PC3 loadings. San Joaquin region stations had strongly negative capture probability trends or chronically low capture probabilities likely due to high water temperature (Stations 910-912).
The stations included in the Suisun region (Figure 1; Figure 5) had the most strongly positive PC2 loadings, representing negative correlations between specific conductance and capture probability. Stations seaward of those included in the PCA were also grouped into the Suisun region.
The Confluence region (Figure 1) comprised the stations that were not included in either of the previous two regions. The boundaries of the Confluence region are somewhat subjective because station 508 was not included in the PCA (Figure 1) and because there was not a clear separation of PC1 or PC3 loadings along the San Joaquin River ( Figure 5). We included Station 508 in the Confluence region because the Pearson correlation coefficient between its capture probabilities and those of the next Confluence region station (513) was 0.74, which was higher than the correlation coefficient between Station 508 and the adjacent Suisun region station (504; r = 0.64). We also included Station 809 in the Confluence region because the correlation coefficient for year versus Station 809 capture probabilities was only 0.42, suggesting a weakly declining trend. The next San Joaquin River station (812) had r = 0.55, suggesting a better-defined declining trend.
The regions were distinct in terms of their mean water quality conditions ( Table 2). The Suisun region had the highest mean specific conductance, and the San Joaquin region had higher mean water temperatures and water clarity than the other two regions. The midsummer water clarity of the upper estuary increased weakly during 1970-2004, driven by a strong increase in the San Joaquin region (Table 3). Water clarity was the only water quality Table 2. Comparisons of mean July water quality conditions for 1970-2004 among three regions of the upper San Francisco Estuary. Statistically significant regional differences (one way ANOVA with Tukey post-hoc comparisons) are separated by lines within each column. Table 3. Results of linear regression analyses of year versus means of three water quality-variables based on the CDFG Summer Tow-Net Survey (TNS). The water quality variables were log 10 -transformed before analysis. The analyses were done at two spatial scales: (1) the entire upper estuary, and (2) regions that are spatially defined in Figure 1. Statistically significant results (alpha < 0.05) are denoted with an asterisk (*). variable that changed significantly at either spatial scale.
Region and Water Quality Variable
Delta smelt relative abundance was typically highest in the Confluence region throughout the study period, though the 1982 step-change (Kimmerer 2002a) is a prominent feature of the Confluence trend ( Figure 6). Another prominent trend was that in the San Joaquin region, delta smelt catches declined rapidly to zero from 1970-1978 and have remained consistently near zero ever since. In the Suisun region, there were two periods of increasing and decreasing relative abundance. Relative abundance was correlated with water clarity in each region (Suisun, Spearman ρ = -0.59; n = 32; P = 0.0004; Confluence, Spearman ρ = -0.51; n = 32; P = 0.003; San Joaquin, Spearman ρ = -0.65; n = 32; P = 0.00005). Relative abundance also varied in the Suisun region in association with specific conductance (Spearman ρ = -0.65; n = 32; P = 0.00005), but specific conductance was not correlated with abundance in the other regions (Confluence Spearman ρ = 0.26; n = 32; P = 0.15 and San Joaquin Spearman ρ = 0.094; n = 32; P = 0.61). At the scale of the entire upper estuary, the water quality variables were not correlated with juvenile delta smelt relative abundance indices calculated from the TNS (F = 2.19; P = 0.11; multiple R 2 = 0.10; n = 32; P-values for individual parameters of > 0.05).
DiSCuSSioN
We found that the three water quality variablesspecific conductance (salinity), Secchi depth (clarity), and temperature-measured concurrently with fish catches in the CDFG TNS all interact to influence delta smelt occurrence (distribution) in the upper San Francisco Estuary. Thus, they are all indicators of abiotic habitat suitability. Long-term associations of water quality variation and relative abundance were discernable at regional spatial scales, most notably on the perimeter of the species' distribution outside of the Confluence region. Delta smelt relative abundance in the Suisun region varied in association with specific conductance, which is a function of river inflow variation. This is consistent with previous findings for larvae during spring-early summer (Dege et al. 2004) and juveniles and pre-spawning adults during fall . Note that Kimmerer (2002a) reported there was no long-term trend in mean January-June X 2 position. This reflects river inflow conditions during the six months preceding the data used in our study. Thus, it is not surprising that we found no long-term trend in July specific conductance. At the landward edge of the estuary, delta smelt have essentially disappeared during midsummer.
Of the three water quality variables, only water clarity had a long-term trend. Jassby et al. (2002) had reported previously that water clarity in the Sacramento-San Joaquin Delta had increased due to significant long-term reductions in total suspended solids during most months between March and November. Thus, we propose that increased San Joaquin region water clarity has constricted delta smelt habitat, and is a major reason for its regional absence during summer. The possibility of habitat constriction was proposed by Bennett (2005) who suggested it as a possible mechanism for apparent 'density-dependence' between the summer and fall based on stock-recruitment analyses of long-term monitoring data-sets.
Our conclusion that there has been a long-term habitat constriction for delta smelt is also consistent with Feyrer et al. (2007), who analyzed fall abundance data. Feyrer et al. (2007) were able to identify chang- Figure 6. Time series of mean delta smelt catch · tow -1 (log 10 + 1 transformed) in three regions defined by principal components analysis (see Figure 1). The splines are loess regression line.
es both regionally and at the scale of the entire upper estuary. They also found simple statistical associations between fall stock size, fall water quality, and abundance the following summer. We suggest that estuary-wide habitat changes are more apparent in fall than in summer because delta smelt habitat suitability progressively deteriorates over the course of the year. Adult and juvenile delta smelt use the San Joaquin region during winter through early summer, sometimes causing conflicts between water export schedules and Endangered Species Act-mandated take levels (Bennett 2005). Presumably, cooler water temperatures and lower water clarity during winterspring flow pulses allow delta smelt to occupy the San Joaquin region early in the year. By July, the San Joaquin region is no longer suitable delta smelt habitat, and by fall, habitat suitability declines further due to a separate long-term trend toward elevated salinity in the Suisun region ).
We acknowledge that the three water quality variables we analyzed cannot fully define abiotic habitat for delta smelt. For instance, estuarine fish distributions can be influenced by dissolved oxygen (Eby and Crowder 2002). Young delta smelt are also exposed to contaminants (Kuivila and Moon 2004), and some individuals show evidence of sublethal toxic exposure (Bennett 2005); the population-level consequences of contaminant exposures are unknown. However, each of the water quality variables we used has wellknown effects on fish ecology. Water clarity strongly affects large river (Quist et al. 2004) and estuarine fish assemblages (Blaber and Blaber 1980). In the cited studies, water clarity was thought to mediate predator-prey interactions; there is experimental evidence for the role of turbidity as a factor influencing piscivore success (Abrahams and Kattenfeld 1997;Gregory and Levins 1998). We suggest that predation on delta smelt may be higher in relatively clear water, or that delta smelt may avoid clear water because it increases their predation risk.
The increased water transparency in the upper estuary appears to be due to the combined effects of decreasing sediment inputs (Wright and Schoellhamer 2004), sediment wash-out from very high inflows during the 1982-1983 El Nino (Jassby et al. 2005), and the proliferation of large beds of submerged freshwater macrophytes, particularly in the San Joaquin region (Nobriga et al. 2005;Brown and Michniuk 2007). These macrophyte beds may act as 'biological filters' for sediment. The invasion of aquatic macrophytes has already substantially changed near-shore fish assemblages. The results of the present study and of Feyrer et al. (2007) suggest the macrophyte proliferation may also have restricted pelagic fish distributions.
Specific conductance is a surrogate for salinity, which strongly affects estuarine fish distributions (Bulgar et al. 1993). The influence of salinity on the geographic distribution of young delta smelt has been noted previously (Moyle et al. 1992;Dege and Brown 2004). Swanson et al. (2000) found the upper salinity tolerance of delta smelt was about 19 psu. This corresponds well with the field data in this study; predicted capture probabilities were virtually zero at a specific conductance of 35,000 µS . cm -1 , which roughly corresponds to 20 psu (Figure 4). Similarly, Feyrer et al. (2007) found that delta smelt capture probabilities during fall were essentially zero at specific conductances higher than 25,000 µS ⋅ cm -1 (about 15 psu). The results of the present study, and of Feyrer et al. (2007), provide idealized salinity response curves that bridge previous findings and show the interactive influence of other water quality variables on delta smelt distribution along the estuarine salinity gradient.
Water temperature is an important determinant of fish metabolic and growth rates, so it affects estuarine habitat suitability through a variety of mechanisms (Lankford and Targett 1994;Marine and Cech 2004). Water temperature was the poorest predictor of delta smelt distribution in the present study, accounting for only about 6% of the null deviance in delta smelt occurrence (Table 1). Water temperature also had no significant regional or estuary-wide effects on delta smelt relative abundance. We think the low predictive power of water temperature was due more to the shape of its response curve than to low ecological importance. Essentially, delta smelt occurrence and relative abundance responded to water temperature only when it neared or exceeded the 25 º C lethal limit reported by Swanson et al. (2000). Currently, the upper San Francisco Estuary averages more than 20 º C during mid-summer, and the San Joaquin region already approaches the 25 º C upper lethal limit (Table 2). Moreover, the shape of the predicted response curve to water temperature suggests the difference between tolerable and not tolerable can be quite abrupt (Figure 4).
Future Trends
Our results, and those of Feyrer et al. (2007), have demonstrated that delta smelt habitat suitability is sensitive to system changes. Our results also suggest that X 2 position variation and other factors need to be considered when evaluating delta smelt habitat suitability and trends. However, there is high uncertainty about future trends in factors that are likely to influence delta smelt habitat suitability that make it impossible to forecast future habitat conditions. For instance, northern California's climate is likely to get warmer by 2100, which would increase water temperatures, but future precipitation trends, which could influence salinity distributions in the estuary, are very uncertain (Dettinger 2005). Longterm salinity trends are further complicated by the potential for catastrophic natural events that could change the upper estuary landscape. Mount and Twiss (2005) estimated there is a 67% chance that flooding or earthquakes will change the landscape of the Sacramento-San Joaquin Delta by 2050. Debate about future policy direction for the Delta adds another level of uncertainty to future habitat conditions for delta smelt (Lund et al. 2007).
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Domain: Environmental Science Biology
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Epidemiological modeling of SARS-CoV-2 in white-tailed deer (Odocoileus virginianus) reveals conditions for introduction and widespread transmission
Emerging infectious diseases with zoonotic potential often have complex socioecological dynamics and limited ecological data, requiring integration of epidemiological modeling with surveillance. Although our understanding of SARS-CoV-2 has advanced considerably since its detection in late 2019, the factors influencing its introduction and transmission in wildlife hosts, particularly white-tailed deer (Odocoileus virginianus), remain poorly understood. We use a Susceptible-Infected-Recovered-Susceptible epidemiological model to investigate the spillover risk and transmission dynamics of SARS-CoV-2 in wild and captive white-tailed deer populations across various simulated scenarios. We found that captive scenarios pose a higher risk of SARS-CoV-2 introduction from humans into deer herds and subsequent transmission among deer, compared to wild herds. However, even in wild herds, the transmission risk is often substantial enough to sustain infections. Furthermore, we demonstrate that the strength of introduction from humans influences outbreak characteristics only to a certain extent. Transmission among deer was frequently sufficient for widespread outbreaks in deer populations, regardless of the initial level of introduction. We also explore the potential for fence line interactions between captive and wild deer to elevate outbreak metrics in wild herds that have the lowest risk of introduction and sustained transmission. Our results indicate that SARS-CoV-2 could be introduced and maintained in deer herds across a range of circumstances based on testing a range of introduction and transmission risks in various captive and wild scenarios. Our approach and findings will aid One Health strategies that mitigate persistent SARS-CoV-2 outbreaks in white-tailed deer populations and potential spillback to humans.
Introduction
Many emerging infectious diseases in animal populations are transmissible between animals and humans, representing a public health threat [1,2]. These diseases are called zoonoses and pose One Health challenges, meaning closely linked human, animal, and ecosystem health challenges that often require coordinated, multi-disciplinary action in the face of socioecological complexity and limited data [3,4]. Epidemiological models are powerful in understanding and responding to One Health challenges posed by zoonoses. Using the best-available science, epidemiological models can project the behavior of zoonotic disease spread across a range of possible conditions, quantify transmission risk between various host species, and examine the drivers influencing the introduction and transmission of zoonotic pathogens in wildlife hosts [5]. These exploratory inferences are particularly valuable with emerging infectious diseases and can complement monitoring efforts documenting the spatiotemporal distribution of infections [6,7].
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the subgenera Sarbecoviruses, subfamily Orthocoronavirinae, is a zoonotic virus that poses One Health challenges around the globe [8,9]. SARS-CoV-2 infection can result in severe respiratory disease (known as COVID- 19) and death in humans, yet in wildlife species SARS-CoV-2 severity is highly variable. Since it was first documented in humans in late 2019, the number of known SARS-CoV-2 hosts has increased and includes a range of companion and wild animals, including wild and captive white-tailed deer (Odocoileus virginianus; hereafter deer) [10,11]. Transmission of SARS-CoV-2 can occur between humans, humans and animals, and between animals [12,13]. Each of these transmission pathways is concerning from a public health perspective for several reasons. First, SARS-CoV-2 circulating in human and non-human hosts can persist, recombine, and evolve into novel variants that change the properties of this pathogen [14][15][16][17]. Second, non-human hosts can act as a reservoir for SARS-CoV-2, posing risks of SARS-CoV-2 persisting outside of human hosts [18,19]. Lastly, SARS-CoV-2 may spill back to humans from non-human hosts as a potentially more virulent form of SARS-CoV-2 [12]. Collectively, these concerns have given rise to surveillance programs of SARS-CoV-2 in wild and captive whitetailed deer across North America [20].
Two introduction pathways may have led to the transmission of SARS-CoV-2 from humans to deer, a process commonly referred to as 'spillover'. First, wild and captive deer could have been exposed to SARS-CoV-2 via direct interactions between humans and deer that are nearby. This direct pathway likely is a result of the aerosolized transmission of SARS-CoV-2 from humans to deer, given the tissue tropism in the upper respiratory tract of both species [21,22]. Direct interactions between humans and deer are possible in some areas of North America where deer are habituated to humans to the point where proximity or even contact is possible [23]. Human-deer interactions are also common in captive settings, ranging from facilities and herd management activities to exposition opportunities for visitors. Second, deer could have been exposed to SARS-CoV-2 indirectly through contaminated surfaces, feed, water, or through intermediate animal hosts [24]. While this indirect pathway has been postulated, evidence of transmission through this pathway does not currently exist.
Like SARS-CoV-2 spillover from humans to deer, the spread of SARS-CoV-2 within a white-tailed deer population could also occur via direct and indirect pathways. Transmission between deer could occur given various social interactions in wild and captive settings, including various agonistic and mating behaviors [25,26]. Direct transmission of SARS-CoV-2 between deer might include aerosolized and fluid transmission. Aerosolized transmission of SARS-CoV-2 between deer could occur within captive facilities where deer densities are high or in wild settings when deer are near one another. Fluid exchange could also lead to the transmission amongst deer given social behaviors such as allogrooming in seasonal social groups [27]. Indirect transmission of SARS-CoV-2 between deer may be possible through fomites, such as contaminated surfaces or feed, however, as previously mentioned, evidence of indirect transmission between deer is lacking.
Although our knowledge of SARS-CoV-2 has greatly increased over the last three years, factors influencing the introduction and transmission of SARS-CoV-2 in wildlife hosts and spillover risk remain poorly understood. Therefore, we develop a SIRS (Susceptible-Infected-Recovered-Susceptible) epidemiological model and apply it to wild and captive deer populations in a range of scenarios to address the following five objectives: Objective 1: Evaluate human-deer (introduction) and deer-deer transmission (spread) in wild and captive deer scenarios to understand the role of pathways in disease dynamics; Objective 2: Examine potential ranges of average prevalence, persistence, and incidence proportion of SARS-CoV-2 outbreaks in deer in wild and captive scenarios; Objective 3: Understand the sensitivity of prevalence, persistence, and incidence proportion to introduction and spread across all scenarios; Objective 4: Test if SARS-CoV-2 outbreaks in deer require continual introduction from humans or just a single introduction event; Objective 5: Identify how contact between deer in captive and wild scenarios through fence line interactions can influence SARS-CoV-2 prevalence and persistence system-wide.
Collectively, this study provides insights into the dynamics of SARS-CoV-2 outbreaks in white-tailed deer populations and provides evidence for different mechanisms of spillover and persistence.
General approach and terms
We modeled SARS-CoV-2 transmission between humans and white-tailed deer, and among deer in several scenarios, including two types of captive facilities and wild deer in rural and suburban environments. We estimated direct (aerosolized) transmission rates from humans to deer as causing initial deer infections (human-to-deer, hereafter HtD). We estimated direct (aerosolized and fluid pathways) transmission rates within wild and captive deer populations following introduction from humans (deer-to-deer, hereafter DtD). We used these transmission rates to estimate two important epidemiological parameters (Objective 1). The introduction of a pathogen, such as SARS-CoV-2 into deer populations, can be quantified as the common Force-Of-Infection metric from humans to deer (FOI HD ; Fig 1) [28]. Then, SARS-CoV-2 transmission within a deer population can be quantified by the basic reproductive metric, R 0 , or the number of new infections, in a completely naive population, originating from one infectious deer over the duration of its infection, with values greater than one indicating sustained infection throughout a population and values less than one indicating pathogen fade-out.(Fig 1 , 28]. We projected the outbreak of infections across 120 days in each scenario to incorporate fall deer behavior (September-December). We focused on the fall season as deer reproductive behavior results in increased DtD contact rates and multiple hunting seasons and seasonal captive activities could increase HtD interactions. We used these fall projections to estimate the prevalence, persistence, and incidence proportion of SARS-CoV-2 in various types of simulated white-tailed deer populations (Fig 1 ; Objective 2). We used our simulated data to investigate the interaction between epidemiological parameters (introduction and transmission) and outbreak characteristics in deer populations (prevalence, persistence, and incidence In each stage outlined above, we describe the stage, illustrate the concept, and define the metric we use to characterize each stage across multiple scenarios of deer in wild and captive environments. We consider the introduction of SARS-CoV-2 into white-tailed deer populations through aerosolized transmission from an infected human, quantified as the Force-Of-Infection (FOI HD ). Transmission occurs as an infected deer (orange circle) interacts with susceptible deer (gray circles), transmitting SARS-CoV-2 through aerosols and fluid over the course of the animal's infectious period (γ). When the individual recovers from its infection (gold circle), it will have stemmed several secondary infections (orange circle), quantified as the basic reproductive number (R 0 = 4). Depending on the magnitude of FOI HD and R 0 (dashed arrows), an outbreak of infections may occur across a deer population. Average prevalence in the Fall season is averaged across daily values (dark line) and incidence proportion can be calculated through the projected fall season (dotted line). This outbreak will either persist or fade determined by the deterministic steady state of the set of ODE equations considered in this study, referred to here as equilibrium (x-axis). The image of a human hand-feeding a deer was created with the assistance of DALL-E 2.
[URL]; Objective 3). We contrasted outbreak dynamics from continuous introduction from humans, compared to those from a single, initial infection event with no further introduction from humans (Objective 4). Finally, we ran the 120-day projection for wild and captive populations connected through a single-layer fence to explore how interactions between captive and wild deer may influence the prevalence and persistence of SARS-CoV-2 in both populations (Objective 5).
Epidemiological model
To understand SARS-CoV-2 transmission between humans and deer and within deer populations, we developed a two-host (captive and wild deer) Susceptible-Infected-Recovered-Susceptible (SIRS) model (Fig 2, 5]. We considered two primary introduction pathways, including aerosolized SARS-CoV-2 transmission in shared airspace, and fluid transmission from sputum or other contagious discharges upon direct contact. For DtD transmission, we integrated both transmission pathways, while for HtD transmission, we estimated aerosolized transmission only. Humans were included as a source of infection, but human disease dynamics were not modeled as a response to disease dynamics in deer. We made several assumptions either inherent in our SIRS approach or that incorporate patterns documented in the relevant literature. We assume that: transmission rates are additive; transmission rates are the same for naïve susceptible deer and recovered deer that have lost temporary immunity and are again susceptible; DtD transmission rates in wild scenarios and captive scenarios mimic wild conditions and are intermediate between frequency-and density-dependent transmission [29]. DtD transmission rates in intensive captive scenarios and across fence lines, and HtD transmission rates in all scenarios are constant and frequencydependent, based on available data; DtD transmission rates via fluids only occurs when an infected and a susceptible individual are in proximity, including along fence lines; human prevalence is constant across each 120-day projection; there is homogenous mixing within captive and wild deer populations; recovery from infection and loss of immunity do not differ between captive and wild deer; there is no viral evolution; there is no disease-induced mortality [22]; there is no spillback from deer to humans (or at least, such spillback does not affect the disease dynamics in the deer population); and deer populations are closed, with no births, deaths, immigration, or emigration. On this last assumption, we recognize that many deer are harvested in the season we chose to simulate. We assume that harvest is random within the population such that the proportion of individuals within the various disease compartments of the SIRS model are unaffected.
The SIRS model was specified with a system of six ordinary differential equations (ODE) [5], and we derived rates for aerosolized and fluid transmission. We tracked the fractions of a population that are susceptible (s), infected (i), and recovered (r), rather than the number of individuals in each compartment. Human prevalence is fixed and not explicitly modeled in this study (i H ). In the equations that follow, our notation includes superscripts to indicate the mode of transmission, including: "Aero", to indicate transmission by aerosols; and "DC" to indicate transmission via fluid exchanged through direct contact. We use subscripts to indicate the individuals in a particular transmission interaction: transmission between wild deer (WW); transmission between captive and wild deer (CW); transmission between captive deer (CC); transmission from humans to wild deer (HW); and transmission from humans to captive deer (HC). We derived transmission rates as the product of HtD and DtD proximity rates and infection probabilities from previous studies. We used expert-elicitation to estimate any parameters unavailable in the literature. For more detail about parameter estimation, see the Scenario Descriptions section below.
Ordinary differential equation
Three ODEs describe the disease dynamics in the wild deer population, with the change in the fraction of the wild population that is susceptible (s W ) given by the change in the fraction of the wild population that is infected (i W ) given by and the change in the fraction of the wild population that is recovered (r W ) given by where α is the immunity loss rate; β is the transmission rate specific to the infectious and susceptible host recipient type (e.g., wild or captive deer) and interactions (i.e., aerosolized or direct contact); and γ is the recovery rate from infection (Fig 2). Three additional ODEs describe the disease dynamics in captive deer, with the change in the fraction of the captive population that is susceptible (s C ) given by the change in the fraction of the captive population that is infected (i C ) given by and the change in the fraction of the captive population that is recovered (r C ) given by We monitored proportions through these projections to reduce assumptions about population size in either wild or captive settings. We note that we summarized these continuous changes into discrete, daily S, I, and R compartment sizes for our analysis for ease of interpretation.
Aerosolized transmission
Aerosolized transmission rates between a host i and recipient j (b Aero ij ) can be described as where ω ij is the proximity rate between host-recipient(i,j) type (human-wild deer, human-captive deer, wild deer-wild deer, captive deer-captive deer, wild deer-captive deer, captive deerwild deer); and σ Aero is the probability of infection from aerosols.
We define proximity ω ij as the frequency per day that host i and recipient j are within 1.5 meters (m) of each other, drawn from existing social distancing guidelines for humans which range from 1-2 meters [30,31]. We estimate the proximity rate for wild deer, ω WW , based on a contact rate model developed by Habib et al. [32] for chronic wasting disease in white-tailed deer that permits density-or frequency-dependent transmission as well as intermediate cases that blend these two standard transmission processes. This rate applies to deer-deer transmission in most scenarios, including cases with and without attractants (e.g., bait, supplemental feed). We apply this model for captive circumstances that mimic natural conditions. It is given by where κ is a scaling constant; q is a concavity scaling constant of the density-contact rate relationship ranging from 0-1, which allows an intermediate blend of density-dependence to frequency-dependence, respectively [32]; N W is the total population size; A W is the area inhabited by the population; ρ attractant is the adjustment for the presence of an attractant (ρ attractant = 1 indicates no attractants present; ρ attractant > 1 indicates attractants present). All other proximity rates, including captive-captive deer (ω CC ), captive deer-wild deer (ω CW ), human-wild deer (ω HW ), and human-captive deer (ω HC ) were not explicitly modeled, and instead were drawn from parametric distributions.
The probability of infection, σ Aero , given proximity, is a function of the instantaneous dose received and a Wells-Riley dose-response relationship given by where θ is the species-specific rate of infection from 1 quantum of SARS-CoV-2; and Q is the dose (quanta) received by a single contact with an infected individual. Buonanno et al. [33] defines a quantum as "the dose of airborne droplet nuclei required to cause infections in 63% of susceptible human individuals."Therefore, θ > 1 corresponds to 1 quantum causing infection in >63% of susceptible individuals, and θ < 1 corresponds to 1 quantum causing infection in <63% of susceptible individuals [33][34][35].
To estimate the dose received by a susceptible individual (Q) we modeled (1) the emission of SARS-CoV-2 from an infectious individual (ER q ) and ( 2) the resulting concentration of SARS-CoV-2 in a designated airspace around an infectious individual, considering viral emission and viral loss. First, an infected individual emits virions at a particular rate (ER q ; quanta/ hr) as the product of the viral load in its exhalation (C v ; RNA copies/ml), a conversion factor (C i ; quanta/RNA copy), the inhalation/exhalation rate (IR; m 3 /hr), and the exhaled droplet volume concentration (V drop ; ml droplets/m 3 exhaled) [36] given by We then use the emission rate to model the instantaneous concentration of virions (C; quanta/m 3 ) in a well-mixed airspace (V air ; m 3 ) around an infected individual (ER q ; quanta/hr). We assumed that the airspace around an infected individual was a half-sphere with a radius of 1.5 m, or 7.07 m 3 . We account for viral loss as the sum of air exchange (AER; hr -1 ), settling (s; hr -1 ), and inactivation (λ; hr -1 ) [33]. Thus, the instantaneous concentration is given by When a susceptible individual enters the contaminated airspace surrounding an infectious individual, the dose (Q; quanta) is the product of the inhalation rate of the susceptible individual (IR; m 3 /hr), the concentration of virions in the fixed volume (C; quanta/m 3 ), and the duration of contact (t contact ; hr) given by ð12Þ
Fluid transmission
We model fluid transmission rate for deer conditional on proximity with another deer (Eq 8).
Fluid transmission rates between a host and recipient (b DC ij ) are given by where ω ij is the proximity rate between host-recipient(ij) type (wild deer-wild deer, captive deer-captive deer, captive deer-wild deer); ε DC is the probability of direct contact conditional on proximity; and σ DC is the probability of infection from direct contact. The probability of infection, σ DC , given contact, was modeled similarly to Eq 9, as a loglogistic function of dose and the reciprocal probability of infection given exposure to a single dose, k [37]. The dose received is a product of the transferred sputum volume given contact, V sputum , and viral concentration in sputum, C v given by where C v is the viral concentration in sputum (in plaque-forming units; PFU); V sputum is the volume of sputum transferred given contact; and k is the reciprocal of the probability of a single PFU causing infection.
Scenario descriptions
We estimated HtD and DtD transmission and outbreak characteristics in four scenarios: (1) wild deer in a rural setting, (2) wild deer in a suburban setting, (3) captive deer in an outdoor ranch, and (4) captive deer in an intensive facility (Fig 2). These scenarios span a range of possible habitat or captive facility conditions, deer densities, and proximity rates with humans; although each of these variables is a continuous metric, we discretized the scenarios to make them easier to interpret. Below, we present parameter estimates used in each simulation (Table 1). For parameters that were unavailable in the literature, we conducted expert elicitation using the IDEA protocol and a four-point elicitation process [38,39]. We included 11 experts on two separate panels: one focused on SARS-CoV-2 virology and another on deer behavior in captive and wild settings. The estimates for 13 parameters we solicited from experts are listed in Table 1. Elicitation methods, the elicitation questions for each panel, and individual (anonymous) and aggregated probability distributions are reported in S1 and S2 Files and S1-S13 Figs. For study Objectives 1 to 4, fence line transmission was fixed at zero to capture outbreak dynamics within these specific scenarios. This transmission rate was restored for the final study objective exploring the influence of linked scenarios across fence lines in outbreak dynamics.
Wild deer in a rural setting-Wild deer are free-ranging in an area with a rural human density (3.1 humans/km 2 ; 15 th percentile of U. S. counties with <100 humans/km 2 overlapping white-tailed deer range; [45][46][47]. We assumed that deer interacted with humans during regulated hunting either using still-hunting, or ground blind or tree stand tactics but were not harvested. We also assumed that baiting and backyard feeding were illegal but may still occur. We calculated wild DtD proximity rates using a population density of 10 deer/km 2 for an area with 26% wooded habitat [32]. For aerosol transmission,we assumed that proximity rates for deer approaching within 1.5m of each other were equal to Habib et al.'s [32] estimated proximity rate of deer approaching within 25m of each other. HtD transmission was derived by estimating the rate and duration of human-deer proximity events and a fixed human prevalence of 5% (Table 1). Wild deer in a rural setting had the lowest rate and duration of these humandeer proximity events (Table 1). We calculated and applied air-exchange rates (AER; 4 -hr ) based on a 15-minute residence time drawn from a range of published values for forest airflow studies (Table 1) [43,44].
Wild deer in a suburban setting-Wild deer are free-ranging in an area of suburban human density (100 humans/km 2 ) [45]. DtD proximity rates were derived using the same parameters as used in the rural scenario, and the AER value used was the same as in the rural scenario (Table 1). Wild deer in a suburban setting experience higher HtD transmission rates, driven by higher HtD proximity rates and longer duration of proximity events, relative to wild deer in a rural setting (Table 1).
Captive deer in an outdoor ranch-We considered captive deer in an outdoor ranch facility typical of a managed, fenced hunting reserve. We assumed that deer stocking densities resulted in the same DtD proximity rates as were estimated in wild scenarios, with an increase in proximity rates due to supplemental feeding (Table 1). We used the same AER value as in wild settings as these captive individuals reside outside. We assume HtD proximity rates are the same as those estimated for the "wild deer in a suburban setting" scenario, but the typical duration of these proximity events is longer in this scenario, reflecting those typical of a captive facility (Table 1). Captive deer in an intensive facility-The last scenario considered was captive deer in a captive breeding or exposition facility. Deer in this type of facility were predominantly indoors at high stocking densities and low indoor air exchange rates (AER; 1 -hr ). Both DtD and HtD proximity rates and duration were highest in this scenario (Table 1).
Objective 1: Differences in human-to-deer and deer-to-deer transmission across scenarios-We quantified the strength of HtD transmission in each scenario using Force-Of-Infection calculations from humans to deer (FOI HD ; Eq 15). These FOI calculations are based on HtD transmission rates (b Aero HD ; Eq 7) and human prevalence (i H ) and equate to the proportion of susceptible deer infected by infectious humans per day.
FOI HD ¼ b Aero HD i H ð15Þ
We also report the probability of at least one HtD transmission per 1,000 deer (N) over the fall season (t = 120 days), using a constant hazard model (Eq 16) [48].
We quantified the strength of DtD transmission for each scenario using the number of susceptible deer infected by a single infectious deer, R 0 , derived from the sum of aerosol and fluid transmission rates over the recovery period from infection (γ; Eq 17). Again, R 0 values greater than one indicate sustained transmission throughout a population, and values less than one indicate pathogen fade-out.
We compared FOI HD , p(HtD), and R 0 estimates across scenarios to evaluate differences in the potential for SARS-CoV-2 to be transmitted from humans to deer and then spread amongst deer. All calculations were conducted in R [49]. We summarized the sensitivity of FOI HD and R 0 to expert-elicited parameters (S14 and S15 Figs). We focused on expert-elicited parameters for these sensitivities as these parameters had the greatest uncertainty in our calculations. We did not present sensitivity of p(HtD) to expert-elicited parameters as p(HtD) was derived from FOI.
Objective 2: Average prevalence, persistence of infection, and incidence proportion in each scenario-We used the six ODEs for the SIRS model, parameters estimated from the literature or expert elicitation, and derived transmission parameters to project continual SARS-CoV-2 introduction and spread across each scenario of interest (Table 1). From these projections, we calculated the proportion of individuals in the wild, captivity, or in both settings that were susceptible, infectious, or recovered. We ran 1,000 iterations for each of the four scenarios. Each iteration had a randomly drawn parameter set, where we randomly drew one value from each parameter distribution during each iteration, resulting in 1,000 parameter sets used to project outbreaks in each scenario (Table 1). Parameters that were constant across scenarios did not vary between parameter sets which ensured that any observed variation was due to differences across scenarios, and not sampling variation from repeated random draws from error distributions.
We projected the proportional size of each SIRS compartment for 120 days for each iteration, using the ODE solver ode() from the deSolve package in R [49,50]. We estimated the average daily prevalence of deer in each scenario during the 120-day projection. We determined if SARS-CoV-2 would persist beyond the 120-day projection for each iteration using the runsteady() function from the rootSolve package [51,52] to estimate the deterministic stable state from the SIRS ODE equation. We assigned each iteration a logical value if infectious compartment at equilibrium was >0.1% for each iteration (at least 1 deer infected out of 1 000). We estimated mean probability of persistence and 95% binomial confidence intervals using the binom.confint()function with the exact method from the binom package for each scenario [53]. Finally, we tracked the incidence proportion, or cumulative proportion of the population infected over the 120 days during these simulations for wild and captive deer (Eqs 18 and 19). This incidence proportion could exceed 1, indicating that all individuals in the population were infected at least once.
Incidence proportion
We summarized these three measures across iterations in each scenario with the median value and 80% confidence intervals. These include median average prevalence, median probability of persistence, and median incidence proportion.
Objective 3: Sensitivity of prevalence, persistence and incidence proportion to spillover and spread-We tested the sensitivity of prevalence, persistence, and incidence proportion of SARS-CoV-2 in white-tailed deer to different levels of spillover (FOI) and spread (R 0 ). After each iteration, we categorized outcomes by one of the following spread categories: unsustained spread (R 0 <1), low, sustained spread (1< R 0 � 3), medium, sustained spread (3 < R 0 � 5), and high, sustained spread (R 0 > 5). We used the stat-smooth() function from the ggplot2 package [54] to visualize trends between HtD transmission, as quantified by FOI, and outbreak metrics for each spread category.
Objective 4: SARS-CoV-2 outbreaks in deer from a single introduction event-We tested whether a SARS-CoV-2 outbreak can occur following a single spillover event, in contrast to the continual introduction modeled above for the other objectives. We simulated this introduction as an initial event that resulted in 0.1%, 1e-4%, and 1e-7% prevalence in deer at the start of the 120-day projection, with no further introduction from humans. We compared differences in prevalence, persistence, and incidence proportion between these initial spillover simulations and the continuous spillover simulation investigated for the other objectives.
Objective 5 Effects of fence line interactions between wild and captive deer on SARS-CoV-2 prevalence and persistence on either side of the fence-We extended our SIRS model to allow fence line interactions between captive and wild deer. To do this we projected outbreaks for paired captive -wild scenarios separated by a fence, using combinations of the two captive and two wild scenarios and associated parameters described above (n = 4 combinations; hereafter systems). We added fence line contact probability and allowed all individuals to interact along fence lines, enabling proximity and direct contact (Table 1).
Objective 1: Differences of introduction and spread for white-tailed deer across settings
The risk of introduction of SARS-CoV-2 from humans to deer varied within and across scenarios (Eqs 15 and 16, respectively; Fig 3 and Table 2). Median FOI HD estimates were 1244-, 85-, and 19-times higher in the intensive facility, outdoor ranch, and wild deer in suburban scenarios, respectively, relative to median FOI HD estimates for rural, wild deer (Table 2). Metrics include: the proportion of susceptible deer infected by humans, per day (Force-Of-Infection from humans-to-deer, FOI HD ); the probability of at least 1 in 1,000 deer becoming infected from a human during the fall season (probability of human-to-deer transmission, p(HtD, 1:1,000)); the number of susceptible deer infected by an infected deer (R 0 ); the average daily prevalence during the fall season (average prevalence); the probability of SARS-CoV-2 persisting beyond the simulated fall season (Persistence); and the total proportion of the population infected during the fall season (incidence proportion). [URL]002 FOI HD was highly sensitive to the frequency and duration of proximity between humans and deer (S14). Median probabilities of at least one HtD transmission per 1000 deer ranged from 100%, 56.1%, 17.7%, and 1.1% in the intensive facility, outdoor ranch, wild suburban, and wild rural scenarios, respectively (Table 2). There was high uncertainty around risk of introduction in each scenario, with detectable differences between the intensive facility and wild deer in rural setting using 80% confidence intervals (Table 2). SARS-CoV-2 transmission between deer (R 0 ; Eq 18) was greater in captive scenarios relative to wild scenarios, with most iterations sustaining transmission of SARS-CoV-2 among the deer population (Table 2). Transmission in both wild scenarios were nearly identical, with 51.3% of iterations resulting in R 0 values too small to sustain transmission of SARS-CoV-2 (R 0 <1; median R 0 = 0.97; Table 2). R 0 values were highly variable in each scenario leading to no detectable differences with 80% confidence (Table 2). R 0 was sensitive to several parameters, including duration of a deer-deer proximity event, the concentration of SARS-CoV-2 in deer sputum, and SARS-CoV-2 dose-response in deer (S15). R 0 in captive, intensive facilities was sensitive to deer-deer proximity rate due to the uncertainty around the aggregate estimate from expert elicitation (S15).
Objective 2: Average prevalence, persistence of infection, and incidence proportion in each setting
Simulated outbreaks of SARS-CoV-2 were variable across scenarios, with higher average prevalence, probability of persistence, and incidence proportion in captive scenarios relative to wild scenarios (Table 2 and Fig 4). Intensive facilities had the highest average prevalence, probability of SARS-CoV-2 persistence, and incidence proportion, followed by the outdoor ranch scenario and both wild scenarios (Table 2). Median outbreak metrics in both wild scenarios, while much lower than captive scenarios, were slightly elevated in the suburban setting compared to the rural setting (Table 2). Overall, there was high variability in these metrics in each scenario, with non-overlapping 80% confidence for the probability of persistence in the intensive facility, outdoor ranch, and wild scenarios (Table 2 and Fig 4).
Objective 3: Sensitivity of prevalence, persistence and incidence proportion to spillover and spread
When we partitioned the relationship between FOI HD and outbreak characteristics, we found evidence that sensitivity to FOI HD differs depending on how quickly SARS-CoV-2 transmits (R 0 , Fig 5). When deer-deer transmission is too low to sustain SARS-CoV-2 infections (R 0 <1), high FOI HD is required for non-zero average prevalence and incidence proportion during the projection, and for a high probability of infections persisting (Fig 5). As deer-deer transmission reaches self-sustaining levels (1< R 0 <3), the role of FOI HD has a greater influence on average prevalence, persistence, and incidence proportion (Fig 5). As R 0 continues to increase to medium (3< R 0 � 5) and high spread (R 0 > 5), the sensitivity of prevalence and incidence proportion to FOI HD diminishes, and persistence is no longer sensitive to changes in FOI HD .(Fig 5).
Objective 4: SARS-CoV-2 outbreaks in deer from a single introduction event
Differences in outbreak characteristics exist between continual introduction of SARS-CoV-2 from humans and a single, initial introduction (Fig 6). However, these differences vary depending on the size of the initial introduction and the scenario and uncertainty prevented high confidence in these differences. If an initial, single introduction resulted in 0.1% prevalence in any context, the average prevalence and incidence proportion were slightly greater than the average prevalence and incidence proportion when SARS-CoV-2 was continuously introduced. However, probability of persistence decreased in all scenarios except for wild deer in a rural setting, where probability of persistence would increase with this initial prevalence compared to when SARS-CoV-2 was continuously introduced. With an initial prevalence of 0.0001%, all scenarios showed median average prevalence and incidence proportion similar to or slightly lower than when SARS-CoV-2 was continuously introduced. The probability of persistence was consistent with those estimated for an initial 0.1% prevalence. Finally, with an initial prevalence of 1e-7%, the lowest tested, all scenarios showed decreases in average prevalence, probability of persistence, and incidence proportion relative to other continuous or initial infection conditions. However, even at this low level of initial infection, deer in the intensive facility scenario had median average prevalence and median incidence proportion that were comparable to when SARS-CoV-2 was continuously introduced, albeit with greater variability.
Objective 5: Effects of fence line interactions between wild and captive deer on SARS-CoV-2 prevalence and persistence on either side of the fence
When fence line interactions occurred between all combinations of captive and wild scenarios, wild deer had a higher prevalence and incidence proportion of SARS-CoV-2 during the fall projection compared to simulations without fence line interactions (Objective 2; Table 3). These increases were highly variable depending on the captive and wild conditions. The probability for persistence did not increase for wild deer when fence line interactions occurred, and captive deer did not experience an increase in any metric (Table 3). Of the four systems, fence line interactions had the greatest effect when dividing captive deer in an intensive facility and wild deer in a rural setting. In this system during the 120-day projection, the average prevalence in wild deer increased by approximately 122% (median), and the incidence proportion of the wild deer in a rural setting increased from 1e-5 to 0.278 (median, Table 3). Smaller increases were estimated in the intensive facility and wild deer in a suburban system (Table 3). We estimated similar patterns when considering systems with fence line interactions between outdoor ranch facilities and wild deer, albeit smaller in magnitude (Table 3).
Discussion
Our study demonstrates the potential for variable, yet widespread risk of SARS-COV-2 introduction and spread across white-tailed deer populations in North America. Our findings indicated that epidemiological conditions and the proximity rates of white-tailed deer may lead to sustained transmission. We estimated sustained infections in wild and captive populations across a wide range of Force-Of-Infection rates from both continual spillover from humans and an initial spillover event. We also demonstrated that wild deer may experience higher prevalence, persistence, and incidence proportion of SARS-CoV-2 infections when sharing a fence line with captive facilities. These results complement ongoing, retrospective surveillance efforts across a range of captive and wild contexts by revealing the spillover risk of SARS-CoV-2 from infected humans and the risk of transmission between deer [20,55]. More broadly, our approach provides a framework for using epidemiological modeling to evaluate the risks of outbreaks and sustained infections of SARS-CoV-2 and other zoonotic diseases in wildlife hosts in a variety of contexts.
Despite lower risks of introduction and transmission, SARS-CoV-2 was still able to transmit and sustain itself in wild scenarios. If R 0 was less than one, indicating unsustainable transmission, our two wild scenarios did not have sufficient FOI HD to sustain infections. However, when R 0 increased above one, wild scenarios showed rapid increases in average prevalence and incidence proportion, and a high probability of SARS-CoV-2 persisting into the future. Our findings generally match those reported by Hewitt et al. [55], who used surveillance data from wild deer across the United States of America to estimate infection rates and prevalence, and estimated R 0 greater than 1 in most of counties monitored across 27 states. In short, our results indicate that there may be broad circumstances where wild deer populations could face repeated introduction and sustained transmission of SARS-CoV-2.
Both captive scenarios showed a higher risk of introduction and a higher rate of transmission, resulting in higher prevalence and persistence relative to wild scenarios. Our findings conform to the available literature on the introduction and transmission of SARS-CoV-2 in captive populations. Roundy et al. [56] reported 94.4% seropositivity for one captive herd and 0% seropositivity in two other captive herds, one of which housed axis (Axis axis) and fallow deer (Dama dama). This contrast could indicate a difference in transmission from humans, as stocking conditions may increase the transmission of the virus. Our study also indicated different epidemiological dynamics in systems where captive and wild deer may interact through fence lines compared to systems without these interactions. However, despite the vulnerabilities of captive conditions to rapid transmission of SARS-CoV-2, we emphasize that the patterns of outbreaks in facilities and increased risk of fence line transmission are likely to vary through space and time. Our captive scenarios did not focus on single facilities with a particular herd size, but rather a pool of captive individuals. Introduction and transmission within individual facilities may be so rapid that a localized infection results in SARS-CoV-2 running out of susceptible hosts and the outbreak extinguishing itself. Spillover to wild populations through fence line interactions during localized outbreaks remain a risk for these individual facilities, though the risk of spillover from wild to captive facilities appears low.
White-tailed deer encounter a wide range of conditions across North America making it challenging to capture this variability in a single analysis. The four scenarios evaluated here are indicative of processes typical of both wild and captive conditions. Our analysis focused on temporal patterns of SARS-CoV-2 introduction and spread across wild and captive whitetailed deer, yet spatial variation undoubtedly plays a role. We did not make our simulations spatially explicit, as we felt that our global approach met our objectives to better understand infection dynamics across typical conditions. Additionally, integrating a spatial component to this study would require specific spatial conditions and assumptions that either generalize across large geographic extents, or limit inferences to conditions in a specific locality. We feel these are important next steps given our inferences from this study and will aid in our understanding of the reported spatial and temporal heterogeneities of SARS-CoV-2 cases in whitetailed deer [10,19,24,57].
We were required to make several assumptions in our parameterization of the SIRS models that may have influenced our inferences. First, we used Watanabe et al.'s [37] reported infection probability for SARS-CoV in mice by intranasal exposure to estimate transmission of SARS-CoV-2 through fluid when deer make physical contact. We join other simulation studies that use this parameter estimate to calculate direct contact probability through fluid transfer and acknowledge the uncertainty of this parameter given it has not been quantified in the literature [58]. Second, we used the stable-state equilibrium of the SIRS model to infer the persistence of SARS-CoV-2. We acknowledge that this assumes that parameter values are not stochastic and do not change past the simulated fall season. Seasonal changes in white-tailed deer behavior are well-documented and affect introduction and spread for multiple pathogens in deer, as with other host-pathogen systems [59][60][61]. Third, parameters used to derive transmission risk between deer in our simulations did not vary by sex. Ongoing monitoring of SARS-CoV-2 in wild white-tailed deer populations indicate higher infection probability and seropositivity in male white-tailed deer, likely driven by sex-specific behaviors [55,62]. We believe that our inferences are robust with our integration of uncertainty around derived parameter estimates and the patterns of prevalence and persistence values documented in multiple studies monitoring ongoing infections [17].
Despite a growing number of studies of SARS-CoV-2 in white-tailed deer, there is no consensus on how SARS-CoV-2 is introduced into deer populations. This is a key detail in mitigating the introduction and transmission of SARS-CoV-2 in a prolific wildlife species that can interact with humans in both wild and captive contexts. In this study, an initial outbreak had to infect less than 10e-7% of deer for there to be an observable decrease in average prevalence, probability of persistence, and incidence proportion compared to those observed during continual spillover. These results indicate that an initial introductory event, even at a low rate, could result in an outbreak in both captive and wild settings. While introduction through aerosolized transmission from humans to deer is presumed to be most probable, our findings indicate that indirect sources of infection could play a role through a single transmission event. Infection from contaminated fomites or wastewater could initiate an outbreak given sufficient dose received by an individual. However, further research remains into the risk posed by these sources.
Sustained SARS-CoV-2 infections in this prolific wildlife species frequently interacting with humans in captive and wild settings creates a One Health challenge that affects human, animal, and ecosystem health. SARS-CoV-2 has demonstrated its ability to spread in wild and captive white-tailed deer populations across much of North America. The outbreak dynamics reported in this study indicate the ease by which the virus can be introduced and sustained in this nonhuman species. Surveillance studies indicate that multiple lineages of SARS-CoV-2 have been introduced and broadly circulated in white-tailed deer populations [10,13,19], with evidence of spillback from deer to humans [14,63]. Our modeling approach provides a foundation to evaluate risks to human, animal, and ecosystem health posed by zoonotic diseases, and to test potential interventions to meet this and other One Health challenges.
S15 Fig. Sensitivity of basic reproductive number (R0) to expert-elicited parameters.
Each row corresponds to a scenario, and each column corresponds to a parameter included in the calculation of R 0 . Points indicate each iteration's draw of each parameter and resulting derived parameter (R 0 ), with a trend line fitted to summarize the sensitivity of R 0 to the range of drawn parameter values. Point and error bars on the top of each plot indicate the mean and 95% confidence intervals of the aggregate parameter distribution from the expert elicitation exercise. Deer-deer proximity rates for captive, outdoor ranch, wild, suburban, and wild, rural scenarios were drawn from Habib et al.'s (2014) [32] contact rate model, with less uncertainty compared to expert-elicited proximity rates in the captive, intensive facility scenario.(TIF)
Fig 1 .
Fig 1. The three stages of zoonotic spillover from humans to persistence in white-tailed deer. In each stage outlined above, we describe the stage, illustrate the concept, and define the metric we use to characterize each stage across multiple scenarios of deer in wild and captive environments. We consider the introduction of SARS-CoV-2 into white-tailed deer populations through aerosolized transmission from an infected human, quantified as the Force-Of-Infection (FOI HD ). Transmission occurs as an infected deer (orange circle) interacts with susceptible deer (gray circles), transmitting SARS-CoV-2 through aerosols and fluid over the course of the animal's infectious period (γ). When the individual recovers from its infection (gold circle), it will have stemmed several secondary infections (orange circle), quantified as the basic reproductive number (R 0 = 4). Depending on the magnitude of FOI HD and R 0 (dashed arrows), an outbreak of infections may occur across a deer population. Average prevalence in the Fall season is averaged across daily values (dark line) and incidence proportion can be calculated through the projected fall season (dotted line). This outbreak will either persist or fade determined by the deterministic steady state of the set of ODE equations considered in this study, referred to here as equilibrium (x-axis). The image of a human hand-feeding a deer was created with the assistance of DALL-E 2.
Fig 2 .
Fig 2. A conceptual diagram of the Susceptible-Infectious-Recovered-Susceptible (SIRS) epidemiological model used for this simulation study. Objectives that focused on specific captive or wild scenarios had no deer-deer fence line transmissions, preventing transmission between captive or wild populations. Objective 5 focused on how fence line transmission in captive-wild systems influence outbreak dynamics on both sides of the fence. [URL]002 in-line equation numbers. Mean and standard deviation (μ and σ), along with error distribution are listed for expert-elicited estimates (S1-S13 Figs). Parameters which do not apply to particular scenarios are indicated (NA). [URL] 3 .
Fig 3. Variation in Force-Of-Infection from humans-to-deer (FOI), probability of at least 1 human-to-deer (HtD) transmission, and basic reproductive numbers (R 0 ) across the four scenarios considered in this study. Human Force-Of-Infection is log10 transformed and presented as odds of HtD transmission per deer, per day. The basic reproductive number threshold between unsustained and sustained transmission from deerto-deer is indicated with a horizontal line (R 0 = 1). Box plots depict the minimum, first quartile, median, third quartile, and maximum, with outliers depicted as single points. [URL] 4 .
Fig 4. Distributions of average prevalence, persistence probability, and incidence proportion values during the 120-day fall projection in each scenario of interest.1000 simulations were run for each scenarioBox and whisker plots depict the minimum, first quartile, median, third quartile, and maximum, with outliers depicted as single points. Error bars for persistence represent 95% confidence intervals. [URL] 6 .
Fig 6. Variation of average prevalence, persistence, and incidence proportion during the 120-day fall projection. Error bars for persistence represent 95% confidence intervals. Plots are faceted by scenario, with variation in outbreak characteristics displayed for continuous introduction from humans, and various degrees of initial, single introductions with no continuous introduction from humans. Box plots depict the minimum, first quartile, median, third quartile, and maximum, with outliers depicted as single points. [URL]006 0.001) CI = confidence interval. [URL]003
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Domain: Environmental Science Biology
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A Predictive Model for Daily Inoculum Levels of Gibberella zeae in Passo Fundo , Brazil
The deposition of spores of Gibberella zeae, the causal agent of Fusarium head blight of wheat, was monitored during 2008–2011, in Passo Fundo, RS, Brazil. The sampling was carried out in a 31-day period around wheat flowering. The numbers of colonies formed were related to meteorological variables. In this study, a hierarchical autoregressive binary data model was used. The model relates a binary response variable to potential covariates while accounting for dependence over discrete time points. This paper proposes an approach for both model parameter inference and prediction at future time points using the Markov chain Monte Carlo (MCMC). The developed model appeared to have a high degree of accuracy and may have implications in the disease control and risk-management planning.
Introduction
Wheat (Triticum aestivum L.) is an important crop in Brazil especially in the South: 90% of the growing area is established in the states of Rio Grande do Sul, Santa Catarina, and Paraná. In this subtropical region, weather conditions during the growing season favor the occurrence of foliar and flowering diseases [1]. Usually, two to three fungicide applications may be needed to control these diseases, thus increasing production costs [2].
Among wheat diseases, Fusarium head blight (FHB) has increased its pressure on crops in many production regions. Apart from losses in grain yield and reductions in baking and seed quality, the major peril due to FHB is the contamination of grain with toxic fungal secondary metabolites known as mycotoxins. The most prevalent mycotoxins are trichothecenes such as deoxynivalenol (DON) and nivalenol (NIV). To protect consumers from mycotoxicosis, many countries, including Brazil, have established maximum allowed levels for the most prevalent Fusarium mycotoxins in cereals and cereal products [3].
The main causal agent of the disease is Gibberella zeae (Schwein.)Petch (anamorph Fusarium graminearum Schwabe) [4], a homothallic fungus that survives in host debris on the soil. Inoculum is made up of ascospores and macroconidia that are dispersed by rain splash and wind, landing on wheat heads and infecting the plant during flowering and grain-filling stages [5]. FHB has worldwide distribution although the severities of the outbreaks are influenced by local weather conditions [6]. The wider adoption of minimum and no tillage, short rotations with maize and global climate variability and change are central in the debate on the causes for the re-emergence and expansion of the disease worldwide [7].
In Brazil, similar to other parts of the world, an increasing frequency of severe FHB outbreaks has been reported over the last two decades (especially after 1990) resulting in severe yield losses [1,8,9]. No wheat varieties are immune to FHB and resistance is generally controlled by several genes of moderate/weak effect and they are defined genetically as quantitative trait loci (QTL). In addition to these, mycotoxins affect production throughout the world, the ability to predict FHB and DON and other mycotoxin contamination is important to reduce the year-to-year risk for producers. Owing to these dangerous consequences of reducing wheat yield and quality around the world, computer models, based on weather variables (temperature, rainfall, and moisture level), have been developed to predict the likelihood of occurrence of FHB and DON contamination in wheat [10].
Inoculum quantification is an important step in processbased model development [11]. It has been shown that weather factors such as precipitation and temperature are highly related to inoculum density in the atmosphere [12][13][14][15]. Statistical models for this purpose have been made using techniques based on linear regression or other generalizations. When the response of the models is binary data, such as inoculum incidence, data fitting with generalized linear models based on logit link function [16] has proven to be the most appropriated. However, when the data are collected at successive time points such as daily or hourly, it may be correlated and under these circumstances an autoregressive structure, specially AR (1), can be used to solve the correlation in the data. Examples of this approach were proposed by [17][18][19].
This study examines the potential impact of climate variability on daily deposition levels of G. zeae propagules using hierarchical logistic model techniques. Our goal is to establish a statistical model of spore deposition that can be used to calculate probabilities of FHB infection as the wheat phenology advances from heading to soft dough stage. Within this framework, we aim, in the future, to relate the risk of FHB infection to the amount of inoculum within wheat fields, host phenology, susceptibility, and weather factors.
Study
Area. Passo Fundo is located at the Planalto Médio Region, northern Rio Grande do Sul State, Brazil (latitude 28 • 15 00 S, longitude 52 • 25 12 W, altitude 684 m) (Figure 1). The region is one of the major wheat production areas in Brazil.
Data Collection.
Patterns of spore deposition were monitored during 2008 through 2011. Each sample period is referred to as a wheat growing season environment. Consecutive sample periods covered the interval of 31 days starting from September 15th. Petri dishes (90 mm in diameter; surface area = 283 mm 2 ) containing Fusarium selective media (FSM) were used to sample viable spores of G. zeae from air. The FSM consisted of a modified Nash-Snyder formulation, prepared as described by [20]. The plates were mounted on a wind-driven sampler previously used by [12]. Two daily samplings performed at 9:00 and 21:00 h.were used with days deemed to begin and end at 09:00 hours GMT for consistency with the meteorological data. Plates were exposed in two periods of 12 h each, called night-and day-time sampling. After exposure to the environment, the plates were transported to the laboratory and incubated in a growth chamber (25 • C and 12 h of darkness) in order to promote fungal growth. Colonies of G. zeae were identified according to color and morphology. Doubtful cases were transferred to Petri dishes containing PDA (potato dextrose agar) for comparison with confirmed true G. zeae colonies. The number of G. zeae colonies was recorded for each plate as CFU (colony forming units). Other Fusarium species were observed but not identified at the species level. Plates were placed, in the local weather station site, at 120 cm above a grass-covered ground.
Daily weather data comprised of maximum and minimum temperature ( • C), total precipitation (mm), sunshine hours (h), and mean relative humidity (%). The data were provided by the National Institute of Meteorology (INMET).
Data Analysis.
Records from the spore sampler were used as daily values (sum of two 12-hour periods), expressed as observed proportion of spores per day. The maximum colony count per Petri dish was fixed to 60 due to operation limitations (visual accuracy during colony identification) in this way the maximum count in each day was 120. The dataset consists of 93 observations where the variable of interest is a binary indicator y [t] with values in range 0-1 at time t. For the climate variables, each observation was centered on climatological normal representing the prevailing set of weather conditions calculated over a period of 30 years (1961-1990) in Passo Fundo this preprocessing step improved the simulation stability and accounted for strong serial correlation intrinsic to environmental data sequentially registered. For example in a given day the total precipitation observed is 25 mm, so the adjusted value is calculated like 25 − 6.2317 = 18.7683where the value 6.2317 corresponded to the precipitation mean observed in the months of September and October from the climatological normal in Passo Fundo.
We fitted an Hierarchical Autoregressive Binary Data Model (HARBDM) to the data. Model development was based on a combined approach from [19,21,22] using the free available software WinBUGS [23]. The statistical analysis and graphs were done in R [24] using the package R2WinBUGS [25]. We ran the simulation with 10000 interactions, in 3 chains, discarding the first 5000. The convergence of the chains was tested using Gelman-Rubin method [26]. We then took percentiles 5, 50, and 95% from the simulation results to get parameter estimates and credibility intervals. The density probability for the median spore incidence, by year, was fitted to a beta distribution.
MCMC (Markov chain Monte Carlo) methodology [27] is adopted to simulate from the full posterior distribution.
Updates were obtained by using the Gibbs sampler [28,29]. The Gibbs sampler split the state vector into a number of components and updated each in turn by a series of Gibbs transitions. Posterior probability estimates for the incidence of spores in a given day were obtained in the context of the group (year).
The data of 2008-2010 was used to construct the model and the data of 2011 to validate the model. (1) In (1) we used terms i for years (2008 to 2010) and t for days (1 to 31) after 15 th of September.
Results and Discussion
A total of 93 sampling days was included in the study. During the sampling time 2076 G. zeae colonies were accumulated. The lowest number of colonies (343) was recorded in 2010. Spores were present in 86 out of 93 days. Summary statistics for each dependent and independent variable are shown in Table 1. The number of rainy days by year were, respectively, 14, 15, and 13.
Visual observations in Figure 2 revealed that climate variability and the number of G. zeae spores present in the air appeared to be associated. Both relative humidity and rain were associated positively with spore incidence while sunshine hours were associated negatively. Temperature amplitude appeared to be weakly related to spore incidence.
The mean and median values of G. zeae incidence were very similar in 2008 and 2009 but contrasted to those observed in 2010 (Table 1).
The monitoring of deposition of G. zeae spores by means of Petri dishes containing selective media provided estimates of inoculum levels in the air of Passo Fundo area. Moderateto-severe Fusarium head blight epidemics occurred during the study period. Thus, the strategy of monitoring spores of G. zeae through different wheat growing seasons was successful in obtaining data from Fusarium head blight epidemic and nonepidemic years. During each sampling period, Fusarium head blight incidence ranged from traces to about 100% of spikes affected. The wheat seasons of 2008, 2009, and 2010 in Passo Fundo area were categorized, respectively, as epidemic, highly epidemic, and nonepidemic. Coincidently, the ENSO phases in each period corresponded to "neutral," "warm," and "cold," respectively. This is in agreement with reports [9] that FHB epidemics are likely to be more severe in "neutral" and "warm" than in the "cold" phase, in this part of the world.
The model constructed with the data between September 15 and October 15 in 2008, 2009, and 2010, respectively, was used to predict the density of G. zeae spores in the air of Passo Fundo. In Table 2, the estimated parameters by group factor (year) are deviations from the climatological normal. In this context we can see that, for a day with no deviation from normal, the probability of incidence of FHB, expressed by e β0 /(1 + e β0 ) for each day, corresponds, by year, to 0.18, 0.20, and 0.06, respectively. The correlation index (φ) (Table 2) between days in 2008 and 2009 were negative and in 2010, positive.
In Table 3, the scale parameter β can be used to estimate the daily inoculum level for a specific year. For example in Passo Fundo, on years with β below a cutpoint (7.0), an alert for moderate-severe status could be set in a monitoring disease system. Otherwise, these parameters (α, β) could be used as priory information in Bayesian model framework.
Another measure of interest is the odds ratio (Table 4) that represents the increase in the incidence by each change in unit deviation from variables from the model.
The adjusted model is showed in (Figure 3) and was then validated by the actual observations (Figure 4). The validation analysis indicates that the model had reasonable accuracy over the predictive period, even though in day 9 the predicted spike was well behind that of the actual peak.
Mechanisms of spore deposition are gravity and scrubbing by rain drops which contribute in a random manner to spore deposition [30]. Wet deposition becomes relatively more important as the distance from a source of spores increases, because dry deposition tends to be limited to the removal of spores near the ground, whereas wet deposition can sweep spores from the entire depth of the spore cloud. For example, a significant portion of ascospores of Venturia inaequalis was collected during hours that rainfall rate was less than 0.25 mm h-1 [31]. Therefore, it is likely that the error range in predicting spore deposition, in our work, is due to the fact that we used total daily rainfall in the model in lieu of actual time courses of rainfall.
Another possible explanation would pertain to packets of air (a localized region of low air density or a descending air current) that settled those days containing higher spore populations due to an earlier massive spore release of some origin in an upwind direction, perhaps at a considerable distance.
The model we developed in this paper describes the deposition probability of airborne spores according to weather factors. In this study, a HARBD model was used in this attempt to develop a G. zeae spore density forecasting system for improving our capacity to predict FHB outbreaks. The developed model appeared to have a high degree of accuracy and may have implications in the disease control and risk-management planning.
The weaknesses of this study must be acknowledged. First, this is a broad assessment of the relationship between climate variability and the incidence of spores of G. zeae at one location. More detailed risk assessment at regional and farm levels may also be required if a comprehensive and systematic risk assessment is to be made. Inclusion of other information (e.g., crop management, stubble characteristics, and other fungal-relevant environmental information) may further improve the model. Second, the model may only be applicable to Passo Fundo and areas with a similar climate background, since only local data were used in the construction of the model.
Conclusions
The autoregressive model is a useful tool for interpreting and applying to local plant disease control measures. Once a satisfactory model has been obtained, it can be used to forecast expected numbers of cases for a given number of future time intervals. Since predictions from HARBD model have the capacity to forecast when an outbreak is likely to occur, it therefore has great potential to be used as a decisionsupport tool for both tactical and strategic recommendations for FHB management. Based on information from the model, we can establish a lower threshold of FHB probability incidence at 0.20.
In the future, combination of this model with an infection process model may result in a complex but more complete model. The combined model may be useful to quantify the impact of FHB epidemics on wheat yield and quality. The development of reliable epidemic forecasting systems should play an important role in FHB management, especially, if associated with expected advances in weather forecasting. Should an outbreak of FHB occur, a farm-scale intervention is usually required. Early warning based on forecasts from the model can assist in improving FHB control. Increasing fungicide spraying during high-risk periods and decreasing it during low-risk periods will improve cost effectiveness of operations. Crop advisers, if anticipating a higher FHB risk of occurrence, can increase vigilance, for example, by alerting farmers, planning for fungicide spraying and preparing for dealing with problem areas. These attempts, if successful, may have significant implications in wheat decision-making and practices, and may help farmers use resources more effectively and efficiently.
Figure 1 :
Figure 1: Location of the sampling site in Passo Fundo, Brazil.
2. 3 . 1 .
Data Model. The functional form of the model is shown below:
Figure 2 :
Figure 2: Relative daily incidence of Gibberella zeae spores (continuous line) and meteorological variables (dashed lines top to bottom graphs: relative humidity, sunshine hours, temperature amplitude, and rainfall), centered on climatological normal in Passo Fundo during 31-day period starting on September 15th of 2008, 2009, and 2010, respectively.
Figure 3 :
Figure 3: Predicted incidence of spores in a given day by year represented by the continuous line and simulated interval (5, 95 percents) dashed line. The vertical lines represent observed values (a). Estimated density for the beta probability distribution function in the different years (b).
Figure 4 :
Figure 4: Validated model of spores incidence using climate variation in Passo Fundo, Brazil. The vertical lines represent the observed values and the continuous line the predicted values (a). Estimated density for the beta probability distribution function (b). The validation period was from September 15 to October 15, 2011.
Table 1 :
Summary statistics from raw data observed from September 15th to October 15th, during three years, in Passo Fundo, RS, Brazil.
Table 3 :
Adjusted parameters for the beta distribution.
Table 4 :
Odds ratio and 95% interval for the autoregressive model.\===
Domain: Environmental Science Biology. The above document has
* 2 sentences that start with 'In this study, a',
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* 2 sentences that start with 'Estimated density for the beta',
* 3 sentences that end with 'spores of G',
* 2 paragraphs that end with 'disease control and risk-management planning'. It has approximately 2831 words, 151 sentences, and 52 paragraph(s).
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Comparative feeding behaviour of native and introduced terrestrial snails tracks their ecological impacts
A developing body of theory and empirical evidence suggest that feeding behaviour as measured by the functional response (FR) can assist researchers in assessing the relative potential, ecological impacts and competitive abilities of native and introduced species. Here, we explored the FRs of two land snails that occur in south-western Ontario, one native ( Mesodon thyroidus ) and one non-indigenous ( Cepaea nemoralis ) to Canada. The non-indigenous species appears to have low ecological impact and inferior competitive abilities. Consistent with theory, while both species conformed to Type II functional responses, the native species had a significantly higher attack rate (5.30 vs 0.41, respectively) and slightly lower handling time (0.020
Introduction
Introduction of non-indigenous species (NIS) is largely a consequence of unintentional and intentional human-mediated mechanisms. Once introduced, some NIS adversely affect native species and alter the communities in which they establish (e.g. Dick et al. 2017a). Elton (1958) proposed that characteristics of a native community could be important in providing resistance by native species against successful establishment of NIS. A large literature subsequently demonstrated that interactions (largely predation and competition) by native species could impair or prevent establishment of NIS (e.g. Levine et al. 2004;Smith-Ramesh et al. 2017), though the opposite may also occur (e.g. Levine et al. 2004;Cobián-Rojas et al. 2018). However, the specific mechanisms and thus the predictability of such species interactions and their outcomes remains poorly studied.
Numerous researchers have explored the role of interspecific competition in invasion ecology and its impacts on native ecosystems (e.g. Paini and Roberts 2005). Cases in which a native species competitively excludes a potential invader are particularly interesting, as they may provide valuable insights into context-specific factors that permit the native species to resist invasion (Paini et al. 2008;Zenni and Nuñez 2013). In many other cases, colonizing species may suppress native ones or limit their distributions (e.g. Petren and Case 1996).
One promising method of studying the possible impacts of NIS and the role of interspecific competition is through the use of the "functional response" (FR; see Dick et al. 2017b). Originally developed to study predator-prey relationships, FRs represent the relationship between resource consumption rate and resource density (Holling 1959). Indeed, competition theory refers to the "functional resource utilization response" of competing plant species (Tilman 1977). Using comparative FRs, Xu et al. (2016b) revealed that the impact of the invasive apple snail Pomacea canaliculata in relation to native Bellamya aeruginosa and introduced Planorbius corneus was predictable from the method. Similarly, comparative FRs were used to highlight the strong ecological impact of the "killer shrimp" Dikerogammarus villosus on native Gammarus pulex (Dodd et al. 2014). Thus, FRs can be used to assess impact on shared resources and hence relative competitive ability of native species and actual or potential invaders with respect to their resource uptake rates (Dick et al. 2017a). In particular, however, this method can move from understanding to predicting invasive species impacts (Dick et al. 2014).
Cepaea nemoralis is a terrestrial snail introduced to North America from its native Western Europe (Örstan and Cameron 2015). Its ornamental value and colourful appearance are responsible for its intercontinental spread by humans (Whitson 2005). The species occupies a wide variety of habitats and can be found in parks and gardens within cities (Ożgo 2012), but does not appear to have significantly harmful effects once introduced (Cowie et al. 2009;Ożgo and Bogucki 2011). This is the case in Windsor and Essex County, Ontario, Canada, where C. nemoralis populations are abundant, particularly in urban and disturbed areas. Despite its commonness in these environments, it is rarely found in largely undisturbed woodlands of the region. It is possible that its absence from undisturbed woodlands is at least partly explained by the presence of the native snail Mesodon thyroidus, a similarly-sized species found mainly in woodlands including Kopegaron Woods Conservation Area (KWCA), where it often occurs on or in downed logs or under leaf litter. Preliminary surveys of KWCA confirmed the presence of C. nemoralis in the more disturbed forest periphery, but the two species never co-occurred in the interior of the forest.
A recent review indicated a significant role of olfaction in detection and selection of food by many terrestrial gastropods, though its importance varies by species (Kiss 2017). It is not clear whether the FRs of species are affected by olfaction nor whether interactions between native species and NIS could be influenced by it.
In this study, we address multiple aspects of the foraging ecology of these two terrestrial snail species, specifically their functional responses, odour detection capabilities and possible interspecific competition. We hypothesized that native, forest-inhabiting M. thyroidus may competitively exclude C. nemoralis from this habitat type. Specifically, we hypothesized that M. thyroidus would exhibit a greater attack rate, shorter handling times (and thus greater maximum feeding rate), shorter search times during olfactory tests, and greater consumption of limited resources in joint foraging experiments with the introduced snail. These predictions follow comparative FR and feeding theory (Dick et al. 2014). To test our hypotheses, we thus used a functional response (FR) framework to compare resource acquisition parameters (i.e.attack rate and handling time) for both these herbivorous snails. We also conducted odour detection experiments to determine whether olfactory cues were important to either species when locating food. Finally, we conducted joint foraging microcosm experiments to observe the relative competitive ability of both snails when placed in a confined environment with limited resources.
Methods
Native Mesodon thyroidus snails were found on wooden logs and leaf litter and handpicked from the ground in KWCA in Leamington, Ontario, Canada, during July 2016. Non-native Cepaea nemoralis snails were collected from various urban areas of downtown Windsor, Ontario. Each species was separately housed in transparent aquarium tanks that were covered with fish net mesh to allow oxygenation while preventing egress of snails. Both tanks were maintained in a light-and temperature-controlled chamber (16:8 light:dark regime at 21 °C). Food for snails consisted mainly of grasses, maple leaves (Acer sp.) and dandelion leaves (Taraxacum officinale) obtained near the Great Lakes Institute for Environmental Research (GLIER), Windsor, Ontario. Snails were fed ad libitum during the acclimation period. Dechlorinated water was added to both tanks daily to maintain humidity.
Functional response experiments
Experimental food consisted of dandelion (Taraxacum officinale), which is a non-native species in both habitats occupied by the snail species. Dandelion has been used in previous feeding experiments with gastropods (e.g. Desbuquois and Daguzan 1995;Hanley et al. 2003Hanley et al. , 2018)). Preliminary feeding trials demonstrated that both snail species consumed dandelion, though Hanley et al. (2018) determined that dandelion seedling contained anti-herbivore phenolics and alkaloids and were only moderately acceptable as food to snails (Cornu aspersum) in feeding trials.
Snails were used for functional response (FR) experiments following a 24 h food deprivation period to standardize hunger levels. Each FR trial lasted 24 h as preliminary trials showed negligible food consumption over shorter (4 h) periods. Transparent boxes (7.6 × 11.4 cm) were used as arenas to hold food and snails during experiments. A grid composed of 1.3 cm squares was fixed below the box to form a 54-square base (6 × 9). Experimental dandelion leaves were hole-punched to produce circular pellets of uniform diameter (7 mm) as food for the snails. Pellets were placed in the centre of each square to standardize distance between adjacent food items. Original pellets (n = 2) were placed at the centre of the box along the short axis, and subsequent food levels (4,8,12,16,20,24,28,32,42,54) were achieved by adding symmetrically along this axis (i.e.non-randomly).
To begin the experiment, adult and subadult snails were placed at the centre of the arena. Five trials were conducted at each food level for the native M. thyroidus and six for the introduced C. nemoralis. The arena was uniformly sprayed with deionized water to provide moisture, and boxes were covered with a lid during the trials. At the end of the test period, dandelion consumption was recorded. An event was recorded as full consumption if at least half a pellet was consumed; partial consumption (<50%) was not recorded. Species' FRs were calculated as described below.
Odour detection experiments
Odour preference experiments were conducted in single-species trials with one randomly selected snail individual each. Mesodon thyroidus ranged between 1.27 and 2.87 g, whereas C. nemoralis ranged between 0.48 and 3.50 g. Fresh dandelion pellets (formed as above) were subjected to one of four treatments: a) desiccation in an oven at 40 °C for 24 h; b) freezing at 0 °C for 24 h; c) pellets from freshly picked leaves; and d) pellets of the same shape but consisting of white paper as a negative control. Freezing significantly reduces volatility of odour compounds in leaves, while oven-drying may cause these compounds to be preserved (Díaz-Maroto et al. 2002). We recorded pellet consumption (as above) for each pellet density (2,4,8,16) and pellet type. We placed a black barrier in the middle of the arena between the pellets and the snail to obstruct its view of the pellets and thereby limited detection by olfactory cues. Time to first contact of a prey item was recorded for each treatment. Each trial was conducted for four hours and repeated with five snails of each species for all food treatments. Species were tested separately (i.e.non-choice experiments).
Joint foraging experiments
The arenas described above for the FR trials were also used to test for possible competition between native and non-native snails. Trials were conducted with a 16:8 light:dark regime at 21 °C. Food pellets hole-punched from dandelion leaves were individually placed in separate squares of the arena (densities 2, 4, 8, 16, 32, 54). Pellets were placed at the centre of the arena and added symmetrically along the short axis of the arena (i.e.successively out to the arena wall as food density increased). For each pellet density tested, five individuals from each species were starved 24 h prior to the trials. We then placed individual native and non-native snails at opposite corners of the shorter edge of the arena facing the pellets. During the 4 h observation, consumed pellets were not replaced, and the number of pellets consumed (defined above) by each snail was recorded.
Data analysis
Statistical analyses were performed in R-3.5.0 (R Core Team 2018). To analyze and model comparative functional responses, we used the FRAIR-0.5.100 package (Pritchard 2017). Rogers ' (1972) Type II equation was used to describe the functional response of both species as food resources were not replaced as they were consumed: where N e is the number of food pellets consumed, N o is the initial number of food pellets, a is attack rate, h is handling time, and T is experimental duration (which was set at 1 in the present study as we wished to compare FR parameters for both species over the same period of time). Maximum feeding rate was thus calculated as 1/h. Models were bootstrapped (n = 2000) to generate 95% confidence intervals for each species' functional response curve. Species differences in attack rate (a), handling time (h) and maximum feeding rate (1/h) were analyzed using frair_compare() option within the FRAIR-0.5.100 package. Here, as the time for feeding was the same for both species and set as 1 above, a and h were used as unitless, comparative metrics consistent with many previous studies (e.g. Paterson et al. 2015;Anderson 2016;Pritchard et al. 2017), though other researchers have applied units (e.g. Rall et al. 2012, Lefébure et al. 2014, Li et al. 2018). In the latter case, attack rate (a) refers to the volume or area searched per unit time by a consumer, whereas handling time (h) refers to the time spent per unit of resource in activities such as capturing, subduing, killing, ingesting and digesting that resource unit (Barrios-O' Neill et al. 2016;Li et al. 2018).
To compare differential responses to food treatments and delineate interactions of independent variables in the odour detection experiments, we conducted an AN-COVA analysis with factors Species and Food Treatment and continuous variable Food Density, and their interactions. From 160 total observations, 52 instances in which individuals made no contact with the food (regardless of treatment type) were omitted. Nine other instances were also removed from the analysis: four cases in which technical/equipment difficulties caused delays in recording time to pellet contact, four in which snails partially consumed the barrier intended to limit detection to olfactory cues, and one where the barrier became damaged from repeated use and was unable to fully hide the pellets. Detection times were Log 10 (x+1)-transformed prior to analysis.
Results from joint foraging experiments were analyzed with a paired t-test by examining pellet consumption by each snail species across each of the six resource level classes. Each food class was represented five times.
Results
Both snail species conformed to a Type II functional response, though C. nemoralis has not reached the curve's asymptote and M. thyroidus individuals exhibited a significantly greater feeding ability with increasing food levels (Fig. 1). There was no overlap in 95% CIs, indicating substantially higher feeding efficiency and rate for the native compared to the introduced snail (Figure 1). M. thyroidus had a significantly greater attack rate (a = 5.30) than C. nemoralis (a = 0.41) (z = −9.97,P < 0.001), as well as a slightly shorter but non-significantly different handling time (h = 0.020 versus 0.023; z = 0.25, P = 0.800). Corresponding maximum feeding rate was higher for the native species (50.0 vs 43.5 pellets over the experimental time; see Fig. 1, Table 1).
Mean food detection times for native M. thyroidus (1585 s, SE = 369 s) across treatments were shorter than for non-indigenous C. nemoralis (1970 s, SE = 266 s). Log 10 (x+1)-transformed detection times for food resources were significantly shorter for M. thyroidus than for C. nemoralis (ANCOVA, F 1,83 = 9.10, P < 0.01). This was the case for all treatments, with the exception of the "paper" treatment, where M. thyroidus took longer to detect the pellets on average (3937 s) than C. nemoralis (2094 s). Food density was also significant (F 1,83 = 7.27, P < 0.01), as average detection times generally decreased with increasing food density for all but one food level (n = 8 pellets). Furthermore, food treatment types differed significantly in detection times (F 3,83 = 4.02, P < 0.05) (Table 2), with "paper" averaging the longest time to detection (2764 s) and oven-dried foods the shortest (1334 s). Time to first contact was also affected by a species*food treatment interaction (F 3,83 = 3.19, P < 0.05) (Fig. 2).
The joint species foraging experiments demonstrated that feeding activity of M. thyroidus was significantly higher than that of C. nemoralis across a variety of food resource levels (paired t-test, t = 4.2, df = 29, P < 0.001) (Fig. 3).
Discussion
Application of comparative functional responses has allowed researchers to discriminate between invader species with high and low ecological impact (e.g. Dick et al. 2014Dick et al. , 2017a;;Xu et al. 2016b), and may elucidate relative competitive ability (Tilman 1977;Dick et al. 2017b). In most cases examined to date, high functional responses of invaders (relative to their native counterparts) are associated with high ecological impact (Dick et al. 2017a); the opposite pattern is expected with low impact nonindigenous species. Bollache et al. (2008) proposed that the method could be used for NIS likely to invade, thereby allowing forecasts of comparative impact of a putative invader with a complementary native analogue. Further, Dick et al. (2017b) argued that, as with plant competition (see Tilman 1977), FRs of animals may uncover relative interspecific competitive abilities. In our study, we thus examined functional responses of native M. thyroidus and introduced C. nemoralis snails that occur in different habitats in south-western Ontario. In line with theory, we observed higher FRs for the native species, a consequence mainly of its higher attack rate and maximum feeding rate. The native snail also had a shorter time to first contact across different food densities. The native snail did, however, have a longer time to contact with non-food (i.e.paper pellets), suggesting it is more discriminating than the introduced snail. Indeed, the native species exhibited much shorter times to contact with actual food than with paper, whereas no such variation was apparent with the introduced species (Fig. 2). These experimental outcomes are consistent with the introduced snail having low (or at least unremarkable) ecological impact (see Cowie et al. 2009;Ożgo and Bogucki 2011). This supports general FR theory (Dick et al. 2014), that high FRs are associated with high ecological impact, and vice versa, that low FRs should be associated with low ecological impact. Our data also suggest that the native species is the superior resource competitor, again consistent with FR theory (see Dick et al. 2014Dick et al. , 2017a)). In particular, the higher attack rate of the native is congruent with competition theory, as superior competitive ability is likely to be exhibited by the competitor that can best utilise food resources at low food abundance (Tilman 1977), and attack rate quantifies this (see Fig. 1). This also is consistent with the hypothesis that the native species exerts some degree of biotic resistance toward the non-indigenous species.
The two snail species used in our study were collected from separate but nearby habitats. There exist many possible reasons for non-overlapping habitat use by species including interspecific differences in habitat preference and environmental tolerance (e.g. Moreno-Rueda 2007;Książkiewicz et al. 2013), or predation and its avoidance (Morris 2003;Green et al. 2011). It is also possible that non-overlapping distributions could result from intense interspecific competition, with species segregating into different habitats to minimize competition or exploit different resources (Cowie and Jones 1987;Kimura and Chiba 2010). Baur and Baur (1990) demonstrated that land snails competed via both exploitative and interference competition, while Parent and Crespi (2009) proposed that interspecific competition constrained phenotypic variation in Galapagos land snails. However, Chiba and Cowie (2016) found only limited support for exploitation or interference competition among land snail species. Experimental field work is required to assess the respective roles of habitat preference or biological interactions in the microallopatric distributions of these two snail species in south-western Ontario. In addition, molecular analyses of gut contents may improve our understanding of overlap in resource use by these and other species (Waterhouse et al. 2014).
Snail feeding behaviour has been well studied in both terrestrial and marine environments. Much of the recent focus on feeding pertains to mechanisms of food detection, particularly olfaction (e.g. Dahirel et al. 2015;Kiss 2017;Cordoba et al. 2018). To date, only a limited number of studies have addressed functional responses of land snails (see Broekhuizen et al. 2002;Haubois et al. 2005;Giacoletti et al. 2016;Xu et al. 2016aXu et al. , 2016b;;Pusack et al. 2018). In our laboratory study, both native and introduced species conformed with a Type II functional response, consistent with previous studies (e.g. Xu et al. 2016aXu et al. , 2016b;;Pusack et al. 2018). Type II curves are important from the context of population regulation of the resource, as relative risk to prey increases as prey density declines, destabilizing the interaction (Dick et al. 2014). Our study highlighted significantly higher feeding rates by the native snail versus the introduced one, consistent with field patterns of low invader impact and low competitive ability. At the other extreme, Xu et al. (2016b) observed that a highly ecologically damaging invasive snail had much higher feeding rates than its native counterpart. Thus, the FR method is able to predict degree of ecological impact and competitive ability, particularly if combined with species abundances, and can be used to both understand current invasions and forecast the outcome of emerging and future invasions (Dick et al. 2014(Dick et al. , 2017b)).
Our study utilized a categorical system to assess pellet consumption. One limitation of this approach was that feeding could be assessed as complete when it was only partial, or nonexistent even though some herbivory occurred (<50%). In addition, our results were potentially affected by trial duration (1 d). Had the duration of these trials been extended (e.g. 2 d), some of the observations in the latter category may have flipped from "non-consumption" to total consumption. Finally, it is important to recognize that our study was conducted with only one invasive and one native species (the only species available) and that differences obtained only demonstrate species differences. Confirmation that these differences were due to the origin of the species would require tests with additional species. However, our data and case study fit closely with current FR theory and, together with these numerous other cases (see Dick et al. 2017a), show great potential in predicting ecological and competitive impacts from benign to highly damaging.
Moving forward, further studies of the context-dependency of snail species impacts should focus on mapping FRs onto impact under different contexts, such as various temperature and humidity regimes that might be expected with climate change. In addition, as invaders with low FRs may still exert ecological impact due to high abundance (see Dick et al. 2017b), the impact of native and invasive snails needs to be monitored as relative and absolute abundances change.
Figure 3 .
Figure 3. Mean (± SE) pellets eaten in joint foraging experiments across increasing food levels by native M. thyroidus (gray) and introduced C. nemoralis (black) snails.
Table 2 .
Results of ANCOVA test assessing effect of Species, Density, and Food Treatment on detection time from the olfaction experiment.
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Domain: Environmental Science Biology
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Harpacticoida (Copepoda) of the northern East Sea (the Sea of Japan) and the southern Sea of Okhotsk: diversity, taxocenes, and biogeographical aspects
Based on novel data and a literature review, an inventory of Harpacticoida from the northern part of the East Sea and the southern part of Sea of Okhotsk is compiled. A total of 151 species belonging to 70 genera and 26 families, of which 16 species are deemed to be new to science, are recognized from the region. Twelve harpacticoid species assemblages are described from marine and brackish water soft sediments and the phytal zone. Estuarine faunas were similar throughout the East Sea and Sea of Okhotsk, both in species composition and dominant-taxon structure. Conversely, the fauna of marine soft sediment and phytal zones differed greatly throughout this region. This distinction may have been caused by differences in dispersal rates or by differences in environmental conditions. The distribution of littoral and sublittoral harpacticoids was determined primarily by climatic factors and the temperature of surface waters. Three basic sub-regions were distinguished within the survey area: Korean, which includes many tropical taxa (39%) and a smaller amount (about 20%) of boreal taxa; Primorye, where boreal and subarctic-arctic species (>40%) predominate, and representatives of the tropical complex are rare (17%); and Soya, with a mix of tropical (22%) and boreal (31%) faunal elements. The faunas of the Korean and the Primorye sub-regions are distinct, whereas that of the Soya has intermediate features.
Introduction
The marine meiofauna of the Russian Far East has not been investigated in detail, with research effort in the region having focused on studies of highly abundant and diverse nematode fauna (e.g. Pavluk and Belogurov 1979;Fadeeva and Belogurov 1984;Pavluk 1984;Fadeeva 1991;Smirnova 2012). Copepods of the order Harpacticoida, the second-most abundant meiobenthic taxon, and one that plays a key role in trophic chains, have not been subject to any comparable degree of research effort (Chertoprud 2013).
Harpacticoids are basic microalgal consumers (Carman et al. 1997). They also serve as prey for many organisms, including commercially important salmonids (Chupachin and Kaev 1980;Kaev et al. 1993;Trofimov 1999;Ivankov et al. 1999;Kaev 2003). Thus far, 144 species have been reported from the Russian Far East *Corresponding author. EmailSalmon fry stomach contents were investigated in two steps: preparation of the fish stomach, then differentiation of food objects. All samples were fixed in 4% formalin solution.
Literature data
Published data on harpacticoids from throughout the East Sea and southern part of the Sea of Okhotsk were used to prepare an inventory of taxa from the region (see Supplement). Faunal data were available for a number of Russian waters (coastal waters of Primorye, Khabarovsk region, Sakhalin Island, and Kuril Islands), Japanese islands, and the southern and eastern coastal regions of South Korea (Figure 1). The most thoroughly investigated region was the Korean Peninsula, for which 57 articles were available. The Japanese island fauna is described in 19 papers, with a detailed analysis of the harpacticoid fauna of Hokkaido Island carried out in five articles. Nine publications report the fauna of the Russian sector of the East Sea. The fauna of the Sea of Okhotsk is described in five published accounts. The world-wide database on shallow-water harpacticoids (Chertoprud et al. 2010;Azovsky et al. 2012) was used to compare the faunas of the Okhotsk and East Seas with those of other regions (South China, Yellow and Bering Seas), in addition to analysing species distribution ranges.
Distribution range typology
We used a number of formal criteria to describe the distribution ranges of species. Cosmopolitan species were those that occurred in at least three oceans, in addition to having a tropical-subarctic/arctic distribution. A similar criterion was used to characterize cosmopolitan species as defined by Finlay and Fenchel (2004). Endemic taxa were those we considered to have restricted or local distributions within an investigated region, though further investigation may reveal these to have wider distributions; accordingly the appropriate term 'provisional endemic' is used. 'Subtropical' and 'subarctic' groups included those species that occurred only in tropical-subtropical and Arctic-Subarctic zones, respectively. Species inhabiting the temperate climatic zone were considered 'boreal.'
Statistical analysis
Taxocene definition and structural comparison The Braun-Blanquet method was used to classify and describe harpacticoid species complexes. This approach is widely applied in plant ecology (Mirkin et al. 2001) and has been modified recently for use with coastal meiobenthos (Chertoprud et al. 2006). This method allows differentiation of assemblages (taxocenes) of species in a series of samples, based on groups of dominant, specific, and discriminating species.
Pairwise similarity of the species composition was evaluated in samples using the Czekanowski index (D) (Magurran 2004): where X i , Y i are the proportion of individuals belonging to the ith species of all individuals found in samples X and Y, respectively.
This index depends on abundance changes of dominant and rare species and is used for quantitative data analysis and to count portions, calculated based on species abundance.
Comparing regional faunas
To compare composition of regional faunas, we calculated the percentage taxonomic overlap between regions, and the proportion of endemic species compared with that of cosmopolitan species. Similarity in faunal composition between regions was estimated using the Kulczynski index (K) for qualitative data (Urbakh 1975): where a is the number of common species in fauna groups x and y; and b and c are the numbers of species restricted to one of the groups. This index is not sensitive to negative coincidences, which is convenient for estimating the similarity between faunas from different regions when individual lists under comparison contain only limited parts of the total species set. This index is used often for biogeographic analyses of Recent faunas (Murray et al. 2002;Azeria 2004). We applied the multi-dimensional scaling method PRIMER (version 6) for graphical representation of similarity in species composition between regions (Clarke and Gorley 2001). This method allows for a comparison of objects on a plane so that all distances between points correspond to a certain value. Consequently, this approach helps to visualize the cluster structure of selected stations.
East Sea
All data sources identify 88 species distributed amongst 60 genera, and 24 families of Harpacticoida in this region. Before our study only 23 species had been reported from it (Chertoprud 2013). The composition of each family and the genus and species diversity are shown in Table 1. Eight of these species were considered to be new to science: Halectinosoma sp. n. 1, 2, and 3; Pseudonychocamptus sp. n. 1, Paramesochra sp. n. 1, Emertonia sp. n. 1, Parastenhelia sp. n. 1 and Tisbe sp. n. 1. Four species from the Primorye region were identified to the genus level because only one sex was found. Overall, the richest families were Miraciidae (11 species) and Ectinosomatidae (10 species). The species:genus ratio was low (1.46).
Epibenthic harpacticoids (inhabiting silt deposits and the surface of soft sediments) dominated the fauna and comprised half of all species (Table 2). The diversity of phytal fauna was high. Obligate interstitial Harpacticoida had the lowest species richness, and were represented by families Darcythompsoniidae, Ectinosomatidae, Cylindropsyllidae, Leptastacidae, and Paramesochridae. The number of facultative interstitial species was not much higher. Only one species, Microsetella norvegica, was considered planktonic.
Provisional endemics constituted almost one third of the fauna (Table 2). Species inhabiting cold waters in boreal, subarctic, and arctic areas comprised 42% of total diversity. Species specific to tropical and subtropical latitudes were relatively rare.
Sea of Okhotsk
The inventory of harpacticoids from the southern part of the Sea of Okhotsk comprised 104 species distributed in 54 genera and 22 families of Harpacticoida (Table 1). Before the present study only 16 species were reported from this region (Chertoprud 2013). Eleven of these species are considered to be new to science: Mesochra sp. n. 1, Cletodes sp. n. 1, Dactylopusia sp. n. 1, Halectinosoma sp. n. 1, 2 and 3; Paraleptastacus sp. n. 1, Amphiascoides sp. n. 1, Schizopera sp. n. 1, Wellsopsyllus sp. n. 1 and Amenophia sp. n. 1. The most species-rich families were Miraciidae (15 species) and Ameiridae (11 species). The species:genus ratio was higher than that of the East Sea (1.92). The numbers of epibenthic and phytal species were similar (Table 2). There were few obligate interstitial Harpacticoida, represented by families Darcythompsoniidae, Ectinosomatidae, Leptastacidae and Paramesochridae. The number of facultative interstitial forms was greater. There were only two planktonic harpacticoids, both of which were species of Microsetella.
Boreal species accounted for one third of the fauna, and the diversity of provisional endemics was slightly lower (Table 2). Typically warm-water species accounted for <20% of all taxa; subarctic and arctic species were rare.
The taxocenes structure
Using the modified Braun-Blanquet method 12 harpacticoid taxocenes were apparent in the northern part of the East Sea and southern part of Sea of Okhotsk. The characteristics of the four most widely distributed taxocenes are presented in Table 3. Below we describe the harpacticoid taxocenes found in different habitats.
A. Marine soft sediments: littoral and upper sublittoral zones Six taxocene types were revealed: three characteristic of silty sand with detritus, and three of washed sand.
Taxocene no. 1 was most widely distributed and associated with silty sands with detritus. The dominant species were Parastenhelia sp. n. 1 and Halectinosoma sp. n. 3, comprising 50-80% of the abundance (Table 3). This taxocene is typical of Kunashir and Sakhalin Islands as well as the Primorye region. The similarity of the stations from different loci was medium (D = 0.48 ± 0.07). Other taxocenes were rarer. The second taxocene occurred in the Primorye region. Paramphiascella was dominant in this complex (68% total abundance). The third was widespread in the upper sublittoral of Kunashir Island, with Nitocra and Halectinosoma representatives of this complex (70% of total). Washed sand taxocenes were localized in distribution and found only in the Primorye region. These taxocenes were dominated by Arenosetella bidenta, Leptastacus japonicus and Paramesochra sp. 1 (>80% of total abundance). No obligate interstitial species were detected in other investigated areas.
B. Estuaries and brackish-water lagoons
We describe four main taxocenes in estuarine and brackish-water lagoon environments. Two of these (nos. 2 and 3) were more widely distributed among brackish waters ( Table 3). The first (no. 2) including Nitocra lacustris, N. spinipes, Halectinosoma sp. n. 2, and Neotachidius parvus (70-90% of total) unites estuaries and lagoons (4‰ salinity) from Sakhalin Island and the Primorye region. The similarity of faunal composition of stations from different loci was medium (D = 0.52 ± 0.07). The second (no. 3) was characterized by the prevalence of Remanea naksanensis and Nitocra spinipes (80-90% of total), typical of brackish water (4-6‰ salinity) at the Kuril Islands and Primorye region. The type habitat for Remanea naksanensis in South Korea is a brackish water habitat near Naksan beach (Back et al. 2011). The remaining two taxocenes had localized distributions. One in which the dominant genus was Geeopsis (64% of total abundance) was typical of cold estuaries experiencing about 4‰ salinity in the Khabarovsk region; the other, dominated by Leimia and Microarthridion (79% of total), occurred in brackish (2‰ salinity) estuarine bays in the Primorye region.
C. Macrophyte zone
Two coastal taxocenes were found in the macrophyte zone in the Kuril Islands and the Primorye region. Tisbe furcata (70-80% of total) was dominant (Table 3) in the most widely distributed of these (no. 4). The similarity of stations from different loci was high (D = 0.72 ± 0.06). The second, dominated by Diosaccus, Dactylopusia and Harpacticus (60-70% of total), occurred only in the upper sublittoral of Iturup Island. Literature revealed different phytal communities in the warm waters of the southern part of the East Sea near the Korean Peninsula, with harpacticoids like Porcellidium and Scutellidium longicauda acheloides being prevalent (Song et al. 2010).
Harpacticus, Tisbe, Diosaccus and Dactylopusia were abundant in the phytal zone (50-90% total harpacticoid abundance in plankton from above the macrophyte zone). Phytal species also constituted about 70% of harpacticoid taxa identified in stomach contents of Oncorhynchus (salmon) fry.
Comparative biogeographical analysis
Distribution of ecological groups Our novel data were obtained from that region encompassing the northern part of the East Sea (mainly the Primorye region; 88 species), and southern part of Okhotsk Sea (coast of Sakhalin and South Kurils; 104 species). Taxonomic inventories from the southern part of the East Sea (Korean Peninsula region; 96 species) were obtained from literature sources.
In general, the benthic harpacticoid fauna was highly specific to particular regions. Some common species from one area have a low occurrence even in a neighbouring region (i.e. the southern part of the Sea of Okhotsk and the northern part of the East Sea). The number of common species decreased with increasing distance between regions, and latitude (Table 4). The similarity in the benthic harpacticoid species lists, calculated using the Kulczynski index (K), did not exceed 0.15.
The number of phytal zone species common to neighbouring regions was greater than benthic species, though this decreased with increasing distance between regions being compared ( Table 4). The similarities in phytal species lists were also two times greater than those of benthic lists.
The brackish-water fauna proved to be the most cosmopolitan in distribution ( Table 4). The number of common species was high for neighbouring and distant regions, with the average similarity in faunal lists from different regions being 0.51. Thus, the brackish-water species group was the most interzonal and has similar features in the wide latitudinal range.
Latitudinal trends in harpacticoid faunal structure
We performed a comparative analysis of taxonomic structure of faunas inhabiting different regions of the East and Okhotsk Seas, Yellow and South China Seas, and the southern part of Bering Sea, using our data and that sourced from the literature (Chertoprud et al. 2010;Supplement). Data span a latitudinal range of 2-56°N, and surface water temperatures of 1-26°C (Figure 1).
Comparative analysis of species lists revealed four faunal groups occupying different thermal regimes ( Figure 2): (1) Cold water fauna inhabiting the northern part of Okhotsk and Bering Seas (Commander and North Kuril Islands) as well as the somewhat different Faunas of geographically separated areas experiencing similar water temperature characteristics are more similar to each other than they are to some areas at comparable latitude which experience different temperature regimes. For example, the fauna of the southern Sea of Okhotsk was similar to the fauna of Hokkaido Island (northeastern East Sea), both experiencing similar water temperatures, yet the fauna of the Primorye region (north-western East Sea) differed demonstrably.
Discussion
Characteristic aspects of the regional harpacticoid fauna Eighty-eight harpacticoid species were found in the northern part of the East Sea, and 104 species were found in the southern Sea of Okhotsk. A similar number of species has been found in some Arctic seas of comparable area, i.e. the Kara Sea (83 species, Garlitska, pers. comm.) or the Bering and Chukchi Seas (110 species, Chertoprud et al. 2010). Other boreal areas, however, are richer, such as the Black or Mediterranean Seas (239 and 652 species respectively). The species lists remain incomplete for the regions explored (East Sea and Sea of Okhotsk), and further studies will likely increase the number of taxa known from each region. The fauna of the northern East Sea has been investigated less than the southern Sea of Okhotsk.
The taxonomic structure of faunas in far eastern seas is relatively typical for boreal regions. The highest species richness was in five families: Miraciidae (21), Ectinosomatidae (15), Laophontidae (13), Ameiridae (12), and Thalestridae (12 species). The first four of these families are typical of soft sediment environments, whereas the last is specific to the phytal zone. The highest species richness is found in the epibenthic genus Halectinosoma (nine species) and in the four phytal genera Dactylopusia (five), Harpacticus (five), Scutellidium (six) and Tisbe (six).
The phytal complex includes a few species-rich genera, whereas the benthic fauna is more genus-rich. Species from the typically warm-water family Porcellidiidae are absent in the northern part of the East Sea and the Sea of Okhotsk and occur only in the southern part of the East Sea. Two families (Longipedidae and Canuellidae) were represented by single species each, despite their usually high abundance in subtropical and temperate latitudes. This observation could be a consequence of some biogeographic peculiarity of the region or an artefact of under-investigation. Further studies in this region are required to more fully address this question.
The composition of the harpacticoid life forms reflected the diversity of studied habitat types. The diversity of interstitial species in all regions is low. Even for regions in which the fauna is relatively well-studied, small obligate interstitial forms are usually underestimated because of limitations in sampling techniques (Chertoprud and Garlitska 2007). Further research will likely increase the number of species in this ecological group. Only two plankton species (both Microsetella) were found, though such low richness is typical of boreal latitudes (Lang 1948).
About one-third (24-32%) of harpacticoids found in the northern part of the East Sea and southern part of Sea of Okhotsk had boreal distribution ranges that corresponded to the latitudinal range of investigated seas. Another third of species are 'provisional endemics,' restricted to one of the studied areas, not having been reported elsewhere.
Our knowledge of harpacticoids of the western Pacific is imperfect, and distribution ranges of many species are not fully documented or understood. Accordingly, the number of endemic taxa may decrease considerably in the future. For example, the present study extended the ranges of Ameira zahaae, Itunella arenaria and Remanea naksanensis, all previously known only from type localities in the southern part of the East Sea and Yellow Sea (Lee and Chang 2008;Back et al. 2011;Karanovic and Cho 2012). These three species were found in the Primorye region and South Kuril Islands. Cosmopolitan species comprised 10-13% of the total number of species detected, of which about half (five species) were brackish-water members of the genera Nitocra, Nannopus, Mesochra and Onychocamptus. However, the taxonomic status and wide distribution of many estuarine harpacticoids remains the subject of debate Chertoprud et al. 2014).
A. Soft bottom communities
The composition of benthic harpacticoid taxocenes is determined primarily by sediment type (Chertoprud et al. 2006). The intensity of harpacticoid reproduction (Chertoprud and Azovsky 2005) and impact of predation (Coull 1985) are related to substratum type. The composition of the life forms in a community is mainly determined by sediment type (Hicks and Coull 1983). Our data clearly distinguished two taxocene complexes: the first typical of silty sand with detritus and the second of washed sand. Relatively large epibenthic forms dominated the first substratum, and small worm-like or flattened interstitial species, the second. According to our data, if muddy bays alternate with sandy beaches along the coast, the distribution of taxocenes would follow the pattern of sediments.
Similar but spatially distant habitats may differ in species composition. Such patchiness may not only be a result of environmental heterogeneity, but reflect the probabilistic nature of colonization events (Chertoprud et al. 2006). Often the speed and success of settlement in new territories is a deciding factor for biotope colonization of one or other harpacticoid species. As a result, relatively stable taxocenes are formed at different loci, and the coadaptation of these species contributes to the resilience of the system against the introduction of 'foreign' elements (Thorson 1958).
B. Estuarine and lagoon communities
Salinity in water bodies ranged 2-8‰. Reduced salinity usually limits the basic set of dominant taxa and the species richness (Van Damme et al. 1984;Moore 1987). Indeed, according to our data, the number of species varied from 5 to 8 in the most common brackish-water taxocenes, whereas the number in marine communities always exceeded 10 ( Table 3). The interzonal nature of estuarine harpacticoid fauna has been hypothesized recently . It was shown that the similar generic composition of brackish-water assemblages was typical over a wide-latitudinal range from the Arctic to the tropics. About half of the species recorded from the Arctic to the tropics are common in the estuaries of southern India, the White Sea and the Russian Far East (Chertoprud et al. 2014). In this study, predominant brackish-water genera (Nitocra, Halectinosoma, Geeopsis and Microarthridion) were distributed across a wide latitudinal range. Other genera were more restricted in distribution, including brackish representatives like Leimia (L. vaga), Huntemannia (H. biarticulatus), Remanea (R. naksanensis) and Neotachidius (N. parvus). These species also reached a large population size, but were not endemics.
There was no immediately apparent relationship between taxocene composition and salinity gradient. For example, a salinity of 4‰ is preferred by inhabitants of three of the four allocated taxocenes with the following dominant representatives: Nitocra lacustris, N. spinipes, Halectinosoma sp. n. 2, Huntemannia biarticulatus and Neotachidius parvus; Remanea naksanensis and Nitocra spinipes; and Geeopsis incisipes. We did not find any clear across-estuary changes in fauna, possibly because of weak salinity gradients and the limited number of stations studied. It is clear that one of the determinants of dominance in the estuarine community is sediment type. Thus, the predominance of the obligate interstitial Remanea naksanensis is typical for coarse washed sand in brackish water areas. Additionally, climate has a significant influence on the formation of species complexes. For example, the subarctic genus Geeopsis dominated the northernmost Tumnin estuary (Khabarovsk district), but was not found in the more southerly regions. Colonization abilities of species probably play a role in some other cases.
C. Phytal zone communities
Macroalgal-associated harpacticoids often have common morphological adaptations, such as flattened (Alteutha, Zaus) or pear-shaped (Diosaccus) bodies; or extensive thoracic segments in combination with a narrow abdomen (Tisbe) (Hicks and Coull 1983). Species with these attributes were abundant in the macrophyte community and formed the basis of taxocenes. As noted previously, the composition of harpacticoid faunas differed not only in respect to algae of different taxonomic groups, but also in relation to different parts of a single macrophyte (Chislenko 1968;De Troch et al. 2001). Thus, clear differences can be traced between the copepod groups inhabiting coastal thickets and Syringodium and Halophila algae (De Troch et al. 2001); as well as the thallus surface, the rhizoid Laminaria (Chislenko 1968), and the leaves and rhizoids of Thalassia (De Troch et al. 2001).
Algal shape and biotope differentiation of the phytal zone are key factors determining the structure of harpacticoid assemblages. The first phytal taxocene with the prevalence of Tisbe was found in a complex dominated by brown algae (Fucus). The second taxocene with Diosaccus, Dactylopusia and Harpacticus predominant, was found on Zostera sp. We investigated the entire fauna of the plants without separating the different morphological fragments. The first taxocene is typical of macrophytes growing in closed and slightly brackish (22‰) bays. Another taxocene was found in bays with a high rate of water exchange, surf, and almost marine salinity (28-30‰). Thus, both abiotic and biotic factors that regulated formation of the harpacticoid species complexes in the phytal zone interacted.
Harpacticoids are a common prey type for different species of fish fry (Borutzky 1960;Ivankov et al. 1999). According to our preliminary data, only taxa associated with phytal forms are common in salmon fry diet. This is probably because the fry feed between macrophytes where phytal species are actively entering the water mass (Thistle 2003;Giere 2009). The copepod composition in plankton samples from coastal waters confirms this hypothesis. Indeed, there is evidence that juvenile fish preferentially eat the fauna of dense substrates (phytal and clay sediments) where organisms cannot hide between sediments particles (Coull 1985). The evaluation of the different ecological harpacticoid group contributions to fish nutrition requires further investigation.
Distribution of ecological groups: the paradox of brackish-water fauna
Comparative analysis of the distribution of the three main ecological groups (marine benthic fauna, fauna of the phytal zone, and brackish-water fauna) showed benthic species to be locally distributed, estuarine species to have the widest distributions, and phytal species to have distributions of somewhat transitional extent. Obviously, the composition of life forms in different species complexes varies significantly. Epibenthic and interstitial forms predominate in marine soft sediments, while phytal forms predominate on macrophytes, and epibenthic species are most abundant in the brackish estuarine fauna. As we have shown previously, planktonic harpacticoids have the greatest distributional range, followed by phytal and epibenthic forms, while interstitial species have the most restricted ranges (Chertoprud et al. 2010). These differences are mainly determined by the dispersal ability of harpacticoids and correlate with habitat mobility (e.g. water masses and floating algae versus sediments) and species leg morphology (Bell et al. 1987;Thistle and Sedlacek 2004). Small interstitial species have lanceolate or vermiform bodies with short legs and are closely associated with certain types of sediments, often avoiding horizontal transfer through water drift by burrowing deeper into the sediment during high tides (Rybnikov et al. 2003). Epibenthic species have relatively well-developed swimming legs, but usually they are clearly linked to their life cycles in a bottom substrate and are rarely abundant in the water column (Hicks and Coull 1983). Epibenthic species are usually transferred by coastal currents over small distances of 10-100 km (Hauspie and Polk 1973). Accordingly, phytal fauna can colonize distant habitats faster than interstitial or epibenthic faunas can. Many phytal species enter the water column and are carried by currents; those representatives that are otherwise strongly fixed on algae can be transferred with dislodged macrophyte fractions (Kurdziel and Bell 1992;Ólafsson et al. 2001). The high colonization ability of phytal species is reflected in the high proportion (31%) of species with transatlantic distribution ranges among them (Chertoprud et al. 2010).
It is paradoxical that brackish-water species are primarily epibenthic taxa with extensive distributions. It seems that the distribution of this ecological group should be similar to that of benthic taxa, or even narrower because of the fragmentation of estuarine habitats. However, in areas separated by more than 1000 km (the northern and the southern parts of the East Sea), over 30% of species are common to all brackish-water loci, whereas this figure for marine benthic species is less than 10%. In Russian Far East waters, 40% of brackish-water harpacticoid species and only 13% of marine harpacticoids are cosmopolitan (Chertoprud et al. 2014). Many widespread estuarine species actually constitute complexes of cryptic species with similar morphology, but which differ genetically . A prerequisite for this is the geographic dispersion of estuaries with rather broad maritime areas, leading to reproductive isolation of local species. In addition, environmental conditions in the estuaries of different latitudes vary considerably, which can also lead to speciation. Molecular analyses of individuals from different brackish-water species, such as Nannopus palustris , Microarthridion littorale (Schizas et al. 1999) and Cletocamptus deitersi (Rocha-Olivares et al. 2001) revealed that these three species are actually groups of cryptic taxa.
Latitude-dependent variability of faunas of the East Sea and southern part of Sea of Okhotsk
The area throughout which our study is focussed is characterized by a distinct latitudinal water temperature gradient, with an average annual surface temperature of 2-16°C (Japan Meteorological Agency 1991), attributable to numerous warm and cold currents throughout the region (Shuntov 2001). For example, the southern part of the East Sea is influenced by the Tsushima and East Korean warm currents. The intensity of these flows decreases gradually towards the northern part of the East Sea. A branch of the Tsushima Current named the Soya penetrates through the straits in the southern part of the Sea of Okhotsk, bringing with it warm water to the southern Sakhalin and the Kuril Islands. Cold currents coming from north of the East Sea affect Primorsky, Schrenk, and North Korea.
The harpacticoid faunas investigated in the East Sea and the southern part of the Sea of Okhotsk corresponded to three temperature regions ( Figure 1) allocated according to Japan Meteorology Agency data (1991), based on the long-term average annual isotherms of the surface water masses: (1) The southern part of the East Sea: the coast of Korea, the Kyushu and Honshu Islands bounded in the south by a 16°C isotherm, and in the north by a 9°C isotherm. This area is characterized by relatively warm water masses throughout the year.
(2) The north-western part of the East Sea: coast of Primorye and Khabarovsky regions of Russia bounded on the south by a 2°C isotherm, in which the sea always partially freezes during winter (for 3-4 months per year). (3) The north-eastern part of the East Sea and the southern part of the Sea of Okhotsk: coast of Hokkaido, southern Sakhalin, and the South Kuril Islands bounded on the south by a 4°C isotherm, and in the north by a 2°C isotherm. The sea only freezes near the northern coasts of Hokkaido and South Kuril Islands for approximately two months of each year.
The taxonomic structure of harpacticoid species complexes inhabiting these three regions (north-western part of the East Sea; the north-eastern East Sea and the southern part of the Sea of Okhotsk; and the southern East Sea) differ significantly. The Kulczynski similarity index (K) value was not more than 0.39 ± 0.1. Not more than 50% of the common species were included in the lists for the three temperature regions. The biogeographical structure of the fauna of the three species complexes was also different (Figure 3). The diversity of species with widespread boreal and subarctic distribution was higher in the northern regions (41-42% of total fauna), and low in the southern part of the East Sea (22%). In contrast, species with tropical and subtropical distribution are highly diverse near the Korean Peninsula (29.5%). The number of subtropical species near Hokkaido and the Kuril Islands was slightly higher than that in the Primorye region: 20% and 17%, respectively. The fauna of the southern part of the Sea of Okhotsk was closer to the fauna of the southern waters of the East Sea (K = 0.44 ± 0.09) than to the fauna of the north-western part of the East Sea (K = 0.28 ± 0.09). The effects of currents on the distribution of copepod fauna has been noted before (Hauspie and Polk 1973). Tropical species prevail in the northern Yellow Sea and South China Sea where they are strongly influenced by the Kuroshio warm current. A transitional area was located between 29°and 32°N and west of 123°E in the East China Sea where the warm-temperate and tropical faunas mix (Chen et al. 1982).
The environment within the East Sea and the southern part of the Sea of Okhotsk is highly variable, and within it at least three relatively isolated complexes of harpacticoids occur within areas subject to different average annual surface water temperature: (1) The Korean sub-region, situated on the southern and south-western East Sea, under influence of Tsushima and East Korea warm currents (average annual surface water temperature 9-16°C), with a total species count of 112. Provisional endemics comprised <40% of the total fauna, which otherwise included many tropical taxa, with a low percentage of boreal species (Figure 3). (2) The Primorye sub-region, characterized by a boreal climate in the northwestern part of the East Sea, where the Primorsky Current is the main influencing factor (average annual surface water temperature not more than 2°C). In total 88 species were recognized within this region; provisional endemics comprised a third of this number, with boreal and typical arctic species dominating the fauna, in which tropical taxa were rare ( Figure 3). (3) The Soya sub-region, including the north-eastern coasts of Hokkaido and South Kuril Islands, with boreal climate influenced by the Soya warm current (with an average annual surface water temperature of 2-4°C). Here 126 species were recorded, with the proportion of provisional endemics being slightly lower than a third of this number. A mix of boreal and tropical faunal elements occurred in this area (Figure 3). This fauna has features transitional to those of Korea and Primorye sub-regions.
Subdivision of the East Sea and Sea of Okhotsk into meiobenthic sub-regions is undertaken for the first time. Similar schemes of zonation for this sector of the Pacific Ocean have been presented for zooplankton, macrobenthos, and ichthyofauna (Kuhn 1975;Kafanov et al. 2000;Golikov et al. 2001). The southern part of the East Sea is diverse with tropical and subtropical zooplanktonic species that are absent in northern parts of the area (Kuhn 1975). Subtropical and boreal zooplankton species cooccur here, the former preferring surface waters, the latter the deeper layers (Nishimura 1969). On the basis of distributions of shell-bearing gastropods Golikov et al. (2001) subdivided this area into four sub-regions, distinguishing the north-western part of the East Sea (with psychrophilic fauna) from the South Kuril Islands (Kunashir and Iturup), an area occupied by thermophilous gastropods. Kafanov et al. (2000), using the data on the East Sea fishes, distinguished as many as 10 biogeographic provinces in this region. The complexes of southern, north-western, and north-eastern parts of the region are different; the tropical and subtropical species mark north-eastern provinces but are absent from north-western provinces.
There is obvious biogeographic heterogeneity in the faunas of the Far East region. Further studies are needed to clarify the proposed zonation of waters of the East Sea and the Sea of Okhotsk for marine harpacticoids, and to extend the biogeographical scheme on the meiobenthic community as a whole.
Conclusion
Our analysis based on original and published data for harpacticoid faunas from the areas investigated in the Far East contributes the following ecological observations: (1) The total species inventory of Harpacticoida from the northern part of the East Sea comprises 88 species; 104 species have been reported from the southern part of the Sea of Okhotsk. As a result of our study, the number of species known from the first region has increased by more than three times, and by more than 10 times for the second.
(2) Twelve harpacticoid species complexes can be distinguished: six taxocenes in soft sediments, two inhabiting the phytal zone, and four characteristic of estuaries and brackish lagoons in the northern part of the East Sea and the southern part of the Okhotsk Sea. (3) The marine benthic (epibenthic and interstitial) harpacticoid fauna showed maximum regional specificity, whereas the brackish-water fauna was the most cosmopolitan; the phytal zone fauna had intermediate values.
(4) The East Sea is highly variable environmentally by virtue of its geographical location and hydrological regime. At least three discrete harpacticoid copepod assemblages exist within it. Further analyses of meiobenthic community structure in the Far East sector will improve our knowledge of observed patterns.
Disclosure statement
No potential conflict of interest was reported by the authors.
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Domain: Environmental Science Biology
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An Examination of Diameter Density Prediction with k-NN and Airborne Lidar
While lidar-based forest inventory methods have been widely demonstrated, performances of methods to predict tree diameters with airborne lidar (lidar) are not well understood. One cause for this is that the performance metrics typically used in studies for prediction of diameters can be difficult to interpret, and may not support comparative inferences between sampling designs and study areas. To help with this problem we propose two indices and use them to evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions. The indices are based on the coefficient of determination (R2), and root mean square deviation (RMSD). Both of the indices are highly interpretable, and the RMSD-based index facilitates comparisons with alternative (non-lidar) inventory strategies, and with projects in other regions. K-NN diameter distribution prediction strategies were examined using auxiliary lidar for 190 training plots distribute across the 800 km2 Savannah River Site in South Carolina, USA. We evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. K-NN and lidar explained 80% of variability in diameters, and Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria.
Introduction
Airborne scanning lidar technology (henceforth referred to as simply lidar) provides vegetation measurements which are highly related to forest attributes needed for forest inventory and monitoring. Some examples of forest attributes which are highly related to lidar measurements include stem volume, basal area, quadratic mean diameter, and above-ground biomass [1][2][3]. The lidar vegetation measurements can be obtained with high precision over large areas enabling wall-to-wall measurements and predictions of vegetation attributes, as well as precise estimates of population parameters. Despite demonstrated advantages to using lidar for inventory and monitoring, there are also omissions from most analyses which inhibit common usage. One of the limitations of typical lidar research studies, is that they do not directly evaluate prediction of tree diameter distributions.
The distribution of diameters at breast height (dbh) is an important component of most forest inventory, management, and monitoring strategies. Dbhs are needed to describe stand properties because variables such as growth, volume, market value, conversion-cost, product specifications, and future forest prescriptions are dependent on trees' dbhs. The use of single-tree level growth and yield models almost always requires dbh distributions. Dbhs are also used to assess forest sustainability based on whether the quantity and sizes of growing stock are suited to replace the current population of harvestable trees [4]. Information about dbhs also informs the type and timing of management strategies and economic value of the stand [5]. Ecological analyses also use dbh density information including, for example, assessments of vegetative diversity [6], insect disturbance mechanics [7], habitat suitability [8], and suitability and distribution of parent stock for coarse woody debris [9].
Dbh distributions are often simplified using a mathematical function with parameters that can be estimated or recovered using lidar measurements or other ancillary data. Dbh distribution functions can be predicted with both parametric and non-parametric strategies. Parametric strategies are based on the assumption that the dbh density can be characterized by a theoretical probability density function. A variety of theoretical functions have been tested including the beta [10], Weibull [11,12] and Johnson's SB [13,14]. Two methods have been used to predict parameters of theoretical functions, the parameter prediction method and the parameter recovery method [15]. As the name indicates, in the parameter prediction method stand attributes (or remote sensing data) are used to predict parameters of the probability density function. In the parameter recovery method moments or percentiles of dbh distribution are predicted or measured using stand variables. The parameters of a theoretical distribution are then recovered by leveraging the known relationships between the predicted attributes and the distributional parameters.
Non-parametric strategies attempt to predict percentiles of empirical distributions [16,17] or directly predict dbh bins or classes. While parametric strategies are advantageous in being able to represent a complete distribution with a few parameters when the empirical distribution is unimodal, they may have limited ability to represent complex and mixed species stands that may not have unimodal densities [18]. Empirical strategies which retain the original data or relative densities by dbh bins provide more flexibility to accommodate many types of stand tables [17], however, some prediction strategies for multiple dbh percentiles can result in illogical behavior, benefitting from the introduction of constraints [19].
Various strategies for predicting dbh distributions with lidar have been evaluated. Gobakken and Naesset [20] compared parameter prediction and parameter recovery methods in the prediction of stem number and basal area distributions. The underlying probability theoretical distribution was a two-parameter Weibull distribution. The precision was slightly better for the parameter recovery method than for the parameter prediction method. Mehtatalo et al. [21] also used the parameter recovery approach, however, they proposed a method where the parameters of the assumed dbh distribution and height-dbh curve are determined in such a manner that they are compatible with the predictions of stand attributes. Maltamo et al. [22] proposed another approach to obtain a compatible stand description. First the parameters of a Weibull distribution and stand volume are predicted with lidar. Then, the estimated stem number distribution is modified to correspond to the volume obtained in the previous step by using the calibration estimation approach proposed by Deville and Sarndal [23].
Percentiles of dbh distributions have also been predicted with lidar. Maltamo et al. [24] predicted 12 percentile points in semi-natural forests in Finland. Bollandsås and Naesset [25] predicted 10 percentiles in uneven-sized Norway spruce stands. Breidenbach et al. [26] used a generalized linear model (GLM) to estimate parameters of theoretical distributions with lidar. A benefit of GLM is that they can be a one-step procedure, without the need to first fit a distribution and then predict its parameters. Thomas et al. [27] studied the prediction of both unimodal and bimodal dbh distributions using a finite mixture model approach. They successfully predicted the parameters of separate Weibull functions, but because there was no lidar-based method for separation of distribution type (unimodal or bimodal), the applicability of the method is somewhat limited for lidar inventory.
An alternative strategy to predict dbh distribution with lidar is using k-nearest-neighbor (k-NN) imputation. An advantage of k-NN is that the k-NN model can be used to simultaneously predict a suite of response variables, including a list of individual trees records (tree-list).k-NN also provides compatible stand if stand attributes are predicted simultaneously with tree-lists. This was the motivation for the work by Packalén and Maltamo [28]. They predicted stand attributes and tree-lists simultaneously for Scots pine, Norway spruce, and deciduous trees and compared the performance of a tree-list approach to the use of a Weibull distribution approach. The k-NN tree-list strategy was able to mimic bimodal dbh distributions of Norway Spruce (the only shade tolerant tree species in the study area) and in general provided clearly lower error index values. Maltamo et al. [29] examined the performance of a k-NN tree-list approach without considering tree species. Their objectives were to investigate the effect of different predictor and response variables and to examine the influence of reduced numbers of training plots. The results indicated that response variables must be selected very carefully in order to obtain accurate predictions of dbh distributions and stand attributes. They also reported that with a low number of training plots (approx.100) precise predictions of dbh distributions could be produced in their study area.
When a diameter prediction strategy is evaluated, inference is typically made on differences between the observed and predicted distribution using hypothesis or goodness of fit tests, such as the Kolmogorov-Smirnov test. However, for reasons described extensively in Reynolds et al. [30] including that the p-values can be wildly inaccurate (for fewer than 40 trees per plot), these types of tests are problematic for many of the same reasons that the statistical community discourages p-value based inference in hypothesis testing (e.g., Halsey et al. [31]). Reynolds et al. [30] instead proposed an index based upon absolute deviations in the units of the response. This index is often referred to as the "Reynolds error index" in the literature. Reynolds et al. [30] also described methodologies for formal statistical inference using their error index.
In this study we wished to understand tradeoffs between various lidar and k-NN based dbh prediction strategies (e.g., numbers of neighbors, distance metrics, and others). While studies have examined dbh predictions with lidar, only a subset of prediction strategies were examined, and the indices used by studies are difficult to generalize to other designs and areas. To overcome these limitations, we propose two indices and use them to examine a variety of dbh predictions strategies. The proposed indices are based on the well-known coefficient of determination (R 2 ), and root mean squared deviation (RMSD) which simplifies their interpretation by users and readers.
Initially, we graphically demonstrate the behavior of the two proposed indices using simulations. We then use the indices to describe the relative performances of a variety of lidar and k-NN diameter distribution prediction strategies. Given the large number of components of a k-NN and lidar prediction strategy, clarity is needed on which k-NN configurations work best with lidar for dbh distribution predictions. Components that we examine include distance metrics (e.g., Euclidean vs. Mahalanobis), numbers of neighbors (the k in k-NN), presence or absence of stratification, and sensitivity of predictions to the choice of response and predictor variables. Based on our findings using the proposed indices, we conclude with recommendations on effective diameter distribution predictions strategies with lidar and k-NN.
Study Site
The study was conducted at the U. S. Department of Energy's Savannah River Site, an 80,267 ha National Environmental Research Park in Aiken and Barnwell counties, South Carolina USA (Figure 1). The Savannah River Site is characterized by sandy soils and gently sloping hills dominated by pines, with hardwoods occurring in riparian areas. Prior to acquisition by the Department of Energy in 1951, the majority of Savannah River Site uplands were agricultural fields or had recently been harvested for timber. The U. S. Department of Agriculture Forest Service has managed the natural resources of the Savannah River Site since 1952 and reforested the majority of the uplands with loblolly (P.taeda), longleaf (P.palustris), and slash (P.elliottii) pines. These pine stands are actively managed for timber and wildlife habitat.
Ground Data Collection
Plot measurements were performed on a grid of fixed radius circular plots designed for modeling forest attributes with auxiliary lidar data. The plot design consisted of two concentric nested fixed area circular measurement plots. The innermost 0.004 ha plot was used to measure trees between 2.5 and 7.4 cm in dbh. Larger trees were measured on a 0.04 ha plot if there were at least 8 dominant or co-dominant trees, otherwise trees larger than 7.4 cm dbh were measured on a 0.081 ha plot. The heights, dbhs, heights to crown base, and species were recorded for trees on the two concentric plots, and additionally trees between 2.5 and 7.4 cm were tallied on a 0.04 ha plot.
Plot locations were selected purposively to cover the range of tree sizes and stand compositions that occur on the Savannah River Site. Field measurements were taken on 194 field plot locations selected purposively to sample across multiple vegetation classes and sizes. Of the 194 plots, 4 were dropped because it was determined that they were measured in locations outside of our target population. A summary of the tree and plot variables used for this study is provided in Table 1. Additionally, a visual representation of the empirical dbh density functions for the 8 most common species occurring on plots is shown in Figure 2. Additional forest attributes (besides dbh) used in our analyses included trees per hectare (TPH), basal area per hectare (m 2 /ha, BA), Lorey's height (m, Lor.), and total cubic stem volume (m 3 /ha, Vol.).
Ground Data Collection
Plot measurements were performed on a grid of fixed radius circular plots designed for modeling forest attributes with auxiliary lidar data. The plot design consisted of two concentric nested fixed area circular measurement plots. The innermost 0.004 ha plot was used to measure trees between 2.5 and 7.4 cm in dbh. Larger trees were measured on a 0.04 ha plot if there were at least 8 dominant or co-dominant trees, otherwise trees larger than 7.4 cm dbh were measured on a 0.081 ha plot. The heights, dbhs, heights to crown base, and species were recorded for trees on the two concentric plots, and additionally trees between 2.5 and 7.4 cm were tallied on a 0.04 ha plot.
Plot locations were selected purposively to cover the range of tree sizes and stand compositions that occur on the Savannah River Site. Field measurements were taken on 194 field plot locations selected purposively to sample across multiple vegetation classes and sizes. Of the 194 plots, 4 were dropped because it was determined that they were measured in locations outside of our target population. A summary of the tree and plot variables used for this study is provided in Table 1. Additionally, a visual representation of the empirical dbh density functions for the 8 most common species occurring on plots is shown in Figure 2. Additional forest attributes (besides dbh) used in our analyses included trees per hectare (TPH), basal area per hectare (m 2 /ha, BA), Lorey's height (m, Lor.), and total cubic stem volume (m 3 /ha, Vol.). Plot locations were surveyed using L1/L2 GLONASS enabled survey-grade GPS receivers. The receiver was placed at each plot's center on a 3 m pole and a minimum of 600 1-second-epoch satellite fixes were collected and differentially corrected. We expect the horizontal RMSE for surveyed plot center positions to be less than 1 m in the pine forest types at the Savannah River Site, based upon our previous experience with positional accuracy using these receivers in a variety of forest types (e.g., Andersen et al. [32]).
Plots were assigned to post-strata using the dominant species group for the stand in which the plot was measured (the most common dominant types include: Hardwood-29 plots; Loblolly P.-76 plots; Longleaf P.-54 plots). All of the hardwood species were combined into a single stratum. Forestry staff for the site developed a tract-wide map of species groups by visually classifying stands in the field. Plots were assigned to strata by intersecting plot locations with the strata map. Plot locations were surveyed using L1/L2 GLONASS enabled survey-grade GPS receivers. The receiver was placed at each plot's center on a 3 m pole and a minimum of 600 1-second-epoch satellite fixes were collected and differentially corrected. We expect the horizontal RMSE for surveyed plot center positions to be less than 1 m in the pine forest types at the Savannah River Site, based upon our previous experience with positional accuracy using these receivers in a variety of forest types (e.g., Andersen et al. [32]).
Plots were assigned to post-strata using the dominant species group for the stand in which the plot was measured (the most common dominant types include: Hardwood-29 plots; Loblolly P.-76 plots; Longleaf P.-54 plots). All of the hardwood species were combined into a single stratum. Forestry staff for the site developed a tract-wide map of species groups by visually classifying stands in the field. Plots were assigned to strata by intersecting plot locations with the strata map.
Lidar Data
Lidar data were collected from 21 February to 2 March 2009 with two Leica ALS50-II lidar sensors in leaf-off conditions. Table 2 provides acquisition parameters.
Point cloud data were processed to create predictor variables for this study using the cloudmetrics executable included with FUSION software [33]. This executable computes a large number of statistics from lidar including, but not limited to, height percentiles (e.g., 90th, 50th, and 30th percentile heights in Table 3) and lidar cover (the percent of returns above a threshold, in our case 1.50 m). We also examined fraction without foliage (fwof), a modeled variable which used normalized intensity to suggest the proportion of the leaf-off lidar which did not intersect live foliage.
k-NN Tree-List Imputation
In k-NN imputation, response variables from measured sites are shared or imputed with sites without measurements based on the degree of similarity in their auxiliary variables. The "similarity" in auxiliary variables is evaluated using a distance metric, e.g., Euclidean distance, where a large number of distance metrics have been demonstrated in the k-NN literature. The distance metrics are functions which determine how one or more auxiliary variables should be weighted and combined. The coefficients of the weight function can also depend on the observed association between response and predictor variables, theoretically weighting predictors which can better predict the response variable(s). If more than one nearest neighbor (k greater than one) is used, then a rule must be formulated to average (continuous) and select (categorical) donor response values. This procedure can be used to simultaneously impute a large number of response variables in a single step.
Procedurally, the process is as follows: (1) a distance metrics is computed between measured and unmeasured (response) observations, then (2) the k observations with the smallest distances (donors) are transferred (imputed) to the observation without a measured response (target).
For this study we relied upon the yaImpute package [34] implemented in R [35] for k-NN imputation. The identities of the donor plots (nearest neighbors) were used to impute tree-lists, as yaImpute is not currently set up to directly impute tree-lists. Based on the donors' identities, all of the tree records from the imputed donor plots were copied to the target observation. Each copied tree was then distance weighted to generate a tree-list for the target observation. The distance weighted tree-lists from the k neighbors then became the basis for prediction of the empirical dbh density for the target observation. The choice of a weighting function for the K imputed neighbors has been shown to have limited effect on performance [28].
k-NN Imputation Strategy Components
We examined the effects of 4 components of a k-NN tree-list imputation strategy including (1) the choice of distance metric, (2) the selected predictors, (3) the set of response variables, and (4) the effect of post-stratification. The distance metrics evaluated include Euclidean distance (EUC.),Mahalanobis distance (MAH), most similar neighbors (MSN), and random forest (RF). MSN and RF distances both use response variables for measured observations in computing distances. Additional details for how these distances are defined can be found in the yaImpute package documentation [34]. The effect of post-stratification on k-NN performance was evaluated by stratifying the data, and predicting separately within strata. Strata with fewer than 10 response measurements were imputed from the pool of all observation.
Dbh Densities
The proportions of trees falling in diameter bins (the empirical dbh density, or just "dbh density") were computed by first binning lists of trees into 2.54 cm (1 inch) dbh bins and computing the proportions of all trees in the dbh classes. In the case of imputed tree-lists, weighted dbh densities were computed using the distance weights from the imputed plots. The bin proportions were then smoothed with a 3-bin moving average centered on the target bins. The smoothing function was applied to emphasize major trends, and de-emphasize fine-scale fluctuations. Individual plot densities can have spikes, pits, and other characteristics that we did not wish to examine.
Measures of Performance
Evaluation of k-NN predictions strategies were performed using Leave One Out (LOO) validation in combination with indices. In LOO, models are iteratively fit to the data while omitting one plot at a time. After fitting a given model, the data are then tested against the omitted plot. The errors in prediction for the omitted plots then serve as the basis for indices of performance. Our first suggested measure of performance is index H. ( Index H is equivalent to the coefficient of determination (or, commonly, R 2 ) which has a straight-forward interpretation-the proportion of variability explained. The index has an advantage over alternative measures of performance that we examined in that it provides a relative measure of performance. The baseline level of variability comes from plot variation in dbh densities around the mean dbh density within a dbh class for all of the plots. As with R 2 , smaller prediction error relative to baseline variability will yield index H values closer to one. Larger prediction residuals will in general cause index H to approach zero, and negative values of H are possible if the prediction strategy is inferior to prediction with the means model. Our inferences for this index are similar to those that would be made from R 2 . We rely heavily on index H for inferences about different k-NN dbh prediction strategies. While the H values are suggestive of general trends in performance, the index is not suited for identifying a single best prediction strategy.
A limitation of index H is that it is only meaningful in comparisons if the baseline variability is similar between compared strategies. In many cases this property will not hold. We expect, for example, to have greater variability amongst variable radius plots than amongst fixed radius plots for the same area; comparing their index H values then would be meaningless. To compare prediction strategies from two different inventory designs or two study areas with different levels of baseline variability, it is important to have an absolute measure of performance. A second limitation to index H is that the index is unitless, and it is often desirable to have an index in the same units as the attribute of interest. To support inferences from multiple designs in the units of the response, we propose a second index, I, which is an absolute measure of performance: Index I is equivalent to the Root Mean Squared Deviation (RMSD) by plot (rather than by bin). The units for this index are the same as for the attribute of interest. As with RMSD, a smaller value of index I indicates better prediction performance. Index I can be used to compare performance across sites, designs, and project areas.
While we do not use p-values in this analysis, we recognize that some users will wish to use p-values.p-value can be fairly easily generated for the suggest indices with simulations. One simulation-based approach to obtain p-value is to randomly assign tree-lists to plots several thousand times, and compute index values for each randomization. This will yield a distribution of index values for the null model where lidar and k-NN provide no explanatory power. The distribution can then be compared to the observed index value for a particular lidar and k-NN configuration. The proportion of values which are as extreme as the observed index value will serve as the simulation-based p-value.
Index Demonstration
To provide a sense of the behavior of our indices as a function of prediction performance, we provide a visual calibration image which shows H values for various levels of departure from agreement between a sample and a prediction (Figure 3). Our quantitative examination of the properties of H relative to prediction properties used simulated dbhs. Our simulated population is a mixture distribution composed of two normal distributions (the solid line in Figure 4). For our examination, we took 100 clustered sample plots of 50 trees from the simulated population, and compared these with "predictions" for the samples. In Figure 4 we can see all three cases: (1) the underlying population, (2) the distribution observed on a plot, and (3) the prediction for the plot. For the purposes of demonstration, predictions were obtained by taking the original sample data and introducing Gaussian noise with parameters (µ ε , σ ε ). In our simulations, we look at various dbh bin widths and four types of errors in our predictions, where the four types of prediction errors are represented as four separate lines and labeled with the parameters of their error distributions. Larger values of index H suggest better prediction performance, where H is bounded by one on its upper end. A value less than zero indicates that the mean by bins (as obtained from all sample plots) is a better predictor of the sample distribution than the prediction strategy under examination.
Results
Results are divided into two sections. In the first section-Index properties-we demonstrate the behavior of H under a variety of prediction scenarios. Our demonstration of index H gives a sense of the behavior of H under various conditions, and the degree of sensitivity of the index to disagreement between predicted and observed dbhs. Simulation results are shown only for index H (not I) for the sake of brevity, since the behavior of the two indices is nearly identical (inversely), although the interpretations are quite different: index H provides a measure of relative improvement over the mean model (higher is better), and index I provides a measure of absolute error in the units of the response that is portable between designs and studies (lower is better).
In the second section-K-NN strategies-we use index H to suggest superior dbh prediction strategies, then conclude with a table of H and I values for the best prediction strategies. We investigate number k of nearest neighbors, which distance metric is used, which sets of predictors and response variables are used for k-NN imputation, and how are predictions for individual species. As with R 2 , higher index H values suggest better prediction performance, but are not necessarily suited for model selection. Instead, they are meant to help interpret general trends in performance for different prediction configurations.
Results
Results are divided into two sections. In the first section-Index properties-we demonstrate the behavior of H under a variety of prediction scenarios. Our demonstration of index H gives a sense of the behavior of H under various conditions, and the degree of sensitivity of the index to disagreement between predicted and observed dbhs. Simulation results are shown only for index H (not I) for the sake of brevity, since the behavior of the two indices is nearly identical (inversely), although the interpretations are quite different: index H provides a measure of relative improvement over the mean model (higher is better), and index I provides a measure of absolute error in the units of the response that is portable between designs and studies (lower is better).
In the second section-K-NN strategies-we use index H to suggest superior dbh prediction strategies, then conclude with a table of H and I values for the best prediction strategies. We investigate number k of nearest neighbors, which distance metric is used, which sets of predictors and response variables are used for k-NN imputation, and how are predictions for individual species. As with R 2 , higher index H values suggest better prediction performance, but are not necessarily suited for model selection. Instead, they are meant to help interpret general trends in performance for different prediction configurations.
Results
Results are divided into two sections. In the first section-Index properties-we demonstrate the behavior of H under a variety of prediction scenarios. Our demonstration of index H gives a sense of the behavior of H under various conditions, and the degree of sensitivity of the index to disagreement between predicted and observed dbhs. Simulation results are shown only for index H (not I) for the sake of brevity, since the behavior of the two indices is nearly identical (inversely), although the interpretations are quite different: index H provides a measure of relative improvement over the mean model (higher is better), and index I provides a measure of absolute error in the units of the response that is portable between designs and studies (lower is better).
In the second section-K-NN strategies-we use index H to suggest superior dbh prediction strategies, then conclude with a table of H and I values for the best prediction strategies. We investigate number k of nearest neighbors, which distance metric is used, which sets of predictors and response variables are used for k-NN imputation, and how are predictions for individual species. As with R 2 , higher index H values suggest better prediction performance, but are not necessarily suited for model selection. Instead, they are meant to help interpret general trends in performance for different prediction configurations.
Index Behavior
In Figure 5 we can see that the effect of bin width on H was observed similarly for each type of prediction error. In each instance both small and large dbh bin widths have reduced values of H, with the highest (best) values of H typically occurring around 4 to 5 cm for our simulations. We can expect lower values of H for small bins because there are few observations in narrow bins, resulting in higher sampling variability in both the plot sample and the plot predictions for each bin. We can also expect lower values of H for larger dbh bins because the shape of the dbh distribution approaches the average density for the plot. In this case (large dbh bins), plot mean densities have greater likelihood of falling near the population mean density, which means there is little variation to be explained by predictions-causing the H values to decline.
Index Behavior
In Figure 5 we can see that the effect of bin width on H was observed similarly for each type of prediction error. In each instance both small and large dbh bin widths have reduced values of H, with the highest (best) values of H typically occurring around 4 to 5 cm for our simulations. We can expect lower values of H for small bins because there are few observations in narrow bins, resulting in higher sampling variability in both the plot sample and the plot predictions for each bin. We can also expect lower values of H for larger dbh bins because the shape of the dbh distribution approaches the average density for the plot. In this case (large dbh bins), plot mean densities have greater likelihood of falling near the population mean density, which means there is little variation to be explained by predictions-causing the H values to decline. Figure 5 also shows that adding errors of increased size to our predictions causes H values to decline. For example, when we introduce Gaussian errors with no bias and a standard deviation of 1.0 ( = 0.0, = 1.0),H values hover around 0.9. When we increase the error by adding a 1 cm bias to predictions, as in the case of the second line in Figure 5 ( = 1.0, = 1.0) it causes all of the H values decline. Interestingly, the magnitude of decline in H values for Gaussian errors ( = 1, = 1.0) is similar to the H values when errors have parameters ( = 0, = 2.0). When we add errors with 2.0 cm bias and 2.0 cm standard deviation ( = 0.0, = 2.0) we see a more severe downturn in performance: the H values at best explain 60% of variability, and at worst do a poorer job of prediction than simply using the mean density from all of the plots combined. Figure 5 also shows that adding errors of increased size to our predictions causes H values to decline. For example, when we introduce Gaussian errors with no bias and a standard deviation of 1.0 (µ ε = 0.0, σ ε = 1.0),H values hover around 0.9. When we increase the error by adding a 1 cm bias to predictions, as in the case of the second line in Figure 5 (µ ε = 1.0, σ ε = 1.0) it causes all of the H values decline. Interestingly, the magnitude of decline in H values for Gaussian errors (µ ε = 1, σ ε = 1.0) is similar to the H values when errors have parameters (µ ε = 0, σ ε = 2.0). When we add errors with 2.0 cm bias and 2.0 cm standard deviation (µ ε = 0.0, σ ε = 2.0) we see a more severe downturn in performance: the H values at best explain 60% of variability, and at worst do a poorer job of prediction than simply using the mean density from all of the plots combined.
k-NN Strategies
Of the four distance metrics examined, the distance metric which had the greatest sensitivity to configuration was MSN distance. Excluding MSN distance, there was little difference in performance amongst the distance metrics used to impute tree-lists (Table 4). For a given number of neighbors, H only varied by a few percent. The range of values for any number of neighbors, k, is sufficiently small to suggest that there is no practical difference in performances amongst distances (excluding MSN). The effect of number of neighbors, k, was larger, e.g., ranging from 0.50 to 0.76 for Euc., and the decline from using a sub-optimal k was greatest for MSN. Performances were generally best for 3 neighbors relative to fewer or more neighbors, with little differences observed in the vicinity of 3 neighbors. To test the effect of auxiliary variables on performance, a suite of auxiliary variables was initially selected which would reflect different types of information (Table 5). The variables were then added or removed to isolate the influences of individual predictors-essentially a manual variable selection approach. Table 5 shows the sorted performances of the various predictor sets. There were only marginal differences among performances for predictor sets 1 through 9, when excluding MSN distance. Prediction performances were clearly sensitive to the predictor sets, although, excluding MSN distance, declines in performance from using inferior predictors sets were fairly modest (from H = 0.65 to H = 0.80). We also examined the sensitivity of the k-NN density imputation strategies to differences in the response sets for MSN and RF. Euc. and Mah., in contrast, do not use response variables when computing distances. We evaluated two sets of predictor variables with five sets of response variables. The results in Table 6 indicate that MSN was sensitive to the choice of response variables, while RF was fairly insensitive to the choice of response variables. Index H values for MSN in Table 6 declined from 0.81 to 0.58, a 28.4% reduction in performance. In contrast, index H values for RF distances varied by less than 4% for the combinations shown. Our final evaluation of k-NN components was on the effect of post-stratification. As can be seen in Table 7, post-stratification on forest type resulted in slightly poorer prediction performance in most cases. Most notably, stratification on the dominant species in a stand did not consistently improve either species group predictions (hardwood or conifer) or individual species predictions.
Comparative Performance
In Table 8 we provide H and I values for simple cases of prediction and estimation with each of the distance metrics. Although most of our inferences were based on index H, Table 8 demonstrates the relationship between the two indices-larger values of H, and smaller values of I suggest better performance, much as with the coefficient of determination and RMSD. The values of H can also provide a baseline for others to use in comparisons and in inventory planning.
Indices H and I
The indices demonstrated in this study facilitate inferences about dbh distribution predictions. The indices were essential to our analysis, and enabled us to demonstrate the behavior of diameter predictions with k-NN and lidar in an easily interpreted fashion. The results can also be compared with other regions, and prediction strategies through the use of index I. The portability of index I, and to a lesser extent H, should help to clarify the ability of lidar-based methods to provide diameter predictions for forest inventory. While we do not compare the performance of lidar-based methods with a traditional inventory system in this study, such comparisons are a natural extension of this research.
In their current implementation, the indices we proposed are based on tree counts by diameter bin, however they are not limited to this formulation. The proposed indices can be easily tweaked to suit various applications. For example, one could weight bins by basal area, or use a completely different strategy which uses maximum bin deviations. These could, respectively, be used in applications where errors in larger trees are more problematic, or in applications where the maximum bin error is of primary concern.
k-NN Imputation Strategies
We observed a number of useful trends with respect to the performance of dbh distribution predictions using nearest neighbor imputation methods and lidar. Our first observation agreed with that of other studies [36,37] in that lidar and nearest neighbor methods were able to provide meaningful predictive power for plot level dbh distributions. We were also able to identify patterns in the behavior of prediction performance with respect to the number of neighbors, k, the nearest neighbor distance type, use of strata in prediction, and the selection of variables used for imputing dbh distributions at the plot level. These results may prove indicative of performances in other areas with similar datasets. Our results are also in rough agreement with those of other k-NN studies, although there are few with which direct comparisons are feasible as studies describing dbh distribution prediction with lidar and k-NN are not common.
Our results with respect to the number of neighbors are fairly similar to those observed elsewhere, although not necessarily in the context of dbh distribution prediction. Most studies have observed that prediction performance improves for k greater than 1, with maximal performance usually falling somewhere in the range of 2-7 neighbors (a more detailed discussion of selection of k is provided by Eskelson et al. [38]). Our results also agree with other studies in that prediction performance is not sensitive to a specific number of neighbors in the indicated range.
With respect to distance metric, while MSN distance achieved equal performance with other metrics in the best case, it was very sensitive to the configuration used for prediction, at time faring much poorer than alternate distance metrics. Euc., Mah., and RF were all fairly robust to configuration, and as a result are preferable to MSN. These results are in contrast to another study which found generally good performance with MSN distance [36] for dbh predictions. Our findings with respect to MSN were surprising given that we hypothesized that there would be an advantage to leveraging the empirical relationship between predictor and response variables. MSN distance did not bear out this hypothesis for dbh prediction, although RF distance, which also relies on response metrics, performed the best according to the indices. Even though it performance the best in terms of the indices, a limitation of RF was that it took a much loinger time to calculate distances. While RF had the best results in terms of top performance and stability, the required additional computational time may not merit the effort. Mah.distance had nearly the same performance as RF (for the configurations tested), was faster, and eliminated the need to select a response set for k-NN-simplifying the analysis process.
The choice of a set of predictor variables also influenced performances, but the results were fairly stable with respect to changes so long as a reasonable set of predictors was provided. Height metrics such as P30 and P90 appeared to be more important than the canopy cover metric. This is a fairly intuitive result as the vertical height metrics are likely to better reflect the vertical forest structure, and thereby, indirectly, the dbh density. Unlike other potential response variables such as Vol., dbh densities do not measure the quantity of vegetation, they measure the distribution of sizes. It doesn't matter if they cover a portion, or all of the plot. The choice of a response set was only important for MSN distance, which was shown to be fairly sensitive to all aspects of the k-NN configuration.
The results from stratification with k-NN suggest that more prevalent species were predicted better without stratification than with stratification. For less common species there was no evidence that one strategy worked better than the other. Previous studies have differed in their conclusions with respect to the effects of stratification on k-NN predictions (e.g., [39,40]), although the studies did not look at dbh predictions. Differing sample sizes between studies likely played a role in the different findings between studies. The number of suitable donors likely also played a role in the observed trend that performances were better for more common species. For dominant subgroups, it is likely dbh densities were simply very similar in form to the combined density from all species, and therefore also effectively predicted without strata.
Conclusions
Tree dbhs are a common requirement of forest inventory systems, but a relatively small number of studies document dbh prediction performances when using auxiliary lidar. In addition, the indices commonly used in lidar studies looking at dbh prediction can be difficult to interpret and compare. This study proposes two interpretable indices and uses them to evaluate various lidar and k-NN dbh prediction strategies. K-NN with lidar was shown to effectively predict dbh distributions at a fine scale (nominally 0.04 ha) for an 80,267 ha pine dominated study area in South Carolina. While the results were fairly insensitive to changes, we identified that Mahalanobis distance, k = 3 neighbors, and no stratification was preferable to other strategies. The proposed indices will facilitate others to make comparisons between prediction strategies, and our findings will enable evaluations of lidar and k-NN as inventory tools. We should note that this was an intensively managed pine forest plantation, and the results may vary greatly from results for other forest types.foliage dataset. Funding support was provided by U. S. Department of Energy, Savannah River Operations Office through the USDA Forest Service Savannah and the USDI Bureau of Land Management, Oregon Regional Office. Finally, we thank Bernard Parresol (deceased), formerly with the USDA Southern Research Station, for his contribution to field protocols, sampling, and data compilation.
Figure 1 .
Figure 1. Map of forested areas of the Savannah River Site.
Figure 2 .
Figure 2. Dbh densities (relative frequency) for the 8 most common species (common names and numbers of trees) measured on fixed radius plots.
Figure 2 .
Figure 2. Dbh densities (relative frequency) for the 8 most common species (common names and numbers of trees) measured on fixed radius plots.
2 i
= a given plot j = a given diameter bin d ij = observed density in diameter class j on plot i dij = predicted density in diameter class j on plot i d ij = mean density in diameter class j for all plots.
Figure 3 .
Figure 3. Examples of values of index H for various prediction behaviors.
Figure 4 .
Figure 4. Probability density function of simulated mixture distribution used for testing overlaid with the predicted distribution for a simulated plot, and the observed measurements for the simulated plot.
Figure 3 .
Figure 3. Examples of values of index H for various prediction behaviors.
Figure 3 .
Figure 3. Examples of values of index H for various prediction behaviors.
Figure 4 .
Figure 4. Probability density function of simulated mixture distribution used for testing overlaid with the predicted distribution for a simulated plot, and the observed measurements for the simulated plot.
Figure 4 .
Figure 4. Probability density function of simulated mixture distribution used for testing overlaid with the predicted distribution for a simulated plot, and the observed measurements for the simulated plot.
Figure 5 .
Figure 5. Effect of bin width on index H values for different combinations of mean and standard deviation values.
Figure 5 .
Figure 5. Effect of bin width on index H values for different combinations of mean and standard deviation values.
Table 1 .
Summary statistics for plot measurements.
Trees, BA (basal area per hectare), Lor.(Lorey's height), and Vol.(total cubic stem volume) are tract level means from plot level calculations, the remaining values were computed from complete tree-lists.
Table 1 .
Summary statistics for plot measurements.
Trees, BA (basal area per hectare), Lor.(Lorey's height), and Vol.(total cubic stem volume) are tract level means from plot level calculations, the remaining values were computed from complete tree-lists.
Table 3 .
Summary of lidar-derived metrics for plots.
FWOF refers to Fraction Without Foliage, P30 and P90 refer to 30th and 90th lidar height percentiles, and cover(1.50)refers to the proportion of first returns above 1.50 m. Min: minimum; Max: maximum; Sd: standard deviation.
Table 4 .
Index H for k-NN by number of neighbors (k) and distance metric using the three predictor variables P30, P90, and cover(1.50)and responses TPH and Vol. for all species combined.
MSN, and RF refer to Most Similar Neighbors, and Random Forest respectively.
Table 5 .
Sorted index H values for k-NN (k = 3) dbh predictions for selected predictors sets for all species combined with responses TPH and Vol. Mah., MSN, and RF refer to Euclidean, Mahalanobis, Most Similar Neighbors, and Random Forest respectively. FWOF refers to Fraction Without Foliage, P30 and P90 refer to 30th and 90th lidar height percentiles, and cover(1.50)refers to the proportion of first returns above 1.50 m.
Table 6 .
Index H values for k-NN (k = 3) dbh predictions for selected predictor and response sets for all species combined., and RF refer to Most Similar Neighbors, and Random Forest respectively. Lor. is short for Lorey's height,. FWOF refers to Fraction Without Foliage, P30 and P90 refer to 30th and 90th lidar height percentiles, and cover(1.50)refers to the proportion of first returns above 1.50 m Items in brackets indicate multiple response variables with different selection criteria including hardwood (HW), softwood (or conifer), and hardwood and softwood (HS) trees; ranges of numbers and greater than symbols indicate selection criteria for dbh values (cm). MSN
Table 7 .
Comparison of k-NN (k = 3) density by dbh class with and without stratification for most common species (N = number of plots with species group-it is only by coincidence that both conifers and hardwoods occur on 176 plots each).
Table 8 .
Index values (H,I) for k-NN prediction and estimation strategies with k = 3 using the three predictor variables P30, P90, and Cover(1.50) and responses TPH and Vol. for all species combined. We also include the baseline variability (k-NN dist = none) describing plot variability around population dbh density.
Euc., Mah., MSN, and RF refer to Euclidean, Mahalanobis, Most Similar Neighbors, and Random Forest respectively.
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Domain: Environmental Science Biology
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This below document has 2 sentences that start with 'In July the maximum at',
2 sentences that start with 'This is consistent with the',
2 sentences that start with '30 year mean',
2 sentences that start with 'Same as Figure 3 except',
3 sentences that end with 'at 40 N',
2 sentences that end with 'wide warm current',
2 sentences that end with 'maximum along 40 N',
3 paragraphs that start with 'Month to month changes in'. It has approximately 3245 words, 130 sentences, and 43 paragraph(s).
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North Pacific Month to Month SST Changes
Month to month changes in the SST of the North Pacific, on the eastern side at mid-latitudes, are studied based on 30 years of ship-injection temperatures. Along both 40 and 35 N the SST maximum shifts west in summer, but it starts west at 35 N two months sooner than at 40 N. In July the maximum at 40 N is at the same location as the maximum at 35 N was in June: 155 W. Since the longitudinal SST maximum in the eastern North Pacific has previously been identified as the signature of a very wide, warm and sluggish current permanently flowing northeast off California, the month to month SST changes are used to estimate its mean speed: 10 20 cm/sec. Also the month to month SST changes indicate that in summer a new body of warm water goes north, in a pulselike movement, to the west of the existing wide warm current. This is consistent with the need of the western equatorial ocean to export more heat northward out of the tropics in summer due to the increased absorption of solar radiation in the surface layer in that season.
Introduction
Seasonal variations of the sea surface temperature of the North Pacific have been described recently [1]. For example, north of 20 N the highest values, in the long-term mean, occur in August and September in broad bands of the open ocean. In addition there is a small patch off the west coast of the US where the highest temperatures of the year are to be found in October!Coldest annual temperatures mostly take place in March and April. Thus there are significant time lags at mid-latitudes between when the sun is farthest north and when the sea surface is warmest as well as between when the sun is farthest south and the sea surface is coldest.
These conclusions are based on an extensive set of ship-injection temperatures collected during thirty years extending from coast to coast. There are literally millions of observations contained in this data set, which is formatted in five degree latitude/longitude squares and one month intervals, starting in 1947. Features of the seasonal variations were interpreted in terms of changes, mainly in horizontal position, of a very wide, warm and sluggish surface current permanently flowing northeast off California.
With the help of the same data base it is possible to say a few things about month to month SST changes in the North Pacific, and that is the purpose of the present essay. This is a natural next step to take, but to my knowledge month to month SST changes have never been studied before for the North Pacific. There are no preconceived expectations existing for how the results might turn out. However, since a connection has been shown between the longitudinal SST maximum and the NPH (North Pacific High), it could become important to know more about these month to month SST changes in connection with weather variations that pass across the US [2].
Therefore, once again the unifying thread used below is the large-scale longitudinal maximum in SST at mid-latitudes, usually on the eastern side of the ocean, which has been identified as the signature of a very wide warm surface current. In fact, this SST maximum appears to locate the approximate east/west mid-point of the northeastward flow. And the NPH usually sits right on top of it.
One of the main physical functions of the wide warm current, and the colder return flow underneath, has been conjectured to be to conduct some of the excess solar heat absorbed mainly at low latitudes by the North Pacific at all times of the year, but especially in the northern summer, from tropical latitudes to higher latitudes in order to help maintain the earth's heat balance [3]. It is clear that heat must be transported out of the tropics because the surface temperatures of the western equatorial waters are no higher in summer than in winter [4] in spite of the fact that more solar energy per unit area and per unit time must get absorbed in the upper 100 m in summer than in winter. Assuming the tropical seas do not give up all that excess heat directly to the atmosphere, then it must be carried northward by ocean currents.
How can the northward oceanic heat flow increase in the summer since the depth scale of the warm flow cannot change, it is tied to the maximum depth of penetration of sun light, which is about 100 m? Possibly the average speed of the current might increase in summer due to the stronger southward surface temperature gradient in summer that is related to the thermal driving force behind the warm/cold surface and near surface circulation. To check up on this idea would be difficult because of the expected sluggishness of the flow [5] caused by a cold source and a warm source at nearly the same level (sea level) but separated by a large horizontal distance (tropics to the sub-arctic).
However, there may be another way that the ocean can increase its net northward heat flux in summer, as suggested by the ship-injection SST data. Either the warm surface current could significantly and rather suddenly widen to the west in early summer [there being no room to the east due to the presence of the North American continental boundary]. Or perhaps equally likely is the possibility that a separate seasonal pulse of warm water could start moving northward in summer temporarily flowing alongside and to the west of the existing permanent northeastward flow (perhaps merging with it) that is in place at all times of the year. Untangling these two options in any given year may not be conclusive with presently available data. In any case, what would trigger the additional pulse of warm water to move north must be a burst of cold water coming south in or near the surface layer, i.e. probably just below it. Such a burst of cold water would not always directly be reflected in the SST data, which documents mostly the position and movement of the warm water.
That there is a return flow of colder water under the wide warm current of the eastern North Pacific is made known by the permanent feature of the shallow salinity minimum [6], where relatively low salinity acts as a dye marking a layer of colder southward flow just underneath the warm northward current at mid-latitudes. Farther to the north at the sea surface is the source of the low salinity water because at that location precipitation exceeds evaporation.
What follows in Section 2 is a straightforward calculation of the month to month changes in longitude of the SST maximum along 40 N, where it is found that the standard deviations are significantly large in the summer months during the westward seasonal migration of the SST maximum. This calculation suggests that in early summer an additional rather irregular pulse of warm water heads north to the west of the permanent northeastward flow.
Further evidence of the extra northward flow of warm water in summer is sought in Section 3 by showing that the mean westward migration of the SST maximum along 35 N starts two months before the similar westward migration along 40 N. With some assumptions these surface temperature observations then allow an estimate to be made of the mean northward flow speed, making the word "sluggish" more quantitative (Section 4).
Month to Month
Along 40 N the longitude of the SST maximum in the eastern part of the ocean was read and recorded for each month for 30 years, beginning with 1947. Then the longitude differences between all consecutive pairs of months was computed: for example J-F, F-M,… etc., where J = January, F = February, M = March… These longitude differences were then corrected by subtracting out the 30 year average month to month longitude differences for the same pairs of months in order to filter out the normal seasonal variation. Finally, the standard deviations of the corrected month to month changes in the longitude of the SST maxima were formed (Figure 1).
On the vertical axis of Figure 1 are the standard deviations in whole degrees of longitude at 40 N. Remember that the longitude of the SST maximum could only be read to the nearest five degrees, due to the format of the ship-injection temperatures. Therefore, standard deviations of five degrees or less have no significance. On the horizontal axis are the consecutive month to month pairs.
What Figure 1 exhibits is that in the summer months the standard deviations rise to about three times those of the noise level (5 degrees). Month to month variations in the longitude of the SST maximum are largest from June through September over the given period. Since the smooth seasonal signal has been subtracted out, Figure 1 suggests a pulse like or episodic flow of additional warm water in the summer months, which presumably comes from the southwest.
Month to month changes in temperature at the location of the longitudinal SST maximum are not significant; they are 1 C or less for all pairs of months [Ship-injection temperatures in those years were based on thermometers that can only be read to the nearest 1 F]. Therefore, east/west positional changes, rather than temperature changes, characterize the month to month variations in the SST at mid-latitude on the eastern side of the ocean.
Figure 2 shows the 30 year average temperature at the SST maximum along 40 N. It is not the monthly mean temperature at a fixed location, but rather following the maximum throughout its seasonal migration east and west. Figure 2 displays a distinctive seasonal variation of the mean SST at the longitudinal maximum along 40 N. There is a regular almost sinusoidal change in surface temperature with a minimum in March and a maximum in August, the total range being about 9 C. The minimum might be described as being shallower and the maximum more peaked than an exact sinusoid would be.
Since it has been proposed that the surface water surrounding the longitudinal SST maximum along 40 N has its source in the western tropical North Pacific (Kenyon, 2013), where the surface temperature is about 28 C year round, by the time these waters have reached 40 N there has been a good deal of heat lost to the atmosphere even in August according to Figure 2: i.e. a seven degree drop in temperature. And it is interesting that the SST maximum at 40 N has such a strong seasonal signal whereas the source region basically has none. Evidently, in traveling from the tropics to mid-latitudes considerably more heat is transferred from the ocean's surface layer to the atmosphere in winter than in summer, at least in the wide area surrounding the longitudinal SST maximum. In the mean the winter SST in Figure 2 is 16 C degrees lower than it was at the source.
Spring into Summer
One feature of the month to month variation of the SST in the North Pacific, not mentioned above, has only been noticed recently. The east/west migration of the longitudinal maximum in sea surface temperature throughout the year has been documented at 35 and 40 N: it moves west in summer and returns east in fall (Kenyon, 1981). What has not been said earlier is that the systematic westward movement starts at 35 N significantly before it begins at 40 N. This is true in the 30 year average as well as in individual years. Typically the maximum at 40 N starts west between June and July whereas the maximum at 35 N moves systematically west two months earlier, starting between April and May, and continues west from May to June (Figures 3-5).
From late fall through winter and into early spring the location of the longitudinal maximum of the SST does not move. Along 40 N it is at 140 W and along 35 N it is at 147.5 W. In any given month, and in the long term mean, the maximum at 35 N is always west of that at 40 N implying that the flow surrounding the maxima between 35 and 40 N is generally northeast. Then in April the maximum at 35 N starts its move to the west and later in June the maximum at 40 N moves west. By November the 40 N maximum has returned to its most eastern position whereas the 35 N maximum requires another month to get all the way back east again.
What is going on here is a periodic phenomenon, the summer migration of the longitudinal SST maximum, because it happens once a year and every year. However, one would not characterize it as being perfectly sinusoidal. The period of time that the maximum is away from its winter position is four months at 40 N but six months at 35 N. By contrast the ultimate forcing function, the absorption in the ocean of short wave radiation from the moving sun, is sinusoidal.
For the months of April, May and June the 30 year means of the SST are plotted in Figures 3-5 against longitude for both 35 and 40 N on three graphs, one for each month. The same temperature scale is used for convenience (so the 40 N curve lies below the 35 N curve in each figure, not above as on a map). It can be seen in these figures that the location of the eastern maximum at 40 N does not move away from 140 W. However, in July it jumps west 15 degrees of longitude (not shown). On the other hand, the analogous maximum at 35 N starts moving west between April and May. This feature appears to be significant and it is what would be expected if an additional body of warm water were to move northward away from the tropics: the temperature signal should cross 35 N first and 40 N second.
Discussion
From the time delay of two months, in the 30 year mean, between the start of the westward migration of the SST maximum at 35 N and the start of the westward migration at 40 N, and the distance of five degrees of latitude, a northward speed for the temperature signal to get from 35 N to 40 N can be estimated. It is 10 cm/sec. What is the meaning of such an estimate?Many cautions need to be stated. First, a long term mean is tricky because it involves averaging many individual cases. From this point of view the estimate might tend to be a low one. If the estimate is to be associated with a flow speed, it will be low also because the mean flow speeds in the area are usually northeast instead of straight north, as inferred from the SST data and assumed in the estimate. Here is another complication. Figures 3-5 show that the SST maximum at 35 N moves west 5 degrees of longitude between April and May and then another 2.5 degrees west between May and June, whereas the SST maximum at 40 N does not move west at all during this time period but then does move west 15 degrees of longitude between June and July. Interpreting the westward movement of a temperature pattern (longitudinal variation) in terms of a northward (or northeastward) movement of fluid would seem to be an impossible task on the face of it.
There is one potential confusion that is not an occasion for much discussion. Even though the longitudinal temperature patterns at 35 and 40 N in the eastern North Pacific look wave-like, the SST data do not suggest that a wave phenomenon lies behind them. So that option can be dropped from consideration right away. No wave phase or group velocity needs to be sought.
Furthermore, the warm water is losing heat to the atmosphere in going from 35 to 40 N, and the amount of heat lost may vary with longitude, perhaps only gradually though, which could have an effect on the longitudinal shape of the temperature signal. However, at least the longitudinal maximum character of the SST stays intact between 35 and 40 N.
Another possible sequence of events comes to mind. Suppose that in the mean sometime before April a new body of warm water is pushed up from the south outside or alongside the permanent wide warm current to its east. As it crosses 35 N this new body of warm water begins to influence the shape of the longitudinal SST maximum, mainly the western limb including the maximum itself, by gradually moving the maximum westward from April through June. Then in July the SST maximum at 40 N has shifted 15 degrees of longitude to the west from 140 to 155 W. That puts it straight north of where the maximum at 35 N was in June (Figure 5). So if the new warm water surrounding the maximum went straight north from 35 N in June to 40 N in July, the mean speed of flow would have to be 20 cm/sec, double the estimate given above (i.e.five degrees of latitude in one month instead of in two months).
Although the most recent 30 plus years of ship-injection temperatures for the North pacific, if they exist, have not been available to me, my guess is that the above conclusions would not change in any significant way were they to be incorporated into present analyses.
Conclusion
Month to month changes in the SST of the North Pacific along 35 and 40 N are described using ship-injection temperatures collected over a 30-year period. These changes are related to the longitudinal SST maximum in the eastern North Pacific, which has been identified as the signature of a very wide warm surface current flowing sluggishly northeast off the coast of California. During late fall to early spring the maximum stays fixed at both 35 and 40 N. Between April and May the maximum at 35 N starts its migration west and then two months later, between June and July, the maximum at 40 N goes west. In July the maximum at 40 N is straight north of where the maximum at 35 N was in June. Using only SST data a mean northward speed of flow is estimated to be 10 -20 cm/sec. In the summer months the standard deviations of the east/west positions of the SST maximum are significantly large, suggesting that an increased episodic northward flow of warm water is occurring just west of the permanent wide warm current. This is consistent with the need for the ocean to enhance its net northward heat flux due to the increased absorption of solar radiation in summer.
Figure 1 .
Figure 1. Standard deviations (degrees of longitude) of the month to month changes in longitude of the SST maximum at 40 N plotted against month pairs. Based on 30 years of ship-injection data.
Figure 2 .
Figure 2. 30 year mean temperatures (Deg. C) at the SST maximum along 40 N plotted against month.
Figure 3 .
Figure 3. 30 year mean SSTs (Deg. C) plotted against longitude at 35 N (top) and 40 N (bottom) for April. California is to the right, Japan to the left.
Figure 4 .
Figure 4. Same as Figure 3 except for May.
Figure 5 .
Figure 5. Same as Figure 3 except for June.
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Domain: Environmental Science Biology
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Impact of invasion by molasses grass ( Melinis minutiflora P . Beauv . ) on native species and on fires in areas of campo-cerrado in Brazil
In the Cerrado Biome of Brazil, African grasses constitute a serious problem, occurring in virtually all protected areas. Molasses grass (Melinis minutiflora P. Beauv.) accumulates more biomass than do most other species of the herbaceous stratum vegetation native to the Cerrado. In this study, our aim was to determine the impact of M. minutiflora on native vegetation, as well as (using simulations of fire traits) on the characteristics of fires, in invaded areas of the Serra do Rola-Moça State Park (Parque Estadual da Serra do Rola-Moça, PESRM), a protected area where fires are frequent, in the state of Minas Gerais, Brazil. Our main results are that M. minutiflora considerably increases the amount of biomass, becoming the main combustible plant in the campo-cerrado (grassy-woody savanna) fires in the PESRM; that the native monocot biomass is inversely correlated with the M. minutiflora biomass, suggesting suppression of the native herbaceous stratum; that eudicots are diminished by M. minutiflora, both in number of individuals and number of species; and that fires are more severe in M. minutiflora-invaded areas. Key-words: Cerrado, BehavePlus, biological invasion, fire intensity, savannah Acta Botanica Brasilica 28(4): 631-637. 2014. doi: 10.1590/0102-33062014abb3390 Impact of invasion by molasses grass (Melinis minutiflora P. Beauv.) on native species and on fires in areas of campo-cerrado in Brazil Rafael Drumond Rossi1,5, Carlos Romero Martins2, Pedro Lage Viana3, Evandro Luís Rodrigues4 and José Eugênio Côrtes Figueira1 1 Universidade Federal de Minas Gerais, Instituto de Ciências Biológicas, Departamento de Biologia Geral, Avenida Antônio Carlos n. o 6.627, Pampulha, Belo Horizonte, MG, Brasil – CEP: 31.270-901. 2 IBAMA/DILIC, SCEN Trecho 2, Ed. Sede do IBAMA, Brasília, DF, Brasil – CEP: 70.818-900. 3 Museu Paraense Emílio Goeldi. Avenida Magalhães Barata, 376, São Braz, Belém PA, 66040-170, Brasil. 4 Universidade Federal de Minas Gerais, Instituto de Geociências, Avenida Antônio Carlos n.o 6.627, Pampulha, Belo Horizonte, MG, Brasil – CEP: 31.270-901. 5 Author for correspondenceIntroduction The most economically important cattle forage grass species in the Neotropics are those native to Africa (Parsons 1972). African grasses are also used for recovery of degraded areas and as slope cover along highway and railway embankments (Reis et al. 2003; Martins 2006). They are easily exportable due to their invasive characteristics (Foxcroft et al. 2010), causing a global problem (Hughes et al. 1991; Barger et al. 2003; Martins 2006; Dogra et al. 2010; Foxcroft et al. 2010) through biological contamination of natural ecosystems (Espíndola et al. 2005). In Brazil, African grasses were introduced during the colonization period of the 16th century (Diamond 1999; Zenni & Ziller 2011) and were commonly used for pasture after deforestation (Parsons 1972; Fearnside 2005). Owing to their great ability to invade open areas, they also became a serious problem in the Cerrado Biome (Pivello 2014), becoming common in several of its protected areas (Pivello et al. 1999a; 1999b). The African grass Melinis minutiflora P. Beauv. (molasses grass), widely distributed in South America, Hawaii, Australia, Central America, Asia and Oceania (Hughes et al. 1991; Barger et al. 2003; Martins 2006), is commonly found in Brazil (Pivello 2014), where it had been the most commonly used species for pastures until the beginning of the 1970s, when programs to replace it with more productive grasses began (Martins 2006). Its introduction into the state of Minas Gerais most likely dates back to the 18th century (Biodiversitas et al. 2006). Although the genus Melinis P. Beauv. comprises 22 species (The Plant List, 2014), only M. minutiflora and M. repens (Willd.) C. E. Hubb. occur in Brazil (Biodiversitas et al. 2006; Filgueiras et al. 2010). Molasses grass grows vigorously in the nutrient-poor and aluminum-rich soils of the Cerrado Biome (Gonçalves & Borges 2006; Martins et al. 2011). It blooms at the beginning of the dry season, in mid-May, producing large amount of seeds with a germination rate of approximately 90% (Alcântara & Bufarah 1951; Martins 2006; Carmona & Martins 2009). Studies within different ecosystems around the world indicate that molasses grass can form dense cover in invaded areas, changing nutrient cycles, light availability and soil microclimate (Barger et al. 2003), thus slowly replacing native species of the herbaceous stratum (Martins et al. 2004; Martins et al. 2009). Because of its higher biomass accumulation
Introduction
The most economically important cattle forage grass species in the Neotropics are those native to Africa (Parsons 1972). African grasses are also used for recovery of degraded areas and as slope cover along highway and railway embankments (Reis et al. 2003;Martins 2006). They are easily exportable due to their invasive characteristics (Foxcroft et al. 2010), causing a global problem (Hughes et al. 1991;Barger et al. 2003;Martins 2006;Dogra et al. 2010;Foxcroft et al. 2010) through biological contamination of natural ecosystems (Espíndola et al. 2005). In Brazil, African grasses were introduced during the colonization period of the 16th century (Diamond 1999;Zenni & Ziller 2011) and were commonly used for pasture after deforestation (Parsons 1972;Fearnside 2005). Owing to their great ability to invade open areas, they also became a serious problem in the Cerrado Biome (Pivello 2014), becoming common in several of its protected areas (Pivello et al. 1999a;1999b). The African grass Melinis minutiflora P. Beauv.(molasses grass), widely distributed in South America, Hawaii, Australia, Central America, Asia and Oceania (Hughes et al. 1991;Barger et al. 2003;Martins 2006), is commonly found in Brazil (Pivello 2014), where it had been the most commonly used species for pastures until the beginning of the 1970s, when programs to replace it with more productive grasses began (Martins 2006). Its introduction into the state of Minas Gerais most likely dates back to the 18th century (Biodiversitas et al. 2006).
Although the genus Melinis P. Beauv.comprises 22 species (The Plant List, 2014), only M. minutiflora and M. repens (Willd.)C. E. Hubb.occur in Brazil (Biodiversitas et al. 2006;Filgueiras et al. 2010). Molasses grass grows vigorously in the nutrient-poor and aluminum-rich soils of the Cerrado Biome (Gonçalves & Borges 2006;Martins et al. 2011). It blooms at the beginning of the dry season, in mid-May, producing large amount of seeds with a germination rate of approximately 90% (Alcântara & Bufarah 1951;Martins 2006;Carmona & Martins 2009). Studies within different ecosystems around the world indicate that molasses grass can form dense cover in invaded areas, changing nutrient cycles, light availability and soil microclimate (Barger et al. 2003), thus slowly replacing native species of the herbaceous stratum (Martins et al. 2004;Martins et al. 2009). Because of its higher biomass accumulation rates, compared to those of the native herbaceous stratum (Berardi 1994;Silva & Haridasan 2007), and its abundant oil secretions (Prates et al. 1993), this grass can also change fire regimes in invaded areas, promoting its further expansion in a feedback mechanism known as the "grass-fire cycle" (Hughes et al. 1991;D' Antonio & Vitousek 1992).
In the Cerrado Biome, several important protected areas suffer from invasion by molasses grass. Some of these are the Brasília National Park, the world's largest natural conservation area in an urban environment (Martins 2006;Zanin 2009); the Emas National Park, an important reserve in the Central Plateau (França et al. 2007); and the Serra do Rola-Moça State Park (Parque Estadual da Serra do Rola-Moça, PESRM), which protects areas of the Cerrado in the iron-stone vegetation and areas of the Atlantic Forest (Biodiversitas et al. 2006). The PESRM is considered a priority area for biodiversity conservation in the state of Minas Gerais because of its high levels of biodiversity and endemism. Large patches of molasses grass in this park increase its vulnerability to fire, one of its main threats (Biodiversitas et al. 2006). However, the impact of molasses grass on native vegetation and its effects on fire properties have been largely unevaluated.
In an attempt to address parts of this knowledge gap, this study had the following goals: to determine whether native species are being suppressed due to invasion of molasses grass; to assess the magnitude of the increase in combustible plant biomass resulting from the molasses grass spread in an area of the grassy-woody savanna (campo-cerrado) vegetation; and to simulate and compare characteristics of fires in the campo-cerrado areas with different degrees of molasses grass invasion.
Study area
The PESRM is located in the northwestern part of the region known as the Iron Quadrangle, a transition area between the Atlantic Forest and the Cerrado Biomes, in the metropolitan area of Belo Horizonte, the capital of Minas Gerais. The main vegetation formations found in the PESRM are the semideciduous forest; the woody savanna (cerradão); the grassy-woody savanna (campo-cerrado, or cerrado sensu stricto); and the sandstone and ironstone grasslands. Molasses grass is found in all of those formations, with the exception of the semideciduous forest. The climate of the region is mesothermal, characterized by an annual mean temperature of 25°C, high daily thermal variation, high winds, and a dry season from April to September (Biodiversitas et al. 2006). The molasses grass patches studied are located near the park headquarters (20º03'07.1"S;44º00'00.0"W). The campo-cerrado vegetation is predominant in the study areas, with sparse trees embedded in an extensive matrix of grasslands.
Biomass increase and elimination of native monocots
Vegetation sampling was carried out in April and May 2009. Twenty plots of 0.5 × 0.5 m were randomly selected in a campo-cerrado field invaded by molasses grass and protected from fires for over 13 years. The vegetation within each square was cut at ground level, collected, bagged in 100-L plastic bags and taken to the laboratory. In the laboratory, the biomass was separated into live and dead fractions of its various components: molasses grass; native monocots; native eudicots; and litter (composed of dry fragments of grass blades, stems, etc.)The biomass components were then dried in an oven at temperatures of approximately 60°C to a constant weight, after which they were weighed with a precision balance. The monocot species of the study area were identified from the specialized literature and by comparison with specimens at the Herbarium of the Federal University of Minas Gerais, in Belo Horizonte (BHCB).
Linear regression, Pearson's correlation coefficient or both were used in order to test the hypotheses that molasses grass is the main component of the total biomass, leads to the biomass reduction of native monocots and is correlated with a large increase in the amount of litter found in the plots. To compare the standing dead biomass between the native grasses and molasses grass, we used ANCOVA. When necessary, log transformations were used in order to linearize the data or to homogenize variances and normalize residuals.
Elimination of eudicots
From June to August 2011, vegetation sampling was also carried out at other field patches that had been invaded, or not, by molasses grass and protected from fires for over fifteen years. Linear transects were set in invaded and noninvaded patches, and samples of all eudicots in 0.7 × 0.7 m plots, one meter apart, were collected. The size of the molasses grass patches limited the number of plots sampled in each area. There were 50 plots sampled for each condition (invaded vs. non-invaded) in area 1; 100 plots for each treatment in areas 2 and 3; and 70 plots for each treatment in area 4. All eudicots were identified, and the number of individuals of each species for each plot (sampling unit) were determined in order to estimate the species richness in the invaded and non-invaded areas, with the richness estimator index ACE (abundance-based coverage estimator). To compare the density of species in invaded and non-invaded transects, we used the Mann-Whitney U test.
Fire simulations
The BehavePlus Fire Modeling System is a collection of over 40 semi-physical models described in 58 reference papers that predict wildland fire behavior and its environmental effects. It is among the most widely used systems Acta bot.bras.28(4): 631-637.2014.
for wildland fire prediction. Planning prescribed fires, fuel hazard assessment, and training are among the BehavePlus applications (Andrews 2009;2014). Using the BehavePlus, Mistry & Berardi (2005) obtained results substantiated by previous studies in the cerrado fires. Fernandes (2003) did simulation with BehavePlus in the PESRM and also obtained satisfactory results. The lowest relative air humidity (11%), and mean wind speeds (13.9±8.7 km/h) in August of the years 2011, 2012 and 2013 (data from the A555 Ibirité-Rola-Moça meteorological station: www.inmet.gov.br), were used for the input data in BehavePlus, in order to compare the expected fire behavior in the campo-cerrado fields without molasses grass (5.6 Mg/ha), with the intermediate invasion (14.0 Mg/ha) and the high invasion rate (18.4 Mg/ha). The biomass surface/volume ratio and biomass energy content were obtained from Mistry & Berardi (2005). Since dead biomass moisture and wind speeds depend on climate conditions, and the moisture of dead grasses and air can reach equilibrium in approximately one hour (Fernandes 2003), simulations were performed with mean wind speeds of 5, 10 and 15 km/h, and the dead biomass moisture ranged from 8 to 23%. These value ranges allow for a direct comparison with the findings of Mistry & Berardi (2005). For simplicity, the topography was considered plane.
The rate of accumulation of the standing dead biomass of native grasses was similar to that of molasses grass (Fig. 1). However, the combined live and standing dead biomasses of molasses grass were greater than were those of native grasses (Fig. 1), indicating a greater contribution of the former to the fine fuel accumulation. The maximum total biomass in the areas invaded by molasses grass was 18.4 Mg/ha, or 3.3 times greater than the 5.6 Mg/ha in the non-invaded areas. As can be seen in Fig. 2, molasses grass became the main biomass component in the areas it invaded, correlating strongly with the total biomass in the plots (r = 0.756). The native monocot biomass decreased in proportion to the increase in molasses grass (native monocot biomass = 69.70− 0.30 molasses grass, r=−0.547,p<0.05). Litter, another important component of the total biomass, was strongly correlated with the molasses grass biomass in each plot (litter biomass = 36.98+ 0.66 molasses grass biomass, r=0.636, p<0.05).
Elimination of eudicots
Taking into account all recorded species of eudicots, molasses grass reduced their individual numbers in the four invaded areas between 1.8 and 4.1 times (Tab.1). The richness estimator index ACE (abundance-based coverage estimator) indicated a reduction in the number of species in the four studied areas invaded by molasses grass (Fig. 3). The reduction was estimated to be between 16% (Area 2) and 43% (Area 4), with a mean of 33%. Species density was higher in non-invaded areas (6.3 species/m 2 vs. 2,6 species/ m 2 , Mann-Whitney U test: p<0.001). Species sensitivity to the molasses grass invasion is listed in Tab. 2.
Fire simulations
Simulations with the BehavePlus 5 indicated that fires would be more destructive in the fields invaded by molasses grass (Fig. 4 and 5). From 2010 to 2013, the relative air humidity was ≤ 18% on 13 days. Considering the more critical periods with 18% dead biomass moisture and 15 km/h winds, in areas with high invasion rates vs. non-invaded areas (Fig. 4 and 5), fire fronts would advance 2.5 times faster (9 m/min vs. 22.1 m/min), the fire intensity would be 17.3 times greater (569 KW/m vs. 9868 KW/m), the flame length would be 2.8 times greater (1.4 m vs. 5.3 m), and the heat per unit area (independent of wind velocity) would be 7.1 times higher (3792 Kj/m 2 vs. 26778 Kj/m 2 ).
Discussion
Molasses grass greatly increased the fine fuel biomass in the campo-cerrado areas of the PESRM, potentially reaching values of up to 18 Mg/ha, similar to the 12.1-21.4Mg/ha estimated for a cerrado area with high rates of invasion in Brasília (Martins et al., 2011). The negative correlation between the biomasses of native monocots and those of molasses grass suggests that the former have been suppressed by the latter. This would also explain the reduction in the richness and abundance of eudicots, some of which are naturally rare. Conversely, eudicots persisted in small non-invaded patches within large areas invaded by molasses grass. Nitrogen use efficiency in molasses grass is known to be higher than is that of native South American C4 grasses (Lannes et al. 2012). Coutinho (2000) estimated that nearly 95% of nitrogen in burned cerrado plants is lost to the atmosphere as smoke. As a consequence, burned sites tend to be nitrogen deficient, with molasses grass often being the dominant species (Lannes et al. 2012). Furthermore, the competition for space and light, as well as allelopa- Hamilton et al. 1999;Levine et al. 2003;Hoffmann et al. 2004;Hoffmann & Haridasan 2008).
In the Cerrado Biome, natural fires are caused by lightning strikes and may be extinguished by subsequent rains. However, human activities have increased the risk of out-of-control fires during the dry season in protected areas (França et al. 2007), as is the case at the PESRM. In addition, the invasion of exotic grasses potentiates the grassfire cycle, characterized by more frequent and intense fires encompassing larger areas, due to the greater and faster accumulation of their biomass compared to that of the native species (D' Antonio & Vitousek 1992;Milton 2004).
The fire simulations for the campo-cerrado at the PESRM indicated potentially greater fire intensity and flame length, with the chance of the fire spreading from the low herbaceous strata to the treetops increasing. The fire properties previewed by BehavePlus are compatible with other studies of the Cerrado Biome (Kauffman et al. 1994;Castro-Neves 2000). Eye-witness accounts from firefighters and volunteers of a large nine-day fire in this park at the end of the 2011 dry season, when 1949 ha (≈60%) of the park area were burned, are impressive: flames 12 m high on the hilltops, invaded by molasses grass, had formed a huge fire front, and the heat could be felt from up to 200 m away. In the invaded valleys, the wind was channeled and the fire spread rapidly. Flame lengths surpassed 6 m (Rodrigo B. Belo, Director for Prevention and Control of Forest Fires and Critical Events, SEMAD). Charcoal marks on the valley trees showed signs of burning at a height > 5 m, and some cerrado trees, like the "barbatimão" (Stryphnodendron adstringens (Mart.)Coville), appeared to be dead.
The results of our study corroborate those of other studies describing a reduction of native species diversity in areas invaded by molasses grass or other exotic grasses (D' Antonio & Vitousek 1992;Hughes et al. 1991;Rahlao et al. 2009). Competition, changes in soil chemistry and fire can act synergistically, leading to a continuous species loss. Recurrent fires favor molasses grass at the expense of native monocots and eudicots that have less resistance to burning and less ability to regenerate (França et al. 2007). Control of molasses grass would be the only solution for a possible re-establishment of native vegetation in invaded preservation areas. At the Brasília National Park, such control was achieved by the combined use of controlled burning, herbicides and seedling removal (Martins et al. 2011). At the PESRM, the persistence of dense patches of molasses grass after decades of fire suppression demonstrates its ability to persist in the absence of such disturbance, as has been reported by other authors (Hughes et al. 1991;Berardi 1994).
The invasion of molasses grass in the campo-cerrado areas of the PESRM is increasing fire intensity, one of the major components of fire regimes (Brooks et al. 2004;Whelan 1995;Bond & Wilgen 1996). In addition, molasses grass invasion increases the risk of fire and the chance of fire spreading from the grass layer to the treetops. Hoffmann et al. (2004;2009) predicted the retraction of gallery forests in the Cerrado Biome boundaries when fires are frequent. The increases in fire frequency and destructiveness associated with the invasion of molasses grass at the PESRM could reduce its gallery forests, bordered by the campo-cerrado, which sustain and protect one of the most important sources of fresh water for the metropolitan area (Euclydes 2011).
Figure 1 .
Figure 1. Relationships between standing dead and live biomasses of molasses grass (filled circles) and native grasses (open circles). ANCOVA: p=0.950 for the intercept, and p=0.887 for the angular coefficient.
Figure 2 .
Figure 2. Contribution of molasses grass (filled circles, continuous line), native grasses (open circles) and litter (stars) to the biomass of the campos-cerrado grass layer (dashed line).
Figure 3 .
Figure 3. Comparison of species richness in non-invaded areas (circles) and areas invaded by molasses grass (squares). Richness was estimated using the abundance-based coverage estimator (ACE) index.t-test for all paired areas: p<0.01.
Figure 4 .
Figure 4. Flame length (a), maximum spread rate (b), and fire intensity (c) for different levels of invasion by molasses grass, with different levels of fine fuel moisture and at different wind speeds. Triangles -high invasion rate; squares -intermediate invasion rate; circles -no invasion.
Figure 5 .
Figure 5. Heat per unit area for different levels of invasion by molasses grass, with different levels of fine fuel moisture. Triangles -high invasion rate; squares -intermediate invasion rate; circlesno invasion.
Table 1 .
Comparison of eudicot counts in areas invaded (INV) and non-invaded (NAT) by molasses grass.
Table 2 .
Species sensitivity to molasses grass invasion.
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Domain: Environmental Science Biology
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Dispersal-Based Microbial Community Assembly Decreases Biogeochemical Function
Ecological mechanisms influence relationships among microbial communities, which in turn impact biogeochemistry. In particular, microbial communities are assembled by deterministic (e.g., selection) and stochastic (e.g., dispersal) processes, and the relative balance of these two process types is hypothesized to alter the influence of microbial communities over biogeochemical function. We used an ecological simulation model to evaluate this hypothesis, defining biogeochemical function generically to represent any biogeochemical reaction of interest. We assembled receiving communities under different levels of dispersal from a source community that was assembled purely by selection. The dispersal scenarios ranged from no dispersal (i.e., selection-only) to dispersal rates high enough to overwhelm selection (i.e., homogenizing dispersal). We used an aggregate measure of community fitness to infer a given community’s biogeochemical function relative to other communities. We also used ecological null models to further link the relative influence of deterministic assembly to function. We found that increasing rates of dispersal decrease biogeochemical function by increasing the proportion of maladapted taxa in a local community. Niche breadth was also a key determinant of biogeochemical function, suggesting a tradeoff between the function of generalist and specialist species. Finally, we show that microbial assembly processes exert greater influence over biogeochemical function when there is variation in the relative contributions of dispersal and selection among communities. Taken together, our results highlight the influence of spatial processes on biogeochemical function and indicate the need to account for such effects in models that aim to predict biogeochemical function under future environmental scenarios.
Introduction
Recent attempts to link microbial communities and environmental biogeochemistry have yielded mixed results [1][2][3][4][5][6], leading researchers to propose the inclusion of community assembly mechanisms such as dispersal and selection in our understanding of biogeochemistry [2,[7][8][9]. Although much work has examined how assembly processes influence the maintenance of diversity and other ecosystem-level processes in macrobial systems [10][11][12][13], our comprehension of how these processes influence microbially-mediated biogeochemical cycles is still nascent [2,8,14]. Thus, there is a need to discern the circumstances under which knowledge on assembly processes is valuable for predicting biogeochemical function.
Community assembly processes collectively operate through space and time to determine microbial community composition [3,7,14,15]. They fall into two predominate categories that can be summarized as influenced (i.e., deterministic) or uninfluenced (i.e., stochastic) by biotic and abiotic environmental conditions. Stochastic processes can be further classified into dispersal, evolutionary diversification, and ecological drift, while determinism is largely dictated by selection [7,16]. We refer readers to a recent review article for a more nuanced understanding of deterministic influences on dispersal and of stochastic influences on selection, which are not discussed here [17]. Experimental research has shown unpredictable relationships between microbial diversity and biogeochemical function (generically defined here to represent any biogeochemical reaction of interest), leading to the hypothesis that differences in community assembly history-and thus the relative contributions of stochastic and deterministic processes-drives relationships between microbial community structure and biogeochemical function [8,9].
Dispersal in particular may vary the relationship between community structure and biogeochemical function [7]. Both positive and negative associations between dispersal and community function have been hypothesized (reviewed in [18]). The 'portfolio effect' argues for enhanced community functioning under high levels of dispersal, proposing that high diversity communities are more likely to contain more beneficial species properties on average than lower diversity communities [19,20]. Additionally, if dispersal increases biodiversity, there should be a greater chance that the community can occupy more niche space (i.e., niche complementarity), reducing direct competition and increasing function [21].
Alternatively, dispersal may decrease community-level biogeochemical function [7,22]. High rates of dispersal can add organisms to a microbial community that are not well-suited to local environmental conditions (i.e., mass effect or source-sink dynamics [23,24]). Maladapted individuals may invest more in cell maintenance to survive as opposed to investing in cellular machinery associated with biogeochemical reactions needed to obtain energy for growth and reproduction. In this case, the community's ability to drive biogeochemical reactions may be depressed. For instance, pH [25] and salinity [26,27] are widely considered strong regulators of microbial community structure. If microorganisms are well adapted to and disperse from a moderate pH or salinity environment to a more extreme environment, they may be maladapted and have to expend energy to express traits that maintain neutral internal pH (e.g., H+ pumps) or maintain cellular water content (e.g., osmotic stress factors). These cell maintenance activities detract from the energy available to transform biogeochemical constituents and may suppress overall community rates of biogeochemical function. In contrast, locally adapted species would putatively have more efficient mechanisms for cell maintenance in the local environment and be able to allocate more energy for catalyzing biogeochemical reactions.
These dispersal effects also interact with local selective pressures and the physiological ability of organisms to function across a range of environments to collectively influence biogeochemical function in uncertain ways. Here, we propose that (1) communities more influenced by dispersal are composed of species that are less well adapted to the local environment and, in turn, that (2) dispersal-based assembly processes decrease biogeochemical function (Figure 1). Our aim is to formalize these hypotheses and provide a simulation-based demonstration of how dispersal-based assembly can influence function. To do so, we employ an ecological simulation model to explicitly represent dispersal and selection-based processes, and we leverage ecological null models that have a long history of use in inferring assembly processes [28]. We link the resulting communities to biogeochemical function through organismal fitness. In our conceptualization, biogeochemical function is a generic representation, and thus, our results can be applied to any process of interest.
Figure 1.
We propose a conceptual model in which dispersal-based assembly processes decrease biogeochemical function. Purple organisms in this figure represent all species that are well-adapted to and are thus good competitors in a given environment. Yellow and green organisms represent all species that are less adapted to the environment than purple organisms. While not displayed for simplicity, we conceptualize multiple species within each color. We acknowledge that the environment influences microbial community composition through effects of both abiotic (e.g., resource availability) and biotic (e.g., competition and predator-prey interactions) factors. We use the term 'selective filter' to indicate influences of both factors on an organism's fitness [29]. (A) In a community structured entirely by determinism, selective filtering restricts community composition to species that are well-adapted to prevailing conditions, resulting in enhanced biogeochemical function.(B) In communities with moderate stochasticity (here, moderate rates of dispersal), there is an increase in the abundance of maladapted organisms in the community. In turn, the community is less efficient and exhibits lower biogeochemical function.(C) Under high levels of stochasticity (here, high rates of dispersal), a large portion of community members are maladapted, resulting in the lowest rates of biogeochemical function.
Materials and Methods
All simulations, null models, statistical analyses, and graphics were completed in R software ( [URL] simulation model consisted of two parts and was followed by statistical analysis. The model builds upon previous work by Stegen, Hulbert, Graham, and others [2,14,15,[30][31][32][33]. Relative to this previous work, the model used here is unique in connecting evolutionary diversification, variation in the relative influences of dispersal and selection, null models to infer those influences, and biogeochemical function. Previous models have addressed some subset of those features (e.g., connecting evolutionary processes with stochastic and deterministic ecology), but as far as we are aware, previous studies have not integrated all features examined here.
A central purpose of the simulation model was to vary the influences of community assembly processes. Previously developed null models (see below) were used to identify parameter combinations that provided a range of scenarios across which the relative balance among community assembly processes varied. As such, parameter values were selected to generate conceptual outcomes needed to evaluate the relationship between assembly processes and biogeochemical function. Specific parameter values do not, therefore, reflect conditions in any particular ecosystem. Likewise, the model reflects a general timescale across which there are (1) large numbers of birth/death events such that community composition closely tracks environmentally-imposed differences in organismal fitness, and/or (2) opportunities for significant immigration into local communities via dispersal. The model's spatial scale is also a general representation that depends on the rate at which individual cells can move through space in a given environment. Therefore, the model's spatial and temporal We propose a conceptual model in which dispersal-based assembly processes decrease biogeochemical function. Purple organisms in this figure represent all species that are well-adapted to and are thus good competitors in a given environment. Yellow and green organisms represent all species that are less adapted to the environment than purple organisms. While not displayed for simplicity, we conceptualize multiple species within each color. We acknowledge that the environment influences microbial community composition through effects of both abiotic (e.g., resource availability) and biotic (e.g., competition and predator-prey interactions) factors. We use the term 'selective filter' to indicate influences of both factors on an organism's fitness [29]. (A) In a community structured entirely by determinism, selective filtering restricts community composition to species that are well-adapted to prevailing conditions, resulting in enhanced biogeochemical function.(B) In communities with moderate stochasticity (here, moderate rates of dispersal), there is an increase in the abundance of maladapted organisms in the community. In turn, the community is less efficient and exhibits lower biogeochemical function.(C) Under high levels of stochasticity (here, high rates of dispersal), a large portion of community members are maladapted, resulting in the lowest rates of biogeochemical function.
Materials and Methods
All simulations, null models, statistical analyses, and graphics were completed in R software ( [URL] simulation model consisted of two parts and was followed by statistical analysis. The model builds upon previous work by Stegen, Hulbert, Graham, and others [2,14,15,[30][31][32][33]. Relative to this previous work, the model used here is unique in connecting evolutionary diversification, variation in the relative influences of dispersal and selection, null models to infer those influences, and biogeochemical function. Previous models have addressed some subset of those features (e.g., connecting evolutionary processes with stochastic and deterministic ecology), but as far as we are aware, previous studies have not integrated all features examined here.
A central purpose of the simulation model was to vary the influences of community assembly processes. Previously developed null models (see below) were used to identify parameter combinations that provided a range of scenarios across which the relative balance among community assembly processes varied. As such, parameter values were selected to generate conceptual outcomes needed to evaluate the relationship between assembly processes and biogeochemical function. Specific parameter values do not, therefore, reflect conditions in any particular ecosystem. Likewise, the model reflects a general timescale across which there are (1) large numbers of birth/death events such that community composition closely tracks environmentally-imposed differences in organismal fitness, and/or (2) opportunities for significant immigration into local communities via dispersal. The model's spatial scale is also a general representation that depends on the rate at which individual cells can move through space in a given environment. Therefore, the model's spatial and temporal scales depend on the environment of interest and may be short (e.g., microaggregates in unsaturated soils or communities with fast generation times) or long (e.g., stream biofilms influenced by hydrologic transport across long distances or communities with long generation times). One hundred replicates were run for each parameter combination in the simulation model.
Regional Species Pool Simulation
First, a regional species was constructed following the protocol outlined in Stegen et al. [15]. Regional species pools were constructed by simulating diversification in which entirely new species arise through mutations in the environmental optima of ancestral organisms. Environmental optima evolve along an arbitrary continuum from 0 to 1, following a Brownian process. Regional species pools reach equilibria according to the constraints described by Stegen et al. [15] and Hurlbert and Stegen [30] and summarized here: (1) we define a maximum number of total individuals in the pool (2 million) such that the population size of a given species declines with an increasing number of species, and (2) the probability of extinction for a given species increases as its population size decreases according to a negative exponential function [population extinction probability ∝ exp(−0.001× population size)].
The evolution of a regional species pool was initiated from a single ancestor with a randomly chosen environmental optimum (initially comprising all two million individuals in the population). Mutation probability was set as 1.00 × 10 −5 . A descendant's environmental optimum deviated from its ancestor by a quantity selected from a Gaussian distribution with mean 0 and standard deviation 0.2. Following mutation, population sizes were reduced evenly such that the total number of individuals remained at two million. The simulation was run for 250 time steps, which was sufficient to reach equilibrium species richness.
Community Assembly
The model's second component assembled four local communities from the regional species pool according to scenarios conceptualized to test our hypotheses. In the model, both selection and dispersal are probabilistic. Selection is based on the difference between species environmental optima and local environmental conditions, while dispersal is unrelated to environmental conditions. Species were drawn from the regional species pool to generate a source community under weak selection and three receiving communities with no dispersal, moderate dispersal, and high dispersal in which organismal niche breadth (n, 0.0075 to 0.175) and environmental conditions (E, 0.05 to 0.95) were allowed to vary across simulations. A simplifying assumption of the model was that all organisms in a simulation had equivalent niche breadth. The purpose of this assumption was to examine tradeoffs between communities comprised of high-functioning, specialist organisms vs. those comprised of lower-functioning, generalist species. Our intent was to simulate communities across a gradient in the degree of specialization (i.e., niche breadth). This allowed for an evaluation of the influence of niche breadth on the relationship between assembly processes and biogeochemical function. All communities had 100 species and 10,000 individuals, drawn probabilistically from the regional species pool. To define species presence/absence in each community, we drew 100 species without replacement from the regional species pool based on selection probabilities described below. In turn, we drew 10,000 individuals with replacement into those 100 species using the same selection probabilities. Selection probabilities (P) of each species from the regional pool were set by a Gaussian function with variance n (reflecting niche breadth) and the deviation (d) of each species environmental optimum from the local environment per the following equation: This equation represents the probability of an individual from a given species surviving in a given environment-and thus the strength of selection for or against it-as directly related to three factors: (1) its own environmental optimum, (2) the simulated environment in which it finds itself, and (3) its niche breadth.
For assembly of the source community, we used one niche breadth (n) for all simulations, which was the maximum value used for receiving communities (0.175). This value represents generalist organisms, which allows for assembly of species representing a broader range of environmental optima than when niches are narrow. The environmental conditions in the source community were also set to a single value using the following procedure: we generated 10 regional species pools and combined species abundances and environmental optima from these pools to generate one aggregate pool representative of the probable distributions of environmental optima yielded by our simulations. We set the environmental optimum of the source community to one end of this spectrum (5th percentile) to allow for comparisons with receiving communities that had the same or larger environmental values. This allowed us to study emergent behavior across a broad range of environmental differences between the source and receiving communities.
For receiving communities, we allowed the environmental conditions and niche breadth to vary across simulations. Environmental conditions ranged from 0.05 to 0.95 by intervals of 0.04736842 to yield 20 conditions. Environmental conditions were static within each simulation. Niche breadth ranged from 0.0075 to 0.175 by 0.008815789 to yield 20 conditions. Receiving communities were assembled under all possible combinations of environmental conditions and niche breadths. Communities for the selection-only case (i.e., no dispersal from the source community) were assembled based only on the selection probabilities as defined by Equation (1), using the same approach as for the assembly of the source community. For moderate and homogenizing dispersal, we modified selection probabilities to incorporate species dispersing from the source community as defined by the following equation: where P disp is the selection probability of a given species accounting for dispersal, S source is the abundance of that species in the source community, and D a parameter reflecting dispersal rate. This equation alters the selection probability without dispersal (Equation ( 1) with an exponential modifier that enhances the probability of selection for species that are abundant in the source community. Parameter D was set to 1 for moderate dispersal and 2 for homogenizing dispersal. All possible communities were simulated with 100 replicate regional species pools such that all possible combination of parameters were used once with each regional species pool. Equation ( 2) simplifies dispersal as a probabilistic function without regard to phylogeny, although we acknowledge that the ability of organisms to disperse is not phylogenetically random in natural settings [17]. In our view of community assembly (and in our simulation model), both selection and dispersal are probabilistic. Selection is based on the difference between species environmental optima and local environmental conditions, while dispersal is unrelated to environmental conditions. In this view, the word 'deterministic' indicates that the environment determines the probability of drawing a given species into a local community, even though assembly is still probabilistic. Likewise, the word 'stochastic' indicates that the random movement of organisms is the only factor influencing local community assembly. Future studies should build upon this work to examine the influence of phylogenetically-structured dispersal probabilities in affecting biogeochemical function.
Our estimation of biogeochemical function is meant to be illustrative and is not associated with any specific reaction. Given this perspective, we make a simplifying conceptual assumption that individuals well-fit to their environment generate higher rates of biogeochemical function than maladapted individuals. The motivation for this assumption is that individuals that are maladapted to a given environmental condition will have to use a larger portion of available energy to maintain their physiological state than well-adapted organisms. In turn, maladapted organisms can invest less in the production of enzymes needed to carry out biogeochemical reactions, thereby leading to lower biogeochemical rates.
In our model, selection probability of a given species in a given environment (Equation ( 1)) defines how adapted an individual of that species is to its local environment. This leads to another simplifying assumption: the contribution of an individual to the overall biogeochemical rate (B) is directly proportional to how well adapted it is to the local environment such that the contribution of each individual is a linear function of its selection probability within a given environment. The biogeochemical contribution of each species is therefore found by multiplying its selection probability by its abundance. To find the total biogeochemical rate for each community, we then summed across all species in a community. Biogeochemical function for each community was thus calculated as: where B is the biogeochemical function for a given community and a i and P i are the abundance and probability of selection for species i, respectively (note there were 100 species within each community). An inherent result of this calculation is that simulations with smaller niche breadth have higher maximum selection probabilities (see Equation ( 1)), which can lead to higher biogeochemical function, relative to simulations with larger niche breadth. Our formulation therefore assumes higher biogeochemical function for specialist organisms, but only if they are well adapted to their local environment. This assumption reflects a tradeoff between the breadth of environments an individual can persist in and the maximum fitness of an individual in any one environment (discussed in [34]).
Ecological Inferences Using Null Models
Following the assembly of communities, the relative influences of stochasticity (i.e., dispersal-based) and determinism (i.e., selection) in structuring communities were estimated using a null modeling approach previous described in Stegen et al. [15,31]. We refer the reader to these earlier publications for full details and provide only a summary of the major elements of the null modeling approach here. The composition of each receiving community was compared to an associated source community that was assembled from the same regional species pool. We first estimated pairwise phylogenetic turnover between a given pair of communities. This was done by calculating the abundance-weighted β-mean-nearest-taxon-distance (βMNTD) [35,36]. A null model was then run 999 times. In each iteration of the null model, species names were moved randomly across the tips of the regional pool phylogeny. This breaks phylogenetic relationships among taxa observed in each community. Using the resulting (randomized) phylogenetic relationships, we re-calculated phylogenetic turnover between the pair of communities and refer to this as βMNTD null . Running the null model 999 times generated a distribution of βMNTD null values. We then compared the observed βMNTD to the mean of the βMNTD null distribution and normalized this difference by the standard deviation of the βMNTD null distribution. The difference between βMNTD and the βMNTD null distribution was therefore measured in units of standard deviations and is referred to as the β-nearest taxon index (βNTI) [32]. Values of βNTI that are <−2 or >+2 are deemed significant in the sense that observed βMNTD deviated significantly from the βMNTD null distribution. The βMNTD null distribution is what's expected when community assembly is not strongly influenced by deterministic ecological selection. Significant deviation from this distribution therefore indicates that selective pressures are very similar (βNTI < −2) or very different (βNTI > +2) between the two communities being compared. Following the convention of Dini-Andreote et al. [37] we refer to βNTI <−2 as indicating homogeneous selection (i.e., significantly less turnover than expected due to consistent selective pressures) and βNTI > +2 as indicating variable selection (i.e., significantly more turnover than expected due to divergent selective pressures). Inferences from βNTI have previously been shown to be robust [15]. This method has also been used extensively across a broad range of systems (e.g., [2,14,[38][39][40]) and is described in detail in previous work [32].
Statistical Analysis
We analyzed differences in model outputs using standard statistical approaches. We calculated the alpha diversity of each source and receiving community using the Inverse Simpson Index [41,42] in the R package 'vegan' [43]. Differences in alpha diversity across communities were evaluated with one-way ANOVA followed by post-hoc Tukey's HSD tests. We used pairwise Kolmogorov-Smirnov tests to compare distributions of species optima between simulations (distributions were non-normal). To compare biogeochemical function of the three dispersal cases, we used one-way ANOVA followed by post-hoc Tukey's HSD tests. We also analyzed how biogeochemical function changed as the environmental difference between source and receiving communities increased; this was done using quadratic regressions due to non-linearity in the relationships. We also compared the influence of dispersal on biogeochemical function across different niche breadths. This was done by first finding the ratio of function in selection-only communities to function in associated homogenizing dispersal communities. Ratios were calculated by comparing communities assembled from the same regional species pool and with identical environmental condition and niche breadth. The resulting distributions of ratios were compared across different niche breadths using one-way ANOVA followed by post-hoc Tukey's HSD tests. To evaluate the relationship between the relative influence of dispersal-based assembly (inferred from the value of βNTI) and biogeochemical function, correlations between βNTI and biogeochemical function were assessed with linear regression. In most studies βNTI values are not independent of each other such that statistical significance requires a permutation-based method such as a Mantel test. Here, each βNTI estimate is independent whereby standard statistical methods that assume independence are appropriate.
Results and Discussion
As ecosystem process models become more sophisticated (e.g., [44][45][46]), there is a need to improve these models by better understanding the linkages among community assembly processes and ecosystem function. Here, we used an ecological simulation model to highlight the importance of dispersal-based microbial community assembly for biogeochemical function. Our results suggest that incorporating assembly processes into ecosystem models may improve model predictions of biogeochemical function under future environmental conditions.
Microbial Community Composition in Response to Dispersal
We found that diversity was highest when both dispersal and selection influenced community structure (Figure 2). In communities assembled with moderate to broad niches, intermediate amounts of dispersal led to the highest diversity (Figure 2A,B). These moderate-dispersal communities were characterized by distributions of environmental optima (across species and individuals) that did not match the source or selection-only distributions, and instead reflect an influence of both dispersal from the source and local selection (Figure 3B,D,F). Both moderate-and homogenizing-dispersal cases exhibited higher diversity than source or selection-only communities (Figure 2A,B). We note that the slight differences in diversity between source and selection-only communities were due to environmental conditions in source communities being defined at one end of the environmental spectrum. This edge-effect truncated its distribution of species environmental optima, causing the distribution to be right skewed (Figure 3A,C,E). Our results suggest a conceptual parallel to Connell's [47] Intermediate Disturbance Hypothesis, whereby intermediate levels of dispersal lead to the highest overall diversity, but only when niche breadth is broad enough to allow for strong contributions from both dispersal and selection (Figure 2). With the narrowest niche breadths (Figure 2C), we observed a distinct pattern of diversity relative to broader niche breadths (Figure 2A,B). Diversity in moderate-dispersal cases decreased substantially as niche breadth narrowed, indicating that moderate levels of dispersal can be overwhelmed when local selective pressures are strong. In contrast, homogenizing dispersal cases maintained consistent levels of diversity across niche breadths and displayed distributions of environmental optima that tracked those of the source community (Figure 3). Diversity in selection-only cases was greatest under the narrowest niche breadth. This was due to only very well-adapted species being part of the community, which led to high abundance across all species in those communities (Figure 3F). For selection-only communities, broader niche breadths resulted in more species with low abundances, and thus lower diversity (cf.black lines in Figure 3B,D,F).
Dispersal, Microbial Community Composition, and Biogeochemical Function
We found that microbial community assembly history altered the degree to which organisms within a community were adapted to their local environment. Given our assumption of the connection between the degree of adaptation and biogeochemical function (see Methods), assembly history was therefore found to have an indirect effect on biogeochemical function. The environmental optima of taxa in selection-only communities more closely matched their simulated environmental conditions compared to communities assembled with dispersal (Figure 3, 1st column, p < 0.001). When niche breadth was broad (Figure 3A), species' environmental optima were distributed around the simulated environment under all dispersal cases. However, as niche breadth decreased (Figure 3C,E), the species distribution of selection-only cases tightened around the simulated environment, with moderate and homogenizing dispersal cases having a wider distribution than the selection-only case. These disparities were maintained when accounting for species abundances (Figure 3B,D,F), in which selection-only communities had unimodal distributions separate from the source community, while moderate and homogenizing dispersal communities had distributions ranging from unimodal to multi-modal, depending on niche breadth. Dispersal from the source therefore resulted in significant numbers of individuals having large deviations between their environmental optima and the local environmental condition. The large number of maladapted individuals in communities experiencing dispersal from the source resulted in selection-only communities having the highest rates of biogeochemical function, on average, regardless of the simulated environment (Figure 4, p < 0.0001).
In natural systems, microbial community compositional differences can be due to competitive dynamics that select for organisms based on their niche optima [48,49] and to immigration of new taxa from the regional species pool [7,32,50]. Strong local selective pressures can lead to more fit species and enhanced biogeochemistry [7]. Due to the lack of immigrating maladapted species in the selection-only simulations, biogeochemical rates were maintained regardless of the difference between source and receiving community environments. This indicates that biogeochemical function can be enhanced by species adaptation to local conditions. Indeed, a plethora of literature demonstrates that environmental features such as pH [25], nutrients [51], and salinity [26,27] impact microbial community structure and biogeochemical function, and our results indicate that the linkage between community structure and function is due to microbial adaptation to local conditions.
Our results also indicate that when immigrating microorganisms are derived from environments that differ from the receiving community (e.g., dispersal across steep geochemical gradients), biogeochemical function may be suppressed. When we included dispersal from a source community, greater differences between the source and receiving communities led to decreases in biogeochemical function in the receiving communities (Figure 4B, p < 0.0001), and this effect became more pronounced as the rate of dispersal increased. Natural systems are influenced by some combination of dispersal and selection and our results indicate that function is maximized when dispersal is minimized and selection is maximized.
Environmental Condition
Moderate: R = 0.40 P < 0.0001 Homogenizing: R = 0.73 P < 0.0001 (B) Biogeochemical function across environmental conditions in receiving communities (vertical axis is the same as in panel A). In the selection-only case (black), biogeochemical function did not vary with environmental condition such that no regression line is drawn. With moderate (blue) and homogenizing (red) dispersal, biogeochemical maxima occurred when the receiving community's environmental condition aligned with the environmental optima of species in the source community (compare to Figure 3). For these two cases, quadratic regression was used and resulting models are shown as solid lines (statistics provided).
Dispersal had the greatest influence on biogeochemical function when niche breadth was narrow (Figure 5). The biogeochemical function of selection-only communities in comparison to homogenizing-dispersal communities was greatest under the narrowest niche breadth (0.0075) and rapidly decreased when transitioning to broader niche breadths. Selection-only communities simulated with narrow niches are comprised of specialist species that can generate high biogeochemical rates and that are well adapted to their local environment. Increasing niche breadth results in the assembly of species with a broader range of environmental optima and that generate lower biogeochemical rates even if their environmental optimum matches the environmental condition (see Methods for a discussion of this assumed trade-off). Thus, high rates of dispersal combined with narrow niche breadth causes replacement of high-functioning specialist organisms with maladapted taxa, thereby significantly reducing community-level biogeochemical function. When niche breadth is broader, immigrating organisms replace lower-functioning organisms (i.e., generalists), resulting in a smaller decreased in community biogeochemical function. We note that our model does not represent dispersal-competition tradeoffs [19], nor does it explicitly represent organismal interactions; exploring the influence of these features would be an interesting extension of the model presented here. Regardless of dispersal, simulations with broader niche breadth led to lower rates of biogeochemical function, supporting a tradeoff between communities comprised of specialist vs. generalist species [52][53][54]. Previous work in microbial systems has posited life-history tradeoffs between specialist vs. generalist species, whereby specialists expend more energy to establish their niches but function at higher levels once established [55]. Specialist species have also been found to be more sensitive to changes in the environment due to strong adaptation to their local environment, with generalists being more resilient to change [56][57][58][59]. While we do not address temporal dynamics in our model, the separation of biogeochemical function based on niche breadth indicates a central role for the balance of specialist vs. generalist microorganisms within a community in determining function, regardless of prevailing environmental conditions.
Impact of Assembly Processes on Biogeochemical Function
We also observed that niche breadth within the receiving community was a key parameter in dictating biogeochemical function when environmental conditions (and thus selective pressures) differed between source and receiving communities. In cases without dispersal, biogeochemical function was dictated entirely by niche breadth regardless of differences in selective environments (as inferred from βNTI) between source and receiving communities (Figure 6A,D). Selective pressures in the selection-only receiving communities were most dissimilar to the source community (βNTI > 2) in simulations with both narrow niche breadth and environmental conditions that were very different from the source community (Figure 6A). This relationship was also apparent (but weaker) in simulations with an intermediate amount of dispersal (Figure 6B). In receiving communities with high rates of dispersal, stochasticity (|βNTI| < 2) was the dominant process regardless of niche breadth or environmental condition in the receiving community (Figure 6C). Across the full parameter space defined by niche breadth and environmental condition, cases with moderate and homogenizing dispersal were generally characterized by a dominance of stochasticity (Figure 6B,C). This increase in stochasticity relative to selection-only cases corresponded to decreased biogeochemical function. This was particularly true as the environment diverged from the source community (Figure 6D-F). Biogeochemical function in these cases was also negatively correlated to niche breadth (i.e., highest under narrow niche breadths), revealing higher functioning of specialist organisms regardless of assembly processes.
Given these apparent associations between assembly processes and biogeochemical function, we directly examined differences in relationships between βNTI and biogeochemical function across a range of environments and niche breadths (Figure 7). Our results suggest that microbial assembly processes may exert the most influence over biogeochemical function when there is significant variation in the relative contributions of deterministic and stochastic processes among communities. We found the strongest relationships between βNTI and function when environmental conditions were dissimilar to the source community, regardless of niche breadth (Figure 7G-I).βNTI had the greatest range in these scenarios, reflecting substantial variation in the contribution of stochastic and deterministic processes. By contrast, scenarios with environments more similar to the source environment had little variation in assembly processes and no relationship between βNTI and biogeochemical function (Figure 7A-F).and (G-I) depict environmental conditions similar, moderately different, and very dissimilar to that of the source community, respectively.(A,D,G), (B,E,H), and (C,F,I) respectively show narrow, moderate, and wide niche breadths. Values of βNTI that are further from 0 indicate increasing influences of deterministic assembly (and decreasing stochasticity). Horizontal gray lines indicate significance thresholds of −2 and +2. Relationships were evaluated with linear regression; fitted models are shown as black lines and statistics are provided on each panel. Panels without regression models had non-significant (p > 0.05) relationships. Note that the vertical axis is scaled the same across panels, but the horizontal axis is not. Black, blue, and red symbols indicate selection-only, moderate dispersal, and homogenizing dispersal scenarios, respectively.
Variation in the balance of stochastic and deterministic assembly processes is prevalent in natural systems [2,7,16,38], as most ecosystems experience spatially and/or temporally variable rates of dispersal. For example, hydrologic connectivity facilitates microbial dispersal and differs with physical matrix structure in soils and sediments. We therefore pose that variation in βNTI may be an effective tool for predicting biogeochemical function when biotic and abiotic conditions lead to a mixture of stochastic and deterministic assembly processes. Natural systems have repeatedly shown such a mixture, and previous field observations have revealed connections between βNTI and biogeochemical function [2,14,60]. These outcomes support our model-based inference that βNTI-as a proxy for assembly processes-offers a practical means to inform models that represent the effects of ecological processes on biogeochemical function.
While our results suggest that maladapted immigrating organisms decrease biogeochemical function, it is important to note that stochasticity may offer buffering capacity that maintains or increases biogeochemical function relative to well-adapted deterministic communities in the context of future environmental perturbations not simulated with the static environmental conditions in our model [56]. Stochastic spatial processes, such as dispersal, may lead to coexistence of species with different environmental optima resulting in a community that can rapidly adapt to changing environment conditions and maintain biogeochemical function in the face of perturbation. Researchers have long demonstrated positive relationships between biodiversity and ecosystem function in both macrobial [61,62] and microbial [63][64][65] systems, and new work has highlighted the role of stochasticity in maintaining this connection [66]. Conversely, a lack of stochasticity may result in species so specialized to a given environment that they are vulnerable to environmental change [56]. While these communities would putatively exhibit high rates of biogeochemical function under stable environmental conditions, their function would plummet in response to perturbation, akin to observations of a tradeoff between function and vulnerability in plant communities [21,67].
Implications for Ecosystem Models
The cumulative impacts of ecological processes through time and how they relate to ecosystem-level processes is an emerging research frontier in ecosystem science [2,44,52,68,69]. We reveal how dispersal-based community assembly can decrease adaptation to local environments and, in turn, decrease biogeochemical function. Our modelling approach demonstrates plausible outcomes of microbial assembly processes on ecosystem functioning, and integrating this knowledge with factors such as historical abiotic conditions, competitive dynamics, and life-history traits could substantially improve ecosystem model predictions.
Previous work by Hawkes and Keitt [52] laid a theoretical foundation for incorporating time-integrated ecological processes into predictions of biogeochemical function. They demonstrate that community-level microbial functions are the accretion of individual life-histories that determine population growth, composition, and fitness. However, they acknowledge their exclusion of dispersal processes from their models and do not explicitly consider dispersal in their analysis. Hawkes and Keitt [52] therefore provide a baseline for future research and call for a holistic understanding of historical processes on microbial function, with a particular emphasis on the underlying mechanisms generating these trends. Our work enhances this framework by demonstrating that community assembly processes are integral to knowledge of biogeochemical function in natural systems.
Microbially-explicit models (e.g., MIMICS, MEND) are rapidly becoming more sophisticated and are readily amenable to modules that represent ecological assembly processes [70,71]. As models begin to consider microbial ecology, there is a need to decipher linkages among spatiotemporal microbial processes and ecosystem-level biogeochemical function. We propose that new microbially-explicit models should go beyond microbial mechanisms at a given point in time or space, and building upon the foundation laid by Hawkes and Keitt [52], incorporate ecological dynamics that operate across longer time scales to influence biogeochemical function. Although there are many available avenues to merge modelling efforts in microbial ecology and ecosystem science, there is little debate that integrated models will increase the accuracy of predictions in novel future environments.
Conclusions
We demonstrate the influence of ecological assembly processes on biogeochemical function. Specifically, we show that dispersal can increase the abundance of maladapted organisms in a community, and in turn, decrease biogeochemical function. This impact is strongest when organismal niche breadth is narrow. We also pose that the explanatory power of microbial assembly processes on biogeochemical function is greatest when there is variation in the contributions of dispersal and selection across a collection of local communities within a broader system of interest. While our work is an encouraging advancement in understanding relationships between ecology and biogeochemistry, a key next step is incorporating assembly processes into emerging model frameworks that explicitly represent microbes and that mechanistically represent biogeochemical reactions.
Figure 1 .
Figure 1. We propose a conceptual model in which dispersal-based assembly processes decrease biogeochemical function. Purple organisms in this figure represent all species that are well-adapted to and are thus good competitors in a given environment. Yellow and green organisms represent all species that are less adapted to the environment than purple organisms. While not displayed for simplicity, we conceptualize multiple species within each color. We acknowledge that the environment influences microbial community composition through effects of both abiotic (e.g., resource availability) and biotic (e.g., competition and predator-prey interactions) factors. We use the term 'selective filter' to indicate influences of both factors on an organism's fitness[29]. (A) In a community structured entirely by determinism, selective filtering restricts community composition to species that are well-adapted to prevailing conditions, resulting in enhanced biogeochemical function.(B) In communities with moderate stochasticity (here, moderate rates of dispersal), there is an increase in the abundance of maladapted organisms in the community. In turn, the community is less efficient and exhibits lower biogeochemical function.(C) Under high levels of stochasticity (here, high rates of dispersal), a large portion of community members are maladapted, resulting in the lowest rates of biogeochemical function.
Figure 3 .
Figure 3. Kernel density of species optima are shown under wide ((A,B), niche breadth = 0.175), moderate ((C,D), niche breadth = 0.086842105), and narrow ((E,F), niche breadth = 0.0075) niches in the mid-point environment (0.476315789, vertical black line). Column 1 displays distributions of species optima without accounting for abundances. Column 2 displays distributions of individuals' optima. Distributions for the source community and its environment condition (vertical line) are displayed in gray. The same communities were selected as examples to generate Figures 2 and 3.
Figure 2 . Figure 3 .
Figure 2. Alpha diversity (inverse Simpson Index) of communities assembled under wide ((A) niche breadth = 0.175), moderate ((B) niche breadth = 0.086842105), and narrow ((C) niche breadth = 0.0075), niches in the mid-point environment (0.476315789). Upper and lower hinges of the box plots represent the 75th and 25th percentiles and whiskers represent 1.5 times the 75th and 25th percentiles, respectively. Colors coincide with labels on the x-axis.
Figure 4 .
Figure 4. (A) Biogeochemical function across dispersal cases. Upper and lower hinges of the box plot represent the 75th and 25th percentiles and whiskers represent 1.5 times the 75th and 25th percentiles, respectively. Different letters indicate statistically significant differences in mean values.(B)Biogeochemical function across environmental conditions in receiving communities (vertical axis is the same as in panel A). In the selection-only case (black), biogeochemical function did not vary with environmental condition such that no regression line is drawn. With moderate (blue) and homogenizing (red) dispersal, biogeochemical maxima occurred when the receiving community's environmental condition aligned with the environmental optima of species in the source community (compare to Figure3). For these two cases, quadratic regression was used and resulting models are shown as solid lines (statistics provided).
Figure 5 .
Figure 5. The ratio of biogeochemical function in selection-only cases to homogenizing dispersal cases across five niche breadths that span the entire parameter range (0.0075 to 0.175). Each column represents all replicates across all environments for a given niche breadth. Different letters indicate statistically significant differences in mean values. Upper and lower hinges of the box plots represent the 75th and 25th percentiles and whiskers represent 1.5 times the 75th and 25th percentiles, respectively.
Figure 6 .
Figure 6. Interpolated contour plots showing average βNTI (A-C) and biogeochemical function (D-F) for each dispersal case across all parameter combinations. Interpolations are based on parameter combinations at each of 20 evenly spaced values across each axis. Values of βNTI that are further from 0 indicate increasing influences of deterministic assembly (and decreasing stochasticity).(A,D) depict selection-only communities; (B,E) depict moderate dispersal communities; and (C,F) depict homogenizing dispersal communities.
Figure 7 .
Figure 7. Relationship between βNTI and biogeochemical function across different niche breadths (columns) and different environmental conditions of the receiving communities (rows).(A-C), (D-F),and (G-I) depict environmental conditions similar, moderately different, and very dissimilar to that of the source community, respectively.(A,D,G), (B,E,H), and (C,F,I) respectively show narrow, moderate, and wide niche breadths. Values of βNTI that are further from 0 indicate increasing influences of deterministic assembly (and decreasing stochasticity). Horizontal gray lines indicate significance thresholds of −2 and +2. Relationships were evaluated with linear regression; fitted models are shown as black lines and statistics are provided on each panel. Panels without regression models had non-significant (p > 0.05) relationships. Note that the vertical axis is scaled the same across panels, but the horizontal axis is not. Black, blue, and red symbols indicate selection-only, moderate dispersal, and homogenizing dispersal scenarios, respectively.
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Domain: Environmental Science Biology
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Relationship Between Stage of Gonad Maturity and Level of Osmotic Work of Sea Cucumber , Paracaudina australis
The holothuroid spawning can be succesful depend on varoius factors such as salinity, temperature, primary productivity and other physiological adaptation toward environment influences. The salinity fluctuation is one of the environment factors, which influence and can cause various osmotic pressure of coelomic fluid of Holothoroid and also gonad maturity stage. Holothuroid (sea cucumber), Paracaudina australis, is overexploited in Kenjeran waters, Surabaya, East Java. The study was aimed to reveal relationship between gonad maturity stage and level of osmotic work of sea cucumber P. australis. Fifty samples of P. australis were collected monthly. Coelomic fluid samples of approximately 200–1000 μl were obtained from all samples using disposable insulin syringes. There were 37 holothuroids classified in the late maturity (stage 5), 74 holothuroid in the earlier maturity stage (stage 1). Gonad maturity stage 5 (late maturity) could be reached on December 2016. Contrasly, the gonad maturity stage 1 (earlier maturity) was found on August 2016. The analysis of coelomic fluid showed that the average value of level of osmotic work ranged 70 to 91 mOsm/L H2O in the earlier gonad maturity stage. Contrasly, in the late maturity, the average value of level of osmotic work ranged 118 to 156 mOsm/L H2O. There is relationship between gonad maturation stage and level of osmotic work. From stage of gonad maturity 1 to 5, there were upward trend level of osmotic work in coelomic fluid of P. australis.
Ecologically all sea cucumbers have important role due to their bioturbation activities and benthic ecosystem (Mangion et al., 2004). Over exploitation of sea cucumber affecting ecosystem, especially in benthic ecosytem. Decreasing stock of the sea cucumber population was caused by large demand for food and medicine purpose in Asian market (Toray et al., 2008;Purcell, 2010). The condition of population sea cucumber P. australis from Kenjeran waters of Surabaya has been over exploited due to the daily activity of fishermen.
Recent studies of P. autralis mainly covered on taxonomical and habitat, e.g. O'Loughlin et al. (2011) and Winarni et al. (2015). Research on gonad maturity stage and level of osmotic work is lacking. Osmotic pressure of coelomic fluid in holothurid is influenced by water quality such as salinity, freshwater from river, temperature, etc. Most of the recent studies showed that echinoderms become isosmotic with the ambient water by exchanging of water and ions in their coelomic fluid, and altering the concentrations of intracellular ions which play an important role in metabolic processes and affect enzymes of metabolism intermediary (Yancey et al., 1982;Diehl, 1986;Stickle and Diehl, 1987). The research reveals relationships between gonad maturity stage and level of osmotic work on sea cucumber P. australis.
Location of study and sampling
This study was conducted in Kenjeran waters, Surabaya, East Java -Indonesia. P. australis were collected at the same day of data collection. On each sampling, 50 individuals P. australis were randomly collected by mini trawl. Their coelomic fluid (200-1000 µl) were then taken carefully from those 50 samples.using a disposable insulin syringe.
Water samples were collected from the study area to determine phosphate, nitrate, nitrite and amonia concentrations. Water quality checker was used to measure salinity, temperature, pH and depth. All water quality data were measured monthly from August to October 2016. Coelomic fluid and water samples were stored in the iced cool box and immediately brought to laboratory and they were frozen to -20°C until analysis and measuring the osmolality. Osmolality was determined using Automatic Micro Osmometer Roebling (Anggoro and Nakamura, 1996)
Analyses
The level of osmotic work was calculated based on difference between osmotic pressure of coelomic fluid in P. australis and osmotic pressure of medium. The measurement of osmotic pressure used Automatic Micro Osmometer Roebling (Anggoro and Nakamura, 1996), as follows: LOW = (P. Osm Coelomic fluid -P.osm medium) Where: LOW = Level of Osmotic Work (mOsm. L -1 H2O) P. osm Coelomic fluid = osmotic pressure of coelomic fluid (mOsm. L -1 H2O) P.osm media = osmotic pressure of media (mOsm. L - 1 H2O).
Gonad maturity stage
The results showed that on August 2016 the dominant stage of the gonad in P. australis is stage 1, i.e. 15 females and 16 males (Figure 1). This condition indicated that gonad of most population of P. australis in Kenjeran waters was still growing in August to November 2016. While in December 2016, the gonad maturity stage 5 indicated the highest number of individuals (17 individuals) than with the others gonad maturity levels (1, 2, 3 and 4). In September 2016, the number of female with stage 2 gonad maturity was 12 individuals, while stage 3 was 12 individuals. Figure 1 also showed that the number of male with the gonad maturity stage 1, 2 and 5 was 1, 3 and 3 individuals, respectively. The number of the male reached gonad marutity stage 1 has found 15 individuals. Whereas, the female reached gonad maturity stage 2 has found 11 individuals. Therefore, the gonad maturity stage 3, 4 and 5 had small number of individual (Figure 1.). The number of the female and male reached gonad maturity stage 2 had a number of individual 11 and 8. Contrasly, for stage of gonad maturity 1, 3, 4 and 5 had small a number of individual. In the Figure 1 showed that on August, September, October and November 2016 has the same pattern of gonad maturity stage except for the December. In December 2016, male reached gonad maturity stage 5 had 17 individuals. Whereas, for the stage of gonad maturity 1, 2, 3 and 4 had small number of individual.
The water quality measurements showed the average value of temperature was 29.82±0.201ºC,salinity was 29.74±0.352ppt, DO value was 3.93± 0.368 ppm, value of pH was 7.22±0.097,value of phosphate was 0.09±0.054ppm and value of nitrate was 0.64±0.172ppm (Table 1.). The value of salinity, temperature and pH were fit with Andriyono et al. (2016). The salinity fluctuations may have significant effects on internal osmotic concentration in internal sea cucumber (Andriyono et al., 2016). Decreasing in ambient salinity medium could change the osmotic pressure of the coelomic fluid and could become stabilize again within 6 hours after the salinity changed (Meng et al., 2011).
The differences in water quality parameters such as temperature, nutrient, and salinity (Table 1.) can cause differences spawning time for P. australis. According to the sampling date, P. australis samples were collected at the new moon for August, September, October and November. Whereas, in the December 2016, Paracaudina australis samples were collected at the full moon. This does not fit with the researchs that showed most of the sea cucumber such as Holuthuria spinifera, Holothuria scabra and Cucumaria frondosa have spawing in the new moon (Conand, 1993;Hamel and Mercier, 1995;Asha and Muthiah, 2005). In the temperate region, most of sea cucumber have spawning time in spring and summer (Hamer et al., 1993), while in (Conand, 1993).
Relationship between gonad maturity stage and level of osmotic work
Most of sea cucumber (Holothuroid) are unique marine invertebrate, being huge considered as osmoconformer and stenohaline, those animal has not tolerate wide variations in sea water salinity or they can not survive. The value of osmotic work Holothuria grisea were exposed to the air under sunshine was the biggest than exposed to the air under cloudy weather and under rain (Vidolin et al., 2002).
Osmolality pressure on the coelomic fluid of P. australis were various value and difference each other depend on gonad maturity stage. Osmotic pressure in the coelomic fluid of P. australis is higher than medium. Most of holothurids conduct water absoption mechanism if the concentration ion in the sea water is lower than in the body mass of holothuroid and thus, the body of holothuroid will become bigger for keeping isotonik in their body (Vidolin et al., 2002;2007). According the research result, in August till December 2016, there were the sama parttern of graph (Figure 2.). The value of level of osmotic work in female was higher than male. It is mean that female was more necessary energy than male for physiology adaptation process at every stage of gonad maturity. From gonad maturity stage 1 till 5, the level of osmotic work increase moderately from August to December 2016 (Figure 2.).
Coefficient regression value between stage of gonad maturity and level of osmotic work for all months of observation is close to 1 and is positive.this means the relationship between gonad maturity level and level of osmotic work was positive (Table 2). The greater the maturity level of the gonads at P. australis and the higher the osmotic work rate. This also means the higher energy required by sea cucumbers in physiological adaptations for gonadal development.
Based on the observation, the coefficient regression value between gonad maturity stage and osmotic work level were greater than 0.9. This indicates that there is a positive correlation between stage of gonad maturity and osmotic work level.(Table 2.). Based on the observation, the coefficient regression value between gonad maturity stage and osmotic work level were greater than 0.9. This indicates that there is a positive correlation between stage of gonad maturity and osmotic work level.(Table 2). Research on coefficient of regression between stage of gonad maturity and osmotic work level on sea cucumber is still rare. However, studies of isosmotic media in other invertebrates have been conducted as the results of Anggoro and Muryati (2007) showed that salinity of the osmotic medium significantly affected Ca-chorionase enzyme activity, energy efficiency and efficiency of egg hatching Metapenaus elegans. Another research showed that salinity fluctuation greatly affected the development of male gonads crab Eriocheir sinensis, increasing osmolarity and ionic concentration in hemolymph (Long et al., 2017).
Figure 1 .
Figure 1. The number of individual Paracaudina australis on gonad maturity stage Note : = Male, = Female, = Undentified The Relationship Between Stage of Gonad Maturity and Level of Osmotic(Widianingsih et al.)
Figure 2 .
Figure 2. The level of osmotic work of coelomic fluid P. australis on gonad maturity stage Note.= Female, = male
Table 1 .
The average value of water quality on Kenjeran Water, Surabaya from August to December 2016.
Table 2 .
The value coefission of regression between stage of gonad maturity and level of osmotic work of coelomic fluid P. australis. ConclusionThere were high positive correlation and relationship between gonad maturity stage and level of osmotic work rate of coelomic fluid Paracaudina australis. The higher value of stage of gonad maturity, the higher value of level of osmotic work for P. australis. According to regression analysis, from August to December 2016, there were regression value between stage of gonad maturity and level of osmotic work rate had range 0.952-0.998. The Relationship Between Stage of Gonad Maturity and Level of Osmotic Work (Widianingsih et al.) development, osmoregulation and metabolism of adult male Chinese mitten crab, Eriocheir sinensis. PloS one, 12(6):p.e0179036. Meng, X. L., Dong, Y. W., Dong, S. L., Yu, S. S. & Zhou, X. 2011. Mortality of the sea cucumber,
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Domain: Environmental Science Biology
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First report on in situ biodeposition rates of ascidians ( Ciona intestinalis and Styela clava ) during summer in Sanggou Bay , northern China
Ascidians are globally important members of marine fouling communities. We measured in situ biodeposition rates of Ciona intestinalis and Styela clava, common biofoulers of aquaculture infrastructure, in Sanggou Bay, northern China, during September. Ascidian numbers were recorded within a scallop Chlamys farreri farming zone to assess biodeposit loading. Both ascidians were most abundant on lantern nets and scallop shells in August and September. The average densities of C. intestinalis and S. clava in the farming zone in September were approximately 329 and 22 ind. m−2, respectively, and their biodeposition rates were 32.1 and 121.2 mg dry material ind.−1 d−1, respectively. Total daily biodeposit production by ascidians in September within the scallop farming zone may amount to 13.24 g m−2, with daily organic matter, C, N, and P biodeposition rates of 1.88, 0.94, 0.11, and 0.98 × 10−2 g m−2, respectively. The predicted daily biodeposit production by C. intestinalis and S. clava within the scallop farming zone in the bay during September was 105.9 t dry material, 7.52 t C, 0.86 t N, and 0.078 t P. By comparison, dropoff to the sea floor was approximately 143.0 t of dry matter for an entire growing season, which would be a relatively small input if averaged on a daily basis. However, some of the drop-off is expected to occur as a short-duration pulse of material (e.g. during cleaning), which may be relatively important in terms of benthic effects. The results suggest that the biodeposition processes and drop-off of C. intestinalis and S. clava may play an important role in coupling material fluxes from the water column to the seabed.
INTRODUCTION
Ascidian species are often dominant members of fouling communities worldwide, and some are welldocumented invaders (usually via anthropogenic vectors) of ecosystems outside of their native ranges, particularly on artificial structures (Lambert & Lambert 1998, 2003, Castilla et al. 2005, Robinson et al. 2005, Qi et al. 2010). They can form dense and heavy aggregations within suspended aquaculture operations, as culturing infrastructure (e.g.buoys, anchors, ropes, and lantern nets) and bivalve shells provide an abundance of hard substrata for them to colonize (Grant et al. 1998, Costa-Pierce & Bridger 2002, Howes et al. 2007, Rocha et al. 2009, Woods et al. 2012).
The filtering-biodeposition process of bivalves can be extremely important in regulating water column processes and pelagic−benthic coupling when they are abundant in coastal waters (Prins et al. 1997, Newell 2004, Zhou et al. 2006, Lacoste et al. 2014). Like bivalves, ascidians have a relatively high filtration capacity (Randløv & Riisgård 1979, Lesser et al. 1992, Petersen & Riisgård 1992). Large populations of ascidians may generate enormous quantities of de posits (e.g.feces and pseudofeces), and act as a conduit for nutrients to the benthos. Haven & Morales-Alamo (1966) reported that biodeposition by fouling invertebrates (e.g.barnacles, tunicates, and other la mellibranchs) may exceed that of the oyster Crassostrea virginica. Therefore, the biodeposition characteristics of ascidians should be investigated to under stand the potential importance of their ecological effects.
The ascidians Ciona intestinalis and Styela clava are common biofoulers of aquaculture infrastructure and crops, and have a well documented history of invading new ecosystems outside of their native ranges via anthropogenic vectors. McKindsey et al. (2009) found that the presence of C. intestinalis on small constructed mussel socks increased biodeposition by a factor of ~2 relative to mussel socks without tunicates. S. clava increased sedimentation rates relative to that of abiotic control socks. Unfortunately, despite their abundance and possible influences on the benthic environment, direct data on the biodeposition rates of these species are relatively limited (McKindsey et al. 2009). Such knowledge is necessary for both a basic understanding of their physiological traits and for the assessment of the ecological impacts of suspended bivalve culture as a result of colonization by ascidians.
In the present study, we aim to provide useful information for the quantitative evaluation of the potential benthic impacts of C. intestinalis and S. clava. We assessed the potential drop-off of these organisms to the seabed, and performed a field experiment to measure their biodeposition rates. This paper is intended as a starting point that contributes to understanding the fouling-related ecological impacts of suspended bivalve culture.
Study site
The study was conducted in Sanggou Bay, a 140 km 2 coastal embayment in northern China (37°01' to 37°09' N, 122°24' to 122°35' E), where large-scale longline culture of bivalves, including the scallop Chlamys farreri and pacific oyster Crassostrea gigas, and kelp Saccharina japonica has been carried out since the 1980s. The average water depth of the bay is 7−8 m, and seasonal water temperatures range from 0 to 25°C. C. farreri is the main cultured species in Sanggou Bay, where it is mainly grown in lantern nets (each lantern net is generally divided into 10 cells separated by plastic perforated plates 30 cm in diameter) suspended underneath buoyed longlines. Approximately 2 million lantern nets are used for scallop culture within the bay.
Densities of ascidians and potential drop-off to the seabed
As Sanggou Bay is located in a temperate zone, Ciona intestinalis and Styela clava are relatively short-lived in the bay. At our study site, their lifespan lasts from early summer to late autumn, with most of them dying with the onset of the cold winter. The numbers of C. intestinalis and S. clava on scallop lantern nets and shells were recorded monthly in 2009 from early summer (July) to late autumn (November). Three lantern nets were randomly collected monthly from a scallop farm in the bay, and were immediately transported to the seaside laboratory. One hundred scallops were randomly selected from each net and were carefully removed by hand to avoid disturbing the ascidians fouling the shells. The numbers of C. intestinalis and S. clava on the lantern nets and scallop shells were recorded. Fifty ascidians including those from both the lantern nets and the scallops were randomly selected, and their wet and dry (after drying to constant weight at 60°C for 48 h) weights were measured. Ash free dry weight (AFDW) was calculated by subtracting the weight following ashing in a furnace at 480°C for 4.5 h. Based on these data, the biomass of ascidian drop-off to the seabed was estimated. Drop-off was expected to primarily arise as a consequence of ascidians being cleaned from the cages during harvest and discarded in late autumn, and from natural die-back in early winter.
Biodeposition measurement
In situ biodeposition rates of C. intestinalis and S. clava were measured during peak densities in September. Ascidians attached to the lantern net partitioning plates were collected while still attached to the plates to avoid disturbing the organisms. Ascidians and substrata were carefully cleaned of any visible fouling epibiota and other material using a soft brush. Before the experiment, the ascidians were acclimated for 24 h in the ambient sea water conditions of the experimental site.
Biodeposition rates of ascidians were measured using PVC cylindrical biodeposit traps (diameter: 20 cm, height: 80 cm). The plastic plates (with ascidians attached) were cut into rounded plates with a dia meter nearly 20 cm and then placed in collecting traps, with the ascidians facing downwards as they were within the lantern nets. The plates were hung inside the trap, ~3 cm under the top of cylinder. The ascidians were placed near the top of the traps so that a sufficient water flow and food supply was maintained, hence deposition rates were representative of actual in situ biodeposition.
The experiment involved 3 treatments with 4 replicates each. One treatment consisted of C. intestinalis (20−25 ind.replicate −1 ), the second of S. clava (10− 14 ind.replicate −1 ), and the third was a control without ascidians. The densities of experimental ascidians were typical of natural densities in the study area. Control traps contained empty plastic plates of a size and shape similar to those in the former 2 treatments. Naturally sedimented material was collected in all of the traps, whereas biodeposits produced by ascidians were also collected in those containing C. intestinalis or S. clava. The biodeposition rate (mg ind.−1 d −1 or mg [g dry weight] −1 d −1 ) was determined based on the amount of material collected in each trap containing ascidians compared to the control traps. All traps were suspended from a longline (located ~100 m from the nearest scallop farm) so that the experimental ascidians were at a depth of ~2.5 m, which corresponds to a routine culture depth for scallops in lantern nets. After the sediment traps had been deployed for ~3 d, they were carefully retrieved and the material in the traps was allowed to settle before the overlying water was siphoned off. After the experiment, ascidians in each treatment were collected and their wet and dry weights were measured.
The traps were taken to the laboratory, where the collected material was rinsed several times with distilled water to remove salts. All of the sedimented material was collected and then dried at 60°C for 4 d, weighed, and then crushed to a fine powder (100 mesh) with a mortar and pestle. Subsamples of the powder were taken to measure the chemical composition. The percentage of organic matter (OM) was determined as AFDW using the method described above. Subsamples were treated with 0.2 M HCl to remove carbonates for analysis of organic carbon (OC) and nitrogen with an Elementar Vario EL III CHN analyzer (Elementar Analysensysteme) stan-dardized with acetanilide. Total phosphorus (TP) and organic phosphorus (OP) content were determined following the method of Zhou et al. (2003aZhou et al. ( , 2006)).
Key physical and chemical parameters of the water column at the experimental site were measured 4 to 6 times every month from July to November. Water temperature, salinity, and pH were measured using a YSI 600 (Yellow Spring Instrument). Water samples were also collected daily during the 3 d experiment at 2.5 m depth. Total particulate matter (TPM), particulate organic matter (POM), and chlorophyll a were measured using Whatman GF/C filters as described by Mao et al. (2006).
Data analysis
Variation in the chemical composition and element ratio of biodeposits and sediment (see data in Table 3) was evaluated using 1-way ANOVA. All data were graphically assessed for normality and homogeneity of residuals (Faraway 2002). When overall differences were significant at the 0.05 level, Duncan's multiple range test was used to compare the mean values of individual groups. Data are reported as means ± SE. All statistical analyses were performed using Statistica 6.0 (Statsoft).
RESULTS
Water column characteristics at the study site are shown in Table 1. From July to November, water temperature ranged from 13.5 to 24.0°C and peaked in September. Salinity varied slightly, from 31.2 to 32.3, with the relatively lower values being observed during the summer months due to rainfall. Water pH values ranged from of 8.10 to 8.24. The mean chlorophyll a (chl a) concentration was 1.43 µg l −1 , with a minimum of 0.87 µg l −1 in November and a maximum of 2.18 µg l −1 in July. Seston concentrations varied markedly among months, with TPM ranging from 4.34 to 10.60 mg l −1 and POM from 1.28 to 2.15 mg l −1 .
Biodeposition
The biodeposition rates of dry matter, OM, organic carbon (OC), organic nitrogen (ON), total phosphorus (TP), and organic phosphorus (OP) by C. intestinalis and S. clava are shown in 2), whereas the biodeposition rate of C. intestinalis, based on per tissue dry weight (mg [g dw] −1 d −1 ), was higher than that of S. clava (see Table 4). The chemical composition (OM, OC, ON and OP) of the ascidian biodeposits is shown in Table 3. There were no statistically significant differences in the chemical composition of the ascidian biodeposits and the control sediment, except that the ON content of S. clava biodeposits was significantly higher (p < 0.05) than that of C. intestinalis and the control sediment.
DISCUSSION
Temperature is one of the key factors controlling the reproduction of ascidians in temperate environments. Recruitment of Ciona intestinalis and Styela clava usually peaks during the summer (Blum et al. 2007, Bourque et al. 2007, McCarthy et al. 2007). We have consistently found that the densities of both C. intestinalis and S. clava are highest in summer in Sanggou Bay. Some previous studies conducted in temperate areas have reported 2 recruitment peaks (e.g. Blum et al. 2007, Bourque et al. 2007), but these studies involved serial cleaning or replacement of the settlement substratum, in contrast to the present study where substrata (i.e.lantern nets and scallop shells) were not manipulated. The majority of the available settlement space in the lantern nets was occupied and retained by ascidians and other set-tlers, leaving little available space on the substratum for recruitment of ascidians produced in subsequent reproductive cycles.
At present, direct data on biodeposition by C. intestinalis and S. clava are limited. McKindsey et al. (2009) used sediment traps to measure the biodeposition of C. intestinalis and S. clava from small groups of individuals on constructed mussel socks. Based on the dry mass of material collected and the mean abundance of tunicates on socks, the biodeposition rates of C. intestinalis and S. clava were approximately 7.12 and 20.53 mg ind.−1 d −1 , respectively. The values in our study were approximately 5 times greater than those of McKindsey et al. (2009). However, it is difficult to perform direct comparisons of the biodeposition rates of filter feeders due to differences in body size and experimental conditions. Since water temperature is an important determinant of biodeposition rates, a possible explanation for the lower biodeposition rates recorded by McKindsey et al. (2009) is that the water temperature in their study area was lower than in the present study. Their measurements were carried out in October in Malpeque Bay, Canada (46°N), a location farther north than Sanggou Bay (37°N). Other factors such as water chl a and suspended particulate matter concentration are also important determinants of biodeposition rates. The biodeposition rates (based on dry tissue weight) of some filter feeders are summarized in Table 4, which shows the wide variation among species. The biodeposition rates of C. intestinalis and S. clava were higher than that of Chlamys farreri previously measured in Sanggou Bay (Zhou et al. 2003b) and Sishili Bay (Zhou et al. 2006) (Table 4).
The biodeposition rates of C. intestinalis and S. clava are based on per unit of total dry weight. These values would be higher if based upon the unit of dry weight of body parts (without tunic) or AFDW, since a relatively high proportion of the tunic has no biological activity. Based upon the biodeposition rates and densities of ascidians, the total daily biodeposit production in September amounted to 13.24 g m −2 , and daily C, N, and P deposition rates were 0.94, 0.11, and 0.0098 g m −2 , respectively. The scallop cultivation area covers about 8.0 × 10 6 m 2 in Sanggou Bay (data from the Fisheries Research Institute of Rongcheng City, Shandong Province; www.rchy.gov.cn). Therefore, the estimated daily biodeposit production by C. intestinalis and S. clava in September was 105.9 t dry weight, 7.52 t C, 0.86 t N, and 0.078 t P. Based on the biodeposition rate of the scallop C. farreri (Zhou et al. 2003b), the daily biodeposit production by 2 yr old scallops would be ~490 t dry weight, 24.5 t C, 3.43 t N, and 0.21 t P in September. The biodeposition of dry material, C, N, and P by C. intestinalis and S. clava amounts to 21.6, 30.7, 25.1, and 37.0% of that by cultured scallops. McKindsey et al. (2009) found that the presence of C. intestinalis significantly increased the benthic loading from suspended mussel culture. The data from the present study similarly indicate that when estimating the im pact of suspended scallop aquaculture on the coastal ecosystem, the contribution of ascidians should be considered.
In addition to biodeposit production, ascidians may also im pact the benthos by drop-off. Most ascidians are either eliminated by net cleaning activities by commercial producers in late autumn, or die during the winter, and subsequently fall to the seabed. According to the den sities and individual weight of the 2 ascidians measured in the present study, dropoff to the sea floor would amount to ~143.0 t of dry matter ([329 ind.m −2 × 0.037 g ind.−1 + 22 ind.m −2 × 0.259 g ind.−1 ] × 8.0 × 10 6 m 2 ). This figure indicates that the biomass of ascidians could be substantial. However, it is noteworthy that this is an estimation for an entire growing season. Therefore, it is a relatively small input to the benthos compared with biodeposition if averaged on a daily basis. On the other hand, the drop-off process would occur in short-duration pulses (e.g. during cleaning), and a short-term large influx of OM may be relatively important in terms of benthic effects. The decompo sition and nutrient release from ascidian drop-off may influence the chemical properties of the surface sediment, and further, potentially modify remineralization and biogeochemical processes.
In summary, the present study has demonstrated that high population densities of C. intestinalis and S. clava may have potential impacts on the culture environment through biodeposition and accumulation of dead and discarded ascidians. These 2 common aquaculture pest ascidians may play an important role in coupling material fluxes from the water column to the seabed, and their role in this process requires further investigation.
Table 1 .
Water column characteristics at the study site in Sanggou Bay, northern China. T: temperature; S: salinity; Chl a: chlorophyll a; POM: particulate organic matter; TPM: total particulate matter. Values are means ± SE
Table 3 .
Organic matter (OM), organic carbon (OC), organic nitrogen (ON), and organic phosphorus (OP) content, and C:N, C:OP, and N:OP ratios in sediments collected in the traps in Sanggou Bay, northern China, including controls (without ascidians) and traps with Ciona intestinalis or Styela clava. Values are means ± SE. Values in the same column with the same superscript letter are not significantly different as determined by Duncan's test (p > 0.05)
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Domain: Environmental Science Biology
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Use of atmometers to estimate reference evapotranspiration in Arkansas
1 UFR S2ATA, Sciences Agronomiques, d’Aquaculture et des Technologies Agroalimentaires, Université Gaston Berger (UGB), BP 234-Saint Louis, Sénégal. 2 Department of Food, Agricultural and Biological Engineering, The Ohio State University, 590 Woody Hayes Dr., Columbus, OH 43210, USA. 3 Laboratoire Leïdi, Dynamique des Territoires et Développement, Université Gaston Berger (UGB), BP 234-Saint Louis, Sénégal. 4 Département Génie Rural, Ecole Nationale Supérieure d’Agriculture (ENSA), Université de Thiès (UT), BP 296/A-Thiès, Sénégal.
INTRODUCTION
In Arkansas, groundwater withdrawal for irrigation doubled from 1980to 2000(Winthrop Rockefeller Foundation, 2008. The same report highlighted that 73% of Arkansas water withdraw were used for irrigation and 80% of the water used for irrigation was groundwater. As a result, irrigation is the main activity contributing to the *Corresponding author. E-mailAuthor(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License increasing of water withdrawal (Valipour 2015a, b). Therefore, particular attention has to be taken in order to better manage irrigation and estimate accurately the crop water requirement.
Reliable estimation of crop water requirements is very important and vital where water resources are limited and crops are constantly under the influence of low rainfall and high temperature (Tabari et al., 2013). Therefore, accurate quantification of crop water requirements is needed for optimizing water productivity, efficient use of water resources and improving management practices to reduce surface and groundwater deterioration (Irmak et al., 2006;Al Wahaibi, 2011;Valipour 2014a, b;.
The evapotranspiration (ET) is generally used for estimation of crop water requirement. Thus, as mentioned by Jia et al. (2013), knowledge of ET is very important for water management and water resource planning. Different methods are developed for estimating ET. Most of them use equations to determine the value of ET at daily, weekly, monthly, or seasonal basis. These equations use weather variables as inputs such as solar radiation, air temperature, wind speed, and relative humidity (Irmak et al., 2005, Valipour 2014c. Among these methods, The Penman-Monteith model is the most accurate and widely used. The Food Agriculture Organization (FAO, 2015) and American Society of Civil Engineers (ASCE) have recommended it for use in irrigation management. However, it demands a lot of weather variables (Irmak et al., 2003) which could not be available everywhere.
The rice research center of University of Arkansas is using Atmometers in some of its fields, to determine ET for irrigation management and scheduling. The same technology has been installed in some farmer fields in order to know when and how much to irrigate. The results from Atmometers are judged accurate and very close to ETP Penman Montheith from some studies conducted in different regions: Hess (1996) and Knox et al. (2011) in England, Irmak et al. (2005 in Nebraska (USA), and Magluilo et al. (2003) in Mediterranean area.
The aim of this study is to compare the Evapotranspiration Penman-Monteith with the evaporation from atmometers (ET_gage) and to evaluate the seasonal variability between same atmometers of commercial types.
MATERIALS AND METHODS
The study was conducted at the Rice Research and Extension Center at Stuttgart in Arkansas (34°28'7.31"N, 91°24'56.14"W) at 62.2 m above mean sea level. Data of four months (May, June, July, and August 2013) of one meteorological station and 6 Atmometers (ET_gages of two types of covers: grass and alfalfa) were used.
Atmometers ( Figure 1) are water-filled devices, in which the actual evaporation of water is measured over time. A graduated glass sight on the water supply tank allows the user to easily measure the evaporation that occurred over a given period. Distilled water was used to fill the cylindrical reservoir of each atmometer made of white PVC, which reflects the radiant energy and is less subject to temperature raising of the water. The individual readings taken from each atmometer (ETgage) at the daily basis was determined by the difference between water levels on consecutive days. If readings are not taken for the week end, we have assumed reading Sunday = Saturday = (reading Monday -reading Friday)/2.
For each type of cover (grass and alfalfa), data from the three atmometers were compared in order to check their consistency. Evapotranspiration from Penman Monteith (ETO_PM) was calculated using the Equation (1). (1) Where ET0 (Penman Monteith grass reference evapotranspiration) or ETr (Penman Monteith alfalfa reference Evapotranspiration) is in mm/day; Rn = net radiation at the crop surface (MJm_2 day_1); G = soil heat flux density (MJm_2 day_1); T = air temperature at 2 m high (°C); u2 = wind speed at 2 m high (m s_1); es = saturation vapor pressure (kPa); ea = actual vapor pressure (kPa); es-ea = saturation vapor-pressure deficit (kPa). Cn is numerator constant for reference type and calculation time step, and Cd is denominator constant for reference type and calculation time step For grass reference and daily step, Cn = 900, Cd = 0.34 and alfalfa reference, Cn = 1600, Cd = 0.38.
The Computer program Cropwat 8 was used to calculate ETo_PM (Allen et al., 1998) at the daily basis. Cropwat 8 is developed by FAO for the calculation of crop water and irrigation requirements based on soil, climate and crop data. Also, the program can be used to develop irrigation schedules for different management conditions and to calculate the water supply for different crop patterns (FAO, 2015). The inputs of the application are maximum and minimum air temperature, humidity relative, average wind speed, and percentage of daytime. The comparisons between Penman Monteith grass or alfalfa reference evapotranspiration (ET0_PM or ETr_PM) and evapotranspiration from atmometers with grass or alfalfa cover (ET0_At or ETr_At) were tested by fitting linear regressions.
ET0_PM or ETr_PM was considered as the dependent variables. The Student's test (t test) was applied to evaluate the significance of the intercept and the slope of the regression. All tests were performed at alpha = 1%. Also a 95% Prediction interval was determined and the regression was bounded by a lower and upper limit values. To evaluate the degree of agreement between evapotranspiration from the atmometers and ETP Penman, coefficients of determination (R 2 ) were calculated. Table 1 gives the average monthly climatic information from May to August 2013. It shows that the average temperature is the same for June, July and August. The month of May with 21°C presents the lowest value. The relative humidity is greater than 80% for May, July, and August and achieves its lowest value at June with a value of 76%. August presents the lowest average wind speed (1.22 m/s), solar radiation (19.9 MJ/m 2 /day), and average hour sun (Hour).
To Penman Monteith and Atmometers (Grass)
A comparison between cumulative values of ET_ At and ET0_ PM during the four months (June to August 2013) is shown in Figure 2. Cumulative ET0_ PM is always greater than the cumulative values of ET_ At . The ETO_ PM exhibits a cumulative value of 526.2 mm. Atmometers 1 and 3 are very consistent and present slightly the same values, 462.7 and 462.5 mm respectively. In contrary, the atmometer 2 shows the lowest values (419.1 mm). These results highlight that atmometers underestimate the value of evapotranspiration during the growing season in Arkansas by 12.5% for atmometers 1 and 3 and 21% for atmometers 2. This result confirms the finding of Gavilán and Castillo (2009) in Spain and Alam and Trooien (2001) under semiarid conditions. Irmak et al. (2005) pointed out that rainfall may play a significant role in this underestimation because the wetness of the canvas cover and the membrane as well as the accumulation of rainwater would cause a reduction in the vapor pressure gradient between the plate surface and the surrounding air on rainy days. These results are different from those of Knox et al. (2011) and Alam and Elliott (2003) which showed that atmometers overestimate the value of evapotranspiration. Another study by Magliulo et al. (2003) in South Italy found that a slight underestimation of pan ET0 by atmometer. The difference can be explained by the climatic differences in these zones or by a reading error (Dukes et al., 2004) because different persons were involved in the data collection and this fact can cause inconstancy in data reporting. The different values from atmometers 1 and 3 on one hand, and 2 on the other hand reveal that it may be by manufactory variability. Gavilan and Castillo (2009) revealed that may be a difference value from atmometer of same cover due sometimes to manufactory variability. It will be interesting to use these three same atmometers for long terms to see how they will perform.
Depending on the geographical area, the model, formula; or method used to calculate evapotranspiration, results are different compared to FAO Penman Monteith method (Snyder et al., 2005). showed that Temperature based formula and temperature and relative humidity based formula overestimated Penman Monteith Evapotranspiration in some provinces in Iran.
Farmers use to irrigate, at average, every three to five days; therefore the mean of the five-day sum values of evaporation were computed using the atmometers and the Penman Montheith. Also, Magliulo et al. (2003) pointed out that for practical purposes, a weekly schedule in ET0 monitoring via atmometers is to be advised to Table 2 provides the standard deviation, the standard error, the coefficient of variation, and the value of t test. It can be seen that the mean The five-days sum evaporation values computed using the different methods (Penman Montheith and Atmometers) were analyzed by using a simple linear regression equation ( Y = Ax +B) where Y represents ETo_PM and X values from the atmometers. A and B arerespectively the slope and the intercept of the regression. The results are shown in Table 3. There is good correlation (R 2 > 0.65) between atmometers 1, 3 and the ETO_PM but the correlation between ETO_PM and atmometer 2 shows a low R 2 value (0.49). This result confirms those shown above. None of the regressions had a slope of 1 or an intercept of 0 (Table 3). All three slopes are less than 0.6 and statically different from 1 and the intercept is statistically different from 0 (Student's t-test at the 0.01 level). These results show that values from atmometers need to be calibrated before using them in irrigation scheduling. Most of the study comparing atmometers and the ETo_ PM showed that a calibration is needed Figure 3 presents the regression with a 95 % interval confidence. It shows that all the point fall in the confident interval showing an acceptable agreement between ET PM and ETO_At.
ETr Penman Montheith and Atmometers (Alfalfa)
Cumulative ETr_PM is greater than those of the three atmometers for all periods (Figure 4). The result reveals that the atmometers underestimate ETr. On the other hand the cumulative ETr of the three atmometers are nearly the same for the four Months (May to August 2013). This shows that the values from the three atmometers reference alfalfa are very consistent whereas the atmometers reference grass showed manufacture variability. Table 4 gives the different statistics for the evapotranspiration from Atmometers alfalfa and Penman Monteith. The mean evapotranspiration reference is smaller for atmometers compared to Penman Monteith with high standard deviation. If we consider the atmometers; they have the same mean 21 m, 21.9 mm and 21.7 mm respectively and the same standard deviation and standard error.
The ratio between average five days sum ETr_PM and ET0_At is 1.19, 1.31, and 1.13 for atmometers 1, 2 and 3 respectively. The mean value of the ETr_PM five day average is significantly different from the mean of the 3 atmometers (Pvalue < 0.005). Like in grass atmometers, a five days sum Evapotranspiration has been calculated and regression on ET_PM against ETr_At is performed; the results show coefficient of determination more than 65% for all 3 regressions. Figure 5 presents the different regressions on evapotranspiration from atmometers against Alfalfa reference evapotranspiration. Overall, all points fall in the area between the lower and upper band of a confidence interval of 95% except for one point which is not representative of the all data points. These results show that the atmometers based alfalfa give best estimation of the evapotranspiration compared to grass atmometers.
The atmometer 1 presents a lower R 2 = 0.68 compared to the atmometers 2 and 3 which show a R 2 of 0.71 and 0.72 respectively (Table 5). Overall, the three regressions present good correlation between ETr_At and ETr_ PM (R 2 > 0.65). The standard error estimates of the three regressions are relatively high with the highest value for atmometer1 (6.43 mm) which has also the lower R 2 (0.68).
Conclusion
This study evaluated the performance of 6 atmometers (3 with grass cover and 3 with alfalfa cover) to estimate reference evapotranspiration against the grass and alfalfa Penman Monteith Equation (ETO_ PM and ETr_ PM , respectively) in Arkansas. Atmometers underestimated reference evapotranspiration during the growing season between 12.5 to 21%. Results obtained from comparison between 5-day ETgage measured by atmometers and estimated ET0_ PM or ETR_ PM using the FAO-56 Penman-Monteith equation showed a relative good correlation resulting in R 2 values varying between 0.48 and 0.72. Atmometer with alfalfa cover had better performance compared to grass cover. Manufacturing variability evaluation between atmometers of same cover showed that Atmometers with grass cover present some\===
Domain: Environmental Science Biology. The above document has 2 sentences that start with 'It shows that', 2 sentences that start with 'This result confirms', 2 sentences that start with 'These results show that', 2 sentences that end with '(UGB), BP 234-Saint Louis, Sénégal', 2 sentences that end with 'Knox et al', 2 sentences that end with 'of the regression'. It has approximately 2214 words, 106 sentences, and 24 paragraph(s).
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The first record of the brown shrimp Penaeus aztecus Ives , 1891 in the central Adriatic coast of Italy
Seven specimens of the northern brown shrimp Penaeus aztecus, a western Atlantic species, were collected in December 2016 and in November 2017 by bottom trawlers off Termoli, on the central Adriatic coast of Italy. The various colonization scenarios put forward as explanations for the species’ sudden, near concurrent presence in distance sites within the Mediterranean Sea and nowhere else, are discussed. The species is already established as a valuable fishery resource in southeastern Sicily, and is likely to do well in the Adriatic Sea, once its population increases.
Methods
On December 2, 2016, several specimens of P. atzecus were caught on a muddy bottom, at depths of 50-60 m, by the M/P Marpesca, a 28 m long commercial trawler equipped with 50 mm net, off Termoli (42° 07' 0122"N 15° 00' 03.90"E), on the central Adriatic coast of Italy; the shrimps were photographed but not preserved. Nearly a year later, on November 13, 2017, seven more specimens were caught by the same vessel at a nearby site (42° 07' 59.69"N 15° 03' 15.81"E), at depth of 50 m. These specimens were photographed and measured (total length, wet weight), preserved in 80% ethanol, and deposited in the Natural History Museum of Comiso, Ragusa, Sicily (MSNC-4560).
The Museo Civico di Storia Naturale of Comiso has engaged fishermen in Sicily and southern Italy in an effort to monitor rare and alien species. Interviews with Apulian fishermen confirm that several large sized specimens P. aztecus have been captured since 2015 between Mola di Bari and Bari (Apulia) and Ortona and Chieti (Abruzzo), at 40-50 m depth.
Discussion
Several colonization scenarios have been put forward as explanations for the sudden, near concurrent records from distant locations within the Mediterranean Sea, and nowhere else (Galil et al. 2016;Scannella et al. 2017). The first, an expansion scenario, posits that P. aztecus was introduced with ballast water to Antalya, Turkey (sometime prior to 2009, Deval et al. 2010), and then by current-driven post-establishment spread along the Turkish Mediterranean coast (Gökoğlu and Özvarol 2013), the western Aegean Sea (Nikolopoulou et al. 2013;Kevrekidis 2014;Minos et al. 2015), and up to Montenegro, in the eastern Ionian Sea (Marković et al. 2014;Kapiris et al. 2014;Crocetta et al. 2015) in less than three years (December 2010-September 2013). This scenario is based simply on the first collection dates, lacking documentation of previous absence at each site, and fails to account for the relatively short planktonic larval stage (less than 15 days) (Cook and Lindner 1970), and mark-recapture experiments that indicated that adults spread parallel to the coastline, with most individuals remaining within 50 km of their release sites (Klima 1963;McCoy 1968). Nikolopoulou et al. (2013: 372) observed that a progressive dispersal from the southern Turkish coast to the northern Aegean Sea "should have required longer period of time than the 2-3 years" and posits multiple introduction events, either additional transfers in ballast waters from the population established in the Turkish Mediterranean or from the native populations in the western Atlantic (Scannella et al. 2017). Yet, it is highly unlikely that ballast introductions are the cause for the burst of records across the Mediterranean Sea. A more plausible explanation for this pattern of spread is that many of the Mediterranean populations issue from direct human introduction (Galil et al. 2016). Mediterranean countries have been notably careless about biosecurity risks due to intentional "unofficial" introductions, movement of stock, feed, and equipment that may result in introduction of marine species (CIESM 2007). The bilaterally ablated female banana prawn, Penaeus merguiensis de Man, 1888, collected in 2006 in the Bay of Iskenderun, Turkey, is certainly an escape or an inadvertent release from a nearby aquaculture facility, since eyestalk ablation is commonly used in aquaculture for inducing maturation of gonads (Özcan et al. 2006) when specimens infected with White Spot Syndrome (WSD) Virus were submitted to the Office International des Epizooties (OIE) Reference Laboratory for WSD at the University of Arizona, USA, by industry representatives in 1997 (Stentiford and Lightner 2011). None of these introductions were reported to the competent authorities of the respective countries. Thus, the absence of official records of importation of P. aztecus into the Mediterranean Sea does not rule out direct human introduction, particularly as several of the recorded specimens were collected in the vicinity of fish and shellfish farms, including the Italian Tyrrhenian and Adriatic records (Sanna 2010, fig. 1). Progressive dispersal with the prevailing current of larvae and adults may be responsible for some adjacent records (e.g. SE Levant, S Turkey, SE Adriatic Sea). In time, population genetic analyses may clarify the colonization history of P. aztecus in the Mediterranean Sea (Darling et al. 2017).
Invasive Erythraean prawns, chiefly P. pulchricaudatus (misidentified as P. japonicus Spence Bate, 1888) and its relatives -P.semisulcatus De Haan, 1844, Metapenaeus monoceros (Fabricius, 1798), M. stebbingi Nobili, 1904 -are highly prized and are considered a boon to the Levantine fisheries (Kumlu et al. 1999;Can et al. 2004;Duruer et al. 2008). It seems that P. aztecus is likely to join them as a valuable fishery resource. Along the southern coast of Turkey, large numbers of commercial-sized individuals were collected by trawling and trammel netting within three years of the initial record (Bilecenoglu et al. 2013). Along the Egyptian coast, where it was first noted in the Damietta branch of the Nile delta in 2012, it has since appeared annually between February and mid-June and the plentiful wild fry is welcomed by fishers (Sadek et al. 2018). Along the Ionian coast of Calabria, Italy, it is commonly trawled at 90-100 m depth and marketed locally (Mytilineou et al. 2016). Off the central Adriatic coast of Italy it is fished in small quantities and offered for sale mixed with other penaeid species. Off southeastern Sicily in past years it fetched 10-12 euros/kg, but its value rose recently, and present prices in the fish markets of Catania and Scoglitti (Figure 2) rose to 15-20 euros/kg as compared to 30-40 euros/kg for the native prawn P. kerathurus (Forskål, 1775).
Figure 2 .
Figure 2. A crate of Penaeus aztecus Ives, 1891, offered for sale in February 2018, in the fish market of Scoglitti, Sicily, Italy. Photograph by B. Zava.
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Domain: Environmental Science Biology
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Influence of Water quality on the biodiversity of phytoplankton in Dhamra River Estuary of Odisha Coast, Bay of Bengal
Dhamra estuarine ecosystem is a hotspot of rich biological diversity which supports a patch of mangrove along with unique flora and fauna. In this study, the diversity of phytoplankton population and other factors that control their growth and biodiversity were investigated. The samples were collected monthly from Dhamra estuary of Bay of Bengal at 6 different stations (grouped under three regions) from March -2008 to February -2009. A total of 41 genera of phytoplankton species belonging to 4 classes of algae were identified. The maximum value of 9.3 X 10 cells l was recorded in post monsoon season. Phytoplankton of Bacillariophyceae, appearing throughout the year, and represent majority of population (75-94%) at all the sampling stations, followed by Dinophyceae (3-14%), Cyanophyceae (3-8%) and Chlorophyceae (0-4%) classes. The Shannonweavers diversity index (H) remains between 0.22 and 2.49. Based on the correlation coefficient data, phytoplankton shows positive relationship with DO, salinity, nutrients and negative relationship with temperature and turbidity. Present study shows that the occurrence and diversity of these primary producers do not subscribe to a single dimensional phenomenon of a single factor, rather than, a consequence of a series of supported factors which will help to maintain and balance such type of fragile ecosystem. @JASEM Estuaries are characterized by the gradient of salinity in a semi enclosed coastal system, flourishing a group of organisms uniquely capable of using the salinity gradient to a competitive advantage. Phytoplanktons represent the base of pelagic food webs in estuarine ecosystem and play a major role in the global cycling of carbon, nitrogen, phosphorus and other elements and the regulation of earth’s climate. The biomass distribution and species composition of phytoplankton have important effects on carbon fixation rates and on transfer of energy in food webs. Studies of the abundance, distribution and composition of phytoplankton communities are, therefore, a fundamental contribution to our understanding of the structure and function of estuarine ecosystems. Phytoplankton communities are multispecies communities, which are highly multifaceted in terms of their diversity and dynamics. Successional shifts in phytoplankton community structure are mainly due to changes in environmental variables such as nutrients and other physicochemical variables which influence the distribution and abundance of plankton communities in estuaries (Cleorn, 1987; Ferreira et al., 2005; Madhu et al., 2007). Estuarine regions are important sinks for several elements. In this study we tried to identify and asses the trend of phytoplankton species diversity at Dhamra Estuarine region with the water physico – chemical parameters. Dhamra river is the union of river Brahmani and Baitarani, the two largest rivers of Orissa, situated at the Bhadrakh district, just north of the boundary of Bhitarkanika wild life sanctuary, famous for its estuarine crocodiles and approximately 10 km from the Gahirmatha marine sanctuary, which is the world’s most important nesting beach for Olive Ridley sea turtles on the Orissa coast of Bay of Bengal. Although, a number of studies have been carried out on the ecological conditions of estuarine region, as Bay of Bengal is considered as a low productive zone (Madhupratap, et al., 2003), very limited work has been done in the east coast of Orissa. Moreover, not as much of information on phytoplankton species composition and diversity is available in this particular estuarine region. MATERIALS AND METHODS Sampling of surface water was carried out throughout the year (March-2008 to February-2009) on a monthly basis at Dhamra River Estuarine region. A total of 6 stations ,where station-1 and 2 covers the lower estuarine region (marine region, depth-63 ft), station-3 and station-4 covers the river mouth region having depth of 27ft and 26ft, respectively and station-5 (26ft) and station-6 (26ft) were the riverine region or fluvial region dominated by freshwater but subjected to tidal action. Water samples for the measurement of salinity, turbidity and nutrient parameters were collected in Polypropylene bottles and for DO and BOD measurement, water samples were collected in DO bottles and analysed by Winkler method. The temperature and salinity was measured in situ using sensor based Multi WTW 340i/set. Salinity was again cross-checked by silver nitrate titration (Oxner, 1920). Turbidity was measured by Nephlometric method using HACH 2100P Turbidimeter. Basic nutrient parameters (nitrite, nitrate, ammonia and
Estuaries are characterized by the gradient of salinity in a semi enclosed coastal system, flourishing a group of organisms uniquely capable of using the salinity gradient to a competitive advantage. Phytoplanktons represent the base of pelagic food webs in estuarine ecosystem and play a major role in the global cycling of carbon, nitrogen, phosphorus and other elements and the regulation of earth's climate. The biomass distribution and species composition of phytoplankton have important effects on carbon fixation rates and on transfer of energy in food webs. Studies of the abundance, distribution and composition of phytoplankton communities are, therefore, a fundamental contribution to our understanding of the structure and function of estuarine ecosystems. Phytoplankton communities are multispecies communities, which are highly multifaceted in terms of their diversity and dynamics. Successional shifts in phytoplankton community structure are mainly due to changes in environmental variables such as nutrients and other physicochemical variables which influence the distribution and abundance of plankton communities in estuaries (Cleorn, 1987;Ferreira et al., 2005;Madhu et al., 2007).
Estuarine regions are important sinks for several elements. In this study we tried to identify and asses the trend of phytoplankton species diversity at Dhamra Estuarine region with the water physicochemical parameters. Dhamra river is the union of river Brahmani and Baitarani, the two largest rivers of Orissa, situated at the Bhadrakh district, just north of the boundary of Bhitarkanika wild life sanctuary, famous for its estuarine crocodiles and approximately 10 km from the Gahirmatha marine sanctuary, which is the world's most important nesting beach for Olive Ridley sea turtles on the Orissa coast of Bay of Bengal.
Although, a number of studies have been carried out on the ecological conditions of estuarine region, as Bay of Bengal is considered as a low productive zone (Madhupratap, et al., 2003), very limited work has been done in the east coast of Orissa. Moreover, not as much of information on phytoplankton species composition and diversity is available in this particular estuarine region.
MATERIALS AND METHODS
Sampling of surface water was carried out throughout the year (March-2008 to February-2009) on a monthly basis at Dhamra River Estuarine region. A total of 6 stations ,where station-1 and 2 covers the lower estuarine region (marine region, depth-63 ft), station-3 and station-4 covers the river mouth region having depth of 27ft and 26ft, respectively and station-5 (26ft) and station-6 (26ft) were the riverine region or fluvial region dominated by freshwater but subjected to tidal action.
Water samples for the measurement of salinity, turbidity and nutrient parameters were collected in Polypropylene bottles and for DO and BOD measurement, water samples were collected in DO bottles and analysed by Winkler method. The temperature and salinity was measured in situ using sensor based Multi WTW 340i/set. Salinity was again cross-checked by silver nitrate titration (Oxner, 1920). Turbidity was measured by Nephlometric method using HACH 2100P Turbidimeter. Basic nutrient parameters (nitrite, nitrate, ammonia and phosphate) were measured according to standard methods (Grasshoff, 1999). For quantitative and qualitative analyses of phytoplankton (Verlencar, 2004), a Sedgwick-Rafter plankton counting chamber was used and samples were examined microscopically by trinocular Nikon 90i Eclipse automated microscope. All 1,000 squares on the chamber were screened (Gilbert, 1942). Empty frustules were not included in the total counts. Phytoplankton cell identifications were based on standard taxonomic keys (Verlencar, 2004;Botes, 2003). The results are expressed as numbers of cells L -1 and phytoplankton diversity was calculated using Shannon's diversity index. (Shannon and Weaver, 1949)
H = -∑ pi ln pi
Where H -is the diversity index; ln -is the natural logarithm; i -is an index number for each species present in a sample; pi -is the number of individuals within a species (ni) divided by the total number of individuals (N) present in the entire sample.
RESULT AND DISCUSSION
Phytoplankton abundance and species composition showed both spatial and seasonal variation. The dominant species recorded at different sampling stations belonged to the genera Coscinodiscus, Skeletonema, Nitzschia, Navicula, Thallasiothrix, Triceratium, Biddulphia, Ceratium, Rhizosolenia, Thallasionema, Bacillaria, Chaetocerous, Melosira, Trichodesmium, Podosira, Pleurosigma. There was succession of different species recorded during different sampling seasons at different sampling points. The numbers of phytoplankton genus under these classes are identified as Bacillariophyceae 31 genus and several species, 2 genus of Chlorophyceae, 6 genus of Dinophyceae and 2 genus of Cyanophyceae. The percentage occurrence of different phytoplankton groups with respect to total phytoplankton taxa at six different stations of Dhamra estuary throughout the year has been given in Figure Table 1. With growth and turnover rates of less than a day, phytoplankton are very susceptible to changes in the environment, and large variations in phytoplankton species composition are often a reflection of significant alteration in ambient conditions within an ecosystem. Basically, Diatom (class-Bacillariophyceae), Dinoflagellates (class-Dinophyceae), Cocolithophores and Silicoflagellates (class-Chrysophyceae) and blue-green algae (class-Cyanophyceae) are the principal phytoplankton taxa in the ocean. In Dhamra estuarine water sample, phytoplankton belonging to class Bacillariophyceae dominated over other classes of phytoplanktons at all the 6 stations in the whole year (March-08 to Feb-09). After Bacillariophyceae, some genus of Dinophyceae, few of Cyanophyceae and occasionally Chlorophyceae genus were observed (Fig 2).
Plankton communities in the estuary can be served as an indicator for the change in ecosystems under the pollution stress. In ecology, a diversity index is a statistics, which is applied to measure the species biodiversity in an ecosystem. A stressed environment typically has a lower number of species with one or two species (those adapted to the stress) having many more individuals than the other species (Gao and Song, 2005). As our study area was identified as a low productive zone (Madhupratap et al., 2003), the diversity index remained between 0.22 and 2.49 throughout the year at all the sampling stations ( Fig 2). During the post monsoon season, as a result of fresh water flushing and changes in salinity, the estuarine region experiences the most dramatic change in phytoplankton species composition, as is evident from the diversity index data (Fig 2). The highest phytoplankton population density reached upto 9.3 x 10 4 cells l -1 at river mouth region during the month of December-2008 (in the post monsoon season). The total phytoplankton community during this proliferation was composed mainly of diatoms and dominated by a single diatom species Pleurosigma angulatum. Abiotic features in estuaries vary depending upon the degree of protection from water motion (waves, tidal currents), the quality of fresh water input and circulation patterns including residence time of the water, depth and salinity gradient. Further abiotic factors change both temporally and spatially, so that a wide variety of habitats exist in estuaries. Diversity and abundance of phytoplankton are related to the physico chemical parameters in general and more particularly to temperature, DO, BOD, salinity and nutrient availability. The correlation coefficient values between physico chemical parameters and the algal population of three distinct regions have been presented in Table 1 . As a matter of fact, positive correlationship was observed between dissolved oxygen and phytoplanktons. Salinity is measured because of its influence on the distribution and diversity of many living marine species. The rate of cell division of these microfloras, as well as their occurrence, distribution and productivity is influenced by salinity and in this study phytoplanktons have shown a positive correlation with salinity value at all the sampling stations because estuarine regions are subjected to considerable fluctuations and these micro flora were well adapted to such vicissitude environment (Kinne, 1972;Lionard et al., 2005). Phytoplanktons need a wide variety of chemical elements but the two critical ones are nitrogen and phosphorous (Dawes, 1981). And we found that phytoplanktons show positive correlation with phosphate and inorganic nitrogenous nutrient parameters but the relationship was not very significant. This may be due to lower concentration or may be rapid recycling of these nutrients. Similar observations were made by Steinhart et al. (2002) and Hergenrader (1980). Temperature and turbidity manifested negative relationship with phytoplanktons. Early workers also reported the same relationship of phytoplanktons with temperature and turbidity (Dawes, 1981).
The overall findings of this study revealed that climatic conditions as well as ionic chemistry of the ecosystem influence the species composition and their relative abundance of phytoplankton in the Dhamra estuarine region. This study of phytoplankton biodiversity at Dhamra estuary of Orissa coast, Bay of Bengal revealed that the population peak was mainly contributed by a single species proliferation, which led to low diversity index. But still it does not impart any harsh effect to the biotic community, because the major organism was not listed under toxic phytoplanktons. Although the bloom was not creating any stressful condition, still it could have been better if the peak population was also associated with highest diversity index. A perfect relationship between specific environment factor and change in phytoplankton community structure was yet to be established. However, this study provides clear information regarding occurrence of an intermediate level of phytoplankton population and minimum diversity during massive blooming period in this particular estuary.
Acknowledgements: The authors are thankful to the Director, IMMT, Bhubaneswar for his kind permission to publish the work. Thanks are also due to the Dhamra Port Co. Ltd., for providing necessary funding to carry out the work.
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Domain: Environmental Science Biology
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The efficiency of pitfall traps as a method of sampling epigeal arthropods in litter rich forest habitats
Pitfall trapping is an approved self-sampling method for capturing epigeal arthropods for ecological and faunistic studies. Capture efficiency of pitfall traps may be affected by external factors and the design of the trap. Pitfall traps set in forests are usually protected with covers or wire grids, but the effect of these constructions on sampling efficiency as well as their practicability and necessity have so far received little attention. During the present study pitfall traps of four different designs (covers, wire grids, litter exclosure, open) were tested in terms of their efficiency in capturing ground-dwelling arthropods (Acari, Araneae, Carabidae, Formicidae, Isopoda, Myriapoda, Opiliones) in order to gain a better understanding of the applicability and reliability of pitfall traps in forests. The study was carried out in an oak-beech forest in Northwest Germany using a total of 40 pitfall traps (ten replicates per trap design). Generalised linear models indicated no significant differences in arthropods counts among catches of pitfall traps of the four different designs, except for woodlice. Ordination analyses (NMDS) and MANOVA revealed no significant differences in spider and carabid beetle species compositions of the catches. In contrast, for both these taxa there were significant differences in the body sizes of the individuals caught. We conclude that the catches of pitfall traps are little affected by their design. Furthermore, the litter layer and litter input have no effect on the capture efficiency and thus there seems to be no need to protect pitfall traps with covers or wire grids in litter rich forest habitats. 69 * Corresponding author. The present study aims to determine whether the design of pitfall traps affects the numbers of epigeal arthropods caught in habitats with abundant litter by addressing the following questions: (i) Do we need to protect pitfall traps with covers and wire grids, respectively, in order to obtain the best possible catches in habitats with abundant litter? (ii) Is the species composition and functional characteristics of the individuals caught by pitfall traps (in the case of carabid beetles and spiders) affected by the design of the traps? (iii) Does the structure of the litter layer affect the capture efficiency of pitfall traps? MATERIAL AND METHODS The study was carried out in an oak-beech forest (FagoQuercetum – vegetation structure: mean vegetation coverage = 5%, height of herbaceous plant layer = 6 cm, litter coverage = 95%, depth of litter = 3 cm) near the city of Münster (51°54 ́37N, 7°44 ́44E) in North Rhine-Westphalia, Germany. The climate is Sub-Atlantic with a mean annual temperature of 7.9°C and an average annual precipitation of 758 mm (MURL NRW, 1989). The area (“Wolbecker Tiergarten”) is one of the oldest forest stands in the region (stand age: ~800 years) and covers an area of nearly 300 ha (Lanuv, 2010). Characteristic elements are deciduous trees (~85%) and a high quantity of dead wood (mean percentage for the study area: 10%). To record the activity densities of ground-dwelling arthropods, pitfall traps were set from 20 June to 7 July 2009 (16 sampling days). The traps (plastic cup, diameter: 8.5 cm, height: 12 cm) were filled up to one third with a formalin solution (3%) and a few drops of detergent to kill and conserve the animals. Traps were set randomly (but > 5 m apart from each other) and level with the surface of the soil. In order to investigate the effect of covers and wire grids, ten pitfalls were covered with white, square plastic covers (14 × 14 cm) 3 cm above the trap opening and ten traps were covered by a wire grid (mesh size: 3.5 cm) (Fig. 1). Furthermore, ten pitfall traps remained open and were thus entirely exposed to the entry of litter. Finally, to test for the possible effects of the litter layer we installed litter exclosures (mesh size: 3.5 cm, height: 30 cm, width: 5 cm around the trap) around ten pitfall traps. Hence, in total there were four treatments with ten replicates of each (altogether 40 traps). The catches were sorted and preserved in ethanol (75%). For the analyses we counted the numbers of acarians (Acari), spiders (Araneae), ground beetles (Coleoptera: Carabidae), ants (Formicidae), woodlice (Isopoda), millipedes (Myriapoda) and harvestmen (Opiliones). The species of spiders and carabid beetles were determined using standard determination keys of Roberts (1987, 1998), Heimer & Nentwig (1991) and Nentwig et al. (2013) for spiders and Müller-Motzfeld (2006) for carabid bee70 Fig. 1. The different types of pitfall traps used in this study: a – wire grid; b – open; c – litter exclosure; d – cover. tles. Nomenclature followed Platnick (2013) (Araneae) and Müller-Motzfeld (2006) (Carabidae). As a functional trait we chose average body size. Data was taken from Nentwig et al. (2013) for female spiders and from Trautner & Geigenmüller (1987) and Dücker et al. (1997) for carabid beetles. All statistical analyses were performed using the free software environment R 3.0.1 (R Development Core Team, 2013) including the packages VEGAN and MASS for multivariate statistics. In order to detect possible differences in total catches of ground-dwelling arthropods (Acari, Araneae, Carabidae, Formicidae, Isopoda, Myriapoda, Opiliones) as well as in numbers of species of carabid beetles and spiders caught by the four types of traps (explanatory variables: wire grid, cover, litter exclosure, open), generalised linear models (GLM) were used. To compensate for over-dispersion, the standard errors were corrected using a quasi-Poisson model (Crawley, 2008; Zuur et al., 2009). Furthermore, the significance of differences in the body sizes of spiders and carabid beetles was tested using generalised linear models (GLM) using a Gamma distribution. A non-metric multidimensional scaling (NMDS) (isoMDS engine) was used to display the species composition for spiders and carabid beetles caught by the four traps. For ordination, species activity densities were square root transformed. The NMDS was based on the Bray-Curtis dissimilarity matrix. In search of a stable solution and the lowest stress value, a maximum number of 100 random starts was used. In addition, a multivariate analysis of variance (MANOVA, 9,999 permutations; ADONIS function in VEGAN) was performed to assess the effect of the treatments on species composition. For all multivariate statistics, sporadic species with less than three individuals in the total catch were excluded. This resulted in 14 species of spiders (1,621 individuals) and 12 species of carabid beetles (781 individuals) being included in the ordination analysis. RESULTS A total of 1,656 Araneae belonging to 37 species, 820 Isopoda, 793 Carabidae belonging to 19 species, 245 Myriapoda, 119 Opiliones, 68 Formicidae and 39 Acari were recorded. We found no significant differences in capture efficiencies of the four traps except for the order Isopoda (Table 1). For this taxon the highest catches were recorded in the covered traps. More species of spiders and ground beetles were caught by the traps within “litter exclosures” (Appendix 1), but the differences were not significant (Table 2). In contrast, for both these taxa significant differences in body size were detected in the catches by the four types of trap (Araneae: F = 4.59, P = 0.008; Carabidae: F = 3.79, P = 0.02), with the largest spiders and carabid beetles more frequently recorded in covered traps. Ordination analyses did not show any significant effect of the different traps on the species composition (Fig. 2): neither the distribution of spiders (F = 1.30, R2 = 0.10, P = 0.21) nor the distribution of carabid beetles (F = 1.19, R2 = 0.10, P = 0.28) showed significant differences among treatments (MANOVA).
INTRODUCTION
Pitfall trapping is an approved self-sampling method for collecting ground-dwelling arthropods in ecological and faunistic studies. Pitfall traps were introduced by Barber (1931) and in general they consist of cups filled with a killing and conserving fluid that are set with the rim of the cup level with the surface of the ground (Balogh, 1958). So set, these traps can be used to catch and determine the activity densities of surface active arthropods, such as spiders and ground beetles (Tretzel, 1955;Greenslade, 1964;Luff, 1975;Uetz & Unzicker, 1976;Adis, 1979;Curtis, 1980). Despite several criticisms (e.g. Bombosch, 1962;Topping & Sunderland, 1992), pitfall traps are widely used as they are easy to handle and inexpensive. In addition, usually high numbers of individuals and species can be caught, making statistical analyses possible (Spence & Niemelä, 1994).
Especially in forests and habitats with abundant litter, field workers often cover pitfall traps to protect them from precipitation, accumulation of leaves or dilution of the conserving solution (e.g., Spence & Niemelä, 1994;Mallis & Hurd, 2005;Schuldt et al., 2008). Moreover, wire grids are often used to keep small vertebrates out. To date, the effect of these constructions on sampling efficiency as well as their practicability and necessity have received little attention except for Buchholz & Hannig's (2009) study, which determined the effect of covers in open and dry grasslands where there is little litter. However, the effect of covering pitfall traps might be quite different in other habitats, such as forests, where falling leaves may affect uncovered pitfall traps more than covered traps. Furthermore, while preventing small vertebrates from falling into traps, wire grids may, depending on their mesh size, reduce the catch ability of, e.g., longlegged spiders and large-bodied carabid beetles. Finally, little is known about the potential effect of the structure of litter, for example, the amount of litter cover and its thickness, on trapping. In this context, it is arguable to what extent covers as well as wire grids operate as an effective protection against litter.
The present study aims to determine whether the design of pitfall traps affects the numbers of epigeal arthropods caught in habitats with abundant litter by addressing the following questions: (i) Do we need to protect pitfall traps with covers and wire grids, respectively, in order to obtain the best possible catches in habitats with abundant litter?
(ii) Is the species composition and functional characteristics of the individuals caught by pitfall traps (in the case of carabid beetles and spiders) affected by the design of the traps?
(iii) Does the structure of the litter layer affect the capture efficiency of pitfall traps?
MATERIAL AND METHODS
The study was carried out in an oak-beech forest (Fago-Quercetum -vegetation structure: mean vegetation coverage = 5%, height of herbaceous plant layer = 6 cm, litter coverage = 95%, depth of litter = 3 cm) near the city of Münster (51°54´37N, 7°44´44E) in North Rhine-Westphalia, Germany. The climate is Sub-Atlantic with a mean annual temperature of 7.9°C and an average annual precipitation of 758 mm (MURL NRW, 1989). The area ("Wolbecker Tiergarten") is one of the oldest forest stands in the region (stand age: ~800 years) and covers an area of nearly 300 ha (Lanuv, 2010). Characteristic elements are deciduous trees (~85%) and a high quantity of dead wood (mean percentage for the study area: 10%).
To record the activity densities of ground-dwelling arthropods, pitfall traps were set from 20 June to 7 July 2009 (16 sampling days). The traps (plastic cup, diameter: 8.5 cm, height: 12 cm) were filled up to one third with a formalin solution (3%) and a few drops of detergent to kill and conserve the animals. Traps were set randomly (but > 5 m apart from each other) and level with the surface of the soil. In order to investigate the effect of covers and wire grids, ten pitfalls were covered with white, square plastic covers (14 × 14 cm) 3 cm above the trap opening and ten traps were covered by a wire grid (mesh size: 3.5 cm) (Fig. 1). Furthermore, ten pitfall traps remained open and were thus entirely exposed to the entry of litter. Finally, to test for the possible effects of the litter layer we installed litter exclosures (mesh size: 3.5 cm, height: 30 cm, width: 5 cm around the trap) around ten pitfall traps. Hence, in total there were four treatments with ten replicates of each (altogether 40 traps).
All statistical analyses were performed using the free software environment R 3.0.1 (R Development Core Team, 2013) including the packages VEGAN and MASS for multivariate statistics. In order to detect possible differences in total catches of ground-dwelling arthropods (Acari, Araneae, Carabidae, Formicidae, Isopoda, Myriapoda, Opiliones) as well as in numbers of species of carabid beetles and spiders caught by the four types of traps (explanatory wire grid, cover, litter exclosure, open), generalised linear models (GLM) were used. To compensate for over-dispersion, the standard errors were corrected using a quasi-Poisson model (Crawley, 2008;Zuur et al., 2009). Furthermore, the significance of differences in the body sizes of spiders and carabid beetles was tested using generalised linear models (GLM) using a Gamma distribution.
A non-metric multidimensional scaling (NMDS) (isoMDS engine) was used to display the species composition for spiders and carabid beetles caught by the four traps. For ordination, species activity densities were square root transformed. The NMDS was based on the Bray-Curtis dissimilarity matrix. In search of a stable solution and the lowest stress value, a maximum number of 100 random starts was used. In addition, a multivariate analysis of variance (MANOVA, 9,999 permutations; ADONIS function in VEGAN) was performed to assess the effect of the treatments on species composition. For all multivariate statistics, sporadic species with less than three individuals in the total catch were excluded. This resulted in 14 species of spiders (1,621 individuals) and 12 species of carabid beetles (781 individuals) being included in the ordination analysis.
RESULTS
A total of 1,656 Araneae belonging to 37 species, 820 Isopoda, 793 Carabidae belonging to 19 species, 245 Myriapoda, 119 Opiliones, 68 Formicidae and 39 Acari were recorded. We found no significant differences in capture efficiencies of the four traps except for the order Isopoda (Table 1). For this taxon the highest catches were recorded in the covered traps. More species of spiders and ground beetles were caught by the traps within "litter exclosures" (Appendix 1), but the differences were not significant (Table 2). In contrast, for both these taxa significant differences in body size were detected in the catches by the four types of trap (Araneae: F = 4.59, P = 0.008; Carabidae: F = 3.79, P = 0.02), with the largest spiders and carabid beetles more frequently recorded in covered traps.
DISCUSSION
Apart from Isopoda, our results indicate that the four types of traps did not differ significantly in the numbers of arthropods caught. In general, woodlice occur mostly in moist and dark habitats such as holes in dead wood, loose litter and under stones (Abbott, 1918). For this reason, Isopoda might be attracted by dark holes beneath covers.
Differences in terms of the numbers of individuals with particular functional traits were found with large species 71 0.87 0.24 3.8 ± 1.7 3.1 ± 1.5 2.7 ± 0.9 2.3 ± 1.0 Opiliones 0.77 0.38 5.5 ± 1.8 5.6 ± 2.9 8.5 ± 3.4 4.9 ± 1.9 Myriapoda 0.02 3.74 18.4 ± 6.0 more frequently recorded in the catches of covered traps. One reason might be that most large species of spiders and carabid beetles are night active and thus seek shelter during the day (Turin, 2000;Nentwig et al., 2013). As large species need large cavities in which to shelter (such as under stones, stems or pieces of deadwood) the covers over the pitfall trap were possibly attractive refuges for them and as a consequence these traps caught higher numbers of large than small species, which tend to stay in the litter layer (Wagner et al., 2003).
There is a vertical stratification in the distribution of ground-dwelling spiders in litter, which is attributed to microhabitat conditions, mainly abiotic factors (Wagner et al., 2003). Due to lower spatial resistance there, large spiders (such as free hunting Agelenidae and Lycosidae) preferentially occur in the spacious, upper litter layer or on the litter surface, while small-bodied species more frequently occur in the compacted, middle and lower litter layers. The same is reported for carabid beetles by Sergeeva (1994). Consequently, the probability of trapping large-bodied species may be higher since pitfall traps mostly catch surface-active species while litter-dwelling species are mostly unrepresented.
Finally, Topping (1993) points out that small-bodied spiders, e.g.those belonging to the family Linyphiidae, can escape from pitfall traps more easily and thus relatively few are caught. The same applies to carabid beetles, which can avoid traps, as shown by Van der Drift (1951) and Greenslade (1964).
In terms of the questions we set out to answer, it seems to be unnecessary to protect pitfall traps with covers or wire grids in forests with abundant litter, as the presence of covers or grids did not significantly affect their efficiency in capturing epigeal arthropods. This is partially in accordance with Buchholz & Hannig (2009), who found no significant effect of pitfall covers in dry grassland habitats. Nevertheless, we recommend the use of wire grids above the trap opening to prevent small vertebrates and debris from entering the traps as this often makes it easier and faster to sort and count the catch.
Furthermore, the type of trap did not significantly affect the species composition of spiders and carabid beetles. One should keep in mind that functional characteristics such as body size (and maybe leg length in spiders) do seem to be affected by pitfall trap design. Finally, the litter layer seems to have only minor direct effects on pitfall traps in forests since they did not catch significantly more arthropods in litter exclosures.
Fig. 1 .
Fig. 1. The different types of pitfall traps used in this study: a -wire grid; b -open; c -litter exclosure; d -cover.
List of species and the numbers caught in each of the different types of pitfall trap used in this study. Nomenclature followsPlatnick (2013) for Araneae andMüller-Motzfeld (2006) for Carabidae.
TABLE 1 .
Total number of individuals of Acari, Araneae, Carabidae, Formicidae, Isopoda, Myriapoda and Opiliones caught by the pitfall traps in the four treatments: wire grid, plastic cover, litter exclosure and open. Data presented as mean ± SE.
TABLE 2 .
Total number of species (no.spec.) and body size [mm] of Araneae and Carabidae caught by the pitfall traps in the four treatments: wire grid, plastic cover, litter exclosure and open. Data presented as mean ± SE. ).
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Domain: Environmental Science Biology
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Reproductive Biology and Histological Study of Red Lionfish Pterois Volitans from Cuddalore, South East Coast of India
The success of any fish species is ultimately determined by the ability of its members to reproduce successfully in a fluctuating environment and thereby to maintain the viable population. Information on the reproductive biology of the candidate species is very much essential for the development of aquaculture industry. The dynamic metabolic activity of a reproduction in most of the fishes and it involves sequential changes in the germ cells. The pattern of these changes in the gonads is typical for each species [1,2]. To understand the physiology of reproduction, the study of the seasonal developmental changes of gonads through both macroscopic and microscopic observations is necessary. In case of hermaphroditic fishes, macroscopic observation may not provide the correct information of the germ cell development during gonadal maturation and has its own limitations [3,4]. Hence, microscopic observation is considered as an important method to get detailed information on the reproductive mechanism of such a fishes. Histological observation can provide information on the internal structural changes in the germ cells. The Pterois, including P. miles and P. volitans, are gonochoristic with males and females, exhibiting minor sexual dimorphism during spawning [4]. The two genders are morphologically identical and thus cannot be distinguished visually. Males typically grow larger than females with the largest male lionfish recorded as 476 mm total length [5]. The red lionfish are external fertilizers that produce a pelagic egg mass following a courtship and mating process that is not well documented. Like many reef fishes, red lionfish larvae are planktonic. After a few weeks in the plankton stage, larvae settle onto reefs as juveniles. In order to proceed with the artificial means of reproduction and to produce good quality eggs, it is necessary to have basic information on reproductive biology of the species. Information on the reproductive biology of Pterois sp completely is not available. Therefore, the present study has been taken up on the reproductive biology of P. volitans from Cuddalore coast.
Introduction
The success of any fish species is ultimately determined by the ability of its members to reproduce successfully in a fluctuating environment and thereby to maintain the viable population. Information on the reproductive biology of the candidate species is very much essential for the development of aquaculture industry. The dynamic metabolic activity of a reproduction in most of the fishes and it involves sequential changes in the germ cells. The pattern of these changes in the gonads is typical for each species [1,2]. To understand the physiology of reproduction, the study of the seasonal developmental changes of gonads through both macroscopic and microscopic observations is necessary. In case of hermaphroditic fishes, macroscopic observation may not provide the correct information of the germ cell development during gonadal maturation and has its own limitations [3,4]. Hence, microscopic observation is considered as an important method to get detailed information on the reproductive mechanism of such a fishes. Histological observation can provide information on the internal structural changes in the germ cells. The Pterois, including P. miles and P. volitans, are gonochoristic with males and females, exhibiting minor sexual dimorphism during spawning [4]. The two genders are morphologically identical and thus cannot be distinguished visually. Males typically grow larger than females with the largest male lionfish recorded as 476 mm total length [5]. The red lionfish are external fertilizers that produce a pelagic egg mass following a courtship and mating process that is not well documented. Like many reef fishes, red lionfish larvae are planktonic. After a few weeks in the plankton stage, larvae settle onto reefs as juveniles. In order to proceed with the artificial means of reproduction and to produce good quality eggs, it is necessary to have basic information on reproductive biology of the species. Information on the reproductive biology of Pterois sp completely is not available. Therefore, the present study has been taken up on the reproductive biology of P. volitans from Cuddalore coast.
Materials and Methods
The present study was carried out at the Cuddalore coast, Tamilnadu from January, 2012 to December, 2012. Adult fishes of P. volitans were collected by using gill-nett, trawlers, hooks and seafood markets of selective landing centres. Further, haphazardly selected ovaries were preserved in 5% formalin and processed according to standard histological methods [6] to investigate pathologies associated. Transverse sections were cut from the same region in the centred of each ovary. The light microscope (LM) was used for the examination. All measurements are in micrometres. The fish nomenclature was followed by Fish Base [7]. The size at 50% maturity was then obtained by substituting P=0.5 in the equation.
to understand that whether fish has attain the maturity and able to produce the eggs in the spawning period. Present observation was made to study the growth of fish and gonadal development along with egg laying capacity (fecundity) and when the fish attains its first maturity.
Estimation of fecundity
For the estimation of fecundity, gravimetric method was applied. Fresh gonads were removed from the fish within a few hours of capture and their reproductive stage was recorded using macroscopic observation. Gonads obtained from recreational fishers could usually be weighed in gm. Two or three transverse cuts were then made through each gonad to ensure proper medium. Gonado-Somatic Index was observed high in fixation.
Fresh samples
Length (cm) and weight (gm) of each fish was measured. Total length (TL) is measured to the tip of the mouth to the end of the tail. Developments of matured oocytes were measured with the help of Oculometer. Gonado-Somatic Index and fecundity have been calculated during the study.
Fecundity (F)=Total wt of the ovary/Wt of sub sample X No .of mature eggs (ova) in sub-sample
Collection method and preservation
Live fishes collected by using fishing vessels were measured accurately to nearest millimetre (mm) for total length, standard length and total weight (gm). Each fish was dissected to remove the gonads. The dissected tissues were covered with aluminium foil, and packed in labelled 4×5 cm polythene bags. The polythene bags were preserved at -20 ºC until the landing of the vessel. After reaching the shore, all the samples were loaded in an ice box and transferred to the laboratory. The ovaries preserved in polythene bags were taken and their weights were recorded up to milligram (mg) level by using electronic balance (Sartorius) for the determination of Gonado-somatic Index (GSI). The GSI for each fish was calculated using the formula of [8,9]. The range and average values of GSI were calculated for each maturity stage. The 'pondreal index' or 'condition factor,' K for each fish was calculated using the formula suggested by [10]. The range and average values of 'K' were determined for each maturity stage. Sex and stage of maturation were determined microscopically.
The gonads were assigned to three different maturity stages as suggested by [11]. The process of oogenesis was studied by utilizing histological preparations of ovaries from females belonging to different gonad maturity stages as recommended by [12] and adopted by [13]. After dissecting the ovary from fresh fish, the ovary was cut into pieces for easy penetration of fixative. The ovary pieces from fresh specimen were fixed in Bouin's fixative and embedded with molten wax (58 °C melting point). The sections (5 mm thick) were stained with Delafield's haematoxylin and counter stained with 1% aqueous eosin.
Oocyte diameter measurements were taken from ovaries belonging to various developmental stages and oocyte size-frequency profiles were constructed to trace the development of ova from immature stage to ripe condition [13][14][15][16]. Fecundity estimates were based on sub sampling of unbiased samples of ovaries from gravid fish collected during the peak spawning period as recommended by [17]. The relationship between the fecundity (F) and total length (L), fecundity and total body weight (W) as well as fecundity and total gonad weight of the fish were determined using regression equations.
Results
The reproductive system of females of P. volitans includes a pair of ovaries, continued into an oviduct and ends in genital pore. The ovaries are paired egg sacs located behind the stomach and duodenum, below the swim bladder and just above the intestine and connected to it by mesenteries. Each ovary consists of a hollow sac. The right and left lobes are usually unequal in size. Right ovarian lobes are relatively larger than the left; both of them join posteriorly and descend as an oviduct to open in the genital pore immediately behind the anus. The urinary bladder is closely bound to the posterior face of the common oviduct. Supporting mesenteries continue forward from the anterior end of each gonad as ligaments that join a complex of ligaments and mesenteries at the anterior end of swim bladder.
P. volitans ovary is the cyst ovarian type in which matured eggs will be released into the ovarian cavity during the ovulation; the ova will pass through oviduct on their way to go out at the genital pore. The genital pore is seen as a smaller pore behind the anus which would be bigger and pinkish during spawning season. The wall of the gonad is covered externally with a peritoneal layer. The tunica albuginea has an intermixture of longitudinal, oblique and circular muscle fibres.
Morphological classification of the ovary
Stage I: The ovary in the immature stage I is relatively small, translucent and white pinkish in colour ( Figure 1).
Stage II:
Mature resting female/maturing female stage II of P. volitans is defined as an ovarian stage II that had undergone extensive vitellogenesis and recovered into resting state. The ovary is larger than the previous stage and white brownish in colour ( Figure 2).
Stage III:
Stage III (ripe) is defined as the ovarian stage III in which active vitellogenesis takes place in preparation for spawning in the mature active female/ripe female. The ovary occupies 2/3 rd of the body cavity and is yellowish in colour. Oocytes are in stages 1, 2, 3 and 4 with stage 3 ovary dominating during early development of this stage ( Figure 3).
Gonado-somatic index (GSI):
In the present study, GSI values of P. volitans showed correlation with the maturation of gonad. The immature ovaries in the maturity stage I showed a GSI value of 0.062, the value was 0.234 for the maturing ovaries in the stage II and in the ripe stage ovaries of the maturity stage III, the value was 3.064 ( Figure 4).
Males
Immature: Testes are appeared in thread-like structure and present within a transparent membrane (Figures 5 and 6).
Developing: Testes developed uniformly as ribbon-like structure.
Surface of testes appears smooth and uniformly textured (Figures 5 and 7). Stage III-Mature ovary: The size of oocyte diameter in this stage was from 80 to 520 μm. The oocyte expands generally and regains its rotundity. The nucleuses are also increased in relation to its size. The ovary is containing early and late vitellogenic oocytes. The stage 4 oocytes are abundant in the ovary (Figure 12).
Stage IV-Spawning ovary: Ovaries are larger in size and filled the body cavity. Most of the eggs are the transparent (hydrated) though some opaque eggs may remain. Eggs are extruded from the body under slight pressure or are loose in the ovary and easily separated from each other ( Figure 13).
Stage V-Spent ovary: Ovaries are larger, but flaccid, watery, and
Histological observation of the ovaries (Based on the procure of oocyte)
Stage 1: The chromatin-nucleolus stage was comprised of the youngest and smallest oocytes. The large nucleus was surrounded by cytoplasm. The oocytes are remained strongly basophilic, and were deeply stained purple with haematoxylin. Oocyte diameters are ranged between 0.04 to 0.21 mm (Figure 16).
Stage 2:
In the pre-nucleolus stage, the cytoplasm had become less basophilic, and stained pale with haematoxylin. At the end of this stage, a number of nucleoli of different sizes were situated in the periphery of the nucleus. Oocyte diameter was ranged from 0.1 to 0.25 mm (Figure 17).
Stage 3:
In the primary yolk stage, the size of oocytes has become larger, but they still stained with hematoxylin. Oil-droplets and yolk vesicles began to appear in the cytoplasm. Some yolk globules began to appear in the cytoplasm. Oocyte diameter was ranged from 0.22 to 0.4mm ( Figure 18).
Stage 4:
In the secondary yolk stage, the accumulation of ova resulted in the rapid growth of oocytes. Yolk globules and oil-droplets rapidly increased in size and number. Oocyte diameter was ranged between 0.32 to 0.84 mm ( Figure 19).
Stage 5:
In the tertiary yolk stage, yolk globules and oil-droplets are continued to increase in size and number. The nucleus which is located at the centre of the oocyte was spherical, and the nucleus was irregularly shaped. Oocyte diameter was ranged from 0.76 to1.9 mm ( Figure 20).
Stage 6:
In the migratory-nucleus stage, the nucleus had moved generally reddish. Scattered unspawned eggs can be seen. Ovaries that are "Recovering" will appear red and contain scattered eggs, but will not be large or quite as flaccid as very recently spawned ovaries, and should be classified as "Early Developing" (Figure 14).
Observation of ovaries
The development of oocytes of P. volitans can be classified into 8 stages (Figure 15) based on cytological characteristics of cells as described below. toward the animal pole of the egg, and a few larger oil droplets were found. The oil-droplets first migrated towards the centripetal nucleus. Oocyte diameter was ranged from 1.6 to 2.3 mm (Figure 21).
Stage 7:
In the mature stage after germinal vesicle breakdown, yolk globules were fused with each other in the peripheral cytoplasm. Oocyte diameter was ranged from 2.2 to3.2 mm (Figure 22).
Stage 8:
In the postovulatory follicle stage, various types of postovulatory follicles with different morphological features were found. Postovulatory follicles were characterized by a large follicular lumen, formerly occupied by the oocyte. It gradually lost its lumen and was invaded by follicular cells (Figure 23).
The vitellogenic oocytes are continue to expand and reach maximum attainable size before ovulation. The nucleus is well defined in early stage 4 ( Figure 21). Yolk vesicles are prominent and usually surround the nucleus in early stage 4 and coalesce towards the centre, when nucleus loses its definition. These yolk vesicles are usually evident in late stage 4 near the oocyte periphery. Acidophilic yolk globules largely replace the basophilic cytoplasm in early stage 4 and become large and well developed in mid stage. The yolk globules coalesce in late stage 4 and present a smooth acidophilic appearance.
Relationship between fecundity and weight of ovary
The number of eggs was plotted against the weight of ovary in a scatter diagram (Figure 24). It was found that the fecundity generally increases with increase in weight of the ovary. The relationship between fecundity and gonad weight in P. volitans was linear ( Figure 24). The regression of fecundity on gonad weight (GW) can be expressed as F=9387.9GW+34026 with an r 2 value of 0.5723. The values indicated that the correlation was significant.
Relationship between fecundity and total weight of P. volitans
The observed values of fecundity for 25 specimens were plotted against the weight of fish in (Figure 25). The relationship between fecundity and weight of fish in female P. volitans was linear and it showed a gradual increase of fecundity with increase in total weight. The regression equation of fecundity on total weight can be expressed as F=11.586TW+72163 (F=fecundity; TW=Total weight) with an r 2 value of 0.0115.
Relationship between fecundity and total length of P. volitans
The number of eggs produced by individuals of P. volitans was plotted against the length of fish ( Figure 26). In the present study, fecundity showed low correlation coefficient with the total length of the fish. The regression of fecundity and total length can be expressed as F=677.14TL+56947 (TL=Total length) and r 2 value was 0.0217.
Relationship between fecundity and standard length of P. volitans
The relation between fecundity and standard length of fish was tested by plotting the observed values in a scatter diagram ( Figure 27). In P. volitans, it showed a linear regression. The regression of fecundity and standard length can be expressed as F=556.21SL+63141 (SL=Standard length) and r 2 value was 0.01.
Discussion
The observations in the present study showed that histological changes in the ovary with the different gonadal developmental stages were recorded in Pterois sp of P. volitions. Reproductive studies of the fish require knowledge of the stage of the gonad development in the teleosts. The structural alterations were observed in the P. volitions oocytes during the oocyte development by the histological studies. In this study, the oocyte developments of the P. volitions were divided into three stages. Reproduction involves changes in growth and development of oocytes during the process of gonad maturation. With the advancement of maturation, oocytes accumulate energy reserves and enlarge further for the onset of embryogenesis. In the present study, P. volitans oocyte increase in size from stage I to stage III of gonad maturation. [18] observed that oocyte increased in size with the progression of gonad maturation in E. malabaricus. They have reported that the size of the oocyte increased from 0.28 to 0.41 mm with the advancement of vitellogenesis. The above results are similar to the observations made in the present study on P. volitans. [19] have noticed that in E. tukula, oocyte diameter increases from immature stage (120 μm) to ripe stage (552 μm), which is very similar to P. volitans. The egg diameter of E. morio was found to be less than 1 mm [12]. [20] have found that in E. guttatus, with the maturation of the gonads, the egg diameter varied between 0.70 and 0.90 mm. [21] have also observed similar trend in oocyte cyclic development in the immature oocyte (54 μm) to ripe oocytes (897 μm) in red grouper, E. morio. In the present study, the largest oocyte diameter was 650 μm in the ripe stage ovary of P. volitans. [22][23] observed the largest oocyte diameter was 600 μm in P. volitans. [24] reported eggs of 0.92 mm diameter in E. striatus.
Fecundity information of a species is essential for estimating seed production capacity and spawning of the species concerned. Fecundity of the individual fish is determined from the total number of mature ova that are destined to be shed at the ensuing spawning season. In the present study, P. volitans gonad weight in relation to the total fecundity showed a significant linear relationship. [25] observed similar relationship between gonad weight and fecundity in P. volitans and E. bleekeri. [26] reported that the fecundity is very closely related to the weight of the gonads in E. aeneus. The total body weight of P. volitans showed a low correlation coefficient with the fecundity. [27] also observed similar relation with total body weight and fecundity in E. malabaricus. It may be due to the fact that weight of the ripe gonads in relation to the total body weight of the fish is small. Fecundity in P. volitans showed linear relationship with total length and standard length of the fish. It has shown low correlation coefficient, r 2 of 0.0217 and 0.01 respectively compared to the gonad weight (r 2 =0.5841). Tessy (1994) made similar observations in E. diacanthus and E. bleekeri. [26] have also observed low coefficient of correlation with the fecundity and standard length in the grouper E. aeneus. However, [28] found correlation with the standard length and fecundity in P. volitans from the Pacific Ocean.
In the present study, the average fecundity of P. volitans estimated was 75,547. Highest fecundity recorded in the present study was 1, 45,755. This observation agreed with previous reports; [29] estimated the total potential fecundity in E. tauvina, as 258.9 million. [30] found that fecundity of P. volitans in the Pacific Ocean ranged from 63,000 to 2,33,000. Bouain and Siau (1983) reported that for equal sizes (standard length=44 cm), E. saeneus (Fecundity=0.64 million) was fecundity more than E. guaza (F=0.60 million) and E. salexandrinus (F=0.43 million). Estimates of potential fecundity in E. tauvina ranged from 0.85 million for a fish of 35.1 cm long to 2.9 million for a fish of 62.3 cm long [31]. Hamsa et al. [25] reported that the average fecundity of P. volitans was 57,458 and the highest fecundity was 1,65,000.
The condition factor (K) is a measure of fish energy reserves. Condition factor values follow internal variations and seasonal cycles [32]. In the present study, condition factor values are in the range of 1.15 to 1.61 in P. volitans. Condition factor has increased in P. volitans from stage I to stage III of gonad maturation. [16] reported an increase of conditional factor with the advancement of maturation in Mugil cephalus. [33] have also observed an increase of condition factor with the progress of the reproductive season in the fish, Diplodus puntazzo. The state of maturity of a fish may be determined by the size of the ovaries. Gonado-somatic index (GSI) indicates the stage and readiness of the ovary for maturation and spawning. Throughout maturation, the GSI values of Dentex dentex females were much higher than males implying a greater proportion in body reserves were allocated to the gonads [34]. Gonadosomatic index has been used by many earlier investigators like [35] explain the degree of ripeness of the ovary in a number of fishes. In the present study, the values of GSI for P. volitans have showed increasing trend from immature (0.06%) to ripe stage (3.06%). [36] also observed GSI values increasing from 0.43% to 5.2% with the maturation of gonads in E. malabaricus. The GSI values obtained in the present study correlated well with the GSI values observed by [37,38] in various size groups of P. volitans. [21] noticed greatest variations in the mean gonadosomatic index of female red grouper, E. morio from 0.27% to 2.14 % in maturing and ripe running stages.
Histological changes in the ovary of several species of groupers have been shown to correspond well with changes in the GSI. The GSI value increases with corresponding histological changes were also noticed in E. morio [39] and in E. merra [40]. The present study clearly indicates that the GSI values of female P. volitans have also showed similar increasing trend associated with histological changes.
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Domain: Environmental Science Biology
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Appraisal of Seedling Stand Vegetation with Airborne Imagery and Discrete-Return LiDAR – an Exploratory Analysis
The potential for combined use of airborne discrete-return LiDAR and digital imagery in the classification and measurement of common seedling stand vegetation was examined in southern Finland (61°50 ́N, 24°20 ́E). Classification was based on spectral and textural image features in addition to geometric and radiometric features of the LiDAR. The accuracy of leaf-on, LiDAR-based terrain elevation models was tested as well as the accuracy of LiDAR in the measurement of vegetation heights. LiDAR-based canopy height and the range-normalized intensity of the LiDAR were strong explanatory variables in vegetation classification. Interspecies variation was observed in the height measurement accuracy of LiDAR for different tree, shrub and low vegetation canopies. Elevation models derived with 1−15 pulses per m2 showed an inherent noise of app. 15−25 cm, which restricts the use of LiDAR in regeneration assessment of very young stands. The spatial pattern of the competing vegetation was reproduced in classification-based raster surfaces, which could be useful in deriving meaningful treatment proposals.
Introduction
The objectives of forest regeneration and early tending of seedling stands can include optimization of the timber production profitability, consideration of scenic preferences, control of fungal root and foliage disease spread, control of nutrient leaching as well as biodiversity and game management issues (e.g. Piri 2003, Huuskonen and Hynynen 2006, Karjalainen 2006). Forest regeneration and tending of seedling stands involves direct costs. Economically, the investments are paid back in increased harvesting revenues and a shortened rotation period. The other ecological and social effects may be more difficult to confirm and quantify. The success of a regeneration is assessed some years after planting, sowing or natural regeneration felling, and the result is typically compared with a target stand, which may be defined by a preferred species/size distribution and spatial pattern of crop trees. The future species mixture is controlled by precommercial thinnings and possible complementary planting. An optimal timing of the first precommercial thinning can help in avoiding a costly second precommercial thinning and it affects the economics of the first commercial thinning (Huuskonen and Hynynen 2006). There may be considerable intrastand variation in the stand conditions that, if accurately known, could be utilized for optimization of forest and stand management. The benefits should however pay for the inventory costs.
Remote sensing (RS) of young stands is an attractive option although the emphasis, concluding from the large volume of topical research, has been on the development of methods for the appraisal of mature, commercially important stands. Airborne laser scanning (ALS, LiDAR), especially, is increasingly used in the measurement of timber volume in boreal forests (Naesset 2004, Suvanto et al. 2005). ALS was combined with analysis of aerial images for enhanced results (Maltamo et al. 2006).
Regeneration assessment in managed boreal forests using airborne RS has mostly been concerned with assessment of the density, size, spatial distribution and condition of conifer seedlings using leaf-off very-high resolution imagery (Hall and Aldred 1992, Pouliot et al. 2002, 2005, 2006, Pouliot and King 2005). Hall and Aldred (1992) used visual interpretation of the stocking, density and species in a prestratification and two-stage random sampling setup (Boreal Zone, Saskatchewan, Canada). Photo-plots were assessed at a scale of 1:500 from stereo pairs that were taken using a helicopter-mounted camera. The detection rate of crop trees was dependent on the seedling height, varying from 0% to 94% for height classes between 0-15 cm and 201+ cm. Species identification accuracy between white spruce (Picea glauca (Moench) Voss) and jack pine (Pinus banksiana Lamb.) was also dependent on the size of the seedling. A scale of 1:5000−1:8000 in which 30-cm-wide crowns would be visible was suggested for the prestratification. Visual interpretation methods tend to be time-consuming and require trained personnel, which has hindered their widespread use. However, the results have provided a basis for the development of automated methods. Pouliot et al. (2005) used semiautomatic tree detection/delineation in 6-cm resolution digital CIR images of 5−10-yr-old planted stands in Ontario (cf. Pouliot et al. 2002, Pouliot andKing 2005). The experiment included variation in competing vegetation. The tree detection rates were 48−70% for heights between 0.06 m and 3.0 m. The abundance of competing vegetation also affected tree detection as well as image resolution, parameters of the algorithm and the spatial arrangement of trees.
Competition by other flora was assessed using both manual and automatic interpretation of 2-cm-resolution leaf-off images in Pouliot et al. (2006). Leaf-on large-scale aerial photography was used for identification of general cover types (Pitt andGlover 1993, Pitt et al. 2000) and veryhigh-resolution digital imagery was tested for cover and leaf-area estimation by Haddow et al. (2000). The requirement for very-high-resolution imagery, valid for both leaf-on and leaf-off, has hindered the adoption of RS in operational regeneration assessment, because the acquisition costs become excessive. Future use of small unmanned aerial vehicles may lead to a reduction in costs. However, in addition to the reduction in data acquisition costs, more reliable and automated analysis methods are needed for the ill-posed task of seedling stand RS.
The potential use of ALS in seedling stands is largely unexplored. Naesset and Bjerknes (2001) examined sparse ALS (1 pulse per m 2 ) for the estimation of tree height and the stem number in 2−6-m-high stands in Norway. The mean height (H) could be derived from the LiDAR features but the estimates of stand density (S), which is important for treatment proposals, were inaccurate. In Finland, Närhi et al. (2008) tested sparse (0.5 pulses per m 2 ) ALS for the derivation of treatment need in three classes (no treatment, within 5 years, immediate) in young Norway spruce (Picea abies (L.) H. Karst) stands, with H of 2−8 m. The three-class treatment need was not assessed in the field, but derived later, using estimates of S and H. Furthermore, regression functions having heightrelated LiDAR metrics as independent variables were used for the estimation of H and S. The classification accuracy was 71.8%. RMSEs of H and S were 16% and 45%, respectively.
Digital, metric multispectral (MS) frame and linear sensors are currently replacing film-based aerial cameras. These new sensors are perceived as well matched for photogrammetric automation due to the improved geometric and radiometric properties of the imagery (Leberl and Gruber 2005). It is feasible to take multiple images of the target and to use this redundancy for enhanced classification and canopy surface reconstruction (Hirschmugl et al. 2007). In forest applications, digital passive sensors are entrusted to improve the species identification task, which is the current bottleneck (Olofsson et al. 2006, Larsen 2007). ALS systems are also evolving with improvements in pulse density, range and orientation accuracy and in the measurement of backscatter reflectance (intensity). Many of the technical problems that were still present in the late 1990s and that resulted in high costs (e.g.accurate sensor orientation, accurate field positioning, elevation modelling and lack of digital workflow; Pitt el al. 2000) now have new solutions (e.g. Korpela 2006, Korpela et al. 2007).
Our aim was to assess the potential of the new data sources, digital MS imagery and discretereturn LiDAR data in the classification and measurement of seedling stand vegetation to support seedling stand management and the derivation of meaningful treatment proposals. For the first time, we tested a newly introduced digital frame camera together with high-density, small-footprint LiDAR in the classification of seedling stand vegetation. To minimize geometric noise in the object-tosensor mapping and data analysis, we used field samples that were positioned at centimetre-level absolute accuracy. Similarly, image orientation was determined with the utmost precision. The spatial sampling density of the imagery and the LIDAR were higher than what perhaps would be affordable in practice. However, it is justified for exploring the upper limits of accuracy achievable. The spectral image features and features extracted from the LiDAR were tested in the classification of individual plant species and species-classes that could be useful in the derivation of treatment proposals. Our exploratory analysis aimed at basic information and we omitted the variation in image-object-sun geometry (bidirectional reflectance) that affects signals in the images. The thesis is that LiDAR is effective in the estimation of canopy heights. However, in seedling stands the inherent measurement errors may become prominent. Errors in a digital elevation model (DEM) are also directly propagated to canopy height observations. A specific objective was to examine the accuracy of LiDAR-based DEMs in seedling stands and the accuracy of canopy height estimation for common plant species. Typically, the tallest trees in young conifer plantations are deciduous and are removed in precommercial thinnings. We also examined whether the LiDARbased height observations in combination with image features could be useful in separating the deciduous trees from conifers and other types of vegetation and biotic material. High canopy closure of the broad-leaved trees as measured by LiDAR, would also indicate immediate treatment need. The study confines to artificially regenerated coniferous Scots pine (Pinus sylvestris L.) and Norway spruce stands at the ages of 3−13 years.
Aerial Imagery and LiDAR
The study area is in southern Finland (61°50´N, 24°20´E) near Hyytiälä Forest Station. Leaf-on aerial imagery consisting of 82 exposures was taken with an UltraCAM D digital frame camera from three flight lines (Table 1). Each exposure is a five-channel perspective image that is combined from 13 similar CCD arrays (4000 × 2700, 9-µm pixels) that are almost simultaneously exposed through eight separate lenses equipped with absorption filters. Four subimages constitute an MS image in R, G, B, and NIR that has a lower resolution in comparison to a panchromatic (PAN) image. The sensitivity curves overlap: 390−530 nm (B), 470−660 nm (G), 570−690 nm (R), 670−940 nm (NIR) and 390−690 nm (PAN). The PAN image is fused from nine CCD arrays that are exposed through four optical cones (Markelin et al. 2005, Honkavaara 2008). We used the level-2, internally preprocessed 16-bit images, in which a small number of defective pixels and rows were corrected by the vendor's processing software.
The exterior orientation of the images was determined using 86 XYZ and 4 Z control points (CPs) and in-flight GPS observations of projection centre positions in a bundle block adjustment. The CPs were treated as error-free and the GPS observations were assigned an a priori accuracy of 0.10 m in the aerial triangulation. Tie points were measured manually and the 765 image observations were assigned an a priori weight of 6 µm. The image residuals of the solution had an RMSE of 5.0 µm. Tables 2 and 3 list the standard deviations of the unknowns. The SDs of the differences in the X, Y and Z coordinates of the CPs were 0.06 m, 0.04 m and 0.16 m, respectively. The inferior accuracy in the Z coordinate is due to the unfavourable base-height ratio of the UltraCAM images.
In a Monte-Carlo simulation with Gaussian image orientation errors, derived using the variance-covariance matrices of the bundle block adjustment, an error-free XYZ point mapped with an average 4-µm error in the PAN image, which corresponds to a half-pixel.
Three leaf-on discrete-return LiDAR (1064 nm) datasets from 2004, 2006 and 2007 were used (Table 4). Acquisition in 2006 occurred 1 week prior to the aerial photography and 4 weeks before the fieldwork. The LiDAR of 2006 was used for vegetation mapping and terrain modelling was tested using all LiDAR datasets.
The geometric accuracy and relative matching of the image and LiDAR data sets were assessed by superimposing LiDAR points in the aerial images and by using surfaces and borders for which the XYZ coordinates were measured using Network RTK (Wanninger 2005) satellite positioning. The XY accuracy of the LiDAR datasets was 0.25 m or better. The Z accuracy was better than 0.1 m. The point densities showed considerable spatial variation, due to the scanning geometry (Fig. 1).
Observations of Vegetation Field Samples
The field data were collected in August, 2006 in six seedling stands, where moraine soils prevailed and soil preparation by mounding or harrowing was applied (Table 5, Fig. 2). Two sampling schemes were used. First, each stand was sampled along lines with an entire coverage. These 947 samples were selected at approximately equidistant locations and represented homogenous samples of the 27 targets listed in Table 6. A second sampling included all trees and shrubs (n = 645) inside square-shaped plots that were subjectively located to include variation in stand conditions. All samples were positioned and measured for height and species. Network RTK positioning, which has an accuracy of 3−5 cm in XYZ, was used (Häkli 2004, Wanninger 2005). The sample height was measured in 0.1-m nominal accuracy, using a reference pole (Fig. 3). Altogether 27 classes were defined, which were further divided into four operational classes (OCs) that were considered important for the derivation of meaningful treatment proposals in coniferous seedling stands (Table 6). The OCs comprised 1) coniferous trees, 2) broad-leaved trees for potential removal, 3) low vegetation and 4) abiotic material.6). The box is drawn from the 25th to the 75th percentile and the height range is marked by the line.
Image Features
As a result of the imaging geometry (diagonal field-of-view 60°) and solar angles (zenith ~60°, azimuth ~108°), the phase-angles ranged from 33° to 89°. Bidirectional reflectance effects were thus present in the images and undoubtedly affected the spectral values observed. Fig. 4 illustrates the effect for Scots pine and Norway spruce seedlings. Shadowed targets also prevailed in the images, due to the low solar elevation. In all, the field observations mapped to 21 images and 5467 image points. The extraction of nine image features was performed with an inhouse photogrammetric workstation. The R, G, B and NIR values of the nearest pixel formed the pixel-level spectral features in 27-cm resolution as well as the normalized difference vegetation index, NDVI: The NDVI separated vegetation effectively in the shaded areas, probably due to the high dynamic range of the images. The textural features PAN-Mean , PAN SD , PAN Min and PAN Max were calculated from 3 × 3 pixel windows of the 9-cm resolution PAN image.
To enable shadow masking and analysis of the effects of shadowing, the first author measured the training data -XYZ points in shadow and direct light (424 + 410), using manual least-square ray-intersection of the aerial images. Random selection of points in all stands was pursued. Image features were extracted for these points and Fisher's linear discriminant analysis (LDA) was applied for the binary classification. The classification accuracy was 96% with NIR, NDVI, G, R and B (Fig. 5). The errors were mainly due to misclassifications in the training data. Namely, a portion of the points that mapped to more than two or three images, i.e. to additional oblique views, were occluded by other (higher) targets.
Mapping of the field observation to the nearest image pixel was examined by systematically lowering the vegetation sample. We hypothesized that this would result in better positioning of the image features in non-nadir views, since the pixels would capture more of the object. The shift downwards was proportional to the object height: ∆Z =h × (1lc). Parameter lc was assigned values between 0 and 0.35 in steps of 0.05 and the image features were stored for analysis. The number of observations classified as being in direct light was maximal when lc was 0.25, which was later applied for trees, i.e. classes 1−9. The image features were highly correlated, except for the NIR band (Table 7).
LiDAR Features
Three LiDAR features were extracted for each field observation, using the first-return points of the 2006 ALTM3100 data: -Proportion of ground returns, PGR -LiDAR-based height above ground, h LiDAR -Intensity of the nearest pulse, INT norm .
We hypothesized that PGR measures canopy closure and leaf density. It may be used to separate dense broad-leaved species from coniferous canopies and tall grasses.h LiDAR is the height of the nearest LiDAR return. The height growth PGR was calculated from points inside a 0.5-m radius. The ground elevation was taken from accurate GPS field measurements. LiDAR points that deviated < 0.5 m from Z ground were considered as ground returns; h LiDAR1 was the height of nearest LiDAR point. The intensity of the nearest LiDAR point was normalized for the variation in scanning range: In Eq. 2, a was 2.5. Theoretically, a = 2 for homogenous surfaces larger than the LiDAR footprint, a = 3 for linear objects and a = 4 for individual large scatterers (Ahokas et al. 2006, Kaasalainen et al. 2007). The value 2.5 is a compromise that was determined using artificial surfaces and natural targets, including understorey shrub, lichen and moss vegetation (Korpela, 2008). R ref in Eq. 2 is an average reference range, which was 839 m. The highest INT norm values were observed for R. idaeus (raspberry, class 10 in Fig. 6). P. sylvestris (class 2) had a lower average LiDAR intensity than P. abies (1), but it may have resulted from mixing with other low vegetation because the P. abies seedlings were rather short. S. aucuparia (5) had a clearly higher intensity than Betula (3), which did not separate from P. abies (1) or P. tremula (4) (Fig. 6.).
Linear Discriminant Analysis for Object Classification
The Stepdisc and Discrim discriminant analysis procedures in SAS/STAT (SAS Institute Inc., Cary, NC, USA) statistical software (version 9.1.3)were used for feature selection, classification and leave-one-out type of cross-validation.
Stepdisc performs a stepwise feature selection and Discrim was later used for LDA.
The classification performance was measured with the proportion of correctly classified objects and the simple kappa (Cohen 1960): In Eq. 3, P(A) is the proportion of correctly classified objects and P(E) the expected proportion from random classification.
In LDA, it is assumed that the multivariate within-class distributions are approximately 6). The box is from the 25th to the 75th percentile and the range is depicted by the line.
normal. Multivariate normality (MVN) was tested using Mardia's skewness and kurtosis tests as well as the Henze-Zirkler's test. The data did not fulfil all of the MVN assumptions, mainly because of the skewness of the distributions. The data was assumed to be outlier-free making LDA applicable.
Terrain Modelling
TerraModeler by Terrasolid (Jyväskylä, Finland) was used for processing the 2004 LiDAR data into a 1-m raster DEM, referred to as 2004-Terra. TerraModeler is based on three principal parameters (Axelsson, 2000) that were tried in 100 combinations and a best-case DEM with an RMSE of 0.27 m was selected for the tests here. The RMSE was observed in 8329 reference height points in mature forests across the same study area (Korpela and Välimäki, 2007). An in-house DEM algorithm was applied for testing the effect of LiDAR point density on DEM accuracy, using all LiDAR datasets. The area in the algorithm flow is first divided into a raster, according to the parameter CellSize. The point with minimum Z is sought in each cell and all points that deviate less than the value of parameter Zbuffer from this minimum are stored for D-TIN estimation (Delaunay -Triangulated Irregular Network). This TIN is iteratively filtered for rising outlier peaks by removing nodes that can only 'be reached' by steep triangle facets. A threshold parameter, SlopeThreshold at 15°, was applied. The filtering is continued typically in 3−4 iterations, until the visual appearance is satisfactory (Fig. 7). Finally, the TIN is converted into a 1-m resolution raster model. This conversion introduces a small random error of 0.03−0.07m. The TIN-estimation was done with fast, streaming algorithms by Isenburg et al. (2006).
The DEM algorithm is not optimal, because it results in higher point densities in planar nonvegetated areas and is somewhat limited in capturing fine topographic details. The parameter CellSize should be minimized to reproduce the details. However, the reduction in CellSize also reduces the probability that the lowest point in a cell is a true ground observation, which may lead to bias. If parameter Zbuffer is set to a very high value, it causes the DEM to float above the true ground.
Raster Analysis of the Canopy Layer
To determine how the sensor fusion and object classification could be applied in practice, we carried out a raster analysis, in which the fully mapped plots were classified in a 0.5-m grid. LiDAR features at each grid point were computed such that the PGR was estimated inside a 1-mwide circle and h LiDAR and INT norm inside a 0.7m-wide square, using the highest LiDAR point. The image features were derived by mapping the highest LiDAR point to the image with the smallest off-nadir angle. The OC was determined, using LDA functions derived from eight image and LiDAR features and the training data of the particular stand. A reference grid was calculated, using the reference trees; in it, the OC was determined by the tallest tree in each grid cell.
Accuracy of Leaf-on LiDAR-based Elevation Models
In all, five DEMs were compared and were all above the reference ground (Table 9). The inhouse DEM algorithm was inferior in comparison to the performance of the TerraModeler algorithm with the same 2004 LiDAR data. Increase in point density from 1 to 15 per m 2 lowered the RMSE 36%, from 0.30 m to 0.22 m. The errors of the different DEMs were correlated (Table 10). We suggest that 36−64% of the random error variance could have been due to the properties of the reference data. The errors may also have been correlated due to the estimation method, since the same XY grid and values of parameter Zbuffer were applied in each stand. Part of the observed random error may also have been due to errors in the reference measurements made with Network RTK. In addition, the subjective selection of the line samples and penetration of the GPS antenna rod into the soil may have been sources of systematic errors in the reference data. However, the low vegetation probably reduced the number of true ground returns, thus lifting the DEMs upwards; while a considerable portion of the random errors were probably due to the small-scale variation in the relief, which cannot be captured by the DEMs. This inherent random error was 0.15−0.25 m and suggests that very low canopies cannot be reliably measured using LiDAR, because this inherent DEM noise propagates directly to the height estimates.
There may also have been small XY or Z offsets at the project and strip levels in the LiDAR datasets. It is evident that changes in low vegetation have occurred between 2004 and 2007 (vegeta-tion succession), which suggests that the results are most reliable for the 2006 DEM, the year of fieldwork. The errors were largest in stand 3, which has a dense 3−5-m-high canopy of sowed, planted and naturally regenerated trees on rather fertile soil that was harrowed in 1994.
Accuracy of LiDAR-based Canopy Heights
The use of h LiDAR resulted in underestimation of tree heights of from 19% to 39% or 0.54−1.09m (Table 11). The coefficients of variation (CVs) ranged from 17% to 37%. The height of the relatively large-leaved raspberry (R. idaeus) was underestimated by 29% but the precision was rather high, 13% in CV. Rosebay willowherb (C.angustifolium), which has a comparable mean height but a different leaf structure, was underestimated by 52% (CV = 13%). The results which are biased and rather noisy imply that the vegetation height measurement accuracy of LiDAR is restricted and dependent on the species.
Vegetation Classification Using LiDAR and Image Features
LDA was first applied in each stand for classifying the four operational target types (Table 12). The classification accuracy varied from 61.1% to 78.9% and was reduced if the shadowed targets were included (Table 13). NDVI and INT norm were strong features. Similarly, h LiDAR1 was significant in stands 1−4, in which the height of the broadleaved trees differed from that of the conifers. The best accuracy, 77.8% for the OCs was achieved in stand 6. The confusion matrix of this classification is given in Table 14.
The classification accuracy for all 27 classes using 3787 sunlit observations from all six stands, was 39% (κ = 0.28) with eight image and LiDAR features. With image and LiDAR features only, the accuracy fell to 28% (κ = 0.19) and 24% (κ = 0.15), respectively.
Raster Analysis
The raster analysis is illustrated in Figs.8−10. Since the 0.49-m 2 raster cells had overlaps in the 0.5-m raster and since the reference raster was computed using the stem positions, comparison of the two surfaces was not straightforward. The reliability of operational class 2 (the competing trees) was considered essential. Those parts of a conifer seedling stand that have a dense dominant canopy cover of broad-leaved trees are likely in need of immediate clearing. In stand 1, the classification accuracy was low and the classified grid overestimates the portion of conifers and partly fails in accurate determination of the broad-leaved canopy (Fig. 9).
The spatial structure of the canopy of the broadleaved trees was reproduced by the classifications in stands 2−4 (Fig. 10). Based on the results, we suggest that further processing of the classified raster surfaces (e.g.segmentation) will enable derivation of maps of intrastand treatment proposals.13). Stand 1.
Due to the random errors in terrain elevation and canopy heights, we feel that airborne LiDAR is not applicable in very young seedling stands. We suggest that multi-image matching (Hirschmugl et al. 2007) may be used to enhance the accuracy of canopy height (surface model).
The potential for use of semi-dense, smallfootprint LiDAR (6−9 p/m 2 , 0.3 m) in estimating the top elevation of individual plants and plant canopies in seedling stands was limited. Underestimation of heights was observed, which was dependent on the species. This complicates sensor fusion, because the LiDAR reflection point that is mapped to the images for the retrieval of image features is not necessarily from the outer, photovisible canopy; in addition the images and LiDAR view the targets in different geometries. The underestimation of tree heights varied from 20% to 40% with a CV of app.30%. The relative height difference of the conifer seedlings and the broad-leaved species, birch and aspen especially, can be 100% in seedling stands because of the differences in juvenile growth (e.g. Elfving 1982, Miina andSaksa 2008). If models are available 13).
for the prediction of juvenile height growth of trees, the model estimates can be used in the LDA classification of seedling stand targets, using the expectances and variances of heights and an empirical covariance structure of the other discriminant features. Naturally, the height estimates need to be unbiased or otherwise the classification will fail. The height growth difference of sprouting broad-leaved stools vs. trees regenerating from seed further complicates the use of LiDARbased heights in classification.
The 27 classes consisting of common tree species, shrubs, grasses, mosses and other surface types that typically occur in seedling stands could not be reliably classified using the tested image and LiDAR features. For the 27 classes, the overall classification accuracy was 39%. In the stand-level tests, the classification accuracy varied from 61% to 79% for the four operational classes of conifers, broad-leaved trees, other low vegetation and abiotic surfaces. These classes were considered sufficient for the determination of meaningful treatment proposals and stand projections. This rather high level of accuracy was attained with considerable training data. The raster analysis revealed that the spatial pattern of the broad-leaved trees is potentially retrievable, using sensor fusion and classification with image and LiDAR features. Further work with more representative field material (stand types, time of season) and different RS material (image scales, sensor types, sampling density) is required for the development of practical applications. For example, we suggest testing of the ADS40 multiview line sensor (Leica Geosystems, Norcross, GA, USA) because its colour registration (beamsplitter) differs from those in metric digital frame cameras (absorption filter). In general, digital cameras offer means of using redundancy for enhanced classification results and we suggest method development in multiview classification. This requires that the object classification occurs in 3D, which is easier to implement using LiDAR with images than by images alone (Hirschmugl et al. 2007). In addition, we suggest investigating the use of more complex image and LiDAR features for vegetation classification.
Range-normalized intensity of LiDAR was a strong explanatory variable in seedling stand vegetation classification. It is invariant to target location, unlike the image features used. Unfortunately, the intensity did not separate the three main species of forest trees in Finland. LiDARbased height was usable in stands, where the conifers were shorter than the broad-leaved trees. The spectral image features were not compensated for the varying image-object-sun geometry even though the spectral values varied with the imaging geometry and that the effect varied between species. This is explained by the varying bidirectional reflectance distribution (BRD) of different targets. Further work is needed to study these directional effects and their compensation, or preferably, utilization in an image-based species classification task. We thus suggest that BRD research be initiated to quantify these BRD effects and to separate them from biological noise (tree vigour, diseases, leaf density, epiphytic lichens etc.) and trends (tree size/age). Such basic research could also aid in constructing specific sensors (radiometric properties) for forest vegetation mapping. Currently, cameras and LiDAR sensors are mainly built for topographic mapping and are commonly used by foresters.
Traditional satellite image-based change detection procedures can be applied for estimating the proportion of deciduous trees in large regeneration stands (Häme 1991) or when existing stand database information supports the interpretation (Varjo 1997). The presence of small-scale structural variation, small size of regeneration areas and the lack of multitemporal data lead to the conclusion that the resolution of traditional (e.g. Landsat) imagery is not sufficient for reliable regeneration monitoring in Finland (Häme et al. 1998, Saksa et al. 2003, Pesonen et al. 2007). Instead of applying the new very-high-resolution (< 1 m) satellite images it may well be more cost-efficient to use digital aerial images, which have become readily available standard products offering extensive temporal and spatial coverage. The use of images from unmanned aerial vehicles should also be investigated.
In the present study, a semi-dense (6−9 pulses per m 2 ) LiDAR data and low altitude (1 km) images were used. Such data acquisition leads to high spatial sampling densities and radiometric accuracy (atmospheric effects) but the costs are high, on the order of 3−4 €/ha for large projects. For practical applications, training data will be needed from the field, which increases the costs. If absolute calibration of the spectral values of the images could be carried out, this would reduce the need for field samples (Honkavaara 2008). In addition, a priori information on the target area, e.g.knowledge of the stand treatment history and site conditions would likely enhance the RS results. Integration of the a priori information will require further research and method development.
Fig. 1 . Fig. 2 . Fig. 3 .
Fig. 1. Left: a 300 × 300-m map of LiDAR point density in seedling stand 6. The greyscale denotes density, 6−29 first-return points per m 2 for the combined data of 2006 and 2007. Middle and right: 17 × 17-m aerial views from the high-density area show the point patterns of the 2006 and 2007 data, respectively.20072006
Fig. 4 .
Fig. 4. NDVI phase-angle scatterplots for pine (left) and spruce (right). The phase-angle is the angle between the object-to-camera and object-to-sun 3D vectors.
Fig. 5 .
Fig. 5. Example of a 150 × 150-m false-colour aerial image of stand 6 and the corresponding shadow classification, in which the shaded pixels are in white.
Fig. 7 .
Fig. 7. Visualization of the nonfiltered and filtered D-TIN of seedling stand 1 over an area of 300 m × 200 m.
Fig. 9 .
Fig. 9. Left: reference grid of operational classes derived from the field-measured trees. Right: Classification using eight image and LiDAR features (cf. Table13). Stand 1.
Table 1 .
Characteristics of the aerial imagery.
Table 2 .
Standard deviations of the exterior orientation parameters, [rad] and [m].
Table 4 .
Characteristics of the LiDAR datasets.
obs , plot Area, m 2 Trees, n/ha Mean h, m Mean h, m Mean h, m
Table 6 .
Hotanen et al. (2000)lassified in the field and their division into four operational classes (OCs). R is the rank by occurrence frequency of understorey species on mineral forest soils in Finland. Similarly, F is the average frequency of occurrence and D the average coverage in stands less than 20 yr old according toHotanen et al. (2000).
Table 7 .
Pearson correlation coefficients of image features. All 5467 observations.
Table 8 .
Pearson correlation coefficients of LiDAR features. All 1592 observations.
Table 9 .
Accuracy of the elevation models, n = 1592.
Table 10 .
Correlation coefficients of the residuals of the five DEMs, n = 1592.
Table 11 .
Accuracy of h LiDAR excluding DEM errors. Classes with over 30 observations were included.
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Domain: Environmental Science Biology
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Survival and cause-specific mortality of female eastern wild turkeys in two frequently-burned longleaf pine savannas
Longleaf pine savannas have declined throughout the southeastern United States due to land-use change. Fortunately, natural resource professionals are currently restoring these ecologically and economically important savannas. Although efforts are underway to restore longleaf pine savannas, little information exists on female eastern wild turkey Meleagris gallopavo silvestris population dynamics in these systems. Therefore, we evaluated survival and cause-specific mortality of female eastern wild turkeys in two longleaf pine savannas in southwestern Georgia. We radio-marked 126 female wild turkeys during 2010–2013 and monitored their survival; 66 (52.4%) radio-marked females died during the study. We estimated causes of death for 37 mortality events with predation serving as the leading known cause of mortality, with 35.1% of mortalities attributed to mesocarnivore predation (e.g., bobcat Lynx rufus, coyote Canis latrans, and gray fox Urocyon cinereoargenteus) and 18.9% to great-horned owl Bubo virginianus predation. One female (2.7%) was hit by a vehicle. Seasonal survival estimates varied from a high during fall (Ŝ = 0.94; 95% CI: 0.86–1.00) to a low during spring (Ŝ = 0.76; 95% CI: 0.68–0.87). Survival of incubating females was 0.82 (95% CI: 0.71–0.93) and survival of nonincubating females was 0.67 (95% CI: 0.52–0.87). Annual survival was 0.55 (95% CI: 0.44–0.67). To ensure sustainable wild turkey populations in longleaf pine savannas, we suggest managers monitor relationships between survival and population productivity.
Survival and cause-specific mortality of female eastern wild turkeys in two frequently-burned longleaf pine savannas Andrew R. Little, John F. Benson, Michael J. Chamberlain, L. Mike Conner and Robert J. Warren A. R. Little ([email protected]), M. J. Chamberlain and R. J. Warren,Warnell School of Forestry and Natural Resources,Univ. of Georgia,Athens,GA 30602,La Kretz Center for California Conservation Science,Inst. of the Environment and Sustainability,Univ. of California,Los Angeles,CA,Joseph W. Jones Ecological Research Center at Ichauway,Newton,GA,USA Longleaf pine savannas have declined throughout the southeastern United States due to land-use change. Fortunately, natural resource professionals are currently restoring these ecologically and economically important savannas. Although efforts are underway to restore longleaf pine savannas, little information exists on female eastern wild turkey Meleagris gallopavo silvestris population dynamics in these systems. Therefore, we evaluated survival and cause-specific mortality of female eastern wild turkeys in two longleaf pine savannas in southwestern Georgia. We radio-marked 126 female wild turkeys during 2010-2013 and monitored their survival; 66 (52.4%) radio-marked females died during the study. We estimated causes of death for 37 mortality events with predation serving as the leading known cause of mortality, with 35.1% of mortalities attributed to mesocarnivore predation (e.g., bobcat Lynx rufus, coyote Canis latrans, and gray fox Urocyon cinereoargenteus) and 18.9% to great-horned owl Bubo virginianus predation. One female (2.7%) was hit by a vehicle. Seasonal survival estimates varied from a high during fall (Ŝ 0.94; 95% CI: 0.86-1.00) to a low during spring (Ŝ 0.76; 95% CI: 0.68-0.87). Survival of incubating females was 0.82 (95% CI: 0.71-0.93) and survival of nonincubating females was 0.67 (95% CI: 0.52-0.87). Annual survival was 0.55 (95% CI: 0.44-0.67). To ensure sustainable wild turkey populations in longleaf pine savannas, we suggest managers monitor relationships between survival and population productivity.
Longleaf pine Pinus palustris savannas are one of the most biologically diverse systems found in North America containing numerous species of flora and fauna (Alavalapati et al. 2002). Historically, longleaf pine savannas occupied over 30 million ha in the southeastern United States (Brockway et al. 2005, Van Lear et al. 2005, and were maintained by fire ignited by lightning or humans. These fires created a grassland-forb system and prevented hardwood encroachment (Komarek 1964, Pyne 1982, Kennamer et al. 1992, Robbins and Myers 1992. However, land-use changes (e.g. conversion from slower growing longleaf pine to faster growing loblolly pine Pinus taeda and slash pine Pinus elliottii; increase in agricultural practices and urban development) throughout the southeastern United States led to a decline in longleaf pine savannas (Frost 1993, Alavalapati et al. 2002, Van Lear et al. 2005. Fire suppression beginning in the late 1890s also led to changes in forest conditions (Alavalapati et al. 2002, Fowler andKonopik 2007).
Fortunately, natural resource professionals have recognized the importance of restoring longleaf pine savannas, which will potentially benefit a variety of species in the southeastern United States. Wild turkeys Meleagris gallopavo have historically been an important species present in longleaf pine savannas, and are adapted to the early-successional understory conditions created by periodic fire that also promotes insect abundance (Hurst 1981, McGlincy 1985, Landers and Mueller 1986, Exum 1988, Provencher et al. 1998). Prescribed fire is the primary tool used to reduce undesirable competing vegetation in longleaf pine savannas while stimulating growth and development of a diverse plant community in the understory (Waldrop et al. 1992, Cain et al. 1998, Barnett 1999, Steen et al. 2013. Various wildlife species found in longleaf pine savannas, such as the endangered red-cockaded woodpecker Picoides borealis and gopher tortoises Gopherus polyphemus are dependent on the use of fire to maintain open, park-like conditions for their survival (Alavalapati et al. 2002). Therefore, land managers commonly apply prescribed fire every 1-3 years to reduce hardwood encroachment and enhance grass and forb development in longleaf pine savannas (Glitzenstein et al. 2012). Wild turkeys have been an economically important upland game bird since reintroduction and restoration efforts (Baumann et al. 1990). Current efforts to restore longleaf pine savannas, coupled with the substantial economic importance of this upland game bird, justify research to address population dynamics of wild turkeys in this ecosystem to help guide management decisions.
Wild turkey population growth is influenced by nest success, brood survival, and adult survival (Roberts et al. 1995, Vangilder and Kurzejeski 1995, Godfrey and Norman 1999. Low survival of females and broods may limit population productivity (Miller and Leopold 1992, Palmer et al. 1993, Peoples et al. 1995, Miller et al. 1998. Therefore, to effectively manage wild turkey populations, biologists and land managers need information on nest success, brood survival and adult survival in longleaf pine savannas. Little et al. (2014) reported a greater initial nest success but similar renest rates and success in longleaf pine savannas relative to other published studies in the southeastern United States (Palmer et al. 1993, Miller et al. 1998, Thogmartin and Johnson 1999, Wilson et al. 2005, Byrne and Chamberlain 2013. Survival of female wild turkeys in longleaf pine savannas has not been previously studied, yet is critical to manage turkey populations effectively and sustainably. Predation is a primary source of wild turkey mortality (Speake 1980, Miller and Leopold 1992, Lovell et al. 1995, Moore et al. 2010. Potential predators of wild turkeys include bobcats Lynx rufus, coyotes Canis latrans, great-horned owls Bubo virginianus gray fox Urocyon cinereoargenteus and red fox Vulpes vulpes, domestic dogs, and raccoons Procyon lotor; Miller and Leopold 1992. No information currently exists on wild turkey cause-specific mortality in longleaf pine savannas. Additionally, previous research has reported lower survival for female wild turkeys during reproductive seasons suggesting nesting females are increasingly vulnerable to predation (Palmer et al. 1993, Roberts et al. 1995, Vangilder and Kurzejeski 1995, Wright et al. 1996, Moore et al. 2010. However, none of these studies have evaluated the influence of reproduction (incubating compared to non-incubating females) on survival. Miller et al. (1998) is the only study that has evaluated the cost of reproduction (or lack thereof ) on survival rates of females during the nesting season. They found reproductively active and inactive females had similar survival rates during the nesting season; however, nesting females were more susceptible to predation while non-nesting females were more susceptible to illegal harvest. Information is needed to fill this knowledge gap in our understanding of wild turkey cause-specific mortality and potential influences of reproduction on mortality in longleaf pine savannas to direct our future management decisions.
Our primary objectives were to 1) estimate annual and seasonal survival rates of female wild turkeys, and 2) document cause-specific mortality. We hypothesized that annual and seasonal survival estimates would be comparable to previous studies in forest-dominated landscapes. We hypothesized that survival would be lowest during the nesting season because incubating females remain on or close to the nest, which could make them more vulnerable to predation. We hypothesized that mesocarnivore predation would be the primary source of mortality. Our secondary objective was to evaluate the effect of nesting status on survival. Specifically, we hypothesized that reproductive activity would negatively influence survival during the nesting season (i.e. incubating females would experience lower survival than non-incubating females).
Study area
Our study was conducted on the 11735-ha Joseph W. Jones Ecological Research Center at Ichauway (hereafter, Jones Center) located in Baker County, Georgia and the 3900-ha Silver Lake Wildlife Management Area owned by the Georgia Dept of Natural Resources located in Decatur County, Georgia (hereafter, Silver Lake WMA; 42 km from Jones Center). The Jones Center was composed of approximately 39% mature pine ( 20 years), 24% mixed pine/ hardwood, 11% agriculture/food plot, 8% young pine ( 20 years), 7% hardwoods, 4% scrub-shrub, 3% wetland, 3% open water, and 1% residential/barren. Wiregrass and old-field grasses (e.g. Andropogon spp.) were the dominant understory conditions in the pine and mixed pine/hardwood stands (Goebel et al. 1997). However, 1000 vascular plant species occur on the site (Drew et al. 1998). Silver Lake WMA was composed of approximately 56% mature pine ( 20 years), 22% young pine ( 20 years), 10% open water, 9% mixed pine/hardwood, 1% shrub/scrub, 1% hardwood, 1% residential/barren, and 1% wetlands and agriculture/ food plots. Paved, gravel and dirt road densities were 5.48 km km -2 and 6.59 km km -2 on the Jones Center and Silver Lake WMA, respectively. Total accumulated rainfall from 14 December to the following 13 December varied at the Jones Center for 2011 (89.15 cm), 2012 (96.42 cm), and 2013 (156.79 cm) (Newton; Georgia Automated Environmental Monitoring Network; < [URL] >), and at Silver Lake WMA for 2011 (73.13 cm) and 2012 (118.57 cm) (Lake Seminole; Georgia Automated Environmental Monitoring Network; < [URL] >). Additionally, the Jones Center was not hunted while Silver Lake WMA was hunted from late March until mid-May for male turkeys only.
To successfully restore and maintain longleaf pine savannas on our study sites, land managers used prescribed fire and mechanical hardwood removal. Fire was applied to mature pine, young pine, mixed pine/hardwood, and shrub/scrub stands. Prescribed fire was conducted throughout the year with 95% of burns conducted during January-June. Prescribed fire application occurred in a mosaic fashion, which promoted landscape diversity. Average patch size burned at the Jones Center was 21.41 ha (SE 0.83; range 0.02-240.57 ha), whereas average patch size burned at Silver Lake WMA was 14.41 ha (SE 0.58; range 0.66-88.27 ha). Fire return interval typically ranged from 1-3 years, but most ( 95%) fires applied to our study sites were 2-years (38.4%, 0-year; 34.9%, 1-year; 21.7%, 2-year; 4.9% of stands with 3-year time-since-fire). Land managers often used mechanical removal to remove large off-site hardwoods (e.g. water oak Quercus nigra) from within mature pine stands.
Turkey capture and monitoring
We captured female wild turkeys using rocket nets baited with corn during December-March of 2010-2013 and June-August of 2011-2012. We fitted all captured females with serially numbered, butt-end (left leg) and riveted (right leg) aluminum leg bands. We also affixed a backpackstyle VHF radio-transmitter, weighing approximately 60-g (Sirtrack, Havelock North, New Zealand; and Telenax, Playa del Carmen, México) to all females. All birds were released at the capture site immediately after processing. The Institutional Animal Care and Use Committee at the University of Georgia approved all turkey capture, handling, and marking procedures (protocol no. A2013 05-034-Y1-A0).
We used a hand-held, three-element Yagi antenna and Wildlife Materials TRX 2000S receiver (Wildlife Materials, Murphysboro, Illinois) to locate radio-marked females 2 times per week from mid-July to mid-March and 1 time per day from mid-March to mid-July. We triangulated each female and recorded locations using a mobile phone containing Location Of A Signal-SD software (LOAS[2010] Ecological Software Solutions LLC. Hegymagas, Hungary, ver. 4.0.3.8.) and a bluetooth-global positioning system unit. We considered a female to be incubating if she did not move for three consecutive days during the nesting season. Once a female was determined to be incubating, we approached to within 25 m of the nest and recorded compass bearings toward the nest. After termination of incubation, we approached nest sites to determine nest fate.
We investigated mortality events immediately following detection of a mortality signal, except during the nesting season when inactive incubating females often triggered the mortality sensor. During the nesting season, we delayed investigation of mortality signals until 28 days from the estimated incubation start date so as not to disturb females that may have been nesting. We classified mortality events into four categories: 1) mesocarnivore (bobcat, coyote and gray fox); 2) great-horned owl); 3) unknown cause of death; and 4) other (e.g. vehicle collision). We based classification on evidence recovered at the site of the carcass (i.e. presence or absence of head and neck, chew characteristics on carcass and radio transmitter, detection of hair or feathers, and evidence of caching; Thogmartin and Schaeffer 2000).
Survival estimation
To evaluate annual and seasonal survival rates of female wild turkeys, we calculated annual and seasonal survival probability estimates using the Kaplan-Meier estimator (Kaplan and Meier 1958) generalized for the staggered entry case (Pollock et al. 1989). Prior to analysis, we censored all mortalities occurring within seven days of capture to mitigate the influence of capture mortality on survival estimates (Vangilder and Sheriff 1990). We censored turkeys whose radio transmitters failed or those that went missing on the last day that we recorded an active signal. We did not suspect that any mortalities occurring during the study were caused by illegal harvest. We structured the data with an annual recurrent time of origin to estimate annual survival, which allowed for re-entry of individuals that survived the previous year (Fieberg and DelGiudice 2009). Specifically, our annually recurrent biological years began on 10 May and ended on 9 May the following year. All individuals that remained alive at the end of each biological year were censored and re-entered on the first day of the next year. Prior to data analysis, we evaluated whether annual survival differed between study sites. We found similar annual survival estimates for each site (Jones Center: 0.562 [0.434-0.728]; Silver Lake WMA: 0.552 [0.407-0.749]). Therefore, we pooled survival data across both study sites. To determine seasonal survival, we delineated biologically meaningful seasons based on the reproductive chronology of turkeys on our study areas (Little et al. 2014) and previous research (Miller et al. 1999, Miller and Conner 2005, 2007. We defined winter as 1 January -31 March, spring as 1 April -30 June, summer as 1 July -30 September, and fall as 1 October -31 December. We estimated survival within these seasons using a seasonally recurrent time of origin such that all individuals that remained alive on the last day of the season were censored and, if still alive, were re-entered on the first day of the same season the following year. All survival analyses were completed in program R using package 'survival' (< www.r-project.org >, Therneau 2014).
Mortality risk
To determine if incubation influenced survival, we estimated survival for incubating and non-incubating females during spring (see also Little et al. 2014). Prior to data analysis, we built a data set that contained only females where apparent nesting status was determined. For example, if radio contact with an individual was lost during the nesting season and the individual reappeared later in the season, we excluded them from the analysis as we could not determine if they initiated a nest. We intended to use a Cox proportional hazards (CPH) model to determine whether incubation status (incubating vs. non-incubating) influenced the risk of mortality (Cox 1972). An important assumption of the CPH model is proportionality, which assumes the hazard function remains constant over time. We tested the proportional hazards assumption using the formal test recommended by Therneau and Grambsch (2000) using the cox. zph function in the 'survival' package. Our CPH model did not meet the required proportionality assumption (p 0.013) indicating that the hazard functions were not constant over time. Given that we were unable to formally test for a difference in survival between incubating and non-incubating females using CPH, we evaluated survival separately for incubating and non-incubating females. We also provide survival curves for both groups to allow for visual evaluation.
Results
We captured and radio-marked 126 female wild turkeys and 66 (52.4%) died during the study (Table 1). We estimated known causes of death for 37 of the 66 (56.1%) mortality events (Table 2), with most deaths attributable to unknown causes (43.2%). Predation was the leading known cause of mortality, with 35.1% attributed to mesocarnivore predation and 18.9% to great-horned owl predation. One female (2.7%) was hit by a vehicle.
We estimated survival for 69 individual females (39 incubated a nest, 29 did not incubate a nest, and 1 incubated a nest one year and not the next year) in which apparent nesting status was determined during the 2011-2013 nesting seasons (Table 3). Female survival for individuals that incubated a nest was 0.82 (95% CI: 0.71-0.93; Fig. 2) and survival for individuals that did not incubate a nest was 0.67 (95% CI: 0.52-0.87; Fig. 2).
Discussion
As hypothesized, survival was greatest during the fall/winter and lowest during the spring. Our findings also indicate that mesocarnivore predation was the greatest known source of mortality. We were unable to test for a difference in survival for incubating and non-incubating females. However, we suggest future research explore whether incubation status may affect survival of female turkeys.
Annual survival was comparable to other forest-dominated landscapes in the southeastern United States (Palmer et al. 1993, Miller et al. 1998, Wilson et al. 2005. Miller et al. (1998) reported a mean annual survival of 0.51 with variation among years ranging from 0.22 to 0.77. However, compared to studies in non-forest dominated landscapes, our findings suggest that our annual survival estimate was low (Hubbard et al. 1999, Humberg et al. 2009). For example, Humberg et al. (2009) found annual survival of female turkeys was 0.80 in a northern Indiana wild turkey population. However, Hubbard et al. (1999) and Humberg et al. (2009) conducted their studies in highly agricultural landscapes, which may have influenced turkey survival. We suggest that lower annual female survival observed on our study sites may be offset by high nest and renest success (Little et al. 2014), in part due to the availability of nesting and brood-rearing cover created by fire (Dickson 1981, Hurst 1981, Landers 1981. For example, Little et al. (2014) observed greater initial nest success relative to other forested-dominated landscapes in the southeastern United States region (Palmer et al. 1993, Miller et al. 1998, Thogmartin and Johnson 1999, Wilson et al. 2005, Byrne and Chamberlain 2013. However, we acknowledge that future research should evaluate the influence of habitat types on annual survival estimates to improve our understanding of potential factors that may influence survival across different ecosystems.
Our seasonal survival estimates were comparable to previous studies (Palmer et al. 1993, Roberts et al. 1995, Hubbard et al. 1999, Wilson et al. 2005, Humberg et al. 2009). Survival was highest during the fall (flock re-establishment) followed by winter (large flocks on wintering areas), summer (brood-rearing), and spring (nesting). Previous studies have documented high survival rates during the fall (Palmer et al. 1993, Roberts et al. 1995, Hubbard et al. 1999, Wilson et al. 2005, Humberg et al. 2009). Increased fall survival is likely attributable to stable foraging resources and a lack of illegal and legal harvest (Wilson et al. 2005). Additionally, survival would be expected to be higher during the fall relative to the spring because females are not nesting (e.g. stationary). We JC fall avian 0 mesocarnivore 0 unknown predator 3 vehicle 0 winter avian 1 mesocarnivore 0 unknown predator 2 vehicle 0 spring avian 3 mesocarnivore 4 unknown predator 6 vehicle 1 summer avian 0 mesocarnivore 2 unknown predator 0 vehicle 0 SL fall avian 0 mesocarnivore 0 unknown predator 0 vehicle 0 winter avian 1 mesocarnivore 5 unknown predator 1 vehicle 0 spring avian 2 mesocarnivore 1 unknown predator 1 vehicle 0 summer avian 0 mesocarnivore 1 unknown predator 3 vehicle 0 Total 2005; 0.91, Humberg et al. 2009). Lower survival during spring is commonly attributed to females remaining on or near a nest site, which may lead to greater risk of predation (Little et al. 1990). Predation was the leading known cause of mortality for female turkeys in our study, which is consistent with many previous studies (Miller and Leopold 1992, Palmer et al. 1993, Wright et al. 1996, Miller et al. 1998, Humberg et al. 2009). However, we could not determine cause-specific mortality for 43.2% of mortality events. Similarly, previous studies have attributed high percentages of mortalities to unknown causes (Miller et al. 1998, Humberg et al. 2009). This finding is likely a result of scavenging activities by various predators, which may delay onset of mortality signals observed similar survival estimates for winter and summer. This finding is partially attributed to a greater risk of predation because 43% (10 mortalities during winter; six mortalities during summer) of 37 mortalities where cause of death could be assigned occurred during winter and summer. Mesocarnivores and great-horned owls were the primary causes of death for female turkeys during these seasons. Summer survival on our study areas was similar to previous studies (Palmer et al. 1993, Roberts et al. 1995, Hubbard et al. 1999, Wilson et al. 2005, Humberg et al. 2009). Survival commonly increases during summer, which is likely due to the end of nesting season and increased mobility of broods. Our estimate of spring survival was within the range of survival estimates previously reported (0.75, Wilson et al. In summary, our results are consistent with previous research in that predation was the leading known cause of mortality for female wild turkeys, especially during the spring. However, females may be able to compensate for lower annual survival by increased nest and renest success, as was observed previously on our study areas (Little et al. 2014). Provision of adequate nesting cover in longleaf pine savannas may be important to decrease predation rates during the spring. Our data also indicated that survival of non-incubating females during spring was lower than for incubating females. Although, we were unable to test for a difference due to the lack of proportional hazard over time for the two groups but note that the confidence intervals for the survival rates overlapped substantially. We suggest further research is needed to evaluate predator -wild turkey dynamics in longleaf pine savannas. Specifically, we suggest future research investigate state-space behaviors on female survival during the nesting season. For example, previous research on our study area documented multiple nesting attempts during the nesting season (Little et al. 2014); therefore, females are changing states during multiple nesting attempts from stationary to mobile to stationary. These behaviors may influence the probability of survival, specifically for individuals that are mobile and are easier to be detected by predators (Lima and Dill 1990). Given that we observed lower annual survival than some studies, we recommend that biologists monitor relationships between survival and productivity of turkeys in longleaf pine savannas to ensure the sustainability of turkey populations. This could be accomplished through mark-capture-resight methods (Weinstein et al. 1995) and line-transect-based distance sampling (Butler et al. 2007). However, we suggest future research also examine improved population monitoring techniques for wild turkeys. until carcasses were completely consumed or displaced from their transmitters (Humberg et al. 2009). Mesocarnivore predation was the most common known source of mortality, followed by great-horned owls. Our findings are generally consistent with previous studies in this regard (Miller and Leopold 1992, Palmer et al. 1993, Wright et al. 1996, Miller et al. 1998, Humberg et al. 2009), but great-horned owls were a significant source of mortality in our study (Table 2). Great horned-owls are known predators of wild turkeys (Miller and Leopold 1992, Palmer et al. 1993, Thogmartin and Schaeffer 2000, but are not typically considered a primary predator such as bobcats, foxes, and other mesopredators (Speake 1980, Wright et al. 1996. Our findings suggest that great-horned owls may represent an important source of mortality to female turkeys because avian mortality was similar across both pine savanna systems. Similar to other studies, survival was lowest during the nesting season (Roberts et al. 1995, Vangilder and Kurzejeski 1995, Wright et al. 1996, Miller et al. 1998, Wilson et al. 2005. Of these, Miller et al. (1998) is the only study that investigated the influence of reproduction on survival of females. They found that incubating females were more susceptible to predation, whereas non-incubating females were more likely to be killed illegally. We found incubating and non-incubating females were primarily killed by predators (e.g. mesocarnivores); however, we did not detect differences in susceptibility to mortality as illustrated by Miller et al. (1998). This is partly due to the large number of unknown cause-specific mortality events. One key difference between our study and Miller et al. (1998) is that we are not aware of any illegal harvest of non-incubating females during our study. Specifically, turkey hunting was not permitted at the Jones Center while hunting was permitted at Silver Lake WMA for male turkeys only from late March to mid-May. Despite the lack of illegal harvest of non-incubating females, our research illustrates the importance of other sources of predation in our study system, in particular, the significant source of mortality caused by great-horned owls.
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Domain: Environmental Science Biology
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Analysis of Soil-Vegetation Interrelationships in a South-Southern Secondary Forest of Nigeria
Soil-vegetation interrelationships in a secondary forest of South-Southern Nigeria were studied using principal component analysis (PCA) and canonical correlation analysis (CCA). The grid system of vegetation sampling was employed to randomly collect vegetation and soil data from fifteen quadrats of 10 m× 10 m. PCA result showed that exchangeable sodium, organic matter, cation exchange capacity, exchangeable calcium, and sand content were the major soil properties sustaining the regenerative capacity and luxuriant characteristics of the secondary forest, while tree size and tree density constituted the main vegetation parameters protecting and enriching the soil for its continuous support to the vegetation after decades of anthropogenic disturbance (food crop cultivation and illegal logging activities) before its acquisition and subsequent preservation by the Cross River State government in 2003. In addition, canonical correlation analysis showed result similar to PCA, as it indicated a pattern of relationship between soil and vegetation. The only retained canonical variate revealed a positive interrelationship between organic matter and tree size as well as an inverse relationship between organic matter and tree density. These extracted soil and vegetation variables are indeed significantly important in explaining soil-vegetation interrelationships in the highly regenerative secondary forest.
Introduction
Soil and vegetation exhibit an integral relationship, in that soil gives support (moisture, nutrient, and anchorage) to vegetation to grow effectively on the one hand, and on the other hand, vegetation provides protective cover for soil, suppresses soil erosion, and helps to maintain soil nutrient through litter accumulation and subsequent decay (nutrient cycling). Hence, vegetation and soil are interrelated and provide reciprocal effects on each other. Vegetation supports critical functions in an ecosystem at different spatial scales. Vegetation strongly affects soil characteristics, including soil volume, chemistry, and texture, which feed-back to affect various vegetation characteristics, including productivity, structure, and floristic composition [1].
Soil nevertheless is fundamental to ecosystem and agricultural sustainability and production because it supplies many of the essential requirements for plant growth like water, nutrients, anchorage, oxygen for roots, and moderated temperature [2,3]. Soil serves a vital function in nature, providing nutrients for plant to grow as well as habitat for millions of micro-and macro-organisms. Healthy soil enables vegetation to flourish, releases oxygen, holds water and diminishes destructive storm runoff, breaks down waste materials, binds and breaks down pollutants, and serves as the first course in the larger food chain [4,5]. The disturbance, compaction, and degradation of soils impact the soil structure and reduce its ability to provide these functions.
The lowland secondary forest of Tinapa Resort is a regenerative forest approaching climax, after series of anthropogenic disturbances notably food crop cultivation, fuelwood gathering, and illegal logging activities. The forest vegetation over nine years of abandonment following its acquisition by the Cross River State government is characterized by a luxuriant forest canopy and diverse tree species. There is therefore a need to identify the basic soil and vegetation parameters encouraging the regenerative capacity of this once degraded forest and impoverished soil [6]. This reciprocal relationship between soil and vegetation demands a multivariate approach in order to determine International Journal of Forestry Research critical vegetation and soil properties that sustain this integrative association. On this premise, multivariate analytical techniques (principal component analysis, canonical correlation analysis, factor analysis, and canonical correspondence analysis among others) are very useful in the analysis of soil and vegetation as each consists of data corresponding to a large number of variables. Thus, analysis via these techniques produces easily interpretable results [7].
Studies explaining the reciprocal effect of soil and vegetation have been conducted in the past by scholars in the fields of ecology, geography, forestry, and soil science using varying multivariate approaches. For instance, studies on soil-vegetation relationships of saline localities have been documented [8][9][10][11][12][13][14]; studies on soil-vegetation relationships in tropical rainforests have also been conducted [15][16][17]. However, in Nigeria, studies on soil-vegetation interrelationships in the rainforest zones show locational bias, as most of the studies were carried out in the South-Western ecological zone [18].
Only perhaps the study by Ukpong [19] used multivariate analyses to determine soil-vegetation interrelationships in the coastal mangrove swamps of Cross River, however, multivariate analysis of soil-vegetation interrelationships using PCA in the rainforest belt of Cross River State has not been fully documented in the literature. It is on this background that the present study attempts to analyse soil-vegetation interrelationships in the secondary forest of Tinapa Resort, Cross River State. The aim is to identify significant soil properties that influence vegetation regeneration and productivity as well as vegetation parameters that help to protect and nourish the soil to sustain its luxuriant outlook. C. The area has an average relative humidity of 80-90% at 10 am during the wet season [20]. The vegetation of the area is a mixture of mangrove and rainforest. The rainforest is further subdivided into the lowland rainforest and the freshwater swamp forest. The mangrove swamp is found in the southern fringe of the area and stretches from the freshwater limits to the ocean beaches [6]. The rich and luxuriant vegetation of the area has been subjected to severe degradation in the past before the advent of Tinapa; as such, the vegetation comprised secondary forest approaching climax. The soils are generally deep, porous and weakly structured, and well drained with low moderate status [20].
2.2.
Sampling Procedure and Analysis. The grid system of vegetation sampling was used to superimpose grids of 2 cm × 2 cm on the lowland forest using the vegetation map of the area, the grid intersections were numbered, and then 15 grids were randomly selected. This approach was used to establish fifteen quadrats of 10 m × 10 m in dimension across the area; and in each quadrat, vegetation and soil samples were collected. The floristic and structural vegetation samples determined included tree density, species composition, tree height, tree size/girth, vegetation/crown cover, species diversity index, and aboveground biomass. Data on vegetation/crown cover was obtained using the line-intercept method [21][22][23]. Tree size/girth was taken at 1.37 m DBH. Species diversity index was determined using Shannon-Wiener's approaches [24,25]. Tree height was determined using the trigonometry method [26,27]. The above-ground biomass was estimated using the allometric formula given by FAO (1989) for tropical areas as cited by Woomer [28] as y = exp (−2.134+2.53InD) , where y = aboveground biomass in kilogrammes, exp = 2.71828, and D is the measured diameter at breast height in cm. However, during the collection of vegetation parameters, only mature trees with ≥ 0.30 m girth were enumerated and analyzed.
In the same way, fifteen (15) soil samples were collected using a soil auger at rooting depth of 30 cm. The soils were put in polythene bags with label; they were thereafter air dried and taken to the laboratory at the Department of Agronomy, University of Ibadan, Ibadan for analysis of soil physical and chemical properties. Particle size composition was determined using the hydrometer method [29]; organic carbon by the Walkley-Black method [30], after which values obtained were multiplied by 1.72 [31] to convert to organic matter; total nitrogen by the Kjeldahl method [32]; available phosphorus was determined by the method of Bray and Kurtz [33]. The soils were leached with 1 N neutral ammonium acetate to obtain leachates used to determine exchangeable bases adapted from the method described by Daly et al., [34]. Soil cation exchange capacity was determined by the summation method, while pH values were determined using a glass electrode testronic digital pH meter with a soil : water ratio of 1 : 2.
Data Analysis.
Two data matrices representing soil and vegetation characteristics were constructed and the SPSS for windows (Ver.17.0) and SAS (ver.9.0) software packages were used for performing principal component analysis (PCA) and canonical correlation analysis (CCA), respectively. PCA was performed to find the main factors determining the reciprocal effects of soil and vegetation. Principal components according to Li et al., [14], are considered useful if their cumulative percentage of variance approached 80%. The scores of rotated component loadings (correlation coefficients) from the PCA output were used to determine the main soil and vegetation components sustaining the regeneration, enrichment, moisture content, and productivity of forest vegetation. The rotated component loadings for the variables were determined using Varimax rotation (variance maximization); this method was applied as it helps to minimize the complexity of the components by making large loadings larger and smaller loadings smaller within each component [7,35,36]. The idea of Varimax rotation is that each variable should load heavily on few components as possible to make interpretation easier [37,38]. Variables were also rotated to obtain new significant and uncorrelated variables called principal components or principal axes, and thereafter, the number of principal components was reduced by eliminating relatively unimportant components [7].
In order to determine main components, only principal components with eigenvalues greater than 1 were selected; components with an eigenvalue of less than 1 accounted for less variance than did the original variable (which had a variance of 1), and so were of little use, as such were not extracted. From each extracted component, variables with coefficients ≥ ±0.70 were selected and considered significant (Johntson, 1980 andWotling et al., 2003 quoted in [39]). However, in order to determine the basic soil and vegetation variables sustaining these interrelationships, the component defining variables (CDVs), that is, those variables with the highest loadings (correlation coefficients) on each extracted principal components for soil and vegetation, were selected to represent the extracted components because they provide the best relationship [40].
What this means is that on every extracted component for soil and vegetation, variable with the highest coefficient, for example, on component 1, tree size was selected as the most significant variable (for either soil or vegetation variable) to represent that component. In addition, canonical correlation analysis (CCA) was performed to examine the main ways in which the properties of soil were related to those of vegetation. The extracted components of soil and vegetation were used to form pairs of linear combinations of the two variables in such a way as to maximize the correlation between each pair. This analysis provides a clearer picture of the complex interrelationships between soil and vegetation variables. Theoretically, canonical correlation does not distinguish between predictor and criterion variables, but for this study, it was done to enhance interpretation. The soil variables were used as predictor variables, while vegetation parameters as criterion variables. The essence of canonical analysis is the formation of pairs of linear combinations of two sets of variables in such a way as to maximize the correlation between each pair.
PCA Result on Vegetation
Parameters. PCA was performed for seven (7) vegetation parameters across the fifteen (15) sampled quadrats in order to identify critical vegetation factors that protect and enrich the soil. Component loadings (correlation coefficients) and the variances (eigenvalues) for the various vegetation parameters were computed. Table 1 shows results of the ordinary component matrix of vegetation parameters with eigenvalues ≥1. It shows that three (3) vegetation parameters loaded heavily on component 1, the parameters/variables included tree size (0.88), aboveground biomass (0.85), and vegetation cover (081). This component accounted for 51.3% of the total variance in the vegetation parameters. On component II, only one variable, tree density (0.71) loaded heavily; this component accounted for 30.1% of the variation in the data set. The lack of spread of variable loadings across the two extracted components and also the overwhelming concentration of significant variables in component 1 affected interpretation as well as understanding of the vegetation data structure. In order to have a fair distribution of variables and to discover the set of vegetation parameters that helps to protect the soil for its continuous nutrient enrichment, the two extracted components were rotated using Varimax method (Table 2). However, the rotation did not affect the sum of eigenvalue (cumulative explanation) but altered the distribution of eigenvalue as well as assigned variable loadings to higher components (Tables 1 and 2).
The loadings of rotated components on vegetation parameters are depicted in Table 2. From the table, two components were extracted, and they accounted for 81.3% of the total variance in vegetation data set. Three vegetation parameters loaded heavily on component 1; they included tree size (0.97), above-ground biomass (0.96), and tree height (0.88). This component was regarded as measuring vegetation structure and accounted for 3.11 of the total eigenvalue loading and 44.5% variance in the linear combination of vegetation parameters; while in component II, three parameters also loaded heavily on it, these variables included tree density (0.96), species diversity (0.93), and species composition (0.72). This component exemplified floristic attributes, and it accounted for 2.58 total eigenvalue loading and 36.9%variance in vegetation dimension. These results based on the criteria of component defining coefficients (CDV) implied that the main vegetation parameters influencing and protecting the soil included tree size and tree density (Table 2).
PCA Result on Soil Parameters.
In the same manner, PCA was performed for thirteen (13) soil properties across fifteen (15) sampled quadrats to determine the main soil factors/variables that facilitate the regenerative capacity of the once disturbed forest to the state of climax. The ordinary component matrix of soil properties is shown in Table 3. Based on the significant threshold for variables, only one soil property loaded on component I, the variable was exchangeable potassium (0.71); this component accounted for 3.34 of eigenvalue loading and 25.7% of the variance in the soil data. Components II and III equally had single soil property; these included available phosphorus (0.77) and silt content (0.73), and they accounted for 18.6% and 14.7% of the variation in soil data, respectively. However, the remaining components (i.e., IV and V) had no soil property loaded on them based on the threshold that only component loadings ≥ ±0.70 are significant, but they accounted for 20.5% of the combined variation in the data set. Again, the lack of spread of component loadings across the five extracted components affected interpretation as well as understanding of the dimension in the soil data. For better distribution of component loadings and interpretation of soil structure, the five components were rotated (Table 4). Table 4 depicts loadings of rotated components on soil properties. It shows that five components with eigenvalue loadings of ≥1 and above were extracted, and they accounted for 79.5% of the total variance in the original data set. On component 1, two soil properties, exchangeable sodium (0.90) and exchangeable magnesium (0.85), loaded heavily; this component measured soil cation concentration and as such accounted for 2.18 eigenvalue loading and 16.8% total variance in soil data set. On component II, two soil properties loaded heavily on it; these included organic matter (0.90) and total nitrogen (0.79); this component represented organic accumulation and as such accounted for 2.18 eigenvalue loading and 16.7% total variance in the linear combination of soil variable. Component III had also two soil properties that loaded heavily on it; this component accounted for 16.0% total explanation in the soil data.
The two soil properties identified on this component were cation exchange capacity (−0.84) and base saturation (0.81); this component in essence measured the effect of CEC content in the soil. Also, on component IV, only exchangeable calcium loaded heavily with coefficient value of 0.90. This component measured the effect of calcium content and accounted for 15.5% of the variance in soil data. In addition, two soil properties loaded heavily on component; they included sand content (−0.87) and clay content (0.82). This component exemplified soil texture and accounted for 14.4% of the total variation in the linear combination of soil variable. Based on this result and the criteria of CDV, the basic soil factors that influenced vegetation productivity and sustainability included exchangeable sodium, organic matter, cation exchange capacity, exchangeable calcium, and sand content.
Canonical Correlation Analysis.
Canonical correlation analysis (CCA) is one of the most general of the multivariate techniques that is used to investigate the overall correlation between two sets of variables. It examines the main ways in which two multivariate measures are related as well as the strength and nature of the interrelationships [18,41]. The basic principle behind canonical correlation is determining how much variance in one set of variables is accounted for by the other set along one or more axes, which are orthogonal (uncorrelated). Unlike many other techniques, in CCA, there is no designation that one set of variables is independent and the other set is dependent, but for clarity, the predictor-criterion language is used. However, from the two variables, a linear combination is derived such that the association/relationship between them is maximum; these pairs of maximally correlated linear combinations are called canonical variates, [18,42].
The results of correlating the five soil properties with the two dimensions of vegetation characteristics are shown in Table 5. The canonical correlations for the first and second canonical functions (or variates) were 0.99 and 0.97, respectively, which were significant using the Bartlett's [43] test at 5 percent significance level. However, in the literature, the significance of the canonical correlation is believed to be insufficient in making valid conclusions, as there are contentious arguments on using the significance of canonical correlation to make conclusion as well as to determine the number of canonical variates or functions to retain for the purpose of making inference. The reason is that significance test tells us absolutely nothing about the magnitude of the relationship (i.e., it does not reveal the amount of variance shared by the two sets of variables), and its statistical significance is heavily influenced by sample size; as it is possible for the test to be statistically significant with large sample sizes (see [41,44,45]). On this note, the use of redundancy coefficient was suggested as it reveals the amount of variance shared by the two sets of variables. Redundancy coefficient or index is an asymmetric index that measures how much variance in one set of variables (say soil properties) is shared by the variability in the other set of variables (vegetation characteristics) [46]. However, the redundancy result in Table 5 shows that the redundancy coefficient for first canonical variate for soil properties indicated that 19 percent of the variance in vegetation characteristics on the first canonical variate was accounted for by the variability in soil properties; likewise, the redundancy coefficient for the second canonical variate for soil properties indicated that 12 percent of the variance in vegetation characteristics was accounted for by the variability in soil properties. The redundancy result for vegetation characteristics equally showed that 83 and 16 percent of the variance in soil properties on the first and second canonical variates were accounted for by the variability in vegetation characteristics. Based on the magnitude of relationships shared by the two sets of variables across the two canonical variates (considering the redundancy coefficient), the first canonical variate was chosen for further explanation, because it explained a large proportion of the variation in soil and vegetation dimensions. The results in Table 5 also showed that two canonical variates were extracted, and each is identified by soil and vegetation components with loadings exceeding 0.60. The first canonical variate of soil properties loaded positively and heavily on organic matter, while the first linear combination of vegetation characteristics loaded positively and heavily on tree size and negatively on tree density. This therefore implied that strong positive correlation existed between organic matter concentration and tree size, while the linear association between organic matter and tree density depicted an inverse relationship.
In essence, the result of the first canonical function/axis showed that organic matter concentration and tree size were positively and directly related; implying that an increase in organic matter concentration in the soil would result in a corresponding increase in tree size and vice versa, while tree density and organic matter concentration showed an inverse relationship, meaning that increase in the density of trees and the continuous addition of nutrient through decomposition of large tree residue would reduce the amount of OM in the soil.
Discussion and Conclusion
Indeed, with the aid of varimax-rotated principal component analysis, the large, intercorrelated soil and vegetation properties initially thought to determine soil-vegetation interrelationships had been reduced to fewer, uncorrelated and more important variables. This is because PCA is a fact-finding tool that reduces measurement problems, such as bias, and reduces the complexity of correlated data, as it extracts only variables that have significant contributions among a set of variables or principal components which account for most of the variance in the observed variables [47,48]. However, the PCA result for this study showed that the reciprocal effects of soil and vegetation were influenced by seven sets of soil-vegetation variables. PCA identified two basic vegetation parameters that sustained and enriched the soil for its continuous support to vegetation regenerative capacity; these included tree size and tree density. These variables stood out as critical vegetation parameters protecting the soil from varying climatic conditions like heavy rainstorm, regulating soil erosion and maintenance of soil moisture among others. These parameters also helped to conserve soil fertility through biomass accumulation and subsequent decay; their combined effects to the soil system were significant through variation in the mineral contents of biomass or litter and the canopy hydrological effects.
Also, the PCA result showed that five fundamental soil properties sustained the regrowth and luxuriant characteristics of the secondary forest; they included exchangeable sodium, organic matter, cation exchange capacity, exchangeable calcium, and sand content. The findings of this study somehow corroborated that of Aweto [18] who identified, based on the threshold of ≥ ±0.70 organic matter status, pH, sand proportion, total nitrogen content, clay, silt, bulk density/porosity, and potassium as paramount soil components; and tree density, tree size/vegetal cover, nanophanerophytes, and species composition as vegetation components influencing soil-vegetation interrelationships. In addition, the result of the canonical correlation analysis indicated a pattern of relationship between soil and vegetation. The only retained canonical variate (the first canonical function) revealed a positive interrelationship between organic matter concentration and tree size as well as an inverse relationship between organic matter concentration and tree density.
This meant that organic matter and tree size were interrelated; the importance of tree size in this association is obvious, as an increase in tree size would lead to an increase in nutrient accumulation in the forest by increasing litter production and protecting the soil against accelerated nutrient (OM) destruction and subsequent loss through erosion. This implied that tree size helped to improve the content of organic matter in the soil by the addition of nutrient in solution form through stem flow as well as through the accumulation and decomposition of biomass (plant residue). Nevertheless, the inverse relationship between organic matter and tree density was expected as simple Pearson's correlation indicated a negative correlation. The negative relationship was attributed to the high rate of OM addition through the accumulation of large plant residue. According to Foth [49], high organic matter contents in soils are the result of slow decomposition rates rather than high rates of organic matter addition. The present area enjoys high precipitation and temperature, which facilitates the quick decomposition of biomass, thereby increasing the rates of organic matter addition. The results of canonical analysis therefore indicated that organic matter and tree size were positively interrelated, while organic matter and tree density were inversely related. It therefore implied that organic matter and tree size were the major soil and vegetation variables sustaining as well as supporting the regenerative capacity of the secondary forest.
Tinapa is located on latitude 05 • 02 and 05 • 04 N and on longitude 08 • 07 and 08 • 22 E. The area falls along the coastal fringes of Cross River State where raining season lasts for about 10 months. The proximity of the Atlantic Ocean has a moderating effect on temperature with highest average daily maximum of 35 2.1. Study Area.
Table 1 :
Ordinary component matrix of vegetation parameters a .
Table 2 :
Rotated components matrix of vegetation parameters a .
Table 3 :
Ordinary components matrix of soil properties a .
a Variables underlined with eigenvectors (coefficients) ≥ ±0.70 are considered significant.
Table 4 :
Rotated components of soil properties a .
Table 5 :
Result of canonical correlation analysis of relationships between soil and vegetation a .
a Variables underlined with canonical loadings ≥ ±0.70 are considered significant.* Significant at 5% significance level.
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Domain: Environmental Science Biology
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Drought and fire stress influence seedling competition in oak forests: fine-root dynamics as indicator of adaptation strategies to climate change
Increased summer drought and wildfires as a consequence of continuing climate change are expected to lead to disturbance of Mediterranean ecosystems. Seedlings recruitment is sensitive to both stresses and, therefore, any adaptation and restoration strategy devised to protect these forests should take into account a careful study on their effects on seedling development. As a substantial fraction of net primary productivity of forested ecosystems is channelled in the belowground compartments, the knowledge of how roots behave under stressful conditions becomes of primary importance to select the right management strategy to be implemented. This work tries to enlighten the events occurring in the fine root portion of the root system in young seedlings of three co-existing oak species (Quercus ilex, Quercus trojana and Quercus virgiliana) under controlled conditions. We have made a comparative analysis of the effect of these two stresses, alone or in combination, with the aim to evaluate the tolerance level of these seedlings and, therefore, to obtain an indication of their recruitment potential in the field. The parameters investigated were biomass and a number of morphological traits. Data obtained suggest that a decrease in diameter could be part of a tolerance strategy in all three oaks tested together with a reduction of root length. In addition, tolerance to water shortage could require a reduction of carbon allocated belowground, in particular in the very fine roots, which leads to an overall reduction of the root system dimension. Q. trojana seedlings seem to be the fastest in resuming growth after stress interruption but a good recovery was also found for the remaining two oak species. Although our study provides interesting information regarding a possible tolerance strategy taking place in the fine root compartment when seedlings of these three oak species undergo water stress and fire treatment, more information is needed before any suggestion can be made as to which species would be best suited to make these forests more resistant to global changes.
Effect of climate change on Mediterranean forests
In the Mediterranean basin water availability is subject to seasonal variability and for this reason forest ecosystems during summer often undergo mild and/or extreme drought leading to the induction in plants of various adaptive mechanisms (Chaves et al. 2003;Montagnoli et al. 2012Montagnoli et al. , 2014;;Tognetti et al. 1999Tognetti et al. , 2000)). Over the last century, this situation has worsened with an increase in temperature of about 0.8°C and a decrease in rainfall of about 5 % (Begni et al. 2001;Xoplaki et al. 2004), leading to an overall increase of evapotranspiration and aridity (De Luis et al. 2003;Piñol et al. 1998). Under these conditions it is not surprising that Mediterranean forests undergo to a) a decrease in productivity (Chaves et al. 2002;Dreesen et al. 2012;Faria et al. 1998;Pereira et al. 2004Pereira et al. , 2007)), b) variations in morphological and physiological traits (Dreesen et al. 2012;Qaderi et al. 2012;Peñuelas et al. 2002), and c) an increase of tree mortality rates (Lloret et al. 2004;Martínez-Vilalta and Piñol 2002). In addition, several other physiological events are affected by drought, such as 1) plant reproduction through the arrest of reproductive development (Moya et al. 2008), 2) seedling establishment (Lloret et al. 2004), and 3) seedling mortality (Neilson and Wullstein 1985). Furthermore, hot and dry summers are often associated with forest fires, which represents an even more severe threat (Sanchez-Humanes and Espelta 2011) for their frequency and severity have increased due to climate change (Giorgi and Lionello 2008;Vilagrosa et al. 2003). It has been shown that drought affects seedlings more than saplings or mature trees, as they are more susceptible to environmental stresses (Chiatante et al. 1999;Chiatante et al. 2005;Di Iorio et al. 2011;Margolis and Brand 1990). In this scenario, in order to evaluate the natural recruitment rate in Mediterranean forests under climate change (Limousin et al. 2009;Ozturk et al. 2010;Sabaté et al. 2002;Scarascia-Mugnozza et al. 2000) any adoption of a forest adaptation strategy (Chapin et al. 1990) should begin from the knowledge of how seedlings respond to concurrent drought and fire.
Tree response to water availability: the role of fine roots
Independently of the climate region considered, the scientific community agrees that, in forest ecosystems, the belowground biomass accounts for a mean of 13-25% of the stand biomass, and fine roots (Helmisaari et al. 2002) represent a percentage of 2-15%. This fact indicates that a substantial fraction of net primary productivity in forested ecosystems is exported belowground in order to produce short-living fine roots (McClaugherty et al. 1982;Joslin and Henderson 1987). Therefore, a better knowledge of forest root dynamics is important for measuring, modelling, and predicting the value of ecosystem services (carbon storage for example) as well as providing useful indications when programming any measure of forest adaptation and/or restoration at landscape level (Stanturf et al. 2014).
Very fine (d<0.5 mm) and fine (0.5<d<2 mm) roots (Zobel and Waisel 2010) represent the most dynamic component of a root system (Hendrick and Pregitzer 1992;Barlow 2010), and they comprise the majority of the length and water/nutrientabsorbing surface area of a root system (Bauhus and Messier 1999;Guo et al. 2008;Xia et al. 2010;Rewald et al. 2011;in Comas et al. 2013). Furthermore, fine roots are characterized by a rapid turnover which is influenced by a variety of internal (e.g.genotype of plant species) and external (e.g.temperature, precipitation, soil properties, nutrient availability and competition between plants) factors (Teskey and Hinckley 1981;Kuhns et al. 1985;Burke and Raynal 1994;Steele et al. 1997;Tierney et al. 2003;Chiatante et al. 2005;Majdi et al. 2005;Scippa et al. 2006;Montagnoli et al. 2012). Thus, fine-root dynamics set limits on shoot functioning by assuring the maintenance of forest plant productivity even under water deficit (Comas et al. 2013). Given their simple anatomical organization, fine roots are also the most sensitive component within the overall root system and, thus, respond rapidly to variations occurring in the rooting environment (Helmisaari et al. 2002). Thin roots are believed to be the belowground equivalent of thin leaves, which have lower metabolic cost (Withington et al. 2006;Ostonen et al. 2007).
Several studies on plant nutrition demonstrated that plants continuously adapt the growth of different organs to nutrient availability in the soil (Metcalfe et al. 2008). Moreover, different tree species appear to adopt different strategies in the attempt to maximize their nutrition capacity (Comas et al. 2002;Curt and Prevosto 2003;Comas and Eissenstat 2004). This species-specific adaptation to nutrient availability in case of shortage can be resumed in two strategies: a) tolerance or b) avoidance of the stressful conditions (Manes et al. 2006 and references therein). In the case of stress tolerance the plant adopts an 'extensive' strategy (Ostonen et al. 2007), with a shift of carbon allocation towards the roots, where photosynthates are used to increase water uptake capability. This hypothesis fits with the demonstration (Ostonen et al. 2011) that across a European climate gradient, Norway spruce forests adapt to low N availability by increasing biomass, length and number of root tip ectomycorrhizas. When the limiting factor is water availability in the soil, the tolerance strategy leads to an increase in root mass and length in the fine-root system as soil moisture declines (Manes et al. 2006;Ostonen et al. 2007;Di Iorio et al. 2011;Montagnoli et al. 2012). In the case of stress avoidance, plants respond to a reduction in water availability by closing their stomata thereby reducing CO2 assimilation and diffusion into the plant (Manes et al. 2006), which leads to a decline in root mass production (Metcalfe et al. 2008). Therefore, it is reasonable to assume that root length is proportional to resource acquisition (benefit) whereas root mass is proportional to construction and maintenance (cost) (Eissenstat and Yanai 1997). In particular, when considering water availability in the soil it is known that the necessity to optimize water/nutrient uptake stimulates finer roots production, explaining why there is a relatively greater root length per unit mass under drier conditions (Metcalfe et al. 2008). In previous studies conducted in Turkey oak and European beech forests, we found that fine roots respond to water content and soil temperature variations independently of their diameter (Montagnoli et al. 2012(Montagnoli et al. , 2014)). In beech forest these studies suggested that the increase in very fine root length results from elongation of their primary anatomical-zone unlike thicker fine roots which grow radially in consequence of vascular cambium activity (Barlow 1997;Montagnoli et al. 2014). In another study, (Di Iorio et al. 2011) we showed that Quercus pubescens seedling survival under severe drought stress might be entrusted on shedding the thinner roots. Variations in diameter of fine roots in response to changes in moisture and/or temperature in the soil have also been demonstrated in boreal trees (Ostonen et al. 2007).
Adaptation and restoration in Mediterranean forests
Several oak species characterizing Mediterranean forests are present in the Apulia region (Southern Italy). In particular, the association Teucrio siculi-Quercetum trojane (Bianco et al. 1998) presents the coexistence of three oak species: Quercus virgiliana, Quercus ilex and Quercus trojana, with the latter being the dominant species. It constitutes one of the habitat types (Q.trojana woods, code 9250) of Murgia di Sud-Est, which has been declared Site of Community Importance (SCI, code IT9130005) by the European Commission and has been included in the Natura 2000 network of protected areas. These three oak species have very different morphological and physiological traits with Q. ilex being an evergreen species whereas Q. virgiliana being a deciduous and Q. trojana have a semi-deciduous habit. The wood quality is completely different too with Q. trojana timber mostly used for ship construction, and timber of the remaining two species prevalently used for energy purpose. The millennium-long exploitation history and the technological advancements have probably imposed repeated changes in management strategy of these forests over time. Therefore, it cannot be excluded that changes in vegetation compositions have been introduced anthropologically (Thompson et al. 2009). More recently, drought and fire events are increasing in this region, both numerically and qualitatively, affecting species distribution and production at both local and regional scales (Matías et al. 2012) thereby putting at risk the very survival of this plant community. The three coexisting Quercus species are likely to exhibit different adaptive responses to climatic constraints. Since Q. trojana represents the dominant species, its seedlings are expected to have fine-root morphological traits that allow them to cope better than the other two species with stressful environmental conditions of drought and fire. In the present paper we test this hypothesis by measuring the variation in fine-root length and mass in relation to two fine root diameter classes (very fine root d<0.5; fine root 0.5<d<2 mm). Our goal is to predict, through fine-root dynamics, the possible responses and adaptation of these species to future aridity increases of Mediterranean ecosystems. Finally, in order to avoid the potential associated impact on forest ecosystems such as a decline in productivity, we evaluated the seedlings' recruitment potential which will allow for a better planning of management strategies for the adaptation to climate change and the maintenance of ecosystem integrity and environmental benefits.
Plant material and growth chamber characteristics
Approximately 200 mature seeds were randomly collected from 15-20 individuals of each species (Q.ilex, Q. virgiliana and Q. trojana) in a forest situated in the district of Martina Franca named Contrada Lanzo and located within the protected area Murgia Sud-Est (40°39′25″N, 17°18′25″E). In the area where seeds were collected, Q. trojana displays a deciduous habit, which might be related to harsh winter condition. Mean annual precipitation of the site measured over an 80-year period is 470 mm. According to Bagnouls and Gaussen (1953), this site experiences a five-month dry season from May to September, with monthly mean precipitation of 26, 18, 13, 20 and 32 mm, respectively. In order to avoid pregermination, immediately after their collection seeds were stored in a cold room at 4°C for 2 weeks before being used in experiments. One hundred seeds for each species were weighed and sown in 3.5 L pots filled with a 3:1:1 mixture of peat, clayey loam, and pumice (440 g dry mass each pot), with the addition of 20 g of a slow-release fertilizer (NPK 14-7-14), and placed in a growth chamber. Seed germination was 58 % for Q. ilex, 60% for Q. virgiliana and 85% for Q. trojana. A single growth chamber was used to allow for a strict control of environmental factors (uniform conditions) and seedling development (coetaneous cohort). Temperature, humidity, light intensity and period, and water availability were regulated to mimick summer weather conditions occurring at the site, with temperature and relative humidity following a diurnal cycle. A maximum temperature of 27°C was applied, which corresponds to the average maximum temperature recorded during the summer season. In fact, these seedlings grow in the shade of a thick canopy and, therefore, the air temperature is relatively low. Temperatures and photoperiods are shown in Chiatante et al. 2015, Table 1. Daily relative humidity cycled from 35 to 76%. Photosynthetically active radiation (PAR, 400-700 nm) at plant height was 350 μmol m -2 s -1 . Light intensity was established as the medium irradiance value corresponding to 17% of full sunlight, which is the maximum light intensity recorded under the canopy cover in natural condition during repeated 4 h measurements (from 11.00 to 15.00 h) in July. Mean seedling age at the initiation of each experiment was 2 months (±18 days) after bud protrusion above the soil, with a seedlings height of 8.50 ± 0.31 cm for Q. virgiliana, 13.82 ± 0.72 cm for Q. ilex and 11.79 ± 1.03 cm for Q. trojana [mean ± standard error (SE)].
Drought experiment
We applied sequentially to the same seedlings (55 for each species) two levels of water shortage indicated, respectively, as mild drought stress (M-stress) and severe drought stress (S-stress). The rationale of using different levels of water shortage, one after the other, was that comparing morphological and physiological traits (Thomas and Gausling 2000) of three different species at two different dose-response treatments during a given period of time (Poorter et al. 2012) allows for a more correct evaluation of stress tolerance and avoidance mechanisms. Soil water potential (Ψsoil) was daily measured by gypsum blocks (Delmhorst KS-D1 Digital Soil Moisture Tester) inserted in the middle of the pot (8 cm from the top of the pot). Six gypsum blocks were used for each treatment. Gypsum blocks measure Ψsoil from -0.03 MPa to about -1.5 MPa. More accurate measurements of field capacity (-0.033MPa) and wilting point (-1.5 MPa) were obtained with a pressure membrane extractor and then related to soil moisture. The experimental design is represented in Figure 1. Five plants per treatment-species combination were collected at each of five harvest points. After 2 months of growth (day 60), a drought stress was applied. To this purpose, watering was completely withheld for a period of 16 days until gravimetric soil water content reached 86.6%. This stage was considered to be a moderate drought treatment (M-stress application; Fig. 1) corresponding to a Ψsoil of -0.19 MPa. From this point on, each pot was weighed daily and a small amount of tap water was added to maintain a constant weight. From day 85 on, watering was withheld again for a period of 10 days until gravimetric soil water content reached a value of 48.2%. This was considered to be a severe drought treatment (S-stress application; Fig. 1), with Ψsoil below wilting point (-1.5 MPa corresponds to a soil water content of 76.1%). Subsequently, soil moisture was kept constant for 15 days by adding tap water when necessary to keep pot weight constant. At day 110, soil moisture was increased to 86.6% (S-stress interruption; Fig. 1) and kept constant for a further 25 days. At day 135, soil moisture was increased to field capacity and kept constant for the final 25 days (M-stress interruption; Fig. 1). Control pots were maintained at constant field capacity (Ψsoil = -0.033MPa).
Fire treatments
To investigate the combined effect of drought and fire, a sub-sample of 10 seedlings for each species was burned 40 days after the beginning of drought treatment and 15 days after the beginning of severe drought stress (day 100; Fig. 1 and Fig. 2). To simulate a fire effect, the pot surface was covered with straw, which was set on fire for 15 seconds. After fire treatment, seedlings remained under S-stress and recovery of this cohort started at the same time as the remaining seedlings, which had undergone only drought treatment.
Fine root measurements
After removing each seedling from the soil, roots were rinsed repeatedly with running tap water, and scanned at a resolution of 400 dpi with a calibrated flatbed scanner coupled to a lighting system for image acquisition (Epson Expression 10000 XL; Fig. 3). Images were analyzed by WinRhizo Pro V. 2007d (Regent Instruments Inc. Quebec) in order to measure 1) measure fine root length and 2) group the different fine root fragments in two diameter classes (very fine d<0.5 mm, fine 0.5<d<2.0mm). Fineroot biomass was determined by collecting five seedlings for each species at each sampling point every 25 days starting on day 60. Fine roots were detached from each seedling and biomass was calculated as dry mass after oven drying (52 h at 75°C).
Statistical analysis
Morphological measurements were square root or log transformed to ensure normal distributions and equal variances for the use of parametric statistics. A threeway MANOVA was used to compare different species, experimental steps and drought treatments. Post-hoc Bonferroni tests were conducted to detect overall differences between species at each experimental step and between control and drought-treated seedlings for each species at each experimental step. Analyses were applied on a 95% significance level. Statistical analysis was carried out using statistical software package SPSS 17.0 (SPSS Inc, Chicago IL, USA).
Results
The effect of different stresses (drought; drought + fire) on plant fine root system development, was investigated by measuring fine-root traits such as biomass (dry weight; g) and length (cm) for two diameter classes (vFR -very fine roots, d<0.5 mm; FR -fine roots, 0.5<d<2.0mm) for each oak species.
Root biomass
In control seedlings of all three considered species, vFR biomass increased significantly (p<0.001)throughout the experiment (Fig. 4a, b, c). Q. virgiliana showed a slower increase than the other two species reaching at the end of the experiment (day 160) an almost two-fold higher biomass compared to the beginning of the experiment (day 60; Fig. 4a). Q. ilex and Q. trojana both showed a linear increase reaching, respectively, a three-fold and two-fold increased root biomass at day 160 (Fig. 4b, c). An opposite trend was observed when considering FR biomass (2.0<d<0.5 mm) which increased throughout the experiment in control seedlings of Q. virgiliana (Fig. 4d) while both Q. ilex and Q. trojana stopped their growth at day 85 and 110 respectively (Fig. 4e, f). Q. virgiliana seedlings showed significant higher (p<0.001)values of vFR biomass than control seedlings at day 85, after 25 days of M-stress application (Fig. 4a). During S-stress and recovery from severe to mild stress, vFR biomass decreased to values lower than those of control seedlings. At day 135, when mild stress was released, vFR biomass increased rapidly and reached the values of control seedlings (Fig. 4a). Q. ilex seedlings under drought stress showed a continuous increase of vFR with values similar to those of control seedlings. Moreover, during the sever stress application Q. ilex drought stressed seedlings keep increasing their vFR biomass reaching values significantly higher (p<0.05)than control seedlings at day 135. When mild stress was released, vFR biomass decreased reaching values significantly lower (p<0.05)than those of control seedlings. Q. trojana drought stressed seedlings showed a continuous increase of vFR biomass throughout the experiment with values always significantly lower (p<0.001)than those of control seedlings (Fig. 4c).
In all three oak species FR biomass did not increase during M-stress and S-stress (Fig. 4d, e, f) resulting in significant lower (p<0.05)values compared to controls. Q. virgiliana started to recover after S-stress release (day 110) and FR biomass values dramatically increased when M-stress was also released resulting in no significant difference to control values at day 160 (Fig. 4d). FR biomass in Q. ilex showed a slight decrease at the beginning of the drought period when M-stress was applied (day 60) and then remained stable until the end of the experiment (day 160) resulting in a biomass significant lower (p<0.05)than that of control seedlings (Fig. 3e). Q. trojana seedlings under M-stress showed a FR biomass increment similar to control seedlings (Fig. 4f) however, when S-stress was applied (day 85) FR biomass decreased significantly (Fig. 4f; p<0.001). At day 110, when S-stress was released, FR biomass increased rapidly reaching almost the same values as those of control seedlings but remaining significantly lower (Fig. 4f; p<0.05). At the end of the growing period, the highest biomass values, i.e. at least two-fold higher compared to the beginning of the experiment, were found in Q. trojana for both root diameter classes and irrespective of stress treatment. Q. ilex showed vFR values similar to those of Q. virgiliana but significant lower values of FR (p<0.001).
Forty days after the beginning of drought treatment (day 100), 10 seedlings per species were subjected to fire treatment for 15 seconds. Although fire treatment was brief, leaves and stems were burned completely and all seedlings showed crown dieback a few days after resumption of the normal watering regime. Seedlings did not show fireinduced mortality. Indeed, on day 135 (35 days after fire treatment), new stems resprouted from dormant buds present at the root collar and new leaves developed in all burned seedlings of all species except for one single Q. virgiliana seedling. After fire treatment, biomass of both diameter classes decreased significantly (p<0.05) for Q. ilex and Q. trojana seedlings. Q. virgiliana, on the other hand, showed a decrease of vFR at day 160 while FR maintained similar values (Fig. 4a, d). At the end of the growing period, 60 days after fire disturbance, all fire-drought stressed seedlings showed significantly lower (p<0.001)values of root biomass than control and drought seedlings for both diameter classes in all three oak species.
Biomass allocation by diameter classes
The allocation of biomass between the two diameter classes of fine roots considered showed differences between species and between control and stressed seedlings (Fig. 5). Control seedlings showed three different patterns of allocation depending on the oak species. Q. virgiliana seedlings showed an increase in the percentage of FR biomass at the beginning of the experiment that remained stable until the end of the growing period (day 160; Fig. 5). Q. ilex seedlings, on the other hand showed a continuous increase of the vFR biomass percentage throughout the whole experiment (Fig. 3b). The same pattern, but less intense, was found in Q. trojana (Fig. 5). Drought-stressed seedlings showed differences in allocation pattern in the two diameter classes in comparison with control seedlings. In particular, Q. virgiliana drought-stressed seedlings showed an opposite pattern compared to control seedlings with an increment of vFR biomass when M-stress and S-stress were applied, recovering the same values of control seedlings when stresses were released (Fig. 5). In the case of Q. ilex, the drought-stressed seedlings pattern did not differ from control seedlings, showing a continuous decrease through the whole experiment but with higher percentage of vFR (Fig. 5). Drought-stressed Q. trojana seedlings only showed a slight increase of vFR biomass percentage when S-stress was applied, recovering the percentage of control seedlings right after S-stress was released (Fig. 5). When fire was applied all the three species showed the further increase of vFR biomass compared to drought stressed seedlings. In particular, Q. virgiliana and Q. trojana seedlings showed the highest percentage of vFR biomass 35 days after fire treatment (day 135; Fig. 5) and Q. ilex seedlings 60 days after fire treatment at the end of the experiment (day 160; Fig. 5).
Root length
In control seedlings of all three considered species, vFR length showed a significant increase (p<0.001) throughout the experiment (Fig. 6). Q. virgiliana showed a lower increase than the other two species reaching at the end of the experiment (day 160) a two-fold higher root length compared to the beginning of treatment (day 60 ; Fig. 6a). Both Q. ilex and Q. trojana reached, respectively, a three-fold and four-fold increase in root length at day 160 (Fig. 6b, c). In the case of FR, control seedlings of Q. virgiliana showed an opposite trend as FR increased throughout the experiment (Fig. 6d) while both Q. ilex and Q. trojana stopped FR growth at day 85 and 110, respectively (Fig. 6e,f).
When the same morphological traits were measured in drought-stressed seedlings, Q. virgiliana showed significant higher vFR values (p<0.05)than those of control seedlings at day 85, 25 days after of M-stress application (Fig. 6a). During S-stress and the recovery from severe to mild stress vFR length decreased to values lower than those of control seedlings. At day 160, despite a recovery during normal watering, drought-treated seedlings showed significantly lower (p<0.05)values of vFR length compared to control seedlings (Fig. 6a). Drought-stressed seedlings of Q. trojana and Q. ilex showed lower values of vFR length compared to controls throughout the experiment (Fig. 6b, c). In particular, Q. ilex stopped vFR growth during drought treatment at day 85, which resulted in significant lower (p<0.05)vFR values than those of control seedlings at day 160 (Fig. 6b). Q. trojana, despite an increase of vFR length during M-stress and recovery after S-stress interruption, showed significant lower (p<0.05)values compared to control seedlings at day 110 and day 160 (Fig. 6c). In all three oak species FR length did not increase during M-stress and S-stress (Fig. 6d, e, f) resulting in significant lower values (p<0.05)compared to controls. Q. trojana started to recover after the end of S-stress treatment showing FR values that were not significant lower than those of control seedlings at day 135 and 160 (Fig. 6f; p=0.535 and p=0.728, respectively). Q. virgiliana started to recover after day 135 during normal watering reaching FR values not significantly lower than those of control seedlings at day 160 (Fig. 6d; p=0.324). In Q. ilex FR length continued to decrease after S-stress interruption and started to recover only during normal watering but values remained significantly lower (two-fold) than those of control seedlings (Fig. 6e; p<0.05). At the end of the growing period both vFR and FR length values were highest in Q. trojana seedlings irrespective of stress treatment (p<0.001;Fig. 6c, f). In Q. ilex seedlings vFR length values were intermediate between those of Q. trojana and Q. virgiliana whereas FR length of Q. virgiliana showed intermediate values between those of Q. trojana and Q. ilex.
Discussion
This paper analyses fine root dynamics in seedlings of three co-existing oak species (Quercus ilex, Quercus trojana and Quercus virgiliana) when subjected to drought or/and fire treatment in a growth chamber. This study has been conducted under controlled conditions in order to exclude the influence of other potential environmental variables. The long-term aim of this study is a) to throw light upon the role of fine roots in supporting seedling survival potential of these tree species in a context of global change and b) to collect data useful to predict the fast-cycling fraction of carbon stored in below-ground organs when seedlings are under normal or stress conditions. The latter point could be used to better estimate the carbon stored below ground in the assumption that the fast-cycling fraction of seedling fine roots could remain proportionally the same for belowground biomass of adult trees. In addition, this type of study regards the possibility to use the turnover rate as an indicator of root efficiency in resource acquisition and sustain plant metabolism (Mainero et al. 2009;Montagnoli et al. 2014). Finally, a better knowledge of seedling survival potential under stressful conditions could help decision makers in choosing the most appropriate management actions (Frelich and Reich 1998;Gauthier et al. 2008;in Thompson et al. 2009).
The data presented here indicate that the fine root fraction in seedlings of the three oak species considered respond differently to drought and/or fire stress. In particular, data referring to Q. virgiliana control seedlings indicate that thicker fine roots increase their biomass and length contemporaneously with temperature increase as has been found in other plant species (Pregitzer et al. 2000;Kaspar and Bland 1992;Larson1970;Wilcox and Ganmore-Neumann1975;Teskey and Hinckley 1981;Bevington and Castle1985;Kuhns et al.1985;Lieffers and Rothwell1986;McMichael and Burke 1998;King et al. 1999;Weltzin et al. 2000;Montagnoli et al. 2014;Barney 1951;Merrit 1968;McMichael and Burke1998;Pregitzer et al. 2000). Different is the pattern in very fine roots where biomass and length show a variation which seems to be largely independent of temperature except the final stage of the experiment. Q. virgiliana seedlings respond to water withdrawal with a rapid surge of very fine root biomass which is higher than that observed in unstressed control seedlings. This response could be an attempt to increase the volume of soil exploited per unit mass invested in fine roots (Ostonen et al.2007;Montagnoli et al. 2012;Comas et al. 2002;Curt and Prevosto 2003;Comas and Eissenstat 2004). However, the peak reverses with the application of severe stress suggesting that there is probably a threshold-value whose exceedance leads to a steep decrease in fine root growth. According to Eissenstat and Yanai (1997), when exceeding such a threshold value the metabolism of fine roots might be arrested and a root shedding induced to achieve a more functional overall root system dimension. Furthermore, our data indicate that as soon as water becomes available again, metabolic and physiological functions of fine roots are fully resumed.
In Q. ilex control seedlings, the temperature-dependent increment of biomass and length seems to be limited to the very fine root fraction indicating that in oak species the response of this root fraction could be really species dependent. In Q. ilex seedlings water withdrawal affects mainly the length parameter independently of the root diameter class whereas biomass is affected only in thicker fine roots. These results could be explained assuming that very fine roots continue to accumulate organic matter even if elongation or new emission of roots is arrested. The thicker fine roots of this oak species are affected by water withdrawal in both their capacity to accumulate organic matter and to undergo further growth. The discrepancy observed compared to increase of biomass and length in the finer root fraction could represent a specific tolerance strategy to counteract lack of water availability (Nardini and Tyree 1999) through starch accumulation as was shown by Deans and Ford (1986) in the root apparatus of Sitka spruce and by Di Iorio et al. (2015) in beech saplings. It would be interesting to investigate whether this tolerance strategy has any advantage when harsh environmental conditions end.
Fine roots of Q. trojana control seedlings respond to temperature increase with very slight differences depending upon their diameter. The absolute values for biomass and length parameters achieved at the end of experiment are twice as high as those found in the other two oak species examined here. Unfortunately, the meaning of this difference in terms of recruitment potential under normal or stress conditions remains unclear at present. When water is withdrawn the effect on biomass and length became clearly visible at a later stage probably when root growth stops and several roots die, as found by Cudlin et al. (2007) in other plant species. Also in the case of Q. trojana seedlings, there could be a threshold value for water withdrawal tolerance before starting an overall reduction of the root system dimension by arresting root growth and/or by starting a root shedding event (Joslin et al. 2000;Chiatante et al. 2005Chiatante et al. , 2006;;Di Iorio et al. 2011). This finding is similar to data reported by us regarding a Q. cerris forest (Montagnoli et al. 2012). However, the data collected here indicate that even when these effects on the fine root fraction are induced, the survival of seedlings at the end of the stress treatment remains preserved as suggested by the resumption of growth in all fine root categories.
The data referring to the effect of fire superimposed on water withdrawal clearly indicate that the addition of this further stress worsens the situation by arresting both biomass and length in a similar way independently of the oak species considered. At the same time it is important to underline that even by coupling two stresses the seedlings of all three oak species resume growth as shown by the emission of new branches and leaves (Chiatante et al. 2015). The resumption of growth in the aboveground organs contrasts considerably with the resumption of growth in the fine root fraction. In fact, even at the end of the experiment a clear-cut resumption of biomass and length growth has not been observed in fine roots. However, it is reasonable to speculate that the carbon stored in the roots and in the stem is used immediately to build new leaves and branches to the detriment of roots. Probably a longer recovery time would have shown the resumption of root growth as was found by us in different experiments conducted in the nursery or in the growth chamber with another oak species (Chiatante et al. 2005;Di Iorio et al. 2011).
Conclusions
The study presented here demonstrates that all three oak species are sensitive to temperature which stimulates an increase in fine root biomass and length independently of their diameter. Both fine root length and biomass are affected when water is withdrawn but a good level of tolerance was observed as long as a threshold limit is not exceeded. In terms of tolerance strategy these data suggest that: 1) all three oak species are able to adjust fine root morphology in response to water shortage by inducing a general decrease of mean diameter; and 2) the very fine roots are probably the most dynamic and responsive root fraction able to respond to climate change. However, the observation that all three oak species tolerate water stress and fire (both alone or in combination) does not provide clues as to which species has a better recruitment potential and, therefore, should be preferred in adapting strategies for this forest in response to the foreseen worsening of climate change conditions. From a biomass point of view, Q. trojana seedlings show the highest biomass storage potential in the fine roots compartment but this trait should be compared with for example the coarse roots. Moreover, only a complete calculation of carbon storage accumulated in the roots of adult trees could provide a more important indication regarding their contribution to a specific ecosystem service. Certainly, the highest values of both biomass and length in fine roots of Q. trojana suggest that resumption of root growth following soil rewetting could be more rapid in this oak species. Furthermore, Q. trojana productivity under recurrent drought could be higher in comparison with the other two oak species. However, all these considerations should be evaluated against other data obtained in a previous study (Chiatante et al. 2015) where we showed that the deciduous or semi-deciduous oak Q. trojana is less resistant to cavitation due to the presence of xylem vessels with a very large diameter. The balance between the contrasting properties of this oak species should take into consideration that it is able to sustain hydration at a less negative value of water potential (Vilagrosa et al. 2012) by developing a larger root system able to explore deeper soil layers. Finally, even though our data suggest that Q. trojana is an adequate candidate for a good natural seedling recruitment under a climate change worsening, the final decision on the correct adaptive strategy for managing this type of forests needs further investigations.
Figure 1 .
Figure 1. Schedule and sequential connection among the three drought/fire treatment combinations. Numbers indicate the experimental day, corresponding to seedling age, of control (black pots), drought stress (white pot) and drought+fire (gray pots); stresses application and release is indicated by the downward arrow. M-stress, mild-drought stress application, Sstress, severe drought stress
Figure 2 .
Figure 2. Time-course pictures in seconds (s) of the fire application to Q. trojana seedling from fire set (1 s) to completely seedling burnt (15s)
Figure 3 .
Figure 3. High resolution image of Q. trojana seedling root system acquired by Epson Expression 10000 XL at day 135 in control, drought and drought + fire stressed seedlings.
Figure 4 .
Figure 4. Very fine and fine -root biomass (rows), of Q. virgiliana, Q. ilex and Q. trojana seedlings (columns) at days 60 (harvest 1), 85 (harvest 2), 110 (harvest 3), 135 (harvest 4) and 160 (harvest 5) under three treatment combinations (see Section 2). Continuous line (-) and dashed lines (---) refer to watering and no-watering conditions, respectively; dotted line (….) refer to burnt conditions. Values are the mean of 5 replicates; standard error of the mean is indicated (±) if it is larger than the symbol. Within each harvest, if written, the means of the different treatment combinations without a common letter are significantly different (P < 0.05).
Figure 6 .
Figure 6. Very fine and fine -root length (rows), of Q. virgiliana, Q. ilex and Q. trojana seedlings (columns) at days 60 (harvest 1), 85 (harvest 2), 110 (harvest 3), 135 (harvest 4) and 160 (harvest 5) under three treatment combinations (see Section 2). Continuous line (-) and dashed lines (---) refer to watering and no-watering conditions, respectively; dotted line (….) refer to burnt conditions. Values are the mean of 5 replicates; standard error of the mean is indicated (±) if it is larger than the symbol. Within each harvest, if written, the means of the different treatment combinations without a common letter are significantly different (P < 0.05).
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Domain: Environmental Science Biology
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The Utilization of Native Freshwater Mussel Pilsbryoconcha exilis as Biocontrol of Pathogenic Bacteria Aeromonas hydrophila in Tilapia Aquaculture Rahman1,*,
This research aims to evaluate the capacity of freshwater mussel Pilsbryoconcha exilis as a biocontrol agent to prevent the transmission of Aeromonas hydrophila in tilapia cultivation. Briefly, 10 tilapia fish with average bodyweight 7,88±0,25 g were subjected to four treatments in the 8-liter aquarium with three replications in a randomized design trial. The treatments were M1 (one mussel for a two-liter of water), M2 (two mussels for a two-liter of water), and two control treatments without mussel ( M+ and M-). All treatments, except the M-, then challenged by adding A. hydrophila live culture to obtain a final density of 105 CFU mL-1 into the aquarium for 7 days duration. The final survival rate of fish, the water-total bacterial count, and the blood profile of animals were assessed. The research revealed that there is a significant impact from the presence of freshwater mussel on tilapia cultivation. Generally, the M2 treatment showed better results with a significant different (P<0,05) according to the survival rate of fish (100±0,00%), water-total bacterial count (4,53±0,03 log CFU mL-1), and fish leucocytes (4,30±0,70x104 cell mm-3). Nonetheless, there was no different (P>0,05) effect on fish erythrocytes among the treatments. Therefore, the feeding activity of freshwater mussel in the water column able to deplete pathogenic bacteria abundance and prevent pathogen transmission along with increasing the survival rate of fish.
Introduction
Tilapia O. niloticus is one of the major aquaculture commodities in Indonesia wherein 2016 there was an increasing domestic production as 14,73% reaching 1.187.812 mt compared to the previous year's production. However, this rate was not able to achieve a proposed target in 2017 where the domestic production had expected reaching 1.246.278 mt (KKP 2018). Environmental alteration and disease outbreak are somewhat relevant to production failure because most of the tilapia production in Indonesia is outdoor activities and often experienced unpredictable outcomes when these factors interacted. The dynamic of the environment able to shape the disease severity when the pathogenic agents are present in the surrounding of a susceptible population (Raiha et al. 2019).
Motile aeromonad septicemia (MAS) which caused by A. hydrophila infection was responsible for the death of 60% of tilapia in Kutai Kartanegara, East Kalimantan (Hardi et al. 2014) and infected as many as 22.22% of tilapia in the Lake Tondano (Tantu et al. 2013). Common symptoms of fish infected with MAS are hemorrhagic, melanization to the fish body, dropsy, and exophthalmia (Rosidah et al. 2018). Consequently, this infection often results in lower fish growth rates and significant economic losses in the national aquaculture industry.
Infections in aquaculture industries usually prevented and treated with chemicals, antibiotics and vaccinations (Austin and Austin 2007), which is relatively expensive or often followed by secondary problems such as increasing the antimicrobial resistance (AMR) incidence in aquaculture (Henriksson et al. 2017) and spill-over of the residues into the environment (Martinez, 2009). The indispensable exploration of ecologically and sustainable methods for preventing diseases in aquaculture production is necessary.
The ecological approach in aquaculture disease management is gaining attention among the scientist since it is offering an environmental friendly practice and propose the aquaculture environment health. The feeding activities of various bivalve in reducing or removing waterborne pathogen from the aquatic environment are notable and have growing literature in many publications. The presence of blue mussel Mytilus edulis proved to lead to the rapid removal of parasitic Paramoeba perurans the causative agent of salmonid amoebic gill disease (AGD) from the water column in an experimental nature (Rolin et al. 2016), and also ingested the infective stage of sea lice Lepeoptheirus salmonis in a lab study (Molloy et al. 2011).
Freshwater mussel Pilsbryoconcha exilis is a native bivalve in Indonesia that naturally dwelling in the aquaculture environment. Dissimilar to the marine mussels that gained considerable studies, this ediblefreshwater mussel is in lack of attention and lack of utilization to be incorporated in aquaculture management by the local institution or by fish farmers. Since the feeding activity of mussel able to deplete the organic suspension in the water column, preventive measures in managing disease is promising. This research will evaluate the biological feasibility and utilization of P. exilis in the reduction of pathogenic bacteria abundance A. hydrophila in the tilapia cultivation system.
Fish and freshwater mussels stocking
The juvenile of tilapia (Nirwana strain) with average bodyweight 7,88±0,25 g obtained from a local fish farms in the Sukabumi district were pooled and reared in 3x2x0,8 m concrete tank and then every 10 fishes carefully transferred to the 8 L-aquarium filled with filtered water for 7 days acclimation. The fishes were fed with a commercial diet (30% total protein) twice a day at satiation and provided with adequate aeration. The freshwater mussels with average bodyweight 72,27±1,65 g obtained from the ponds of the experimental facility, Fisheries and Marine Science IPB University. The mussels were pooled and reared in separate 3x2x0,8 m concrete tank which filled with naturally enriched-ponds water.
The pathogen A. hydrophila
A local isolate of A. hydrophila obtained from the Fish Health Laboratory of Aquaculture Department, IPB University. Prior to use in the experiment, the isolate was axenically cultured overnight in a slant medium of trypticase soy agar (TSA, Himedia, India) at room temperature and subsequently recultured in 8 ml trypticase soy broth (TSB, Himedia, India) for another overnight before harvested and used in a passage trial to recover the virulence. The passage has done to the African catfish juvenile by mean injecting the live cells of pathogen intraperitoneally at dose 10 5 CFU ml -1 ind -1 . The fish that showed the symptoms then sacrificed and dissected to reisolated the pathogenic A. hydrophila from the kidney to the Rimler-Shotts (RS) medium (Himedia, India). The yellowish colonies in RS medium then purified in the TSA medium and a confirmation test was done by using KIT API 20E (Biomerieux, France). Regular maintenance of the selected and the confirmed colonies of A. hydrophila was performed in a slant agar of TSA, while to obtain a mass of pathogen live cells were conducted by harvesting an overnight culture of A. hydrophila in 50 ml of TSB medium
Experimental design
The experiment was performed in a set aquarium within randomized design with three replications. The animals were left for another 48 hours for acclimation. Afterwards, once stable condition achieved (noticeable by no mortalities among animals) the treatment then run for total 14 days duration. The fishes fed with commercial diets containing 30% total protein twice a day at satiation and provided with adequate aeration. The cohabitation of fish and mussels was conducted by adding 10 fishes for each aquarium and the mussels density was set according to Table 1 below. Positive control, no mussels added and the fishes were challenged by A. hydrophila M-Negative control, no mussels added and the fishes were not challenged by A. hydrophila M1 One mussel for a two-liter of water, the fishes were challenged by A. hydrophila M2 Two mussels for a two-liters of water, the fishes were challenged by A. hydrophila
Challenge test
On day 8, all treatments were challenged (except the M-) by mean the immersion method. One liter of an overnight culture of A. hydrophila (approximately density 10 9 CFU ml -1 ) used as a stock culture. The live cells were harvested by precipitation (3000 rpm, 20 min) and resuspended within the sterile water to obtained its initial volume. A direct simple dilution was performed by adding the suspension of A. hydrophila live cells into the aquarium to achieve a final density of 10 5 CFU ml -1 . This procedure was conducted only once and animals left for another 7 days until day 14. During the course of the challenge test, the fish were fed twice a day with commercial fish diet.
Water quality maintenance
The quality of water was maintenanced regularly by replacing 20% of the water from total volume daily before the challenge test. To prevent a biased result in the experiment, at the day of challenge test and afterward, the water replacements were halted in order to avoid the decreasing of pathogen population in the water column.
Parameters
The parameters were observed at random sampling including the survival rate (SR), the blood profile of fishes, total hemocyte count (THC), and total bacterial count. The SR data were observed daily during seven days of challenge test while other parameters were observed three times during the course of the challenge test. The blood profiles were done by taking the fish blood through the vein vessels of the tail. For total erythrocytes, blood is taken as much as 0.5 scales, it then diluted 200 times with a solution of Hayem and counted in a hemocytometer. Total leucocytes will be determined by diluting fish blood 20 times in Turk's solution and then counted in a hemocytometer. For hemocyte count, a freshly 0.10 mL of hemolymph withdrawn from adductor muscle then gently mixed with 0,10 mL of anticoagulant and diluted with formaldehyde to make a 1:1 ratio. Cell concentration then counted with a hemocytometer.
For assessment of the bacterial load in the water column, one mL of water sample withdrawn from each trial and serially diluted in test tubes containing sterile water. An aliquot (50µL) then spread on the surface of the TSA medium and then incubated in 37 0 C overnight. The grown colonies were counted, expressed as CFU mL -1 .
Data analysis
All collected data were presented in either graphical or table using Microsoft Excel 2010. Data analysis of survival rate, total erythrocytes, total leucocytes, total hemocyte count, and water-bacterial load were processed using SPSS 10 and one-way analysis of variance (ANOVA) to determine the difference between treatments and then processed to the Duncan's test.
Tilapia survival rate
Before challenged by the pathogenic A. hydrophila, there were no mortalities in fish population among the treatments. However, after a challenge test during 7 days period, the treatment of M+ and M1 showed a significant decreasing in fish survival rate while the treatment M-and M2 were sustaining 100±0.0% of the fish population. The lowest value of the final survival rate was recorded in M+ with a value of 40±14,14% and marked a significant difference (P<0.05) with M2 and M-.
Total erytrocytes of tilapia
A high value of total erythrocyte noticeable at the beginning of the infection which indicating a level of stress among fish in all treatments. There were significantly different (P <0.05) total erythrocyte values before the challenge test between control and mussel treatments as shown in Figure 2. However, a remarkable decline occurred on the 4th day after the challenge except for the M2 treatment. The M2 treatment was sustaining the highest total erythrocyte reaching 2.53 ± 0.35 × 10 6 cells mm -3 and significantly different (P<0.05) from other treatments. On the last day of the challenge test, all treatment showed insignificant differs from others (P>0.05).
Total leucocytes of tilapia
There was a significant increase in the total leucocytes in the M+ treatment until the end of the challenge test as seen in Figure 3. The peak on the last day of testing, the total leucocytes in the M+ was 7.40 ± 0.77 × 104 cells mm -3 , significantly different (P <0.05) with the M2 treatments, whereas in the M1 and M2 treatments the total leucocytes were fluctuating with a tendency to decrease when compared to day 1. The graphic shows the total leucocytes of M2 treatment on the last day of testing was the lowest with 4.30 ± 0.7 × 10 4 cells mm -3 significantly different (P <0.05) with M+ and M1 treatments.
Total hemocytes count (THC) of mussel
Due to no mortalities among the mussel in the course of challenge test, then the impacts of pathogenic bacteria on the immune status of the feeding mussels was assessed by comparing the dynamics of circulating hemocytes among the mussels in the treatments. Since the control treatments were not used the mussels, the comparison of THC fluctuation was done between M1 and M2 only as shown in Figure 4 below. There was no significant fluctuation of THC on the M1 mussel during the challenging test. Different dynamics were found in the M2 with significantly different fluctuation on the mussel's THC mussel (P <0.05). The lowest value was found on M2 mussels on the 7th day with a value of 4.75 ± 0.35 × 10 2 cells mm -
Total bacterial count
Generally, the total bacteria in the water column from all treatments showed a tendency to increase until the last day of observation. However, a significant different results (P <0.05) between treatments always consistent at day 4 post infection onwards. The M+ treatment always showed the highest bacterial abundance in the water column while the lowest one is shown by the M2 treatment as seen in Figure 5 below. Figure 5. Total bacterial count in the water column in the course of the challenge test. The different letter above the bars in the same day showed the significant difference (P<0.05) among the treatments.
Discussion
The ecological services by filter feeder animals in the aquaculture system indeed may have either benefits or adverse impacts to the production i.e. by improving water quality (Brown et al 2011, Chopin et al 2001, Joyni et al 2011 and entrap the swarming pathogen in water column (Molloy et al 2011, Rolin et al 2016, Webb et al 2013 or may also be detriment to the farm biosecurity by accumulating the pathogen and transmitting the pathogen from one species to another (McConnachie et al 2013, Ben-Horin et al 2015, Desrina et al 2013. In this context, the fate of the infection may depend on the particle selectivity of bivalve, the degree of infectivity of pathogens, the transmission mechanism of the pathogen, and the ability of pathogens to resist degradation in the gut of bivalve (Burge et al 2016).
In this research, we were able to prove that the presence of P. exilis in the tilapia cultivation system was able to suppress the transmission of a waterborne-pathogen A. hydrophila to the fish-host. Comparing to the M+ treatment which was experiencing a severe infection and had lost a total 60% of its initial population, the native mussel in M1 and M2 treatment obviously showed a decreasing of the disease severity in the fish population, thus maintaining the survival rate at 85±5% or even reaching 100% as the same level to the M-treatment.
The pathogen A. hydrophila has the ability to excrete several compounds such as hemolysin, protease, cholinesterase, enterotoxin, endotoxin, and adhesin which play a role in the development of virulence of pathogenic bacteria (Citarasu et al. 2011). Therefore, observation of the blood profile is somewhat relevant as an indicator of fish health. The highest total erythrocyte was observed in the M-treatment, expecting that the fishes were not infected or affected by the releasing toxins of A. hydrophila. However, it did not significantly differ to the outcome of M2 (P>0.05), this findings strengthen the evidence that the feeding activity of P.exilis in the M2 treatment may play an eminent role in depleting A.hydophila from the water column to a level where the pathogen was not able to infect and then prevent them in poisoning the red blood cells of fish.
Leukocytes in the fish have an important role to destroy the infecting pathogens through phagocytosis activity as a part of non-specific defense (Sukenda et al. 2008). A high pathogen burden in the water column increasing the probability of infection to the fish body. The presence of the pathogen in the body of fish usually provoking the immune system to generate more leucocytes circulating in the fish blood (Austin and Austin 2007). A noticeable increasing leucocytes count in the M+ treatment in the course of challenge test was related to an immune response against infection. On the contrary, decreasing of leucocyte count in the M2 treatment indicating that the fishes were not in suppressive activity against the pathogen due to insignificant infection. However, the increase in leucocytes number is not always related to infection. Stress condition, nutritional factors, and also age factor often tributed to the increasing of leucocytes number in fish (Maftuch et al 2011).
Invertebrate animals relied on their innate immunity to respond to an infection. Their circulating hemocytes playing a role to deliver the immune response and their cell abundance have a strong relationship with its immune status. In our research, it was seen that the presence of foreign live particles of A. hydrophila inducing the proliferation of hemocytes of P. exilis on day 4th either in M1 or M2 treatment. However, the declining of THC occurred on the last day of observation. It seemed that in the final day, the M2 showed insignificant THC fluctuation ( Figure 4) and its bacterial load was lesser ( Figure 5) compared to the M1. This findings were indicating the pathogen was neither amplified within the mussel body nor exhibiting considerable hostile activities to the immune system of mussel. A non-target host mussel already proposed as a pathogen biocontrol due to its ability to reduce pathogen numbers in the aquatic environment (Burge et al 2016) without showing an alteration to their physiological parameters. The high number of mussels in the M2 treatment may have a contribution to the rapid pathogen depletion, lowering the pathogen burden in the mussel, thus recovering the THC to the normal state.
Moreover, even though the details of pathogen reduction by P. exilis was not elaborated in this works, we are guessing that the mechanism was related to the feeding activity of the mussel which was, in turn, depleting the pathogen abundance in the water column, similar to what Othman et al (2015) already observed on Streptococcus agalactiae in the previous works. He founded that P. exilis able to decrease the mortality of tilapia fish and inhibit the outbreak of streptococcosis without accompanied by negative impact from the presence of mussel. Another remarkable mechanism is related to the living space-shifting which was also already observed in V. anguilarum. This pathogen previously was in high abundance in the water column and becoming lesser when the blue mussel present. However, the viable of pathogenic cells become accumulated and denser in the pseudofeces of blue mussel and sank on the floor of the codfish tank (Pietrak et al 2012). Proper consideration must be taken if the latter mechanism has exactly occurred because a massive spill-back of the pathogen to the susceptible fish host would lead to disease outbreak in the future (Burge et al 2016).
Conclusion
The native freshwater mussel P. exilis able to prevent the transmission of waterborne pathogen A. hydrophila when cohabitated with the tilapia. Overall, the best treatment is the M2 based on the survival rate of fish, total leucocytes, and total bacterial loading in the water with value as 100±0,00%, 4,30±0,70x10 4 cell mm -3 , and 4,53±0,03 log CFU mL -1 respectively. Most of freshwater mussel species have a parasitic stage where their glochidia are the ectoparasite temporarily infesting on the freshwater fish, thus a studies to evaluate the possible trade-off that may occur, and the fate of swallowed pathogen would be a necessity to be addressed in order to obtain the whole picture of this ecological approach before being adopted in the farm level.
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Domain: Environmental Science Biology
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Forest Health Management and Detection of Invasive Forest Insects
The objectives of this review paper are to provide an overview of issues related to forest health and forest entomology, explain existing methods for forest insect pest detection, and provide background information on a case study of emerald ash borer. Early detection of potentially invasive insect species is a key aspect of preventing these species from causing damage. Invasion management efforts are typically more feasible and efficient if they are applied as early as possible. Two proposed approaches for detection are highlighted and include dendroentomology and near infrared spectroscopy (NIR). Dendroentomology utilizes tree ring principles to identify the years of outbreak and the dynamics of past insect herbivory on trees. NIR has been successfully used for assessing various forest health concerns (primarily hyperspectral imaging) and decay in trees. Emerald ash borer (EAB) (Agrilus planipennis), is a non-native beetle responsible for widespread mortality of several North American ash species (Fraxinus sp.). Current non-destructive methods for early detection of EAB in specific trees are limited, which restricts the effectiveness of management efforts. Ongoing research efforts are focused on developing methods for early detection of emerald ash borer.
Introduction
Non-native, invasive species are one of the greatest threats to biodiversity and ecosystem stability worldwide [1,2]. Even the most remote forests have the potential risk of exotic introductions due to globalization, continually shifting trade patterns, and climate change [1,3]. Invasive phytophagous (plant feeding) insects also cause substantial economic losses in the United States, particularly to municipal governments and homeowners [2]. The vast majority of exotic introductions fail to establish or have little impact, but some are considered invasive if they cause catastrophic changes to ecosystem structure and function [1,4]. The term invasive is typically associated with non-native species causing severe damage, however, many introduced species are not considered invasive in their new habitat. While select species of nematodes, fungi, bacteria, viruses, plants, animals, insects and other arthropods have all been classified as non-native, invasive species, the main focus of this paper will be on invasive, forest insects. Primary reasons why an insect can become invasive in a new habitat include their arrival without natural predators, parasitoids, and parasites, and the new host plants lacking co-evolved, natural defenses [1,5,6]. Many invasive insects are not detected until well after they have established and begin to spread in their new habitat. Examples of exotic insect species considered to be invasive in North American forests include: emerald ash borer (EAB) (Agrilus planipennis), gypsy moth (Lymantria dispar L.), and hemlock woolly adelgid (Adelges tsugae) [2,7]. The objectives of this review paper are to provide an overview of issues related to forest health and forest entomology, explain existing techniques for forest insect pest detection, and provide background information on a case study of emerald ash borer.
Forest Health Overview
Definitions of forest health are broadly varied but can generally be classified as either utilitarian or ecological [8]. Several definitions of forest health include: "a fully functioning community of plants and animals and their physical environment; an ecosystem in balance; a forest resilient to change; the ability of forest ecosystems to recover from natural and human stressors; and a condition where biotic and abiotic influences on forests do not threaten management objectives now and in the future" [5]. The Society of American Foresters defines forest health as "the perceived condition of a forest derived from concerns about such factors as its age, structure, composition, function, vigor, presence of unusual levels of insects or disease, and resilience to disturbance" [9]. With multiple and often contradictory definitions of "Forest Health", to reduce misunderstandings (particularly for environmental policy and management), Raffa et al. [10] recommends using the term when describing how well ecosystems function within their historical range of variability and using modifiers when including human objectives when describing a forest. Ultimately, defining forest health is a human construct and to describe a forest as "healthy" depends on management objectives and ecosystem processes.
A quantitative, ecologically based concept for describing forest health is the idea of baseline mortality [11,12]. Baseline mortality is the number of stems in a specified size class that must die in order for population stability, and is often represented by a negative exponential (more small trees and less large trees) [11,12]. Mortality can be achieved naturally through competition or disturbance, or by anthropogenic factors. Disturbances are events that alter the structure, composition, or function of an ecosystem by increasing the availability of limiting resources, and traditional types include native insects and diseases, fire, drought, and invasive species [3,5]. While disturbances harm individual organisms, they can be an essential and normal component of overall ecosystem health [10]. For a forest to be considered healthy, a proposed description is for it to be both sustainable and productive [9]. A sustainable forest has been defined as "one that maintains a stable diameter distribution or structure, and has a balanced relationship between growth and mortality; a forest in which observed mortality is not significantly above or below baseline mortality" [9]. A productive forest is one that meets management objectives [9]. As they are not within their historic range of variability, intensively managed plantation forests are generally not considered as healthy ecosystems, but if they meet management objectives, they can be sustainable [10]. Forest health management does not focus on individual organisms or species, instead it is considered in an ecosystem or landscape level [5,8,10].
Causes and Symptoms of Forest Health Issues
Many forest health problems can be induced by direct human action [9]. Species loss, insect and disease epidemics, excessive wildfires, air pollution, water quality problems, impacted wildlife, nutrient imbalances, and soil and watershed damage are some of the main issues associated with anthropogenic forest health problems [5]. Examples of human actions that negatively impact forest health are past and current land use activities, fire suppression, pollution, anthropogenic climate change, desertification, introduction of invasive species, and recreational activities [9,10]. Forested areas have seen an increase in land use and management over the previous two centuries resulting in land cover conversion and reversion to earlier successional stages [13]. These past management activities have altered ecosystem structure and function which often results in increases in the severity and impact of tree diseases and insects [14]. Urban trees are particularly predisposed to stress, as they are often planted in unfavorable sites, grow in compacted soils, have high exposure to pollution, are often planted as monocultures, and are physically damaged by human actions [15,16].
Disease is defined as "a chronic condition or irritation that prevents the affected host from reaching its maximum genetic potential" and can be caused by biotic or abiotic causal agents [17]. Biotic causal agents of disease are broadly referred to as pathogens and include fungi, oomycota, bacteria, viruses, plant-pathogenic algae and vascular plants, and nematodes [18]. Insect feeding is mostly limited to short term or localized damage, and insects are generally not a chronic condition [17]. Some species of insects will interact with and transmit pathogens to facilitate a disease [14]. An example is Beech bark disease, where the scale insect, Cryptococcus fagisuga, introduces the fungus Nectria coccinea to American beech (Fagus grandifolia), causing tree decline and mortality [14]. Abiotic causal agents of disease include but are not limited to, nutrient deficiencies, flooding, drought, unfavorable site conditions, and air pollution [17]. A third category of disease are declines (often referred to the decline disease spiral), which only impact populations of mature trees and results from multiple, interacting causal agents grouped into predisposing, inciting, and contributing factors [17,19].
Native insects and pathogens that feed on trees are essential, natural thinning agents that reduce competition through mortality and contribute to overall ecosystem health [10]. Natural disturbance agents (i.e., forest insects) are only considered pests due to human values and expectations, and the term "pest" has been defined as, "an organism that interferes with our management objectives" [10]. Tree mortality due to native organisms is often not widespread, but there are cases of naturally occurring, periodic outbreaks of native insects, such as the spruce beetle (Dendroctonus rufipennis), the mountain pine beetle (Dendroctonus ponderosae), spruce budworms (Choristoneura fumiferana and C. occidentalis), and the southern pine beetle (D. frontalis) in North America which can cause extensive tree mortality [17,20]. While most widespread outbreaks of native forest insects can be severe, often they are reoccurring and natural parts of the ecosystem process [17,20]. However, in the case of the mountain pine beetle, the recent increase in frequency and severity of outbreaks has been associated with climate change, overstocked stands, fire suppression (which has increased the proportion of overmature, susceptible trees in these forests), and prolonged drought [5,20].
The term invasive species in this paper will refer to non-indigenous organisms whose introduction was facilitated by humans, and which are currently causing significant ecological and or economic harm as defined by Parry and Teale [3]. There are several key stages of a biological invasion by an invasive species from its native region to the area it is introduced [21]. The sequential stages of invasion include arrival (transportation from native habitat to new location), establishment (population growth), and spread (movement of species in the introduced habitat to new areas) [7]. However, the actual stages and terminology is not always agreed upon and using the stage based method for explaining invasions can imply that they only occur one at a time [21]. For each phase of an invasion, the organism is faced with barriers and ultimately only a small portion of species from the original source establish and even fewer become invasive in a new habitat ( [1,3]. Nearly all non-indigenous introductions today can be classified as human-assisted and the number of introductions has greatly increased with the expansion of international trade [22,23]. However, the establishment rate in the United States has remained relatively constant since around 1860, with an average detection of 2.5 new phytophagous species a year [1]. Many insects are unintentionally transported on infested nursery and seed stock, wood packing material, and lumber that is shipped internationally [24]. The horticultural and ornamental trade industries in particular, are an important pathway for many invasive invertebrates, pathogens and plant species [22]. Invasions of forest insect pests worldwide are directly influenced by continuously changing modes of transportation, shipping practices, shifts in trading patterns, and international plant transport [1,23]. Many countries, including the United States, have established regulatory practices including quarantines and inspections (particularly for borders and ports of entry), in an attempt to prevent exotic introductions [1].
An explanation for the ability of certain non-native insects to become invasive in a new habitat is that plants develop mechanisms for deterring or surviving attack through co-evolution with their herbivores [6,25]. When insects are introduced to new habitats they may be able to feed on hosts due to a lack of suitable defense mechanisms [25]. Another reason for invader success is that when they are introduced to the new habitat, it is often without natural enemies that typically keep their populations in check [25]. The emerald ash borer is a phloem feeding beetle native to Asia that causes widespread mortality of the North American species in the genus Fraxinus [26]. To date, the emerald ash borer is considered to be the most destructive forest insect pest in North America [2,26]. The gypsy moth (native to Europe and Asia) is currently the most destructive forest defoliator in the United States and can feed on a wide range of host species [27]. The gypsy moth, like other defoliating insects, mainly causes tree mortality only when severe defoliation occurs consecutively over several years, or in conjunction with additional disturbance factors [2]. The hemlock woolly adelgid (native to Japan) is a sap feeding insect that causes localized decline and mortality of eastern hemlock (Tsuga Canadensis) in the eastern United States [2,28].
Applied Forest Entomology
Forest entomology is the study of forest insects, both beneficial and those considered pests. Since it was first established, the field has undergone substantial changes and continues to evolve [25,29]. Forest entomology was first developed in Germany in the 1800s and the first member of this field, Julius Theodor Christian (J. T. C.) Ratzeburg is credited as being the "father of forest entomology" [30]. For many years, forest entomology in North America was primarily focused on developing methods of pest control to protect forest resources [25,29]. During the time period from around 1945 to 1965, potent insecticides were widely used to kill forest insect populations [25,30]. Around the 1960s in the United States, the advancement of knowledge in forestry, diminishing old growth forests, and changes in societal values allowed the entire field of forestry to shift from focusing on timber harvesting towards managing forests for both marketable and non-timber values [25]. Entomologists around this time also realized that many forest insects play important ecosystem services and that feeding induced tree mortality is sometimes beneficial for overall forest health [25]. Widespread usage of environmentally persistent insecticides which also had unintended non-target effects were also cause for concern. These changes in societal values, increased scientific knowledge and more frequent exotic introductions have made forest entomology much more complex today than it once was.
At least half of insect species are estimated to be herbivorous (phytophages), but of the 29 insect orders, only nine include or contain species that feed on live plants [31]. These orders include: Thysanoptera (thrips), Orthoptera (crickets, grasshoppers), Phasmatodea (stick insects), Hemiptera (true bugs, aphids, and scale insects), Coleoptera (beetles), Lepidoptera (moths and butterflies), Hymenoptera (ants, wasps, bees, hornets, sawflies), Psocoptera (bark lice), and Diptera (flies) [5,31]. There are 16 orders of insects that include species that eat dead or dying plant material (detrivores, decomposers, and shredders) [31]. For most plant species, multiple insect herbivores can simultaneously exploit almost every part of the plant [31]. Coevolution with insects has allowed trees to develop barriers to herbivory [6,25]. Insect herbivores of living plants can be classified by diet breadth (host range) and feeding guild (similar feeding mechanism of the same resources) [31]. Host ranges include monophagous (limited to feeding on a single species or genus), oligophagous (feeding in a single family for multiple genera), and polyphagous (ability to feed on multiple families of plants) [1,31]. The hemlock woolly adelgid is considered monophagous in North America as it only feeds on trees in the Tsuga genus, while the gypsy month (Lymantria dispar) is polyphagous as it is capable of feeding on several hundred tree and shrub species in multiple families ( [28,32]. In some cases, the diet breadth of a species can change when in its native range compared to where it is non-native [31]. A comprehensive study of the known 450 non-indigenous insect species established in the United States saw that 38% were monophagous, 33% were polyphagous, and 29% were oligophagous [1]. Foliage and sap feeding insects (exophages) make up the majority of non-native introductions to the United States [1]. Concealed feeding guilds (endophages) include stem and wood-borers, fruit borers, seed and pod borers, gall insects, and root feeders [31]. Introductions of endophages, such as phloem feeding and wood boring beetles, have increased recently as they are often transported internationally in wood packing material and lumber [24]. Wood boring and phloem feeding invasive insects are associated with the highest economic and ecological impacts compared to defoliators as they typically cause greater mortality [2].
Outbreaks of forest insects (both native and non-native) are controlled by the components of integrated pest management (IPM): (1) mechanical and physical control; (2) chemical control, (3) biological control [5,21]. Mechanical and physical control involves methods that destroy or remove the insects themselves, or change their habitat so that it is no longer suitable such as silvicultural methods (e.g., thinning) [5]. For example, mass trapping with attractive traps and heat treatment of lumber can be used to kill any insects living in the material [5]. Using chemical treatments alone is often not economically feasible in most forests to reduce insect populations and the negative environmental effects of many pesticides often outweigh the benefits [5]. Semiochemicals are compounds often used by insects during host and/or mate-location and selection [4,5]. Semiochemicals are an important tool for monitoring and detecting native and exotic insects as part of pest management practices [4,5]. Classical biological control involves the importation, release, and establishment of an invasive insect's specialist predators from its native habitat to the new one ( [7,25]. Negative impacts have occurred when introducing generalist predators and current methods now incorporate quarantined screening for potential, unintended hosts to avoid introducing a new pest [25]. Integrated pest management is an approach that combines multiple, suitable control methods in order to reduce or manipulate populations to acceptable levels that limit the degree of economic related damage (since complete eradication is rarely feasible) [5,27,31]. An example of a successful integrated pest management program is the slow the spread program for gypsy moth (Lymantria dispar) [27]. This program deploys grids of pheromone-baited traps ahead of the population front, and once detected, colonies are delineated and treated [27].
In the United States, the U. S. Department of Agriculture (USDA) Forest Service, Forest Health Monitoring (FHM) established in 1990 is responsible for annually monitoring the status, changes, and trends in national forest conditions [5,33]. The U. S. Forest Service Inventory and Analysis (FIA) Program is responsible for conducting sampling in the FIA national field plot network [33]. The FHM program works with the FIA, and state and Federal Agencies to monitor forest health and to provide information on invasive species and updated conditions of forest insects and diseases [33]. The USDA Forest Service started the Early Detection and Rapid Response program in 2001 for detecting non-indigenous phloem feeding and wood boring insects in the United States [1], and by 2016 this detection list has grown to 355 species [34].
Detection Techniques of Invasive Insects
Early detection of potentially invasive insect species is a key aspect of preventing these species from causing damage [3]. In practice, this is difficult and often, non-native species are not detected until after they are already established [3]. Studies have indicated that invasion management efforts are typically more feasible and efficient if they are applied as early as possible [25,35,36]. Eradication may be possible if new colonies of potentially invasive insects are detected early when the insect is still in a limited geographic area, particularly urban areas [25,36]. After initial detection, trace-forwards and trace-backwards are conducted if an interception is associated with a particular commodity. Delimitation surveys are implemented to determine where the invasive insect population is distributed in the new environment [3,37]. Control measures cannot be applied without information of where the insect is located. Detection methods are often specific to the species causing the damage, the life stage under observation and type of damage caused/feeding behavior [3,38]. General survey methods may utilize aerial and ground based surveys; observations of the insect itself; the signs, symptoms, or damage caused by the insect; and baited traps [3]. Trapping detection methods for low population levels typically require effective attractants such as plant compounds or pheromones for the intended species [37]. One example of early detection is the mitigation efforts of gypsy moth, which have been successful in the United States largely due to detection and delimitation of outlying populations using highly effective pheromone traps [37].
Insects that feed within trees are typically more difficult to detect than insects that feed on external tree tissues [35]. In the United States, introductions and detection of non-indigenous phloem feeding and wood boring insects have increased over the past few decades [1]. Bark beetle and wood borer detection methods involve ground surveys in well-roaded areas, aerial surveys for large areas, and the use of host volatile or pheromone baited traps [5]. Attractants are not available for many beetle species and their detection is difficult [4,37]. Recent developments include DNA-based methods. DNA "barcodes" for example, lead to both detection and identification species with 100% accuracy [38]. This method has great potential, but requires the development of global DNA databases for all relevant taxa in order to provide a standardized tool for worldwide use [38]. Locations for implementing insect surveillance methods are also important as a recent study in Italy concluded that detection of non-native wood boring beetles was improved by surveying in wood waste landfills in addition to ports of entry [39]. Dendrochronology has also been used to reconstruct past outbreak dynamics and spread of invasive insect species [40][41][42][43], as will be discussed in the following section.
Spectroscopic and imaging techniques have been applied successfully for plant stress detection, particularly in agriculture and have potential for rapid, non-destructive, and cost-effective detection of damaging insects [3]. Hyperspectral imaging is used in agriculture and some forestry applications by acquiring the spectral reflectance in the visible and infrared regions of the electromagnetic spectrum [44]. In particular, studies have shown that hyperspectral remote sensing can produce detailed maps of forest health conditions and species distribution on a landscape scale [45], but require ground-truthing and have limited temporal resolution. There has also been studies looking at the volatile organic compounds (VOC) released by vegetation which are influenced by humidity, temperature, light, soil conditions, fertilization, growth and developmental stage, and insect presence/damage [44,46].
Dendrochronology
Dendrochronology is the study of tree ring dating to explain surrounding environmental information including climate, disturbance events, stand composition and insect herbivory throughout a tree's lifetime [47]. Previous studies have shown the usefulness of dendrochronological methods for observing the impacts of climate, disturbance events, insects and diseases on radial growth [40,48,49]. In dendrochronology, valid inferences can be made indirectly about the mechanisms of tree response to their environment [50]. Dendroclimatology (a subfield of dendrochronology) is the study of past and present climates using climatic information and tree ring growth [51]. Trees are highly responsive to their surrounding environments and climatic factors (i.e., temperature and precipitation) in particular have been shown to be primary controlling factors on ring growth [47]. Trees growing in different regions are often limited by different climate factors; for example, trees growing in the American Southwest are typically limited by moisture availability while at higher latitudes, temperature is the primary limiting factor [52,53].
Dendroentomology utilizes tree ring principles to identify the years of outbreak and the dynamics of past insect herbivory on trees. While dendrochronological methods have been successfully used to identify past outbreaks and reconstruct the spread of some invasive insects, it is less likely that dendrochronology can be used for early detection of acute insect infestations. Dendrochronological studies have shown that the mountain pine beetle (Dendroctonus ponderosae Hopkins), a bark beetle native to North America that occurs primarily on lodgepole pine (Pinus contorta Douglas ex Loudon) shows periodic outbreaks approximately every 40 years in central British Columbia [54]. Rentch et al. [43] used dendrochronology to relate changes in radial growth with crown condition in eastern hemlock (Tsuga canadensis) infested by hemlock woolly adelgid (Adelges tsugae). A previous study was conducted on green ash (Fraxinus pennsylvanica) to reconstruct the spread of emerald ash borer near the epicenter of initial EAB establishment in North America (southeast Michigan) [40]. Using a systematic grid, Siegert et al. [40] collected samples from a geographic area in southeast Michigan (around 1.5 million ha) in order to reconstruct the initial establishment and spread of EAB. Siegert et al. [40] concluded that EAB was established in Michigan by the early to mid-1990s, several years before it was officially discovered in 2002. Dendrochronological studies are also more challenging in tropical forest regions because of the lack of seasonality in temperature but seasonality in precipitation can provide the opportunity for examining annual growth dynamics [55,56].
Near Infrared Spectroscopy
Near-infrared spectroscopy (NIR) is becoming an increasingly popular technique in a wide range of industries (agricultural, pharmaceutical, industrial, wood-products industry and forestry). NIR is an appealing technique as it investigates the vibrational properties of materials rapidly and nondestructively [57]. The near-infrared region is located in the 780 to 2500 nm region of the electromagnetic spectrum between the visible and infrared regions [58]. Modern NIR spectroscopy (post 1960s) incorporates high performance and commercially available spectrometers with multivariate analysis to provide chemical and physical information for both biological and manufactured materials [58]. Compared to chemical analysis, which is destructive and costly for relatively small amounts of samples, NIR can non-destructively analyze bulk materials rapidly with minimal sample preparation [58]. For NIR studies measuring wood, the spectra are either collected in diffuse reflectance or transmission modes [59]. Transmission is typically limited in its application because this method requires more sample preparation such as milling or slicing [59]. Diffuse reflectance has a wider application as it can measure intact, solid samples in addition to samples whose original state has been altered (although changing the physical state of samples will influence spectra) [59]. A NIR diffuse reflectance spectrum is a composite of chemical and physical properties of a material [58]. Diffuse reflectance works by sending a beam of light to the object being measured, where it interacts with the sample before being reflected back to the spectrometer [59]. NIR spectra are unique for every substance, and if two or more samples have similar spectra, it can be assumed that they have similar chemical and physical composition [58]. Conversely, if the spectra of multiple samples are different, it can be assumed that the materials are physically and/or chemically different [58].
In NIR studies, multivariate calibrations are often required for spectral analysis. While there are a wide variety of multivariate analytical methods that are used for NIR analysis, they can be separated between two distinct groups, quantitative and qualitative analysis (for the purposes of this paper we will only discuss qualitative methods). A qualitative method, discriminant analysis, is used for sorting spectra by sample type and applying the technique to compare the different groups [60]. Discriminant analysis is used for NIR spectroscopic analysis in order to qualitatively determine whether a sample is similar or different compared to samples from one or more predetermined groups [60]. Discriminant analysis has been successfully used in several previous NIR studies for classifying spectral samples based on distinct groups [61][62][63]. Ertlen et al. [61] successfully used multiple discriminant analysis to distinguish between grassland and forest soils. Evans et al. [62] saw that while discriminant analysis improved the identification of wood from two tree species compared to existing identification methods, classification was still not perfect. Watanabe et al. [63] were able to successfully identify intact wood compared to wood with wet-pockets using two different variants of discriminant analysis.
The majority of NIR research done for trees is implemented in the wood products industry for wood structural quality and decay; however this technique is increasingly being explored for alternative applications in forestry [64,65]. NIR studies for assessing various wood properties (i.e., wood density) have been applied to both hardwoods and softwoods [66]. Wood properties are highly influenced by the amount of moisture in the sample, the effects of which can be minimized by oven drying all samples before collecting NIR measurements [59]. While not extensively, NIR has been successfully used for assessing various forest health concerns and decay in trees [45,67,68]. The majority of NIR based studies for tree decline due to insect infestation have measured foliage samples, while studies on decay will measure wood samples [45,67,[69][70][71]. Field-based NIR spectrometers and satellite-based hyperspectral NIR has the potential to detect early stages of hemlock wooly adelgid induced hemlock decline by measuring foliage [69]. Watanabe et al. [63] reported that spectroscopy in the visible light and near-infrared ranges (VIS-NIR) could discriminate between wood with wet-pockets and wood free of wet-pockets. To assess oak decline due to outbreaks of the red oak borer (Enaphalodes rufulus) a handheld NIR spectrometer measured foliage samples to describe plant stress [67]. However, the widespread implementation of hyperspectral imaging is currently limited by high costs [72]. For example, as of 2015, the costs associated with unmanned aerial vehicles used for hyperspectral imaging can be over $50,000 [72].
Several studies have explored the potential of utilizing hyperspectral imaging for detecting and mapping emerald ash borer infestations [45,[73][74][75][76]. Bartels et al. [75] found that when using hyperspectral images and LIDAR data for identifying hardwood tree species and EAB declining ash trees, misclassification of different tree species occurred. However, they were able to use hyperspectral imagery for classifying multiple ash health categories with accuracy up to 60%-70% [75]. Maps created using hyperspectral imagery and vegetation indices were able to identify five different ash decline classes (due to EAB infestation) at 97% accuracy [45]. A recent case study for EAB detection in Canada using multisourced data (both a variety of commercially available remotely sensed data and archived maps), highlighted that current challenges prevent effective use of hyperspectral technologies for detection and mapping EAB infestation [76]. These challenges include labor intensive and lengthy manual corrections of segmentations, limited data sources, and timing of image acquisitions [76].
Case Study: Emerald Ash Borer
The emerald ash borer is a phloem-feeding beetle native to Asia identified in southeast Michigan in 2002 as the cause of extensive decline and mortality of ash (Fraxinus spp.) [77]. Current methods in detecting emerald ash borer infestation early and at a large scale are limited, restricting the ability to effectively manage for this insect. It is imperative to develop cost-effective approaches in implementing regional scale methods for early detection of EAB. By using cross-dating techniques on cores collected from the initial infestation area of Detroit, Michigan, dendrochronological data has shown that EAB was established for around 10 years before first being detected [40]. As of 2015, EAB has killed tens of millions of ash trees in the eastern United States as well as parts of Canada and annually causes billions of dollars' worth of damage [2,78]. EAB is not considered a major pest where it is native in Asia, and does not cause widespread mortality of the more resistant Asian ash species including Fraxinus mandshurica and F. chinensis [15]. Ongoing research efforts include developing methods for early detection of emerald ash borer [78].
Life Cycle
The life cycle of an individual emerald ash borer is typically completed in one year, although some individuals may need two years to complete their development [79]. Two year life cycles have been observed on recently infested trees with low population levels of EAB, in areas with cooler climates, or associated with late summer oviposition [26,80]. Adult beetles emerge in May or June leaving behind D-shaped exit holes on the tree and live for about 3 to 6 weeks, during which they feed and mate [81]. The adults are typically more active on warm (>25 ˝C), sunny days [80]. After feeding on ash foliage for at least two weeks, mated females lay 60 to 80 eggs in bark crevices from late June through August, which hatch within 2 weeks when temperature are around 25 ˝C [80]. The larvae then chew their way into the bark and create serpentine shaped galleries packed with frass as they feed in the phloem, cambium, and outer xylem [80]. Extensive feeding by the larvae disrupts translocation of nutrients, and can girdle and kill a tree in 2-4 years after crown dieback becomes noticeable [26]. EAB will typically complete 4 instars before overwintering as prepupal larvae in the outer 1-2 cm of sapwood or bark beginning in October [26,81]. Two year life cycles occur when the larvae overwinter as early instars, continue to feed a second summer, and overwinter the second year as prepupae [79]. Pupation will take place from the middle of April through May for about 3 weeks, followed by adult emergence [15]. As the damage is done beneath the bark, infestation is difficult to detect in newly infested trees as there is often a delay between initial infestation and when visual symptoms develop [78].
Host Species and Spread
Ash is a fast growing woodland tree and has been planted extensively in urban areas which are often unfavorable sites [15]. The 16 endemic species of ash in North America are susceptible to EAB induced mortality in the United States, and potential hosts in the US range from Northern Maine to Southern California [15]. Where EAB is native, it typically only targets stressed or dying species of Asian ash, but has caused high mortality on imported North American ash species [80]. The two most common North American ash species, white (F.americana) and green (F.pennsylvanica) are taxonomically and economically important, but identification is complicated by their ability to hybridize [82]. While there appears to be strong preference for some species over others, all North American ash species located in its current range have proven to be susceptible to EAB infestation and decline to some extent [26,83,84]. The North American species, blue ash (Fraxinus quadrangulata Michx.) can be colonized by EAB but has been shown to have higher resistance compared to other native ash species [84]. The supercooling point for EAB is ´35.3 ˝C: at and below this temperature EAB freezes and dies [83]. It is expected however, that North American EAB distribution will be limited more by host range instead of climate [26]. EAB adults can fly away from the tree where they emerge, but the spread is primarily facilitated by people moving infested ash into uninfested areas [26]. Human transportation of infested ash material further predisposes urban forests for introductions and establishment of EAB [26]. Due to the ability of EAB to spread rapidly, research efforts have focused on identifying and developing new methods for early detection.
Evidence suggests that adult EAB use visual and olfactory cues in locating and selecting hosts [85]. There also appears to be adult preference for rough-barked trees over smooth-barked trees and will generally target trees grown in open conditions compared to shaded conditions [77,86,87]. Adults are also attracted to specific shades of green and purple [88]. Host volatiles produced by ash are attractive to adult beetles and bark sesquiterpenes and green leaf volatiles have been identified [78,[89][90][91]. These volatiles often increase with host stress and girdled ash trees in particular are highly attractive to adult EAB [86,87,89,91].
Past and Present Detection Methods and Treatment Options
After the discovery of EAB in North America, initial monitoring was based on the visual signs and symptoms of infestation such as D-shaped exit holes left by the emerging adults, longitudinal cracks in the bark over the S-shaped larval galleries, canopy dieback, epicormic shoots and woodpecker damage [90]. Accurate detection and monitoring methods of EAB populations and newly established infestations have been difficult to develop, which greatly hinders the ability to effectively manage for this pest. In addition to the lengthy amount of time before visual symptoms develop, on the ground detection is further complicated by the fact that adult EAB will typically target the upper portion of a tree during the initial infestation [15]. Furthermore, a long range pheromone has not yet been detected for EAB [92]. Two short range, contact pheromones produced by adult emerald ash borer females have been identified that are antennally attractive to the males and have been used to improve trap captures [80,90,93,94]. Currently, key survey methods include the use of external signs and symptoms, green and purple sticky prism traps baited with ash volatile lures, green multi-funnel traps, trap logs, and using girdled trap trees which are an expensive and destructive method [15,88,92,95]. Trap trees involve girdling (removing a band of bark and phloem from around the tree) individual trees which become attractive to the adult beetles [86,87]. After one to two years the tree is felled and debarked in autumn to inspect for EAB larvae and S-shaped galleries [96]. Artificial traps include the sticky prism traps [87,88], double-decker traps [95,97,98], and Lindgren green multi-funnel traps [78,99]. The manufactured sticky prism traps currently used for EAB are visually attractive purple and green made from corrugated plastic covered in sticky glue, which are typically baited with host volatile lures [88,90]. Efforts in the United States include deploying thousands of these baited purple prism traps [100,101]. Purple traps generally catch more females than males, and green prism traps (when hung ~13 m in the canopy) will catch more males than females [90]. Green multi-funnel traps treated with a slippery coating have demonstrated potential as an effective and reusable detection and survey tool [88]. These multi-funnel traps have recently started being deployed in the United States in addition to the purple prism traps [101]. While artificial traps are visually attractive to adult beetles, the effectiveness of traps depends on lure types and combinations and placement of the traps [78]. Improvements to EAB lures are still a focus of EAB detection research and the (Z)-3-hexanol lure is the current recommendation by the USDA APHIS (Animal and Plant Health Inspection Service) for trap deployment [78,88,101]. Girdled trap trees are more likely to detect EAB at low populations compared to artificial baited traps [102]. The study by Ryall et al. [103] developed a detection method for urban trees that involves the collection of branches that are subsequently debarked and inspected for EAB feeding activity. Preliminary studies utilizing hyperspectral remote sensing have explored as a potential method for emerald ash borer detection and surveying [45,73,75].
The long-term outlook for North American ash survival does not look promising, but treatment options do exist for high value urban and shade trees and efforts are underway to slow the spread of EAB [104]. Research has shown that even with large EAB populations, some insecticide options can be highly effective in keeping treated trees alive [104]. Some estimates have indicated that the cost of treating high value landscape and urban ash trees with systemic insecticides can be less than if the trees were removed [104]. There are four categories of insecticides that are currently being used to control EAB: (1) systemic insecticides applied as soil injections; (2) systemic insecticides applied as trunk injections; (3) systemic insecticides applied as lower trunk sprays; and (4) protective cover sprays applied to the trunk, branches, foliage [104]. These insecticide treatments are most effective when applied as soon as possible to relatively healthy trees, as trees that have lost more than 50% of its crown are usually too far gone to save [104]. In 2008, a pilot project to slow the impact of EAB, called SLAM (SLow Ash Mortality) was initiated as an integrated strategy for dealing with recently established outlier sites [100]. The SLAM pilot project used girdled ash trees, a systemic insecticide, and removal of ash trees [100]. Long-term conservation of ash by reducing EAB populations in North America have invested in classical biological control and three parasitoids native to China are currently being released in the United States [105]. At this time, the long-term impact of the biological control agents on EAB populations is uncertain [26].
Conclusions
The ability to detect and delineate infestations of destructive forest insects is essential for management and mitigation of damage. As current detection methods for individual trees are often destructive, this review evaluated the potential of two non-destructive methods as indicators of emerald ash borer infestation. That is, this review explored the potential of dendrochronology and near-infrared spectroscopy (NIR) as non-destructive indicator tools of emerald ash borer infestation in white ash. Dendrochronology is the study of tree ring dating and is used to explain environmental information (climate, disturbance, stand composition, etc.) throughout the lifetime of a tree. Valid inferences can be made using dendrochronology to indirectly explain the ecophysiological mechanisms of a tree's response to its environment including the impact of insects (i.e., dendroentomology). While Dendrochronology can be useful in characterizing past invasions, the potential as a rapid detection tool is less likely. Near-infrared spectroscopy measures the vibrational properties of objects in the near-infrared wavelength range of the electromagnetic spectrum (780-2500 nm) rapidly and non-destructively. NIR provides physical and chemical information by producing a spectrum along the near-infrared wavelength range unique to the measured sample. Cost limiting factors currently restrict the widespread application of NIR for early detection of insect invasions. Future research would be required for further development and refinement of these techniques as detection tools.
Since its discovery in 2002, emerald ash borer (EAB), (Agrilus planipennis Fairmaire) (Coleoptera: Buprestidae), has killed millions of ash trees (Fraxinus spp.) in the eastern U. S. and parts of Canada, and is continuing to spread throughout North America [26]. Emerald ash borer is considered to be the most destructive invasive insect pest in North America to date [26]. As this exotic insect spreads across the U. S. the economic damage is estimated to reach billions of dollars [2]. Research is ongoing for improvements to existing detection methods and developing new methods of effective EAB detection.\===
Domain: Environmental Science Biology. The above document has
* 2 sentences that start with 'Emerald ash borer',
* 2 sentences that start with 'Ongoing research efforts',
* 2 sentences that start with 'The gypsy moth',
* 2 sentences that start with 'The hemlock woolly adelgid',
* 2 sentences that start with 'Two year life cycles',
* 2 sentences that end with 'past insect herbivory on trees',
* 2 sentences that end with 'in their new habitat',
* 2 sentences that end with 'overall ecosystem health [10]',
* 2 paragraphs that end with 'of emerald ash borer'. It has approximately 6743 words, 282 sentences, and 43 paragraph(s).
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Abundance patterns of early stages of the Pacific sardine (Sardinops sagax) during a cooling period in a coastal lagoon south of the California Current-Patrones de abundancia de los estadios tempranos de la sardina del Pacífico (Sardinops sagax) durante un periodo de enfriamiento en una laguna costera al sur de la corriente de California
Abundance patterns of eggs and larvae of the Pacific sardine, Sardinops sagax (Jenyns, 1842), in Bahía Magdalena, Baja California Sur, were analysed during a cooling period south of the California Current from 2005 to 2009. The thermohaline characteristics and zooplankton abundance were good descriptors of the potential spawning habitat. Individual quotient analyses showed a predominance of eggs and larvae within a SST range of 16 to 18°C, at low salinities (33.9-34.1) and at low density gradient variability (0.009-0.029), associated with deeper waters (25-40 m) near the main entrance, where the transparency was intermediate (6-8 m) and zooplankton abundance was relatively high (>316 ml/1000 m3). Increments within different class intervals meant that neither dissolved inorganic nitrogen (DIN), phosphates nor chlorophyll a predominated. The large interannual fluctuations in sardine spawning activity and preferential temperatures observed in historical and recent data suggest that two sardine stocks spawn in Bahia Magdalena: one stock spawned in the period 1981-1989 and one stock spawned in the period 1997-2009. The influence of cooling and warming periods as additional components of the regional environmental framework is analysed and discussed.
INTRODUCTION
Most small pelagic fishes along the northeast Pacific coast show variations in the extent and duration of spawning in relation to water masses , Smith and Moser 2003, Emmett et al. 2005. The spatial range and location of spawning can be critical for eggs and larvae due to interan-nual changes in population structure and geographic variations in environmental conditions (Planque et al. 2007, Smith and Moser 2003, Emmett et al. 2005, Ibaibarriaga et al. 2007, Coombs et al. 2006. However, few comprehensive surveys of eggs and larvae are available to determine preferred spawning locations and little is known of the stock structures of various important species (Agostini and Bakun 2002), which are influenced by physical processes that provide favourable habitat requirements for larval growth and survival (Lasker 1978, Parrish et al. 1981, Bakun 1996, Logerwell and Smith 2001, Lynn 2003, McClatchie et al. 2007, Planque et al. 2007, Aceves-Medina et al. 2009).
The spawning season and temperature range of the Pacific sardine, Sardinops sagax, show marked latitudinal variation over its wide distribution range as there are a number of stocks (Parrish et al. 1989, Lluch et al. 2003) separated by distinct preferential ranges of sea surface temperature (SST) in the Northeast Pacific (Tibby 1937, Ahlstrom 1954, 1965, Lluch et al. 1991, Funes-Rodríguez et al. 1995 and the Gulf of California (Hammann et al. 1998). In the vicinity of Bahía Magdalena (BM), in the southern part of the California Current, two stocks (warm and temperate) of Pacific sardine overlap because temperate stock migrates in the winter with the strengthening of the California Current to its southern distribution limit, as evidenced by increased catches in spring-summer, conversely its northward movement begins in summer with the onset of the equatorial countercurrent (Félix-Uraga et al. 1996, Félix-Uraga et al. 2004. Morphometric results showed differences between the two stocks associated with different SST intervals along the Pacific coast of the Baja California Peninsula. These differences suggest that there are different morphotypes, but the current molecular data do not clearly support the existence of a phylogeographically structured population (García-Rodríguez et al. 2010).
The presence of two stocks coincides with a winter spawning period in BM (Torres-Villegas et al. 1995), probably associated with the warm stock and, a second period during summer outside the bay , Hernández-Vázquez 1994 related with the temperate stock. The spawning season of the winter stock in BM (warm stock) occurs in a preferential temperature range between 19 and 20°C (Saldierna- Martínez et al. 1987, Funes-Rodríguez et al. 2001, similar to the temperature range in the Gulf of California (Hammann et al. 1998. However, the temperate stock in the area during the summer may not spawn inside the bay due to the high water temperature reached in this season (>25.0°C) (Funes-Rodríguez et al. 2007).
The abundance and recruitment of the Pacific sardine are highly variable, probably due to long-term environmental processes (Lluch-Belda et al. 1992, Deriso et al. 1996, Félix-Uraga et al. 1996, Schwartzlose et al. 1999, Rodríguez-Sánchez et al. 2001, Melo-Barrera et al. 2010, Cota-Villavicencio et al. 2010). There is a good correspondence between the warming periods from 1956-1959 and 1976-1980 and the expansion northwards of sardine spawning. The main difference is that while there was a sustained cooling trend after 1959, sardine spawning was progressively restricted to the south (Lluch et al. 2003). The Pacific sardine fishery collapsed in California and Ensenada in the early 1950s, which coincided with the Mexican fishery moving south towards new fishing areas, such as Isla Cedros, BM and the Gulf of California (Félix-Uraga et al. 1996). Sardine eggs and larvae were most abundant towards the southern part of the sardine distribution range, south of Punta Eugenia and north of BM , Hernández-Vázquez 1994, Lluch et al. 2003. Subsequently, the sardine fishery showed signs of recovery in California in the early 1980s (Deriso et al. 1996) but not in BM during this same decade (Félix-Uraga et al. 1996). Catches have increased in the last decade (Félix-Uraga et al. 2007, Melo-Barrera et al. 2010, coinciding with a cooling period (Peterson and Schwing 2003). Populations of small pelagic planktivores generally have wide interannual variability in reproductive success, which results in extreme variability in their population sizes. This has large effects on the trophic levels and may be critical for biological communities with trophic structures that exhibit a striking "wasp waist" configuration (Bakun et al. 2010).
This study analysed the relationship between hydrographic fluctuations and the abundance of eggs and larvae of the Pacific sardine, Sardinops sagax, during a cooling period from 2005 to 2009 in Bahía Magdalena, Baja California Sur. The study assesses for the first time the sardine's potential spawning habitat in this bay, based on the occurrence ranges of early developmental stages in relation to various explanatory variables (SST, salinity, nutrient concentration, chlorophyll a concentration, water column density gradient and zooplankton biomass). The likelihood of spawning activity of different sardine stocks in BM is discussed in relation to interannual variations in commercial catches and the abundance of spawning products recorded in historical and current data.
MATERIALS AND METHODS
Bahía Magdalena, Baja California Sur, is one of the most extensive areas in Mexican Pacific waters. The system is located to the south of the area of influence of the California Current, on the west coast of Baja California Peninsula, Mexico (24°15'N 25°20'N and 112°30'W 112°12'W). Bahía Magdalena (BM) (649.7 km 2 ) comprises three zones: A northern zone with shallow channels (3.5 m, average depth) surrounded by estuaries bordered by mangroves; a western zone connected to the neritic zone (4.5 km wide and 40 m maximum depth); and a shallow eastern zone with sandy bottoms (3.5 m depth) (Fig. 1).
Between 2005 and 2009, 12 oceanographic surveys were carried out at 14 stations during the Pacific sardine spawning season from winter to early spring (Total of 166 plankton tows). Temperature and salinity (psu) were recorded with a CTD (Seabird 19) to 40m maximum depth. Nutrient (nitrites, nitrates, ammonium and phosphates) and chlorophyll a (CHL) concentrations were determined following Strickland and Parsons (1972) and Venrick and Hayward (1984), respectively. The sum of nitrites, nitrates and ammonium was considered to be the dissolved inorganic nitrogen (DIN). Samples were collected at the surface, filtered under less than one third of atmospheric pressure and then frozen (-40°C) prior to analyses. The stability of the water column was estimated by calculating the vertical density gradient (Peterson et al. 1988). A Secchi disc was used to measure water transparency (m). As supplementary information on the region's environmental framework, time series of the multivariate ENSO index (NOAA, Earth System Research Laboratory, Physical Science Division, www.esrl.noaa.gov), the average monthly upwelling index (m 3 /s 100 m of coastline) and SST anomalies (24°N, 113°W) (Pacific Fisheries Environmental Laboratory, www.pfeg.noaa.gov) were obtained to assess interannual environmental variations. Zooplankton samples were collected with a conical net with a standard 0.5 m mouth diameter and 0.505 mm mesh size, fitted with a calibrated flow-meter, towed at the surface (1 m depth) following a semicircular trajectory at about 1 m/s for five minutes. Integrated vertical plankton tows were not possible due to equipment limitations. However, high velocities that were measured and modeled (up to 1.1 m/s) suggest a well-mixed water column during the flood, when a strong tidal flow produced intense vertical mixing of near-bottom cold water with upper layer water, which led to reduced SST values (Zaytsev et al. 2010). Sam-Samples were preserved in 4% formalin with sodium borate as buffer. Zooplankton volume (ml/1000 m 3 ) was determined by measuring the displaced volume (Beers 1976). Eggs and larvae of S. sagax were sorted and their abundances expressed as number of individuals per 10 m 2 of sea surface. Historical information about the abundance of S. sagax (January to April) in BM (1982-1989 and 1997-2004) was obtained from the Centro Interdisciplinario de Ciencias Marinas database, La Paz, Baja California Sur, Mexico. Analyses of Variance (ANOVA) and box plots were applied to test for monthly differences (winter-spring 2005-2009) in abundance and environmental variables. These analy-These analyses were carried out for each data set, 12 months at 14 stations (N=166 stations). Spearman's rank correlation tests were conducted to identify relationships between abundance and environmental variables (each environmental variable was tested separately; P <0.05). The spawning season was characterized in terms of the explanatory variables recorded by means of quotient analysis. This technique is commonly used to identify preference or avoidance of spawning zones by assessing the distribution of eggs and larvae in relation to covariates of interest (Emmet et al. 2005, Ibaibarriaga et al. 2007. A canonical corre-A canonical correspondence analysis (CCA) was applied to correlate egg and larval abundance with environmental variables. Prior to analysis, S. sagax and zooplankton abundance data were log transformed as ln (x+1).
RESULTS
Individual quotient analyses applied to identify the range of those variables considered relevant in the Pacific sardine spawning habitat showed a predominance of eggs within a SST range between 16 and 18°C, at low salinity (33.9-34.1) and low density gradient variability in the water column (0.009-0.029) associated with deeper waters (25-40 m) near the access inflow, where the transparency was intermediate (6-8 m) (Fig. 2a-e). The egg quotients in relation to dissolved inorganic nitrogen (DIN), phosphates and chlorophyll a (CHL) did not show a clear trend because there were increments within different class intervals (Fig. 2f-h). In contrast, the shift in quotients towards the right of the zooplankton class intervals evidenced a predominance of spawning products associated with an increase in zooplankton (>316 ml/1000 m 3 ) (Fig. 2i). However, fish larvae quotients never surpassed a value of 1 with any hydrologic variable. (Fig. 3a). Salinity and average density showed less variation than SST (34.71 and 24.7 kg m -3 , respectively); however, density decreased as a result of the increase in SST (2005 and2009) (Figs. 3a, 3b and 3c). Nitrites and nitrates increased mainly in April (0.07 and 2.06 µM, respectively) and were usually lower early in the year (Figs. 4a and 4b). Ammonium increased in 2007 and early 2008 and 2009 (>3.7 µM) (Fig. 4c), while phosphates varied around or above the mean value (≥0.79 µM) during the study period, decreasing in early 2005 (Fig. 4d).
Chlorophyll a (>2.00 mg m -3 ) generally followed the increase in nutrients and phosphates (Table 1 (Table 1; Fig. 5). Spearman's rank correlations (P<0.05) revealed a significant correlation between phosphates and the CHL concentration but no significant correlations with nitrite and nitrate concentrations. Nevertheless, the chlorophyll a, nitrite and nitrate concentrations as well as the egg and larval abundances in the area around the inflow were correlated with both depth and transparency, which suggests an oceanic influence. Phosphate concentration and zooplankton abundance were negatively correlated with depth ( Table 2).
The first two axes of the canonical correspondence analysis (CCA) accounted for 23.5% of the total variation. The first canonical axis revealed an environmental gradient related to salinity, density, transparency and phosphate concentration (-0.592, -0.566, 0.611 and -0.65, respectively). The second canonical axis Bold numbers indicate singnificant differences between means (±95% confidence interval). was directly associated with SST (0.661), nitrate concentration and DIN (-0.552 and -0.514, respectively). There was a low correlation with nitrite, ammonium and the density gradient in the water column (-0.441, -0.196 and 0.252, respectively) (Table 3). The chlorophyll a concentration was associated with intermediate values of nutrients and density, whereas zooplankton abundance was associated with an increase in SST and salinity. Egg and larval abundances were correlated with transparent and deep waters (Fig. 6) in accordance with the outcome of the Spearman's rank correlations ( Table 2). The CCA of sites and environmental variables indicated that stations near the main entrance (stations, K1, L1, M1, N1, L2, M2) were largely related to transparent and deep waters (Axis 1, right), while shallow stations (Axis 1, left) were related to warmer waters with higher salinities, which indicates that there is high evaporation in the northern and eastern zones (I, J, K2, K3, M3, N1, N2, O). For some years, the relative positions of stations with respect to density and nutrient vectors have corresponded to sites located near the main entrance (Fig. 7).
DISCUSSION
Early stages of small pelagic fishes display latitudi-arly stages of small pelagic fishes display latitudinal differences separated by preferential SST intervals in different ecosystems around the world (Parrish et al. 1989, Lluch et al. 1991, Coombs et al. 2006, Ibaibarriaga et al. 2007). In the northeast Pacific, spawning of the Pacific sardine, Sardinops sagax, occurs over a wide SST range (13-25°C) across the area of influence of the California Current and Gulf of California. In California, the early stages of sardine occur in a temperature range between 13.5 and 16.5°C (Ahlstrom 1954(Ahlstrom , 1965, with a second peak between 19 and 23.5°C, which corresponds to the warmer part of its distribution range off the west coast of Baja California (Lluch et al. 1991). This second peak is similar to the that observed in BM (19 to 20°C) during the 1980s (Saldierna-Martínez et al. 1987) and in the Gulf of California (Hammann et al. 1998, which was even slightly higher during the El Niño years in 1983 and 1997 (19.0 to 21.5°C) (Saldierna-Martínez et al. 1987, Funes-Rodríguez et al. 1995. However, under the current cooling conditions (2005)(2006)(2007)(2008)(2009), the SST range includes lower temperatures (14 to 22°C) off Baja California (unpublished data) and in BM, with similar preferential temperature ranges (15.5-16.0°C and 16-18°C, respectively).
Physical processes that combine to yield a favourable reproductive habitat for coastal pelagic fishes have been called the 'ocean triad' (enrichment, concentration and retention) (Bakun 1996, Agostini andBakun 2002). In this case, food production determines survival and re-productive success of small pelagic larvae (Lasker 1978, Lynn 2003, Logerwell and Smith 2001, McClatchie et al. 2007, Planque et al. 2007, Aceves-Medina et al. 2009). Survival of first-feeding larvae improves when there is food of an adequate size and layered concentrations associated with a stable water column (Lasker 1978). However, the evidence of a correlation between water column stability and larval survival varies (Cury andRoy 1989, Planque et al. 2007). In Sardina pilchardus from the Bay of Biscay, egg abundance drops as water stratification increases (Planque et al. 2007), while the egg abundance of S. sagax in South Australia is significantly related to water column stability (McClatchie et al. 2007). In this study, sardine eggs and larvae were more abundant in non-stratified waters, although this was not statistically significant, probably because larvae occurred at stations both close to the bay entrance and in stable and shallow waters inside the bay. Intense coastal upwelling takes place in spring (Zaytsev et al. 2003(Zaytsev et al. , 2010 so that sardines are caught mainly in spring and summer in BM (Félix-Uraga et al. 2007), but they do not spawn inside the bay due to the high water temperatures reached in summer (>25.0°C) (Funes-Rodríguez et al. 2007). This implies that the high seasonal variability in sardine egg and larval abundance in BM is mainly related to temperature and the migratory movements of different stocks.
The biological productivity that characterizes BM results from nutrient enrichment entering from the adjacent sea and also nutrient regeneration in the bay itself (Gómez-Gutiérrez et al. 1999, Gómez-Gutiérrez et al. 2007, Palomares-García and De Silva-Dávila 2007. In line with this, the stations near the bay entrance were positively correlated with nutrient and CHL concentrations, whereas shallow waters were associated with phosphates. However, the correlations between spawning products and nutrient and CHL concentrations were not significant. This seems reasonable because spawning takes place mainly from January to March before the onset of the intense coastal upwelling in April and May (Zaytsev et al. 2003(Zaytsev et al. , 2010) that leads to a peak in CHL (Cervantes-Duarte et al. 2007 and phytoplankton (Gárate-Lizárraga and Siqueiros-Beltrones 1998).
Although a direct relationship between spawning products and zooplankton was observed, zooplankton abundance and diversity are known to be relatively low during the winter and high in the summer (Palomares-García and Gómez-Gutiérrez 1996). Thus, this relationship between sardine spawning and zooplankton might be related to food type. Early larvae feed on copepod nauplii (Arthur 1976, Turner 1984, which might correspond to the temperate species typical of the California Current that are usually present at the bay entrance (Acartia clausi, Calanus pacificus), including the euphausid Nycthiphanes simplex, or the species that are abundant inside the bay (Paracalanus parvus and A. lilljeborgii ) (Palomares-García and Gómez-Gutiérrez 1996, Gómez-Gutiérrez et al. 1999. In BM, the sardine fishery decreases with anomalous warming events (El Niño) but recruitment increases the following year (1983-84, 1992-93 and 1998), particularly in the case of the warm stock (Félix-Uraga et al. 1996. A similar trend was observed in spawning products, which decreased with intense El Niño warming events (1982)(1983)(1997)(1998) and increased after the warming event (Fig. 8a). Nevertheless, changes in the abundance of small pelagic species are associated with distribution shifts that occur over decades and in large geographic areas (Lluch-Belda et al. 1992, Deriso et al. 1996, Félix-Uraga et al. 1996, Schwartzlose et al. 1999, Rodríguez-Sánchez et al. 2001. This suggests that during the warm period (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)) the main reproductive group in BM is the warm stock (winterspring). Due to its distribution south of the Peninsula, this stock can only be caught in BM, whereas sardines from the temperate stock can be caught in any of the three fishing areas (BM, Isla Cedros and Ensenada) (Félix-Uraga et al. 2004. In fact, even though commercial catches in BM were relatively small during the warm period (10000 t; 1981-1989), they were comparatively smaller (5000 t) in Isla Cedros and Ensenada during the 1980s (Félix-Uraga et al. 1996). Consistently, a winter spawning season in BM corresponds to the warm stock (Torres-Villegas et al. 1995, Funes-Rodríguez et al. 2001, whereas the temperate stock, located between Punta Eugenia and BM , Hernández-Vázquez 1994, is caught in spring and summer in BM (Félix-Uraga et al. 2007) but does not spawn in the bay due to the high SST in the summer (Funes-Rodríguez et al. 2007).
Over the last decade, a cooling period off BM (Peterson and Schwing 2003), which has been confirmed by climatic indices (Figs. 8b and 8c), markedly impacted the sardine spawning activity in BM. This is evidenced by the comparatively low preferential SST ranges. More recently, although peaks in egg abundance were observed to alternate every two or three years (>1000 eggs per 10 m 2 in 2001,2005,2008), drastic decreases occurred in consecutive years (100 eggs and 15 larvae per 10 m 2 in 2000,2003,2004,2006,2007,2009). In contrast, the warm period between 1981 and 1988 had marked peaks (in 1984, 1987, 1989) but smaller interannual decreases (<500 eggs; >50 larvae per 10 m 2 ), except in 1983 and 1988 when intense El Niño and La Niña events occurred (Fig. 8b). Under the current cooling conditions (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)) and a likely expansion of the temperate stock, the total catch of Pacific sardine increased in the fisheries located on the peninsula's western coast, and particularly in BM (50000 t) between 2000and 2008(Félix-Uraga et al. 2007, Melo-Barrera et al. 2010. Based on these observations, reproduction of the warm stock might be delimited in BM, whereas the temperate stock may be making a larger contribution in the present conditions. This is related to the presence of mature and post-spawning individuals throughout the year (2006)(2007)(2008), although there is a larger percentage (90%) of males and females early in the year in addition to a second, smaller peak in June-July (Melo- Barrera et al. 2010). However, the reasons why large increases followed by drastic decreases in the early stages of sardine have been observed during the present cooling period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009), seem to be related to migration processes, fluctuations in SST anomalies and moderate El Niño events.
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Domain: Environmental Science Biology
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Influence of climate and regeneration microsites on Pinus contorta invasion into an alpine ecosystem in New Zealand
In many regions, alien conifers have spread widely at lower elevations and are increasingly found colonizing alpine areas. Although studies have addressed conifer invasions at low elevations, little is known about the rates and constraints on spread into higher elevations. Here, we assess the relative importance of climate and the availability of regeneration microsites on the establishment of the alien species Pinus contorta into a high elevation site in New Zealand. Spread has occurred from two stands planted at the elevation of the native treeline (1347–1388 masl) in the 1960s. Most stems established between 1350 and 1450 masl and P. contorta individuals were found up to 270 m above the original plantings. Although the population has increased by 180% in the last 20 years, population growth rate has been declining. Furthermore, comparisons with studies from other mountain ranges around the world and at low elevations in New Zealand suggest this is a relatively limited spread. Our results suggest that climate variation did not have a significant effect on establishment patterns, as opposed to availability of regeneration microsites. Soil and alpine mat microsites favoured establishment of P. contorta and, although these microsites did not appear to be saturated, microsite availability may be an important limiting factor for the spread of P. contorta. Thus management strategies should focus on preventing spread in addition to removing already established stems.
Introduction
Compared to lower elevations, high elevation environments (i.e., alpine zones above the natural treeline) have been considered to be at low risk of plant invasion because of the harsh climatic conditions and lower human population density [1][2][3][4]. However, in the last decade, the presence of alien species in alpine zones has been increasingly documented around the world [4][5][6][7][8], and this trend is postulated to reflect greater human impacts at high elevations [4,9]. Together with increasing anthropogenic influence, propagule pressure and disturbance, climate change may further facilitate establishment of alien species in previously unsuitable environments [10][11][12] by mitigating unfavourable conditions associated with high elevation habitats.
Recent studies suggest that the suite of factors likely to promote range expansion of alien species into high elevation are precisely those that may lead to a steady decline in native species [12,13]. Thus, larger efforts are needed to estimate the risk of invasions into native communities. However, other factors potentially hampering the spread of alien species into alpine zones, such as biotic interactions [14], soil conditions [15] and microsite availability [16,17] may have been overlooked [18]. Therefore, a primary focus of current ecological research is to gain a better understanding of the drivers of invasion in order to develop more targeted management strategies.
As yet, current understanding of the consequences of plant invasion remains limited [19] and this is especially true in alpine zones. In contrast to the many alien herbaceous species that have colonized alpine zones, it might be expected that the establishment of alien trees above the native treeline would result in much more marked changes to ecosystem processes. Examples of alien tree invasions above native treelines are rare, but this phenomenon is increasingly being observed for alien conifer species [20][21][22][23]. This situation is relatively common in some regions of the Southern Hemisphere such as New Zealand, where treelines are composed of slow growing tree species such as Nothofagus sp. that show little or no upward expansion in response to climate change [24]. Such unresponsiveness to climate warming appears due to the many requirements this species has for successful establishment such as shelter, shade, mycorrhiza and nutrients [25]. The low tolerance of native tree species to the harsher climate found at high elevations results in a low competitive ability against invasive conifers [4,26]. Therefore, introduced conifers, such as pines, that are often pioneers and tolerant to temperature and drought stress as well as disturbance, can colonize high elevation habitats with limited interference from native tree species. This invasion has the potential to alter the structure of local treelines and impact subalpine vegetation.
Of particular concern is lodgepole pine, Pinus contorta, which has been widely introduced in several regions of the Southern Hemisphere for wood production, erosion control and forestry purposes [22,23,27,28]. Pinus contorta has been shown to have substantial and long lasting impacts in its invaded ranges, as besides reducing the diversity and abundance of native and endangered species [26], it negatively affects soil carbon and water balance, ultimately facilitating the establishment of other alien species at the expense of the local flora [29].
In New Zealand, P. contorta was introduced at the beginning of the 1900s and -wilding pines‖ have since spread across the lowlands covering approximately 150,000 ha by 2001 [30,31]. Multiple introductions exacerbated the invasion process, but provided evidence of the effect of propagule pressure on its spread [32]. Although numerous studies have addressed the invasion of P. contorta at low elevation, similar studies in alpine areas are limited. Thus, recent reports have raised concerns about the risks of spread into alpine areas [33], where P. contorta can recruit above the treeline, located at 1350 masl, in the absence of competition from other trees [23,34]. Although it has been reported that the species can grow well at elevations up to 1600 masl [23], to our knowledge no study has quantified the rate of spread of P. contorta into high elevations. In the present study, we examined the spread of P. contorta from planted stands at one of the few alpine sites in the South Island of New Zealand where planting history and propagule pressure are known. We asked the following questions: 1. How rapid is the spread of P. contorta into alpine areas and is it comparable to rates observed in the lowlands? 2. Does climate variation influence recruitment and, if so, which variables are most important? 3. Is the availability of suitable regeneration microsites an important factor limiting the establishment of P. contorta in alpine areas?
Using the answers to these questions we explore the potential for further P. contorta invasion and discuss possible management options.
Study site
The study site was located on a steep, highly eroded east-facing slope in the Craigieburn Range, South Island, New Zealand (43°10' S; 172°45' E). The native treeline-forming species (Nothofagus solandri var. cliffortiodes (Hook. f) Poole) gives way quickly to rocky scree fields, shrub and tussock grasslands above 1370 m [24,25]. As part of a series of forestry trials, two stands of P. contorta spp. contorta were planted above the Nothofagus treeline to examine the elevation limits of commercial forestry. The two stands, approximately 300 m apart, were planted for research purposes in 1962 (24 individuals; 1347 masl) and 1964 (multispecies trial planting including Pinus contorta, Pinus ponderosa and Pinus mugo, with a total of 40 individuals; 1388 masl) [25]. These two stands unintentionally provided an opportunity to assess the degree to which P. contorta could establish above the native treeline under conditions of relatively high propagule pressure.
Study species
Pinus contorta Dougl. ex. Loud., is native to the northwestern region of North America and Canada [35,36], and was initially planted in New Zealand for forestry purposes [8] and erosion control in mountain lands [23]. The species can begin reproducing after as little as five years [37]. Most cones mature within 12 months [38] and, in New Zealand, are non-serotinous [30]. Seeds are released shortly after maturation [39] in early autumn (March in New Zealand) or before the following growing season, generally beginning in October or November [40] when wind speed tends to be greatest [41]. Seeds of P. contorta are smaller than those of most pines [32], weighing approximately 4 mg [42], they are winged [43,44] and can be dispersed by wind up to 40 kilometers [30,37]. The species is shade intolerant [43,44] and previous studies showed that water holding capacity and soil moisture have a critical influence on the germination and early survival of P. contorta in its native range [45,46].
Field sampling
To assess establishment patterns of P. contorta into alpine zones, we established in February 2009 four sampling blocks running upslope starting at 1350 masl up to the maximum elevation reached by P. contorta (see Supporting Information for scheme of the sampling blocks- Figure S1). The maximum elevation was determined after a thorough search for individuals from the edge of the two planted stands to the ridgeline (1790 masl). Sampling blocks were situated between 10 and 150 m from the closest planted stand, and the maximum distance between sampling blocks was 120 m. Within each sampling block, we established at least five transect belts 50 m long and 2 m wide, with a total of 75 transect belts. Each transect belt ran parallel to the edge of the planted stands at intervals of 12.5 linear m, starting at 1350 m asl and ending at the maximum elevation reached by the species. Five transects were also laid within the planted stands to quantify these populations. The position of each transect belt was determined using a handheld eTrex GPS unit.
We identified and measured all P. contorta stems rooted within each transect. For each stem, we recorded the distance along the transect, stem height, basal diameter, presence of cones, tree class and age estimate. We categorized individuals in four tree classes according to their height and diameter: seedlings (basal diameter < 0.5 cm), saplings (0.5 < basal diameter < 4 cm), sub-adults (4 < basal diameter ≤ 10 cm) and adults (basal diameter > 10 cm). We used two methods to estimate stem age and year of recruitment, we counted internodes [47] for stems with diameters less than 3 cm, and counted the number of rings from increment cores or cross-sectional disks for stems with diameters greater than 3 cm. We cored stems or took disks by sawing stems at 20 cm above the ground because of the difficulty of coring stems at the root collar. We processed cores and disks according to the methods of Stokes and Smiley [48], and subsequently estimated the age by counting the rings with a binocular microscope, correcting for missing rings following Duncan [49]. It is well recognised that age estimates taken from above the root collar will underestimate age since establishment, because of the time taken for stems to grow to coring height [50,51]. To correct for this, we fitted a log-log regression between age and height for those individuals whose age was estimated by counting internodes. This allowed us to estimate the time taken to grow to coring height (on average, 4 years to reach 20 cm height) and so we added 4 years to the ages of the cored and sawed samples to estimate age since establishment ( Figure 1).
Microsite occupancy and availability were also assessed along each transect. We determined the microsite into which each P. contorta stem had established by characterizing the area around each stem into six classes (Supporting information- Figure S2): rock outcrop, scree, bare soil, alpine mat (mainly composed of short-statured plants and bryophytes), tussock grassland, and shrubs (Dracophyllum sp., Podocarpus nivalis and Aciphylla sp.). Thus we used substrate characteristics as a proxy for the environmental conditions in which P. contorta individuals were growing. Microsite availability along each transect was estimated using a point intercept method, where the microsite was recorded at 1m intervals along the center of each transect (i.e., 50 samples per transect). The regression line was used to estimate mean age at coring height (20 cm) to correct age estimates derived from cores and sections.
Climate data
Climate data were available from a meteorological station located at 914 masl, 4.2 km from the study area. Monthly temperatures (mean, minimum, maximum) and precipitation were downloaded for the period 1964-2008 ( [URL]/). We then calculated the annual average temperature and total precipitation for the austral growing (November through April) and dormant (May through October) seasons for each year.
Statistical analyses
Our data consist of the estimated age structure of P. contorta and the elevation and microsite in which trees were found. We used regression analyses to relate elevation (i.e., distance from the planted stands) to the number of recruits. To investigate population increase of P. contorta and predict its invasion potential, we applied non-linear regressions to population size overtime by fitting two different models that assumed exponential or logistic growth, and compared the fit of these models to the data using AIC. The best model was then used to estimate the rate of population increase when rare (r) and the carrying capacity (K) of the sampled transects. These models assume a smooth rate of increase over time driven by constant values for r and K, but deviations from this average population growth curve will occur if the recruitment rate was lower (if below the curve) or higher (if above the curve) than expected in a given year. Such deviations could be driven by climate variation, with higher rates of recruitment in climatically favourable years and lower rates in less climatically favourable years. To test whether variation in climate could explain deviations in yearly recruitment away from the average population growth curve, we correlated the residuals around the growth curve with seasonal rainfall and temperature data. Furthermore, to allow for potential inaccuracies in our age estimates, we grouped individuals into age intervals of two and four years respectively, to allow for a dating imprecision of ±1 or ±2 years, and repeated the analysis.
To compare the microsites occupied by P. contorta stems with microsite availability, we first calculated availability as the percentage of all point intercepts classed as each microsite class. We then calculated occupancy as the percentage of microsites occupied by pines within each microsite class. Microsite preference was assessed as the ratio between occupancy and availability [52]. A ratio <1 indicates that the microsite is occupied by P. contorta less than it would be expected given its availability, a ratio = 1 indicates that the microsite is occupied in proportion to its availability, and a ratio >1 indicates a microsite occupied more often than would be expected given its availability. Significance was evaluated using chi-square tests. All statistical analyses were conducted using the statistical software R 3.1.2 [53].
Demography
In total, 242 P. contorta individuals were sampled, with similar numbers of seedlings, (83) saplings (73), and subadults together with adults (86), with over half of the latter being reproductive (56). In total, we found 4 dead individuals, only one of which was found above the planted stands. Nearly one third of all stems (70) occurred within 10 m of the closest planted stand. The remaining individuals occurred up to 435 m linearly from these sources, across an altitudinal range of 272 m, and no stems were found above 1623 masl. Overall, we estimated a density of 290 trees/ha. Reproductive stems were observed up to 1601 masl and none of the individuals bearing cones were less than 12 years old. The majority of seedlings (93%) occurred within 10 m of a reproductive stem, but no seedlings were found above 1450 masl, indicating a lack of recent establishment, even though reproduction occurred above this elevation. We did not find evidence of establishment beyond the planted stands until 1987 (Figure 2, 3), over twenty years after the original planting date. Since the late 1980s, establishment has occurred annually up to 1450 masl, whereas above 1450 masl it has been episodic, primarily occurring since the 1990s (Figure 2).
Based on the spatial and temporal patterns of establishment, and the different distribution of tree classes across the elevation range, we divided our sample into two bands: a mid-elevation band (1349-1450 masl) composed of 219 individuals and a high-elevation band (1451-1623 masl) composed of 23 individuals, none of which were seedlings. For the 5 transects measured along the edge of the planted stands, we recorded 156 individuals, with the oldest individual having established by at least 1960. Our age estimates suggest that approximately 25% of these individuals established between 1960 and 1987. Of these stems 18 individuals (12%) established between 1965 and 1987. Seedlings (55/156) and saplings (55/156) both accounted for 35% of stems, and adults and subadults together accounted for 30% of stems.
Establishment patterns, climate and microhabitat
The number of P. contorta stems recorded on each transect declined with elevation (R 2 = −0.0247, F = 18.84, df = 239, p-value < 0.05). The logistic model (AIC = 131.2) was chosen over the exponential (AIC = 148.2) as the best descriptor of population growth (Figure 3), showing that the cumulative number of individuals increased through time, but that the rate of population growth was slowing. The rate of population growth when rare (r) was estimated as 0.22, and the population carrying capacity on the sampled transects (K) as 529. We did not find any significant correlations between climate variables and deviation in recruitment from the average growth curve (Supporting Information-Table S1, Figure S3). These results were unchanged when individuals were grouped into age-classes of two-and four-year intervals (Supporting Information-Table S1, Figure S4, S5). The prevailing wind direction during the period of seed release since the plantations were established was SE to SW, which was downhill.
Discussion
Pinus contorta has invaded alpine areas in the Craigieburn Range in New Zealand, but at a slower rate compared to lowland invasions. Variation in climate did not account for annual fluctuations in recruitment around the overall population growth curve, whereas the availability of favourable regeneration microsites greatly affected species establishment. The limited availability of favourable regeneration microsites, together with the decline in population growth rate over time, suggest that the population of P. contorta at the Craigieburn range may be approaching saturation. However, given the high colonizing ability of the species, constant monitoring and implementation of management strategies are highly desirable.
Demography of invasion
The naturally regenerating P. contorta population above the planted stands has reached a density of approximately 300 trees/ha; a value at the lower limits of observations of P. contorta density in the Andes at 1420 masl [8,26]. Similarly, the population density and rate of population increase at low elevation sites in New Zealand is substantially higher than above the treeline. Ledgard and Paul [47] estimated that P. contorta density at 850 masl increased by almost 70 times (i.e., from 500 trees/ha to 34,550 trees/ha) in 10 years, whereas during the same period, the population at Craigieburn increased by approximately 25 times. Furthermore, a recent study estimated the density of a P. contorta population in New Zealand located between 785 and 1040 masl [54] to be 24,700 trees/ha, which is 85 times higher than the density estimated from our survey.
The increase in population size over time at Craigieburn is consistent with the high invasive potential of P. contorta [55] which is mirrored in the native range where the species is encroaching into meadows with negative effects on plant diversity [56]. In both the native and introduced range the species exhibits a wide tolerance for climate extremes [30,57]. However population growth at the Craigieburn Range may be inflated if it is primarily driven by recruitment of a large number of young stems, which then suffer high mortality. Although we did not monitor mortality, we recorded the presence of dead stems, finding only four dead individuals, three of which were within the planted stands, suggesting relatively low mortality rates of established stems. Our model suggests that P. contorta invasion into high elevations is unlikely to reach densities observed at lower elevations in New Zealand or in the Andes [8,26]. The current population appears to have reached more than half of the maximum number of individuals that can be supported at the study site, as evidenced by the estimated carrying capacity (K = 529). Such a trend is not entirely novel as decreasing density due to competition for limited microsites have been observed previously in New Zealand [47].
At the Craigieburn Range, a temporal lag in establishment is evident. Establishment above the planted stands only commenced in approximately 1987, almost 20 years after planting. Temporal lags in the spread of alien species after their introduction have been observed for pine species [4,58] and may reflect specific life history traits or changes over time in climatic and habitat conditions that assist spread [59]. A similarly low rate of establishment between 1975 and 1987 was observed at the edge of the planted stands, suggesting that the reason for this temporal lag may be at least in part due to unfavourable environmental conditions. In New Zealand, seeds of P. contorta are known to be dispersed over distances up to 40 km [27,30], however in our survey we found that the first individuals to establish were located within about 0.1 km beyond the planted stands in bare soil. Remarkably, this is in contrast with a recent study showing that dispersal ability is a dominant factor at the early stages of a P. contorta invasion [60]. Furthermore, previous studies indicated that P. contorta produce cones as early as at 5 years [37], whereas we found no coning individuals younger than at least 12 years old. Thus both reproduction and establishment appear strongly constrained at these elevations.
Climate, although a critical factor affecting spread of numerous treeline species [61], was not a significant factor accounting for variation in the rate of population increase of P. contorta at our site (Supporting Information- Figure S3). The population has increased relatively steadily over time, suggesting relatively constant conditions for establishment ( Figure 3). This suggests P. contorta recruitment is not tightly linked to climatic variation, which is consistent with the wide environmental tolerance of the species [62].
When looking at microsites, P. contorta stems were found mostly in bare soil and alpine mats, despite the relatively low availability of these two microsites classes (Table 1, Figure 4). Bare soil and alpine mats retain humidity that is beneficial for seedling survival [27], whereas tussock and shrubs may result in stronger competition for water and light especially during the early life stages [26,39,43]. Conversely, on rocky outcrops and scree, seedlings are exposed to harsh conditions and water run-off rapidly causes drought stress. Pinus contorta seedlings and saplings have shallow roots that penetrate soil only up to a depth of 10-15 cm, thus the species is highly susceptible to drought [35,36,45]. This effect may be especially relevant in high elevation habitats where steep slopes and shallow soil do not provide high water retention.
At our study site, the spread of P. contorta appears to be limited by source effects (i.e. higher establishment occurring close to the planted stands), longer time to reproduction and availability of microsites with higher potential water availability. Limited availability of favourable microsites may likely hinder successful establishment of seedlings, causing death during early life stages and curbing population growth rates.
Management implications
There is growing consensus among management and conservation experts that preventing recruitment of P. contorta (and in general alien conifers) should be emphasized over removing existing stands [33]. The focus on prevention is not only motivated by the elevated cost of removal of wilding conifers, but also by the fact that alien conifers, P. contorta among them, permanently offset soil abiotic and biotic properties preventing the recolonization of native species. Studies have shown that alien pines lead to soil acidification and to a reduction of exchangeable nutrients [63,64]. Furthermore, alien pines have been associated with a reduction of mycorrhizal species diversity compared to that found in Nothofagus forests [29,65]. Research by Paul & Ledgard [66] also showed that dead pine stands can have deleterious effects on the local vegetation as they favour the invasion of exotic grasses over native species [21,29].
In such a framework, our results fill a knowledge gap, as most of the data used by conservation strategists come from studies conducted at low elevation. Consistent with other studies [4,26], we show that the establishment of P. contorta into high elevation, although less dramatic than at lower elevations, remains a potentially large problem, as at high elevation there are no native species that can effectively outcompete and replace it.
Although considerable effort has been invested in eradicating P. contorta the species is still widespread in New Zealand. As highlighted by our results and by previous findings [67], not all microsites are favourable to the establishment of P. contorta. Thus, one step would be to increase the cover of native species such as tussock and shrubs where the survival of seedlings is hindered by shading and competition. This could be implemented by ameliorating grasslands through addition of fertilizer [31], which would increase their competitive ability against seedlings in the early life stages [68]. In addition, consistent with previous research [2], we recommend special attention should be paid to the removal of juvenile P. contorta in alpine areas, before individuals start coning, a practice that will prevent further spread at a lower economic and ecological cost compared to the removal of reproductive individuals. The unique setting of our study site, namely known initial propagule pressure and date of planting, was also its main limitation, as we could not extend our survey to other sites. Therefore, we have to be cautious in generalizing our results, and further studies should be carried out to validate the feasibility of our recommendations. Finally, our results suggest a physiological limit to expansion that will likely transfer to other sites; principally the availability of suitable microsites that limit population growth and spread. Regardless, colonization above the treeline should not be underestimated as, due to the lack of competitors, P. contorta cannot be replaced by native species at later successional stages [33,69] and should be closely monitored.
Conclusion
Our study found that Pinus contorta has been spreading into high elevation subsequent to plantings established in the 1960s at the Craigieburn Range. The establishment pattern is mainly constrained by limited availability of favourable microsites, whereas climate variation had surprisingly little effect on the rate of population growth. Our findings suggest that P. contorta may be approaching saturation of favourable microsites and thus it may not represent an immediate threat to high elevation native species. However, considering the potential for long distance dispersal and pioneer ability of this species, we recommend that studies examine in more detail the patterns of establishment in different mountain areas of New Zealand. Furthermore, constant monitoring of such populations is desirable to allow for early detection and removal of seedlings.
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Domain: Environmental Science Biology
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Decreased Water Use in a Super-Intensive Olive Orchard Mediates Arthropod Populations and Pest Damage
In Spain, water use in agriculture is expected to become limited by resources in the future. It is pertinent to study the effect of decreased irrigation on the presence of pests, plant damage, and arthropod communities in a super-intensive olive orchard examined from 2017 to 2019. Arthropods were studied with visual and vacuum sampling methods in two irrigation treatments (T1—control and T2—Regulated Deficit Irrigation (RDI)). Univariate analyses showed that the total arthropod abundance was significantly greater in T1 than in T2 in 2018 and 2019, mostly due to Diptera Nematocera. Visual sampling revealed that the feeding damage produced by Eriophyidae (Trombidiformes) was significantly lower in T2 in 2018 and 2019: 10–40% of shoots were affected in the late season compared with 50–60% affected for T1. The feeding symptoms caused by Palpita unionalis Hübner (Lepidoptera: Crambidae) and Zelleria oleastrella (Milliere) (Lepidoptera: Yponomeutidae) were significantly less for T2 than for T1. Multivariate principal response curves showed significant differences between irrigation strategies in the 2018 and 2019 data for both sampling methods. In conclusion, irrigation schemes with restricted water use (T2—RDI) help to reduce the abundance of several types of pests in olive crops, especially of those that feed on the plants’ new sprouts.
Introduction
Super-intensive olive management has increased rapidly in Spain, especially in Andalucía, with a 116% acreage increment between 2015 and 2018 [1]. Trees are planted in hedges to form continuous vegetation (a different management strategy from the usual olive crop) with the main objective of increasing harvest for oil production and with the advantage of drastically reducing harvest costs and other cultural practices [2]. This trend is currently observed in areas with sufficient water supply for irrigation that have been prepared to use harvesting machines and other mechanized operations, but irrigation in super-intensive olive orchards can double the water usage of a more traditional olive orchard [3,4]. Olives are one of the most important crops in Spain, with most of the agricultural acreage located in Andalucía [1] and have been the focus of research about the efficient use of water [5,6], together with other crops [7,8]. It is essential to study how to manage this crop to use the available water as efficiently as possible; in Andalucía, climate projections show that precipitation will decrease by 25-30% by the end of the 21st century [9]. For this reason, different strategies based on deficient irrigation are of great interest in arid and semiarid zones-such as the Mediterranean basin-to sustainably use water [7].
A rational approach to the sustainable use of water must include how it can affect different aspects of the crop, such as the arthropod community and specific pests. Studies about how irrigation affects arthropod pests [10][11][12][13] or arthropod communities on crops [14], including how manipulated soil moisture can affect arthropod communities Agronomy 2021, 11, 1337 2 of 16 in woods [15,16], are gaining interest, although obtaining accurate predictions is difficult due to the high variability in how herbivores respond to water-deficit, stressed plants [13]. Several studies have highlighted the non-monotonic relationship between water stress in different model plants and the feeding of several pests upon them [17][18][19][20], relating the stress level with different components in the plants. Pests with chewing mouths were favored with high levels of water stress [17][18][19], whereas phloem-feeding pests were favored with no stress to low stress levels [18,20]. A recent review [21] outlined how intermittent and moderate drought can result in increases in carbon-based and nitrogen-based chemical defenses in trees, with a consequent effect on arthropod populations. No specific studies have been conducted in olives, and even studies on the effects of nitrogen fertilization on important olive pests are scarce [22]. There are more studies relating the type of management of the crop or the landscape structure with the biodiversity and/or the presence of pests and natural enemies in olive [23][24][25][26]. Usual pests and diseases already described in this crop are also the most common in super-intensive (high irrigated) olives [27], but the higher tree density, the vigor, the orientation, and even the mechanical operations (pruning and harvesting) likely play important roles in pest presence and their abundance [28,29], although no specific references have been found comparing arthropods and/or pest community in super-intensive versus more traditional olive orchards.
The present study aims to improve the knowledge about how crop management can impact the presence of arthropods (pest and beneficial) in olive orchards. Specifically, this study focuses on how the rational use of water can affect the abundance of pests and other arthropods in a super-intensive olive orchard, comparing two different irrigation schemes and thus providing a more complete view of its effect. This three-year study provides a sound basis for the results: the most rationally watered treatment (T2) had fewer symptoms of Eriophyidae feeding damage and other secondary pests compared with the most irrigated treatment (T1).
Experimental Design
This research was carried out in a 9-ha olive orchard located in Seville, Spain (37 • 29.459 N; 5 • 40.440 W). The orchard was planted in 2006 with the cultivar "Arbequina" using a planting layout of 4 × 1.5 m (1667 trees per ha) forming a hedgerow. The farmer applied a super-intensive management, in which the most important cultural practices were mechanized: irrigation, pruning, harvesting, weed control, and pesticide application. Integrated Pest Management protocols promoted by the regional government were applied with the direction of a qualified technician.
The experimental design was randomized blocks, with four blocks (each of 120 m length and 12 m width) and two irrigation treatments. Each experimental plot was 30 × 12 m, with three rows of olive trees, and sampling was performed only in the central row. One repetition of each irrigation treatment was randomly assigned within each block, making four repetitions of each irrigation treatment for the whole experiment.
This study focused on two different irrigation strategies: no limitation of irrigation (control) and Regulated Deficit Irrigation (RDI) [8]. The control (T1) plots received irrigation to avoid any water stress in the trees and to meet their evapotranspiration (ETc) necessities. RDI decreased the use of water during specific growing states of the olives, specifically during stage II (fruit pit hardening), but had non-limited water use during stage I (blossoming) and during most of stage III (pre-harvest rehydration). The Confederation RDI (T2) plots were irrigated with this strategy but limited to the water resources allowed by the Guadalquivir River Water Authority (Confederación Hidrográfica del Guadalquivir); this resulted in a 69% reduction in the total amount of water used for irrigation compared to the control treatment. The parameters of irrigation for each treatment can be found in Appendix A. The average annual water irrigation received for each treatment during the three years of this study were T1 = 647 ± 165 mm and T2 = 198 ± 36 mm.
Sampling Procedure
Arthropods were sampled from March to October each year of study. Sampling was performed on a biweekly basis but was modified sometimes due to limitations in time and/or personnel. There were 16, 16, and 14 sampling dates in 2017, 2018, and 2019, respectively.
Two sampling methods were used: vacuum and visual. Vacuum sampling provides an abundance of arthropods and groups not easily observed in the canopy, whereas visual sampling preferentially informs about arthropods that are not easily captured-especially for symptoms developed by crop-specific pest arthropods.
Visual sampling was performed immediately upon arrival at the orchard. Eight small branches (of around 8 cm, with or without sprouts) were randomly examined visually on each side of the central row of every plot for a total of sixteen branches per plot and sampling date. During the blossoming period, the same number of inflorescences were examined-sixteen per plot and sampling date-and after pollination, an equal number of fruits were also examined-sixteen per plot and sampling date. We recorded the presence/absence of arthropods and symptoms of feeding or damage that could be assigned to them. Feeding spots (produced by a sucking arthropod) on leaves were relatively frequent, and the proportion of the leaf surface with feeding spots was also recorded with an ordinal scale (0 (no damage), 1 (1-20% of surface damaged), 2 (21-50% of surface damaged), 3 (>50% surfaces damaged)). Venturia oleaginea (Castagne) Rossman and Crous (peacock spot), the most important disease in olive orchards, was also included due to its importance and ease of observation, although our records are incomplete because the leaves were not taken to the laboratory and introduced in a dilution of 5% NaOH or NaCl to observe latent peacock spots. The most important variables used in the visual sampling are presented in Table 1. Samples of the leaves, inflorescences, and fruits were taken to the laboratory sometimes to confirm or elucidate the observations. A garden blower powered by a battery (Sterwin, Leroy Merlin, Seville, Spain) was used for vacuum sampling the canopy. It was used as an aspirator in reverse mode, with an aspirator capacity of 8.9 m 3 ·min −1 and a 36 V and 4 Ah battery that allowed for enough time to take the samples on each date. A nylon sock was put at the entry to collect all of the individuals for each plot and sampling date. The procedure involved moving the aspirator up and down at four spots in each plot-two spots on each side of the central row-to take a sample. Considering the width of the aspirator and the height of the hedge, about 1.9% of the canopy surface was sampled for each plot. The eight samples on each date-four from the T1 treatment and four from the T2 treatment-were brought to the laboratory and put in a freezer (at −18 • C) for at least 24 h prior to opening the socks and separating the specimens on a white tray with the help of a dissecting microscope at 45×. The specimens were separated according to different taxonomic groups-class, subclass, order, suborder, superfamily, and family-following different generic taxonomic guides [30,31]. Genus and species were determined with the help of different experts only in particular cases when it was important in relation to the crop. Samples of the most relevant specimens are kept in a laboratory collection. 'fl' and 'sh' means 'flowers' and 'shoots', respectively; Feeding spots (a) indicates the proportion of leaves with the presence of feeding spots; Feeding spots (b) indicates the proportion of a leaf surface with feeding spots. The Euphyllura data were pooled together to perform the analysis of the three years (2017 to 2019); (·) 0.10 > p ≥ 0.05; (*) p< 0.05; and (**) p < 0.01. (1) Not registered. (2) Not calculated due to the very low numbers of individuals.
Data Analysis
Repeated-measures ANOVA was used to analyze how each taxon (in vacuum sampling), and observation (in visual sampling) was individually affected by the irrigation treatment with a method of analysis for the time-series abundance data. SPSS (v15.0 for Windows) was used to test whether irrigation treatment (between-subject effect, with two treatments), time (within-subject effect, with 16, 16, and 14 sampling dates), and interaction of time and irrigation treatment were significant in the response variables for each of the years of study. A repeated-measures ANOVA was also performed pooling data from the three years for each response variable in visual and vacuum sampling, using treatment, year, and treatment × year as factors to test whether a general pattern was present.
Multivariate principal response curve (PRC) was used for synthesis and global observation of the possible effects of the treatments under study when multiple variables are concerned. This method was initially developed by Van Den Brink and Ter Braak [32] to assess the effect of toxicants in freshwater communities-especially macroinvertebratesbut PRC has been used in agricultural entomology [14,[33][34][35][36] with the same objective of analyzing the effect of a treatment on the complex of arthropods.
In PRC, the community response under study is represented by a canonical coefficient, which measures their response to abundance in a designated control, expressed as deviations from a control community over time. The treatment designated as control is represented by a horizontal line, which serves as a reference to assess its relationship with the other treatment [32]. PRC analyses generate a species-or higher taxonomic groups and observations in our case-weight plotted in the right vertical axis; weights are used to indicate which of them follow the plotted community pattern, but only weights higher Agronomy 2021, 11, 1337 5 of 16 than |0.4-0.5| are considered significant. A visual interpretation of the PRC graphs can be found in Auber et al. [37].
Quantitative tests of whether a PRC diagram displays significant variance because of treatment were performed in R (v3.6.3) with the package Vegan (v2.5-2), which uses a Monte Carlo procedure to generate up to 999 permutations.
The data from vacuum sampling were transformed with log (x + 1), and the data from visual observations were transformed with arcsin ( √ p) prior to applying PRC and repeated-measures ANOVA in both cases [38].
Vacuum Sampling
There were no statistical differences in the total arthropods aspired in both treatments in 2017 (F = 0.24; df = 1, 6; p = 0.640). In the other two years, there were significant differences between the irrigation treatments in the total arthropods aspired in 2018 (F = 10.95; df = 1, 6; p = 0.016) and in 2019 (F = 8.70; df = 1, 6; p = 0.026), and with more abundance in T1 (control) than in T2 (Confederation RDI). Taking together the three years of study, T1 had a significantly larger arthropod population than T2 (F = 15.6; df = 1, 18; p = 0.001). Results in Table 1 (significance) and Table 2 (arthropod abundance), and more statistical outputs are presented in Table A3 (Appendix B). Diptera Nematocera was the most abundant arthropod group in the aspiration sampling with 6794 individuals (Table 2). They presented significant differences between treatments only in 2018 (F = 21.52; df = 1, 6; p = 0.004). Nevertheless, considering all three years together, T1 had a significantly larger Nematocera population than T2 (F = 16.2; df = 1, 18; p = 0.001). Diptera Brachycera had much less individuals, and there was no statistical difference between treatments in all three years taken together or separately. Results are in Table 1 (significance) and Table 2 (arthropod abundance), and more statistical outputs are presented in Table A3 (Appendix B).
Hemiptera Heteroptera was the second-most abundant arthropod group, with 2126 individuals, but no significant differences were found between treatments for all three years taken together or separately, although there were larger populations in treatment T1 than in T2. One of the most frequently captured species was Brachynotocoris puncticornis Reuter (Heteroptera: Miridae). Results are in Table 1 (significance) and Table 2 (arthropod abundance), and more statistical outputs are presented in Table A3 (Appendix B).
Hymenoptera was another arthropod group well represented in the different groups observed (1399 individuals). Neuroptera is an important group for its role in biological control of several olive pests, and it appeared constantly in the three years, but no differences between irrigation treatments were observed (F = 0.81; df = 1, 6; p = 0.381). Other secondary groups showed significant differences between treatments, such as Araneaesignificantly greater in T1 than in T2 for the three years (F = 6.31; df = 1, 18; p = 0.022)-and Coleoptera (F = 6.51; df = 1, 18; p= 0.020, analysis of the three years)-with populations in T1 significantly greater than in T2. Sternorrhyncha and Ichneumonoidea showed no differences between treatments in the analysis of the three years together, although they were significantly more abundant in T2 than in T1 only in 2019. However, their numbers were low and did not impact the total very much. Results are in Table 1 (significance) and Table 2 (arthropod abundance), and more statistical outputs are presented in Table A3 (Appendix B).
The PRC of vacuum sampling showed no differences between treatments in 2017 (p = 0.400, Figure 1a Hymenoptera was another arthropod group well represented in the different groups observed (1399 individuals). Neuroptera is an important group for its role in biological control of several olive pests, and it appeared constantly in the three years, but no differences between irrigation treatments were observed (F = 0.81; df = 1, 6; P = 0.381). Other secondary groups showed significant differences between treatments, such as Araneaesignificantly greater in T1 than in T2 for the three years (F = 6.31; df = 1, 18; P = 0.022)and Coleoptera (F = 6.51; df = 1, 18; P= 0.020, analysis of the three years)-with populations in T1 significantly greater than in T2. Sternorrhyncha and Ichneumonoidea showed no differences between treatments in the analysis of the three years together, although they were significantly more abundant in T2 than in T1 only in 2019. However, their numbers were low and did not impact the total very much. Results are in Table 1 (significance) and Table 2 (arthropod abundance), and more statistical outputs are presented in Table A3 (Appendix B).
The PRC of vacuum sampling showed no differences between treatments in 2017 (P = 0.400. Figure 1a), but the treatments were significantly different in 2018 (P = 0.039, Figure 1b) and 2019 (P = 0.001, Figure 1c), confirming the results of the ANOVA analysis. Diptera Nematocera and Neuroptera (in 2018, Figure 1b) both have high negative weights (in the vertical right axis) and are of opposite signs to the canonical coefficient of T2, indicating that, in general, their populations were larger in T1 than in T2. Ichneumonoidea (in 2019, Figure 1c) also have a high negative weight and are of equal sign to that of the canonical coefficient of T2, thus indicating that their populations were higher in T2 than in T1. As seen before, the total arthropods were significantly more abundant in T1 than in T2 in 2018 and 2019 (Table 1, vacuum). In both years, in general, T1 showed a larger population, although with a similar pattern than T2 (Figure 2a,b), with a peak at the beginning of the season. Only in 2019 there was a significant effect for Treatment × sampling date (F = 3.94; df = 3.9, 23.4; P = 0.014, Table 1, Table A3), especially at the end of the season, when total arthropods were more abundant in T1. As seen before, the total arthropods were significantly more abundant in T1 than in T2 in 2018 and 2019 (Table 1, vacuum). In both years, in general, T1 showed a larger population, although with a similar pattern than T2 (Figure 2a,b), with a peak at the beginning of the season. Only in 2019 there was a significant effect for Treatment × sampling date (F = 3.94; df = 3.9, 23.4; p = 0.014, Table 1, Table A3), especially at the end of the season, when total arthropods were more abundant in T1.
The PRC of visual sampling showed no differences between treatments in 2017 (P = 0.786, Figure 3a), but in 2018 (P = 0.038, Figure 3b) and 2019 (P = 0.031, Figure 3c), statistically significant differences were found. The vertical right axis shows the weights of the different observations, their importance in the effect, and their response to the irrigation treatments. Eriophyidae (2018 and 2019, Figure 3b,c, respectively) and Lepidoptera (2018, Figure 3b) showed high negative values for their weights, opposite to the canonical coefficient of treatment T2 in the second part of the sampling period, which indicates that their presence was contrary to the evolution of treatment T2 and, thus, more abundant in T1.
The PRC of visual sampling showed no differences between treatments in 2017 (p = 0.786, Figure 3a), but in 2018 (p = 0.038, Figure 3b) and 2019 (p = 0.031, Figure 3c), statistically significant differences were found. The vertical right axis shows the weights of the different observations, their importance in the effect, and their response to the irrigation treatments. Eriophyidae (2018 and 2019, Figure 3b,c, respectively) and Lepidoptera (2018, Figure 3b) showed high negative values for their weights, opposite to the canonical coefficient of treatment T2 in the second part of the sampling period, which indicates that their presence was contrary to the evolution of treatment T2 and, thus, more abundant in T1.
Eriophyidae symptoms in the shoots were significantly more frequent in T1 than in T2 in 2018 and 2019 (Table 1, visual), and it was more evident in the latter part of the season (Figure 4a,b), when 50-60% of the shoots were affected in T1 whereas 10-40% of shoots were affected in T2, with the confidence intervals not overlapping. Lepidoptera presence and damage in shoots was significant in 2018 (Table 1, visual), and more evident in the middle and in the latter part of the season (Figure 4c), when 40% of the shoots were affected in T1 and 10-20% were affected in T2. The proportion of leaf surfaces with the presence of feeding spots was only significant in 2018 (Feeding spots (b), Table 1, visual), with more presence in T1 than in T2 (Figure 4d) throughout the season, although the percentage of the leaf surface affected was low and constant in both treatments. shoots were affected in T2, with the confidence intervals not overlapping. Lepidoptera presence and damage in shoots was significant in 2018 (Table 1, visual), and more evident in the middle and in the latter part of the season (Figure 4c), when 40% of the shoots were affected in T1 and 10-20% were affected in T2. The proportion of leaf surfaces with the presence of feeding spots was only significant in 2018 (Feeding spots (b), Table 1, visual), with more presence in T1 than in T2 (Figure 4d) throughout the season, although the percentage of the leaf surface affected was low and constant in both treatments. Finally, no effect of the irrigation treatment was observed for the presence of peacock spots (V. oleaginea) on the leaves during the three years of study (F = 0.01; df = 1, 42; P = 0.948, Table 1, visual), although the method of observation was not exhaustive. More statistical outputs are presented in Table A3 (Appendix B).
Discussion
The general picture that can be drawn from this study is that more arthropods were present in T1 (control) than in T2 (Confederation RDI) and, more importantly, that some pests were also more abundant in T1 than in T2. Interestingly, the abundance of the most important groups of beneficial insects-Neuroptera, Ichneumonoidea, and Chalcidoidea-was not especially affected by the irrigation treatments, although Araneae was more abundant in the more watered treatment (T1). Water management and its effect on the Finally, no effect of the irrigation treatment was observed for the presence of peacock spots (V. oleaginea) on the leaves during the three years of study (F = 0.01; df = 1, 42; p = 0.948, Table 1, visual), although the method of observation was not exhaustive. More statistical outputs are presented in Table A3 (Appendix B).
Discussion
The general picture that can be drawn from this study is that more arthropods were present in T1 (control) than in T2 (Confederation RDI) and, more importantly, that some pests were also more abundant in T1 than in T2. Interestingly, the abundance of the most important groups of beneficial insects-Neuroptera, Ichneumonoidea, and Chalcidoideawas not especially affected by the irrigation treatments, although Araneae was more abundant in the more watered treatment (T1). Water management and its effect on the arthropod community were also studied by Frampton et al. [14], although with different objectives, but they also found that irrigation increased the presence of arthropods in agricultural plots. Studies in woods also found that higher humidity (natural or not) increased in general arthropod abundance [15,16].
In our research, the irrigation treatments did not show any pattern in the first year of study (2017), but significant differences were found in the following two years (2018 and 2019). This is probably because the differential effect of irrigation was not evident in the first year of study.
For vacuum sampling, the most abundant group of arthropods was Nematocera, which was significantly more abundant in T1 than in T2 for all three years. Treatment T1 had more weeds in the tree rows and humidity in the soil, which could help to increase their presence [15,16]. The other groups were less abundant and of little influence in the general effect of irrigation, although there were significant differences between irrigation treatments for Araneae, Ichneumonoidea, and Sternorrhyncha in some years or even in the pooling of the years, such as for Araneae and Coleoptera; Collembola and Formicidae showed significant effects only in the interaction Treatment × Sampling Date.
The order Neuroptera-with the family Chrysopidae being the most relevant-is regarded as an important biological control agent for several pests in olive orchards [24]; in our research, it was reasonably present during the three years of study, but no effect of the irrigation treatment was observed in relation to its abundance.
Regarding visual sampling, Acari (Trombidiformes: Eriophyidae) was the most important group of arthropods observed for the symptoms produced in the shoots due to their feeding, which are clearly distinguishable and assigned to a group of gall mites or eriophyids species commonly present in the crop [39], of which Aceria oleae (Nal.) is the most important species. Some samples of leaves with these symptoms were taken to the laboratory on different occasions, and the presence of eriophyids was confirmed, although no species was positively identified. Eriophyid damage was significantly greater in T1 than in T2 in 2018 and 2019 (and for the combination of all three years). English-Loeb [12] also observed that Tetranychus urticae Koch (Trombidiformes: Tetranychidae) populations were most abundant on well-watered and on severely stressed Phaseolus vulgaris (bean) plants and least abundant on slight to moderately stressed plants. The presence of Lepidopterawhich comprises two species, P. unionalis and Z. oleastrella-larvae and damage on shoots was also significantly greater in T1 than in T2 in one year (2018). This behavior is similar to that observed for several experiments in which pot plants with low water stress (comparable with the T2 of our study) were less preferred than very stressed or well-watered plants by different lepidopteran pest larvae [17,18], a response that could be due to different components of the plant.
The presence of feeding spots on the leaves was also significantly greater in T1 than in T2 when taking all three years of study into account, although only in 2018 was relevant. Feeding spots were present on the leaves-mainly old leaves-and we hypothesized that they were produced by sucking feeding arthropods. The most abundant group with this type of feeding was Heteroptera-observed in the vacuum sampling data, mainly from the family Miridae, which has many phytophagous and zoophytophagous species [31] -but they were not easily observed during visual sampling, and there were no differences in its numbers between irrigation treatments, both in vacuum and visual sampling.
Some groups that were visually observed were also captured in vacuum sampling. Euphyllura olivina Costa (Hemiptera: Psyllidae)-included in the Sternorrhyncha suborder of the Hemiptera-is considered a secondary pest of olives in Spain [27]. Its nymphs feed on the shoots and inflorescences and are surrounded by a web of whitish threads, which makes them easily detectable. Vacuum sampling captured several adults and some nymphs of this species, but in both types of sampling, there were no significant differences between the irrigation treatments-except for 2019 in vacuum sampling-and their populations were low and of little significance.
There was almost no presence of Bactrocera oleae (Rossi) (Diptera: Tephritidae)-the most important pest of olive crop in Spain. Vacuum sampling is not the best method to study its populations, but fruit sampling in October 2017 and 2018 did not detected the presence of these larvae or any damage. The cultivar "Arbequina" is one of the least preferred by this pest [40], so this result is understandable. Another important olive pest in Spain is Prays oleae Bernard (Lepidoptera: Praydidae), which is easily detected when sampling inflorescences visually in April-May, but in the three years of this study, its presence was very low, and thus, it was not included in further analyses. The number of adults captured with the vacuum were also very low in the three years.
The interaction Treatment × Sampling Date was remarkable in Eriophyidae (visual sampling), and total arthropods (vacuum sampling) also showed a significant effect in the interaction (but only in 2019). In both cases, more damage/individuals were observed in T1 (compared with T2) at the end of the sampling period, when trees had a new sprouting period, especially in the most watered T1 treatment.
Visual sampling was more appropriate to scout specific pests of this crop-such as Eriophyidae, the Lepidopteran shoot feeders P. unionalis and Z. oleastrella, or symptoms of feeding activity on leaves. In this way, we were able to detect significant differences in 2018 and 2019 between treatments, as was represented in the PRC analysis and the repeated-measures ANOVA. More twigs and shoots were observed-not quantified-in the T1 treatment in spring and autumn, and it can be hypothesized that excess water produced more vigorous vegetation, where most of the detected pests proliferated. Another aspect that could influence this difference in population in both treatments is that moderate and intermittent drought-as in T2-can result in a surplus of carbohydrates (fewer carbohydrates are used for growth) becoming available for the synthesis of carbonbased defenses (terpenes and phenolics) [21]. A collateral benefit of less irrigation is the reduction in the vigor of these trees and, thus, the intensity of crop operations, such as mechanical pruning.
Conclusions
As a conclusion of this three-year study, although it was carried out only in one orchard, irrigation schemes with limited water use-e.g., treatment T2 (Confederation RDI) used in this study-can reduce the abundance of different pests in olive crops, especially of those who feed on the plants' new sprouts. This is an important effect that should be considered in super-intensive olive orchards because it allows for not only the sustainable use of water but also more rational management of several pests in the crop, with collateral benefits in the cultural operations as well.
The theorical amount of irrigation water to be received in Treatment 3 (Confederation RDI) was around 1300 m 3 ·ha −1 per year, but it was increased in 2019 due to low rainfall, resulting in an annual average of 1980 m 3 ·ha −1 for the three years of study.
The water stress integral (SI , Table A2) was calculated (Equation (A1)) to describe the accumulative effect of deficit irrigation strategies from the beginning of pit hardening to harvest: where SI is the stress integral, ψ is the average midday stem water potential for any interval, and n is the number of the days in the interval.
Appendix B
The between-subject analysis of the repeated-measures ANOVA used the following factors: Treatment (2 levels), Block (four levels), and Treatment × Block. Tables 1 and A3 only show the results from the Treatment factor.
In visual sampling, there were two different sets of observations for each plot (one on each side of the hedgerow), so the total number of observations is 16 The within-subjects analysis in the repeated-measures ANOVA was performed with the time factor (Sampling Date) and its interaction with the other between-subject factors (Treatment, Block, and Treatment × Block), but only the results of the Treatment × Sampling Date are presented in Tables 1 and A3. First, we tested whether the Mauchly's test of sphericity was significant. In most of the cases, the test was significant, and the Greenhouse-Geisser degree of freedom correction was applied.
The analysis of the three years together is included in the table, although for some variables (both in visual and vacuum sampling), the data are available only for two years or, in some cases, for only one year. The data were analyzed also with repeated-measures ANOVA, but only the between-subjects effects for Treatment (two levels), Year (three levels), and Treatment × Year are presented. (1) Not registered. (2) Not calculated due to the very low numbers of individuals.
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Domain: Environmental Science Biology Agricultural and Food Sciences
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Impact of Microbial Application on Growth & Development of Dalbergia latifolia & Dalbergia sissoo under Nursery Condition
The forest tree species belonging to Dalbergia are important timber trees, valued for their decorative and often fragrant wood, rich in aromatic oils Thurlough et al. [1], Vasudeva et al. [2] Dalbergia latifolia (Roxb) Family-Fabaceae, a large glabrous tree a single stem with characteristic smells, grey bark, thin with irregular short cracks, exfoliating in fibrous longitudinal flakes Parrota [3], Troup [4]. It is distributed in Bihar, Bundelkhand and Central India (Figure 1). Quality of having a long cylindrical bole, high strength and high density. The high quality wood properties make this species highly valuable and great demand. Hence, facing problem of depletion in population Raghava Swami et al. [5]. Besides wood demand it has great medicinal properties due to tannin which is being used for leprosy and worm Kirtikar & Basu [6]. It is slowing growing and prone to jungle fire which also promotes its depletion in the natural forest. Hence, prioritization of large scale production and plantation is required Sujatha et al. [7].
Introduction
The forest tree species belonging to Dalbergia are important timber trees, valued for their decorative and often fragrant wood, rich in aromatic oils Thurlough et al. [1], Vasudeva et al. [2] Dalbergia latifolia (Roxb) Family-Fabaceae, a large glabrous tree a single stem with characteristic smells, grey bark, thin with irregular short cracks, exfoliating in fibrous longitudinal flakes Parrota [3], Troup [4]. It is distributed in Bihar, Bundelkhand and Central India (Figure 1). Quality of having a long cylindrical bole, high strength and high density. The high quality wood properties make this species highly valuable and great demand. Hence, facing problem of depletion in population Raghava Swami et al. [5]. Besides wood demand it has great medicinal properties due to tannin which is being used for leprosy and worm Kirtikar & Basu [6]. It is slowing growing and prone to jungle fire which also promotes its depletion in the natural forest. Hence, prioritization of large scale production and plantation is required Sujatha et al. [7].
Dalbergia sisso is important plantation tree species and useful for afforestation program due to excellent coppicing properties. This species is endowed with many medicinal properties Ahmad et al. [8]. Hence, its large scale propagation & plantation is needed for the point of huge demand as well as environmental concern. Soil is composite system and inherent with many microbes including bacteria and fungi. Their population depends upon all multifarious condition also. Most of the tropical soil are "P" fixing and do not make available. This mineral content to the medicinal plant. Hence an additional dosage of P' is require to fulfill the plant demand. To save the cost of chemical fertilizers as well to get rid of its ill effect over plant health, an alternative as bio-fertilizer is required. Many fungi and bacteria are reported to have capability to solubilize the bonded form of P' content White law [9]. It may happen due to secondary metabolites and for enzymes Holford (1997). The application of such types of organism either alone or in
International Journal of Environmental Sciences & Natural Resources
combination with the other useful organism especially nitrogen fixing make a suitable and worthy combination to help plants for microbial leaching and subsequent uptake Lee & Bressan [10]; Chen et al. [11].
In the present study, both the legume tree species are (one) facing problem of slow growth (D. latifolia) and (second) high demand (D. sissoo) were taken into consideration to develop a nursery technology so that a quality planting material could obtain. Quality material of any tree species will certainly be useful for the large scale plantation program which can save fertility and produce healthy plant products and fulfill the demand.
Materials and Methods
Present experiment was carried out during April-August, 2016 in the experimental field of Regional Plant Resource Centre, Bhubaneswar, Odisha, India. The experiment was done in poly bags containing 5.5 kg red soil. Pot soil was fumigated with 1% formalin for 48 hrs prior to the experiment. Deep brown colored flat seeds were decapsulated and sown in presaturated poly bags. Seeds were germinated in the poly bags. Two Rhizobium and two phosphate solubilisng fungal isolates (confirmed earlier through plate culture test performed on Pikovskaya' medium) identified as Aspergillus kanagawaensis, Aspergillus niger were supplemented into the experimented plants, individually and combination of both. The seven day old culture developed in Sabouraud Dextrose broth and Nutrient broth at 30°C were inoculated (@ 100 ml /poly pot ) separately & thrice with monthly interval.
International Journal of Environmental Sciences & Natural Resources
In present ex periment to evaluate the effective plant growth promoting ability of phosphate solubilizing fungi on forest trees was set up on Dalbergia sissoo & Dalbergia latifolia in pot culture condition which was supplemented with liquid cultures of phosphate solubilising fungi namely: Aspergillus kanagawaensis, Aspergillus niger and two Rhizobium sp-1 & 2 ( Figure 3). The different fungal strains used in the present study were having phosphate solubilising potential tested in plate culture method in laboratory earlier. Previously, Odee et al. [18] recommended inoculation of liquid cultures for the healthy seedlings in the nursery conditions. This also helps in increasing fungal population in the rhizosphere and finally mineral solubilisation (Figure 4). In the present experiment revealed the effect of fungal and Rhizobium inoculation and their usefulness as bioinoculants for plant productivity. In present study Aspergillus niger indivisually and combined with Rhizobium sp.-2 found to be most effective for overall plant growth promotion and establishment in both the experimented plants.
Conclusion
This experiment illustrates that the co-inoculation of A. niger and Rhizobium has improved nutrient status of the rhizospheric region. This combination brought about significant increase in the growth parameters of Dalbergia plants, suggesting their application for better growth and establishment.
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Domain: Environmental Science Biology Agricultural and Food Sciences
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Invasion risks posed by ornamental freshwater fish trade to southeastern Brazilian rivers
A model was developed to assess the risk of invasion of ornamental non-native fishes to six rivers in the state of Minas Gerais, southeastern Brazil, with focus on species popularity. Thirty-nine aquarium shops, in six cities, were visited monthly from January to December 2007. In each city, fish species were identified, and their biology and invasion history information was obtained from the literature. We calculated the annual frequency of occurrence and average number of specimens monthly available in stores. Quarterly water temperature and dissolved oxygen data from 1997 to 2007 were obtained for the Velhas, Muriaé, Uberabinha, Sapucaí-Mirim, Doce and Todos os Santos Rivers from public databases. The invasion risk of each species was assessed through a model comprising nine parameters grouped in four variables: (i) Invasiveness (thermal and dissolved oxygen ranges, diet, parental care or fecundity), (ii) History of invasions (establishment), (iii) Propagule pressure (commercial success, comprising annual frequency of occurrence and number of specimens available monthly at stores), and (iv) Invasibility (water temperature and dissolved oxygen in the target river compatible with the species ranges). Of the 345 ornamental fish species for sale, 332 are non-native to either Minas Gerais (n = 151) or Brazil (n = 194). Based on the proposed cutting values, in particular the compatibility between species and recipient thermal ranges, five ornamental non-native species (Cyprinus rubrofuscus, Carassius auratus, Xiphophorus hellerii, Poecilia reticulata, and P. latipinna) can potentially invade the Velhas and Muriaé Rivers, four species (Cyprinus rubrofuscus, Carassius auratus, X. helleri, and P. reticulata) the Uberabinha River, four species (Cyprinus rubrofuscus, Carassius auratus, X. maculatus, and P. reticulata) the Sapucaí-Mirim River, three species (Carassius auratus, X. hellerii, and P. reticulata) the Doce River, and three species (Cyprinus rubrofuscus, P. reticulata, and Amatitlania nigrofasciata) can potentially invade the Todos os Santos River. Six recommendations are suggested to reduce the invasion risk of non-native fish on the rivers surveyed posed by aquarium trade.
Introduction
Worldwide, keeping freshwater and marine ornamental fish in aquaria is a popular hobby in both homes and public spaces. Currently, the global ornamental fish trade generates about US$ 3 billion/year and an equipment and literature industry that exceeds US$ 15 billion/year (Dawes, 2001). In spite of the popularity of fishkeeping, not all amateurs persevere in adequately caring for their aquaria due to unwelcome difficulties such as excessive growth of individuals and aggressiveness of some species. Upon giving up their hobby, many aquarium owners are reluctant to sacrifice their pets, and end up discarding them directly in artificial (Fuller et al., 1999) and natural environments (Semmens et al., 2004).
Aquarium dumping, according to Fuller et al. (1999), is the second main cause of non-native species introduction in the US. In Brazil, most of the information about fish introductions concerns escapees from fish farms for food production, while reports of aquarium dumping are rare (Magalhães, 2010). Attention has been paid to non-native ornamental fish species in Brazil, as well as elsewhere in the world, only after they have settled into a new environment (Alves et al., 2007). Once established, it becomes virtually impossible to eradicate aquatic species (Gozlan et al., 2010). In order to prevent future invasions in Brazilian water bodies via aquarium trade, it is necessary to identify species of ornamental fish that pose high invasion risks, as well as to monitor the events that may lead to these introductions (such as new species offered by the market, environmental changes, and urban growth).
According to Bomford & Glover (2004), simple risk assessment models that predict invasion risk by non-native species are a low-cost alternative to guide management policies. Risk models were originally developed to deal with chemical pollution, and have only recently been adapted for other stressors, including non-native species (Calado & Chapman, 2006). Invasion risk models by non-native ornamental fish exist for temperate regions, such as those of Gertzen et al. (2008), Kolar & Lodge (2002), Rixon et al. (2005), Chang et al. (2009), Strecker et al. (2011) for North America, and for sub-tropical/tropical regions such as the one developed by Bomford & Glover (2004) for Oceania. Kaplan & Garrick (1981) define risk as the probability of ocurrence of an unwanted event, along with an assessment of its consequences. In the case of aquarium trade, the undesired event is the release of non-native ornamental fish by hobbyists, while consequences include establishment through reproduction in new environments and ensuing damages such as changes in the original structure of native fish communities. Our study applies for the first time a simple risk model of ornamental non-native fish invasion in southeastern Brazil, based on species biological traits and invasion history, popularity, availability, and abiotic features of the recipient environment, along with a discussion of aquarium dumping consequences and recommendations to restrain this practice.
Predicting potential invaders in six rivers of Minas Gerais
State. A model was applied to predict probable non-native fish invasions through aquarium fish trade in rivers of Minas Gerais State. This southeastern Brazilian state is the fourth in size, and the second most populous, with a current population of more than 18 million (Alves et al., 2007). A donor source of species, represented by ornamental fish trade, was identified and adopted as a vector for deliberate human dumping, in order to create a predictive model that combined biological, historical, and commercial variables, as proposed by Rixon et al. (2005), with abiotic criteria, as proposed by Chang et al. (2009).
The first of these four variables is the invasiveness ability of each species, assessed by four parameters: thermal range, dissolved oxygen tolerance, type of diet, and parental care or fecundity (representing reproductive attributes). The attributes assumed to facilitate the invasion of ornamental fish were the ability to tolerate changes in water temperature and dissolved oxygen levels, omnivorous diet (feeding on at least two items among organic debris, micro-or macrophytes, micro-or macro-invertebrates) and either parental care (by male, female, or both) or high fecundity (high numbers of oocytes). These are important prerequisites for the establishment of ornamental non-native species in any freshwater body (Moyle & Marchetti, 2006). The second variable is the history of worldwide invasions, adapted from Drake (2007), defined as H = Pe/Pi ×100, where H = history of invasions, Pe = number of countries where the species has successfully established itself, Pi = number of countries where the species was introduced. The third variable is propagule pressure, estimated by the number of individuals released, a primary factor in establishment success (Vander Zanden & Olden, 2008). Assuming that popular species (i.e.hardy and easy to feed) sold in considerable numbers (> 100 specimens per month) have a greater probability of being released in the environment by hobbyists, we used the annual frequency of occurrence in stores, and number of specimens of each species available monthly for sale as measures of commercial success. The fourth variable is invasibility of the recipient river, assessed as the compatibility of its water temperature range (mean autumn-winter and spring-summer, Table 1) with the thermal ranges tolerated by the invading species, as proposed by Chang et al. (2009). Dissolved oxygen was also assessed because urban portions of most Brazilian rivers are usually polluted by domestic and industrial sewage and would be less suitable for fish survival (IGAM, 2009). All these variables are important steps in successful biological invasions by aquatic species according to Lockwood et al. (2007). To be considered a potential invasive species in any of the six rivers, a species had to fulfill rigorously the cutting values of all four variables and nine parameters described above (Table 2).
Data sampling and analysis
Nineteen aquarium shops were visited monthly from January to December 2007 in the city of Belo Horizonte (Velhas River, São Francisco River basin), four in the city of Muriaé (Muriaé River, Paraíba do Sul River basin), four in Uberlândia (Uberabinha River, Paranaíba River, upper Paraná River basin), four in Pouso Alegre (Sapucaí-Mirim River, Grande River, upper Paraná River basin), four in Governador Valadares (Doce River, Doce River basin) and four in Teófilo Otoni (Todos os Santos River, Mucuri River basin) (Fig. 1). The number of stores surveyed corresponds to 100% in Muriaé, Pouso Alegre and Teófilo Otoni, and approximately to 90% in Belo Horizonte, Uberlândia and Governador Valadares. This estimate was obtained consulting the specific store directory records of each city. The rivers were chosen because part of their course is within these urban areas, where dumping is more likely. We calculated the annual frequency of occurrence and the monthly average number of fish for sale at stores in each region.
freshwater fish was taken from Lawson (1995).
Quarterly data from 1997 to 2007 of water temperature and dissolved oxygen for the six rivers were obtained from the public database of the Minas Gerais Institute of Water Management (IGAM) (2009). Water temperature (ºC; thermometer) and dissolved oxygen (mg/L; Winkler's method by Golterman et al., 1978) were measured downstream and upstream of each city (average distance: 19,8 ± 11,6 km) at 1 m depth in the main channel of each river.
Results
We found 345 species of ornamental fish belonging to 58 families on sale at stores, of which 332 were non-native either to the watersheds studied (151 species from the Pantanal and Amazonian basins) or to Brazil (194 species from North America, Central America, South America, Africa, Asia, and Oceania). In Belo Horizonte we recorded 166 fish species (five native and 161 non-native) belonging to 37 families, with an average number of 40.2 ± 30.2 species per store. In Muriaé, we recorded 52 species (eight native and 44 non-native) belonging to 15 families, with an average number of 33.7 ± 7.7 species per store. In Uberlândia, 83 species (all of which non-native) belonging to 16 families were found, with an average of 26.3 ± 16.7 species. We recorded 29 species (all of which non-native) belonging to seven families in Pouso Alegre, with an average of 11.1 ± 0.5 species per store. In Governador Valadares, we found 15 species (all of them non-native) belonging to six families, with an average of 10.2 ± 3.5 species per store and finally, in Teófilo Otoni, 14 species (all of them non-native) belonging to five families, with an average of 10.4 ± 2.5 species per store. Fishes well-known by hobbyists such as cyprinids, poeciliids, and osphronemids, all with non-native representatives, were the most traded. Usually large (e.g., the striped catfish Pangasianodon hypophthalmus and the clown knifefish Chitala ornata), aggressive (the golden mbuna Melanochromis auratus and the giant snakehead Channa micropeltes), expensive (the discus fish Symphysodon discus and the sixbar distichodus Distichodus sexfasciatus), and rare species (the short-tailed pipefish Microphis brachyurus and the marble goby Oxyeleotris marmorata) were available in less than 50% of the stores.
Based on the model proposed (Fig. 2) only seven non-native species may potentially invade and establish themselves in the rivers surveyed if dumped by hobbyists, and most of them are common to several rivers. Five non-native species (Cyprinus rubrofuscus, Carassius auratus, X. hellerii, and P. reticulata, P. latipinna) are a threat to both the Velhas and Muriaé Rivers. In the Uberabinha River we found four potential invaders (Cyprinus rubrofuscus, Carassius auratus, X. hellerii, and P. reticulata), four in the Sapucaí-Mirim River (Cyprinus rubrofuscus, Carassius auratus, X. maculatus, and P. reticulata), three (Carassius auratus, X. hellerii, and P. reticulata) in the Doce River, and three species (Cyprinus rubrofuscus, P. reticulata, and Amatitlania nigrofasciata) in the Todos os Santos River (Table 3).
Discussion
Our survey of ornamental fish stores in Minas Gerais State showed that the number of species available for sale is relatively large (n = 345), and that although these species are of tropical origin, the great majority is non-native to the state or country (96,23%). For these reasons, their trade as an introduction pathway deserves better investigation and regulation by environmental authorities. Concern about the predominance of non-native fishes in aquarium trade was recently addressed by Gertzen et al. (2008) in Canada, and by Chang et al. (2009) and Strecker et al. (2011) in the states of California and Washington, US. Of 252, 432 and 400 species available at stores in the respective regions surveyed by these authors, the absolute majority was of tropical origin. In our survey, Cyprinidae, Poeciliidae, and Osphronemidae were the most popular families in stores. A similar trend was found by Duggan et al. (2006) in Toronto, Canada, and Macomb County, Michigan, US. According to these authors, popular families available to hobbyists have a tendency to be introduced more frequently and in greater numbers than rare families. Members of these 'high-risk' families are usually sturdy, have low aggressiveness, and are small enough to be kept in large numbers, thus adding to propagule pressure. Some exceptions are Cyprinus rubrofuscus and Carassius auratus, which are comparatively large, yet sold in large numbers. Most of these sales, however, are of juveniles, and both species have high indices of rejection when they attain their adult size. The model created for Minas Gerais State predicts that only four poeciliids, two cyprinids, and one cichlid are potential invaders of the six rivers surveyed. These families were also highlighted by Bomford & Glover (2004), whose model for the Australian tropical region identified poeciliids and cyprinids as the highest risk taxa, followed by cichlids, which posed moderate risk. The very short list of potential invaders, when compared to the number of available non-natives, is the result of several restrictive model parameters, and mainly of the mismatch between species thermal ranges and the recipient river temperature. This rigorous selection also occurs in the model proposed by Chang et al. (2009), which detected five potential invasive species for the cold scenario (autumn-winter) in the San Francisco Delta Bay, California, and 27 species for the warm scenario (spring-summer). However, when both scenarios were considered, only five ornamental species were qualified as potential invaders.
Aside from the thermal incompatibility discussed above, diet, parental care, fecundity, and number os specimens for sale contributed to eliminate 11 other species as potential invaders. Fish species that have thermal ranges incompatible with the location to be invaded, affecting embryo and egg development (Milton & Arthington, 1983), that have specialized feeding habits, lack parental care, have low fecundity (Moyle & Marchetti, 2006), and are commercially undesired by humans (Duggan et al., 2006), are seldom considered potential invaders (Strecker et al., 2011).
Except for Cyprinus rubrofuscus (common variety) and P. reticulata introduced in the Velhas River (unknow introduction pathways, no evidence of reproduction) (Alves & Pompeu, 2001), there are no published records regarding the introduction of Carassius auratus, Cyprinus rubrofuscus -koi, P. latipinna, X. maculatus, X. hellerii, and A. nigrofasciata in this river. Similarly, there are no records regarding the introduction of Carassius auratus, Cyprinus rubrofuscus -koi, P. reticulata, P. latipinna, X. maculatus, X. hellerii, and A. nigrofasciata in Muriaé, Uberabinha, Sapucaí-Mirim, Doce and Todos os Santos Rivers. There are also no records of introduction of A. nigrofasciata in other sites in Brazil. Of the seven potential invaders traded in Minas Gerais and detected by the model, Carassius auratus, P. reticulata, X. maculatus, and X. hellerii have been introduced by Table 3. Biological, historical and species frequency/numbers variables in 39 stores surveyed in Minas Gerais State. Only species with at least 50% occurrence are listed, in decreasing order of annual frequency of occurrence (FO). Cities: B = Belo Horizonte, M = Muriaé, U = Uberlândia, P = Pouso Alegre, G = Governador Valadares, T = Teófilo Otoni. Sources : Richter (1988), Degani (1990), Lawson (1995), Ford & Beitinger (2005), Çek & Gökçe (2005), Froese & Pauly (2006).hobbyists (with no evidence of reproduction) in the São Francisco River basin (X.hellerii) (Chaves & Magalhães, 2010), in the Paraíba do Sul River basin (Carassius auratus, P. reticulata, X. maculatus, and X. hellerii) (Melo et al., 2006;Alves et al., 2007), in the Doce River basin (Carassius auratus and X. hellerii) (Alves et al. 2007), in the Jequitinhonha River basin (P.reticulata) (Neto, 2010), and in the Mucuri River basin (P.reticulata) (Pompeu, 2010). P. reticulata was also introduced and is reproducing in the São Francisco and Doce River basins respectively (Magalhães, 2008). These data attest the imminent risk of invasion posed by aquarium dumping and reinforce the need of aquarium trade regulation in Minas Gerais State. Cyprinus rubrofuscus (common variety) that escaped from fish farms has great importance in commercial fishing in several lotic and lentic environments in the Doce River basin (Vieira, 2010). The same trend of spread may occur with Cyprinus rubrofuscus -koi if released by hobbyists in the studied rivers. Feral koi carp released by aquarium amateurs has established itself and dispersed in a number of rivers and lakes of New Zealand (Tempero et al., 2006). There are no studies showing the adverse ecological effects of introducing Cyprinus rubrofuscus -koi, Carassius auratus, P. latipinna, X. maculatus, and X. hellerii by hobbyists in Minas Gerais or elsewhere in Brazil. On the other hand, the ecological effects of P. reticulata are known not only in the state but other locations in Brazil: this poeciliid has negatively changed fish community structures in streams of southern Brazil (Vieira & Shibatta, 2007;Cunico et al., 2009).
The model selected relatively few species as potential invaders because it requires that all nine parameters be met, but according to Chang et al. (2009), this does not mean that the risk may be low. Firstly, the list of potential invading species is constantly increasing due to the aquarium trade search for new species that appeal to its customers. Recent examples are the cichlids, the flowerhorn Amphilophus trimaculatum (= 'Cichlasoma' trimaculatum) × Amphilophus citrinellum, the black diamond cichlid Paratilapia polleni, and the freshwater stingrays Potamotrygon spp.(A. L. B. Magalhães, pers. obs.). Secondly, there are presently in the state more than 2,000 dammed water bodies (Alves et al., 2007), many located in the surroundings of the six cities studied. Impoundments often exhibit marked fluctuations in nutrient content, a condition that increases invasibility for aquarium fishes pre-adapted to lacustrine environments (Magalhães, 2010). Lastly, with the demographic growth of the six cities in the last ten years, together with the aquarium hobby expansion in Minas Gerais and Brazil (ANFALPET, 2010), the frequency of introductions and subsequent establishment of aquarium fish species through this vector will surely increase with time.
Management recommendations. This study focused on the risk-enhancing role of physical aquarium shops in six cities of Minas Gerais State. The negative consequences of ecommerce via virtual shops and auctions were not analysed, but should not be underestimated. For example, Carassius auratus, Cyprinus rubrofuscus, Poecilia latipinna, P. reticulata, Xiphophorus maculatus, X. hellerii, and A. nigrofasciata are available on e-commerce at very affordable prices (Magalhães & Jacobi, 2010). In view of this, the potential impact of e-commerce as a disseminating source of non-native species in Minas Gerais State ought to be urgently investigated. The quantities sold by means of these two types of retailing (fixed location and online) should in turn reflect the probability of escape from fish farms, a problem which has already caught attention of the Brazilian environmental authorities (Magalhães, 2010).
Brazil is signatory to the Convention on Biological Diversity. Article eight of this treaty establishes that each country that is party to the Convention has to make efforts to avoid the introduction of non-native species (Alho et al., 2011). One way to greatly reduce the potential impact that aquarium trade may cause on the water bodies of Minas Gerais State is by better informing those involved in this market, such as sectors dealing with importation, breeding, transportation and trading of aquarium fish, as well as hobbyists.
To put this into practice, we suggest the following recommendations for aquarium trade in Minas Gerais State, and Brazil as a whole: (i) instruct suppliers and retailers to sell only males of the potential invader cyprinids Cyprinus rubrofuscus -koi (veil variety), Carassius auratus (veiltail, telescope-eye, bubble-eye and celestial-eye varieties) and poeciliids Xiphophorus hellerii, X. maculatus, Poecilia reticulata, P. latipinna (veiltail and lyretail varieties) and female of the cichlid A. nigrofasciata (striped variety). These are more colorful (cyprinids, poeciliids, cichlid) and swim with difficulty due to the long fins (cyprinids, poeciliids) or have protuberant eye bulbs (Carassius auratus), which renders them more susceptible to native predators; (ii) inform hobbyists of proper locations to hand over their fish (Brazilian federal or state environmental agencies, return to the fish farm or aquarium shop), thus avoiding indiscriminate dumping in nature; (iii) with the assistance of qualified personnel, identify species that may be used as viable alternatives in each region without risks of invasion; (iv) acknowledge and apply laws of fish import and quarantine; (v) inform the hobbyist about the species characteristics when young and adult, so as to reduce rejection and fish dumping, and allow for proper care; (vi) display posters and flyers informing about "non-native species", along with a notice in bags, packaging paper and website (if any) of the aquarium shop. This notice must inform customers that in case they no longer want their fishes, they should never free them in either natural (creeks, streams, rivers or lakes) or artificial water bodies (channels, ponds or dams).
It should be made clear that these recommendations are not intended to harm aquarium trade activities, because its economic importance for the Minas Gerais State and for Brazil is undeniable. Instead, they should be seen as a powerful set of tools for disseminating environmental conservation. Already in the early 1970s, Courtenay et al. (1974) suggested similar measures for aquarium trade in the state of Florida, US, in order to prevent non-native ornamental fish species dumping by people practising this hobby. Since our recommendations are more than 35 years late in comparison to the US, and the Brazilian culture traditionally remediates environmental disasters rather than prevents them (Guerra, 1995), it is adviseable that such measures be put in practice without delay. Otherwise, the future of many water bodies in Brazil and especially in Minas Gerais State is foreseeingly disturbing adding the environmental damages caused by nonnative aquarium fish species to those of ongoing landscape changes, among which urban pollution, siltation, riparian forest destruction, and impoundments. Finally, it should be reminded that our recommendations were intended for ornamental aquarium species, and that other commercially important nonnative fishes (such as those for human consumption), deserve equal attention and specific invasion risk studies.
Fig. 1 .
Fig. 1. Cities and watersheds within the Minas Gerais State, Brazil, where the 39 ornamental fish stores were visited.
Fig. 2 .
Fig. 2. A model describing the invasion stages that species must pass in order to represent an invasion risk for rivers in Minas Gerais State, Brazil.
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Domain: Environmental Science Biology
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Summer habitat selection and impacts of human disturbance on leopard cats (Prionailurus bengalensis)
ABSTRACT Introduction: As a consequence of habitat loss and degradation, the leopard cat (Prionailurus bengalensis) in China has become endangered and in need of urgent protection. In situ conservation of leopard cats must be based on an understanding of their habitat selection patterns. We studied the summer habitat of leopard cats using line-transect surveys in the northern Taihang Mountain region surrounding Beijing, China. We compared used plots with non-used plots in elevation, tree canopy, and 20 other ecological variables, and used Vanderploeg＆Scavia’s resource selection index (VSI) to analyze habitat preferences. Outcomes/others: Results show that tree canopy, tree height, tree density, and stump quantity of used plots were significantly lower than non-used plots in summer, and that leopard cats preferred habitats located on northern, flat slopes with lower slope, shrub-dominated, dry soil, and less fallen-wood. Leopard cats had a strong tendency to use habitats near human disturbance areas with moderate levels of disturbance intensity. Conclusion: The results suggest that future conservation efforts should emphasize: (1) strengthening the protection and management of forest fringe shrub habitats to improve summer habitat suitability, and (2) environmental education and animal protection campaigns to promote community biodiversity conservation.
Introduction
The leopard cat (Prionailurus bengalensis), a small cat specie, is distributed in east, south, and southeast Asia (Ross et al. 2015). In China, leopard cats are widespread and have a strong ecological adaptability (Luo, Zhu, and Li 1995). However, due to habitat loss and fragmentation, leopard cat populations in many regions of China have declined and even become extirpated in some regions (Han et al. 1995;Wei 2006;Liao et al. 2018). The leopard cat has been listed in CITES Appendix II and as a vulnerable species (VU) in the China Biodiversity Red List, and is now anational second-class protected specie in China, which means they are in need of effective protection.
In situ conservation is the most effective way to protect endangered wildlife when it is based on a thorough understanding of the ecological characteristics of the species, particularly its preferred habitat (Yang et al. 2003). Wildlife habitat selection and utilization are related to such key influencing factors as food abundance, water quantity, shelter from disturbance, and interspecific competition (Morris 2003;Panthi et al. 2012;Pradhan, Saha, and Khan 2001). Among these factors, food availability is one of the most important determinants of habitat selection and utilization (Jiang, Jian Zhang, and Stott 2010), especially for predators who base habitat selection on maximizing food and acquisition (Wasko and Sasa 2012). Moreover, human activities can affect food availability thereby affect wildlife habitat selection (Jiang, Zhang, and Ma 2007). As an example, Petroelje et al. (2019) reported that some top prey species preferred habitats with human activities that subsidized food sources such as hunter-killed prey, livestock carcass dumps, refuse and landfills, showing their ability to adapt to anthropogenic influence.
Moreover, wildlife habitat selection is a system of stable behavioral strategies which allows some species to adapt more readily to environmental changes (Aryal et al. 2014), and have relatively flexible responses to interspecific and intraspecific interaction and competition (Jiang, Zhang, and Ma 2008). Pérez-Barbería., Hooper, and Gordon (2013) reported that, at low population densities, individual-red deer (Cervus elaphus) selected more suitable habitat, while at higher densities some individuals had to select suboptimal habitats that were relatively avoided in the past. Wysong et al. (2020) stated that predator-prey relations make predators select preferred habitats where their prey occurs, but small or medium-sized carnivores will avoid the most suitable habitats to avoid interactions with larger apex predators in their home range and habitats.
Habitat selection of leopard cats has attracted the attention of many researchers for several decades. A survey in Malaysian commercial forest reserves carried out by Mohamed et al. (2013) showed that leopard cats preferred habitat with low canopy density and a low ratio of climax plant species to pioneer plant species. Oh et al. (2010) reported that habitat selection by Tsushima leopard cats (P. b. euptilurus) was influenced by weather and human activities. Leopard cats would choose human buildings as a shelter during bad weather, and rely on the food provided by human communities in the winter when food resources were scarce, which meant they did not avoid communities and villages. A study in Thailand found that the behavior of leopard cats was not affected by roads and traffic density (Sean et al. 2007). However, in another study area in Korea, leopard cats tended to choose habitats away from roads (Rho 2009). These studies provide important information for leopard cat protection and habitat management.
Compared with other areas in the world, little research has been conducted on leopard cats in China. Previous studies in China mainly have focussed on biological research, taxonomic differentiation among subspecies, population size, distribution, and diet analysis (Wei 2006;Liu et al. 2018;Luo, Zhu, and Li 1995;Han et al. 1995;Shao et al. 2019). Studies on the habitat selection of leopard cats, especially in regions near large cities such as Beijing, are rare. Such studies, however, could reveal ecological characteristics of leopard cats under anthropogenic impacts and be used to identify suitable habitat so that wildlife managers might reduce unnecessary development activity in the habitat and improve the efficiency of in situ conservation. When habitat is protected, it could reduce the interactions between human and wildlife, which has a positive effect on not only wildlife population management and protection but also animal disease prevention and control because free-roaming pets like cats and dogs have the potential to transmit pathogens to leopard cats .
In this study, we analyzed the summer habitat selection of leopard cats in the Northern Taihang Mountain region around Beijing, China. We predicted that the summer habitat selection of leopard cats is related to numerous variables, but most importantly to food abundance. Moreover, we predicted that human disturbance, an important factor in the study area, will play a significant role in affecting the habitat selection of leopard cats through changes in food abundance balanced by natural avoidance behavior. The results can provide information for in situ conservation of leopard cats, as well as management of wildlife habitat and wildlife-compatible human development.
Study area
We conducted this study in the northern Taihang Mountain (N: 36°01 '~42°37ʹ, E: 113°04 '~119°53ʹ) in the Capital Circle region around Beijing, China ( Figure 1). Influenced by the sub-humid, warm temperate continental monsoon climate, the study area is characterized by a hot rainy season with an average annual temperature of 13.5°C and average annual precipitation of 700 mm. The highest precipitation occurs in July (132.3 mm) and the lowest in December (4.4 mm). Vegetation in the mountains occurs in elevational belts, with deciduous broad-leaved forest distributed below 700 m, mixed coniferous broad-leaved forest between 700 and 1500 m, and coniferous forest between 1500 and 2000 m. Fruit trees are planted in the valleys and alluvial terraces, which is one of the main deciduous fruit tree regions in China. Over 70 mammal species occur in the region, including leopard (Panthera pardus), red fox (Vulpes vulpes), leopard cat, jackal (Cuon alpinus), yellow weasel (Mustela sibirica), Siberian polecat (Mustela eversmanni), and raccoon dog (Nyctereutes procyonoides) (Ning 2012;Zhou 2012).
Surveying variables and definitions
The investigation was conducted from 10 July to 31 August 2019. After considering land use, geographic factors, vegetation type, and human disturbance, 74 survey grid cells (10 km×10 km) were selected out of 315 grid cells (10 km×10 km) in the study area ( Figure 1). Within each of the 74 grid cells, we established two-line transects that were approximately 3 km in length and perpendicular to the mountain ridge. The distance between the two line transects was greater than 3 km. We ensured that all habitat types were contained in the field survey areas.
The investigators walked each line transect and determined which areas were used based on activity signs of leopard cats, such as feces, footprints, shelters, etc. When a sign was located, a 20 m × 20 m signcentered "used" plot was established. When no signs were found, a 20 m × 20 m nonused plot was established at the beginning, middle, and ending of a line transect. Five 4 m × 4 m small plots were set in the center and four corners of used and nonused plots respectively.
Based on reported studies about leopard cat (Mohamed et al. 2013;Rajaratnam et al. 2007) and other mammals (Zhou et al. 2013;Wang et al. 2014;Tang 2014), 22 variables were selected to describe the summer habitat of leopard cats. The classification, definition, and determination methods are summarized in Table 1.
Data analysis
The Mann-Whitney U test was used to compare differences in 11 continuous variables between used plots and nonused plots, including elevation, tree canopy, tree DBH, tree height, tree density, shrub height, shrub canopy, ground-plant cover, stump quantity, fallenwood quantity, and withered grass cover. A Chi-square Test was used to compare the differences in 11 discrete variables between used plots and nonused plots, including slope aspect, slope gradient, slope position, vegetation type, concealment, Lee condition, soil moisture degree, distance from the nearest water source, distance from the nearest residential area, distance from nearest anthropogenic disturbance and disturbance intensity (Tong et al. 2010).
We used Vanderploeg Scavia's Resource Selection Index (VSI, Zhou et al. 2013) to analyze the habitat preferences of leopard cats. VSI can be used for both continuous and discrete variables. The VSI is calculated as follows: In the VSI, w i is the resource selection coefficient, E i is the resource selection index. In the formula, r i is the number of plots with a certain ecological factor used by leopard cats; P i is the sum of plots with a certain ecological factor; n is the number of items in a variable. Results for the VSI are interpreted as follows: E i = 1, especially preferred (EP), indicating a complete preference of the habitat characteristic. 0.1< E i <1, preferred (P), indicating a slight to a strong preference for the habitat characteristic.
-0.1< E i <0.1, almost randomly selected (AR), indicating a very weak preference, or avoidance of the habitat characteristic. E i = 0, randomly selected (RS), indicating that leopard cats did not show any preference for or avoidance of this habitat characteristic.
-1< E i <-0.1, not preferred (NP), indicating a slight to strong avoidance of the habitat characteristic. E i = -1, not selected (NS), indicating complete avoidance of the habitat characteristic.
Comparison between used and nonused summer habitats
Results for continuous variables of used plots (n = 26) and nonused plots (n = 251) are shown in Table 2. Tree canopy (25.0 ± 5.3%), tree height (6.2 ± 1.6 m), and tree density (8.2 ± 2.2/plot) in used plots was significantly lower than those in nonused plots (P < 0.01). The number of tree stumps (0.2 ± 0.1/plot) in used plots was significantly lower than that in nonused plots (P < 0.05).
There were highly significant differences among slope position, vegetation type, and soil moisture degree (P < 0.01) between used plots and nonused plots (Table 3). Leopard cats preferred to select a lower slope (54.17%) with dry soil (50.00%) and shrub (80.77%) covered.
Leopard cats preferred habitats with an elevation higher than 800 m and avoid those lower than 800 m (Table 5). They preferred north, flat, and gentle slope under bad Lee condition, and avoided west, gentle, and middle slope. Leopard cats preferred dry soil and avoided wet soil.
Leopard cats tended to choose habitats with a medium distance from water and avoid habitats far from water. They preferred habitats near to anthropogenic disturbance with a moderate disturbance intensity and avoided habitats with weak disturbance intensity (Table 6). Thus, the characteristics for habitat selection of leopard cats are as follows: low tree canopy (<50%), low tree height (<5 m), thick DBH (>30 cm), sparse tree density (<20/plot), high shrub height (>2 m), high shrub cover (>75%), moderate ground-plant cover, and withered grass cover (25 ~ 50%), less than 3 stumps, less than 5 fallen-woods, moderate concealment, dry soil, bad Lee condition, and above 800 m, located in the north, flat, and lower slope. Moreover, leopard cats showed a preference for habitats near to anthropogenic disturbance with moderate disturbance intensity.
Discussion
Habitat selection is an adaption to the environment (Aryal et al. 2014) that allows animals to survive, reproduce, and perpetuate their species. Habitat selection is influenced by many factors (Tong et al. 2010) such as food abundance, vegetation type, elevation, temperature, landform, and roads (Zhang and Ma 1999;Oh et al. 2010;Bashir et al. 2014). Previous studies have shown that leopard cats are widely distributed and are broadly adapted to a wide variety of habitat, and in different areas, their habitat has included a great diversity in vegetation type, including meadow, shrub, evergreen broad-leaved forest, mixed forest, and coniferous forest (Oh et al. 2010;Rabinowitz 1990). Notes: Chi-Square Test; **, highly significant difference (P < 0.01).
ECOSYSTEM HEALTH AND SUSTAINABILITY
In this study, habitat selection of leopard cat reflected not only natural ecological requirements but also influence from human disturbance and human activities. We found that leopard cats in northern Taihang Mountain preferred habitats with shrub vegetation and avoided habitats with trees, which is probably related to their prey. Predators prefer habitats with high prey density (Davidson et al. 2012). As carnivores, leopard cats primarily prey on small rodents (Han et al. 1995;Rajaratnam et al. 2007), so their habitat selection will be mainly determined by the spatial distribution of rodents. Small rodents are more abundant in early successional and highly disturbed habitats, and studies have found that most of rodent species in Taihang Mountain are more likely to live in shrub habitat (Si 2017;Huang et al. 2019), such as Niviventer confucianus and Apodemus peninsulae (Fan et al. 2020;Bao, Li, and Shi 2005), which were the main prey species of leopard cats in our study area. By providing suitable habitat for prey, shrub habitat provides an abundance of food for leopard cats. Hence, leopard cats had a tendency to choose shrub habitats. Similarly, Oh et al. (2010) reported that leopard cats preferred to use mountain ridges because the dense shrubs growing on ridges could be preferred habitat for prey. The highly consistent spatial characteristic of habitat selected by leopard cats suggests that prey distribution can have a strong impact on the habitat selection of predators. The landform is also an important factor in the habitat selection of leopard cats. One study found that leopard cats preferred to move through valleys (Lee et al. 2017). Similarly, in this study leopard cats showed a tendency to use lower slopes, which generally make up valley topography in the Taihang Mountain regions. This preference might be explained by the need for efficient movement and antipredator behavior. Leopard cats spend most time resting and moving on the ground (Rabinowitz 1990); therefore, they tend to choose habitats with a gentle slope, fewer stumps, and fallen-woods to avoid obstacles on the ground, which might hinder them from hunting or getting away from dangers quickly. Moreover, there are prey rodents, leopard cat' preferred food, living in such habitats because of many human activities here (Liu et al. 2018).
Temperature regulation might also affect habitat choice by leopard cats. Preference of carnivores for temperature also affects their habitat selection; in the summer many carnivores avoid high temperatures in the daytime by nocturnal activities (Marinho et al. 2018). In this study, leopard cats preferred to choose the northern slope, which is shadier in the summer time and could help leopard cats avoid high temperature.
The anthropogenic disturbance may significantly affect habitat selection by wildlife (Ge et al. 2015). Our study area is located near Beijing, one of the largest cities in China where development has been proceeding rapidly, and the level of urbanization has been close to 65%. The main kinds of human disturbance in the study area are farming, animal husbandry, collection of medicinal plants, and transportation. Our research found that leopard cats tended to choose habitats near human disturbance with moderate disturbance intensity, which indicates that leopard cats in Taihang Mountains could tolerate and even adapt to human activities in their habitats. Similar results were reported by other studies, e.g., Sean et al. (2007) found that leopard cats in Thailand were not affected by roads and vehicles, and Mohamed et al. (2013) reported that leopard cats in Malaysian Borneo preferred to occur in areas with high human disturbance intensity. Although roads are known to cause negative impacts on wildlife, some studies have documented animals' more complex responses to this anthropogenic disturbance. Habitats close to roads can contain a large number of small mammals (Meunier et al. 1999), and roads could increase the spatial overlap of predators and preys, so predators can use roads to improve predation rate (Demars and Boutin 2018). Moreover, some animals can also use roads to increase travel efficiency, which may be positive for their survival (Muhly et al. 2019;Mumma et al. 2019). Hence, in this study, choosing habitats crossed by dirt roads suggests that leopard cats may use roads for higher movement efficiency and more abundant food resources. Human communities provide good habitats for rodents like Rattus norvegicus and Mus musculus, and in recent years, increasing agricultural activity has helped small rodent populations expand (Ye et al. 2015), which can be used as food species by leopard cats. Leopard cats also occasionally prey on poultry and eat garbage (Han et al. 1995), leading them to use habitats with high disturbance.
Habitat selection is also influenced by interspecific competition (Suhling 1996;Jiang et al. 2009;Jiang, Jian Zhang, and Stott 2010), especially among species with similar food niche. In Taihang Mountainous regions of this study, there are sympatric species such as leopard (Panthera pardus), red fox (Vulpes vulpes), jackal (Cuon alpinus), yellow weasel (Mustela sibirica), and raccoon dog (Nyctereutes procyonoides), which have similar food niche with leopard cat (Ye et al. 2015;Han et al. 1995). Therefore, leopard cats need to avoid and reduce competition with co-occurring species. As the results of interspecific competition and niche partitioning, leopard cats are inclined to select and utilize habitats closer to human communities, which means that leopard cats have a relatively higher tolerance of anthropogenic influence compared with the above mentioned sympatric predator species. Further studies of the entire carnivore community might help elucidate these relationships.
Overall, our study demonstrates that the habitat selection of leopard cats in Taihang Mountainous regions in northern China was determined by both natural ecological factors and anthropogenic factors. With regard to natural factors, in the summer leopard cats preferred to choose habitats with shrub vegetation and lower air temperatures, and avoided habitats with abundant ground obstacles such as stumps or fallen woods. Food resources were the key factor influencing habitat selection and shrub habitats are known to support more small mammal prey species used by leopard cats. In addition, human disturbance appeared to attract leopard cats, likely because of increased prey abundance in areas with human activity. As the main areas of human disturbance, communities, agricultural lands, and roadsides have been shown in other studies to have high densities of preferred rodent prey species, so leopard cats showed a preference for habitats near human disturbance areas with moderate levels of disturbance intensity. Niche differentiation caused by human disturbance also induces leopard cats to occur in areas near human activities with less competition from other native carnivores which prefer to avoid human disturbance.
Conservation policy and suggestion
Most of the distributed areas of leopard cats are not within reserves or key monitoring areas, so we should enhance the monitoring, protection, and management of those areas to maintain habitat suitability. In our study, leopard cats showed a strong tendency to use habitats near human and communities, so we need to promote animal conservation and environmental education to improve wildlife conservation and minimize negative interactions with wildlife such as the loss of domestic animals. On the other hand, species related to special habitat types can be good indicators by providing information of particular environment for wildlife managers (Carignan and Villard 2002), and top predators can be ideal indicator species because they can represent the changes of food webs (Ramirez et al. 2014). Hence, leopard cats are the potential to be indicator species for peri-urban nature ecosystem assessment. Further study is recommended on estimating the density of the population of leopard cats, which can reflect the ecosystem restoration and protection effectiveness.
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Domain: Environmental Science Biology
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Toxicity and Disruptive Impacts of Novaluron, A Chitin Synthesis Inhibitor, on Development and Metamorphosis of The Olive Leaf Moth Palpita unionalis (Hübner) (Lepidoptera: Pyralidae)
The olive leaf moth Palpita unionalis (Lepidoptera: Pyralidae) is an economic pest of the commercial olive groves in Egypt and different Mediterranean countries. The present study was conducted aiming to assess the effects of Novaluron, a chitin synthesis inhibitor, on survival, growth, development and metamorphosis of this pest. The newly moulted last instar (6th) larvae had been treated with six concentrations (100.0, 10.0, 1.00, 0.10, 0.01 and 0.001 ppm), via the fresh olive leaves, as food. Different degrees of toxicity were recorded on all developmental stages. LC50 was calculated in 0.97 ppm. The somatic weight gain of larvae was drastically reduced and the larval growth rate was severely regressed, regardless the concentration. The larval duration was generally shortened but the pupal duration was remarkably prolonged, in a dosedependent manner. The pupation rate was regressed, especially at the higher four concentrations. The metamorphosis program was impaired, since larvalpupal intermediates had been produced at some concentrations. In addition, the pupal morphogenesis was disrupted, since some pupal deformities had been observed at some concentrations.
I. INTRODUCTION
From the Zoogeographical point of view, the Mediterranean Basin was reported as the original area of the olive leaf moth Palpita unionalis (Hübner)(Lepidoptera: Pyralidae). Now it is an international lepidopterous migratory pest in the tropical and subtropical regions of the Old World [1, 2]. P. unionalis is one of the most dangerous pests of olives in Egypt and other Mediterranean countries [3][4][5][6]. The most important damage of this pest occurs on young trees, nurseries and shoots of old trees [7,8]. The control of P. unionalis on olive trees has relied upon the use of traditional synthetic insecticides [9]. Different pesticides exhibited a good control when applied on the early larval instars [10]. Insecticide residues have been detected in olive oil and in the environment where olives are grown [11]. In addition, the extensive use of conventional insecticides has caused resistant insect strains to emerge [12,13] and serious toxicological problems to humans and the environment [14,15]. Therefore, alternative materials have been initiated recently to minimize the pesticide hazards and introduce of new effective and safer ways and negligible effects on ecosystem.
Over the past four decades, efforts have been made to develop insecticidal compounds with selective properties that act specifically on biochemical sites that are present in particular insect groups but with properties that differ from conventional insecticides [16][17][18]. Insect Growth Regulators (IGRs) belong to a group of compounds which are not directly toxic, but act selectively on normal growth, development metamorphosis and/or reproduction in insects via disrupting the hormonally regulated physiological processes [19][20][21][22][23][24]. Because of their desirable characteristics, such as low toxicity, less environmental pollution, high selectivity, and low impact on natural enemies and people, IGRs are used to control various insect pests [25][26][27]. Several IGRs have been extensively studied for investigating their effects on metamorphosis and reproduction in a number of insect species [28,29]. On the basis of the mode of action, IGRs had been grouped in three categories: (i) Juvenile hormone analogues (JHAs) (also called as Juvenoids), ( inhibitors [30,16,31]. They had been, also, grouped in CSIs and substances that interfere with the action of insect hormones (i.e. juvenile hormone analogues, and ecdysteroids) [32].
CSIs interfere with chitin biosynthesis in insects and thus prevent moulting, or produce an imperfect cuticle [33]. By affecting the hormonal balance, they disrupt several physiological processes in insect body [33]. Also, CSIs are less toxic compounds to the non-target organisms and beneficial biota and have no residual effects [34]. One of the novel benzoylphenyl ureas is the Novaluron. It inhibits the chitin formation on larvae of various insects of different orders [35,36] and exhibits a high toxicity against several dipterous species [37][38][39][40][41][42]. It is, also, a powerful suppressor of lepidopteran larvae [43] and whiteflies [44,45] as well as some species of Hemiptera [46,47] and Coleoptera [48][49][50]. [63]. Larvae were daily provided with fresh olive leaves Olea europaea L, as a food. After the larval stage, the developed pupae were collected and transferred to Petri dishes (5.5×1.4cm). The emerged adults were daily collected and released in plastic jars (3L) provided with cotton pieces, soaked in 10% sugar solution, for feeding, as well as olive twigs ( 20 cm in length) as an oviposition site. After egg deposition, adult males and females were transferred into new plastic jars. The jars of eggs were provided with fresh tender olive twigs fixed in a small bottle containing water, so as to keep the leaves flat and fresh, for feeding of the newly hatched larvae. The fresh tender olive leaves were renewed daily until pupation.
Bioassay of Novaluron.
Novaluron [1-[chloro-4-(1,1,2trifluoromethoxyethoxy) phenyl] -3-(2,6difluorobenzoyl) urea] has the molecular formula: C 17 H 9 ClF 8 N 2 O 4 . It was supplied by Sigma-Aldrich Chemicals. A series of concentration levels of Novaluron was prepared by diluting with distilled water in volumetric flasks as follows: 100.0, 10.0, 1.0, 0.1, 0.01 and 0.001 ppm. Bioassay tests were carried out using the newly moulted last instar (6 th ) larvae. Fresh olive leaves were dipped in each concentration of Novaluron for 5 minutes and air dried before introduction to larvae for feeding. Control larvae were provided with water-treated olive leaves. Ten replicates of treated and control larvae (one larva/replicate) were kept separately in glass vials. The larvae were allowed to feed on treated leaves for 24 hrs. Then, they provided with fresh untreated olive leaves and all biological and physiological parameters were recorded daily. Weight gain: Each individual larva (treated and control) was carefully weighed every day using a digital balance for calculating the growth as follows: Initial weight (before the beginning of experiment) final weight (at the end of experiment).
Growth rate: Growth rate (GR) can be calculated according to Waldbauer [66] as follows: GR = fresh weight gain during feeding period / feeding period X mean fresh body weight of larvae during the feeding period.
Developmental rate: Dempster's equation [67] was applied for calculating the developmental duration, and Richard's equation [68] was used for calculating the developmental rate.
Pupation rate: The pupation rate was expressed in % of the successfully developed pupae.
Deranged metamorphosis: different features of impaired metamorphosis program of P. unionalis were observed as larval-pupal intermediates, pupaladult intermediates or extra moult and calculated in (%). Also, impaired pupal morphogenesis was observed as pupal deformations and calculated in %.
Various features of impaired metamorphosis and morphogenesis were recorded in photos.
Pupal water loss.
Pupal water loss was calculated depending on the data of the initial and final weights of the pupae, as follows: Water loss % = [initial weight -final weight /initial Weight] × 100 4. Statistical analysis of data.
Data obtained were analyzed by the Student's tdistribution, and refined by Bessel correction [69] for the test significance of difference between means.
III. RESULTS
Toxicity and lethal effects.
After treatment of the newly moulted last instar (6th) larvae of P. unionalis with six concentrations of Novaluron (100.0, 10.0, 1.00, 0.10, 0.01 and 0.001 ppm), via the fresh olive leaves, as food, data of toxicity and lethal effect on all developmental stages were distributed in Table ( 14±0.37 and 1.75±0.50 days, at 0.001, 0.01, 0.10, 1.00, 10.00 and 100 ppm, respectively, vs. 3.60±0.69 days of control larvae). Developmental rate of larvae is another parameter indicating an enhancing action of Novaluron, since the treated larvae developed in faster rate than control congeners. As obviously shown in the previously mentioned table, a reversal action of Novaluron was exerted on the developed pupae, since their duration was remarkably prolonged, in a dosedependent manner (9.25±1.98, 9.28±0.75, 9.60±0.54, 10.66±1.52 and 12.50±0.70 days, at 0.001, 0.01, 0.10, 1.00 and 10.0 ppm, respectively, vs. 9.20±0.78 days of control pupae). This prolongation of pupal stage was reflected in a retarded development, i.e., pupae developed in slower rate than that of control pupae (for detail, see Table 2).
Because the pupal death may be due to the desiccation caused by Novaluron, loss of body water was estimated in %. In general, the successfully developed pupae from treated larvae lost more body water than control pupae (28.6, 31.0, 31.0, 31.0 and 38.7%, at 0.001, 0.01, 0.10, 1.0 and 10.0 ppm, respectively, compared to 28.2% of control pupae).
With regard to the effects of Novaluron on metamorphosis and morphogenesis of P. unionalis, data listed in Table (2) exiguously revealed various disruptive effects such as the regressed pupation rate, especially at the higher four concentrations (70,60,80 and 40%, at 0.10, 1.0, 10.0 and 100 ppm, respectively, vs. 100% pupation on control larvae). The metamorphosis program was impaired, since larvalpupal intermediates had been produced at some concentration levels (10, 30 and 10% at 0.10, 1.00 and 10.0 ppm, respectively). Description of these intermediate creatures was provided in Plate (1). Moreover, 10% of pupal-adult intermediates had been produced only at 0.01 ppm (see Plate 2). In addition, the pupal morphogenesis was disrupted, since some pupal deformities had been observed at some concentration levels (12.5 and 20.0%, at 10.0 and 1.0 ppm, respectively). Some malformed pupae had been observed in non-tanned segmented body or segmented body with tanned part and incompletely tanned part, depending on the concentration level of Novaluron (see Plate 3).
As reported in the available literature, LC 50 values of Novaluron and lufenuron against S. litura were determined as 350.45 and 453.78 ppm, respectively [100]; LC 50 of Pyriproxyfen was found to be 0.025% against S. litura larvae [86]; LC 50 of Hexaflumuron against H. armigera was 8.47 mg /L [101]; LD 50 values of RH-5849 and Tebufenozide against E. kuehniella were 0.05 and 0.005 μg/insect, respectively [90]; LC 50 of Methoxyfenozide against Culex pipiens was calculated in 24.54 µg/L [89]; LC 50 of Lufenuron against G. pyloalis was 19 ppm [91] and LC 50 values of Chlorfluazuron, Cyromazine, Lufenuron and Precocene I against C. felis were 0.19, 2.66, 0.20, and 10.97 ppm, respectively [96]. Also, a variation in LC 50 values was reported for Novaluron on S. littoralis, since LC 50 values were 2.71 and 2.65 ppm, after treatment of penultimate instar larvae and last instar larvae, respectively [55]. In the current investigation on P. unionalis, LC 50 of Novaluron was calculated in 0.97 ppm. Thus, LC 50 value depends on several factors, such as susceptibility of the insect and its treated stage or instar, lethal potency of the tested compound and its concentration levels, method and time of treatment, as well as the experimental conditions.
To explicate the recorded toxic effect of Novaluron on larvae, pupae and adults of P. unionalis, in the present study, IGRs exhibit their toxic effects on insects with a mode of action other than that of conventional insecticides. Furthermore, CSIs interfere with the synthesis and/or deposition of chitin on the exoskeleton or other chitinized internal structures, such as the peritrophic matrix, hindering the role of peritrophic membrane in protecting the secreting cells from damage [102,103]. Furthermore, it was suggested that the tested CSI interferes with the transport system of UDP-N-acetyl amine across the membrane [104].
For some detail, the larval deaths of P. unionalis by Novaluron, in the current study, may be attributed to the failure of larvae to moult (lethal moult) owing to the inhibition of chitin formation [105,106], to the inability to shed their exocuticle [107], or to swallow volumes of air for splitting the old cuticle and expand the new one during ecdysis [108]. Also, these larval deaths may be due to the prevented feeding and continuous starvation of the present insect [109].
Although the disturbance of hormonal regulation or the disrupting of normal activity of the endocrine system in insects by IGRs was reported [110,111] and suggested for some mosquito species [35,112], the pupal deaths in P. unionalis, in the present investigation, could not be directly relate to the hormonal activity of Novaluron, but to other causes, such as suffocation, bleeding and desiccation due to imperfect exuvation, failure of vital homeostatic mechanisms, etc. [113]. This suggestion can easily be substantiated since Novaluron exerted a predominant desiccating action on the successfully developed pupae of P. unionalis to lose more body water than control pupae, in the present study. In addition, the adult mortality of P. unionalis after treatment of newly moulted last instar larvae only with 0.01 ppm of Novaluron, in the current study, can be explained by the retention and distribution of this compound in the insect body as a result of rapid transport from the gut of treated larvae into other tissues, by the direct and rapid transport via the haemolymph to other tissues, and/or by lower detoxification capacity of adults against the tested CSI [114].
2. Disturbance of growth and development of P. unionalis by Novaluron.
Depending on the currently available literature, some authors have taking into account the body weight gain by the insect larvae as a valuable indicator for growth [115]. In the present study, both larval weight gain and growth rate had been determined after treatment of newly moulted last (6 th ) instar larvae of P. unionalis with different concentrations of Novaluron. The somatic weight gain of larvae was drastically reduced and the larval growth rate was severely regressed, regardless the concentration. Also, larval duration was generally shortened and the developmental rate of these larvae was enhanced.
On the other hand, the present results of shortened larval duration and enhanced developmental rate of P. unionalis larvae were in agreement with the reported results of shortened larval duration of P. gossypiella after treatment of newly hatched larvae with Methoxyfenozide [99] and other insects, such as Rhynchophorus ferrugineus by Lufenuron and Diofenolan [121], A. ipsilon by Flufenoxuron [122] and Schistocerca gregaria by Lufenuron [123]. On the contrary, the present results disagreed with the reported results of prolonged larval duration of S. littoralis larvae after treatment of penultimate or last instar larvae with by Novaluron [55] and Cyromazine [78]; prolonged larval duration after treatment of 5 th instar larvae of Spodoptera frugiperda with LC 10 and LC 25 of Methoxyfenozide [124] and prolonged larval duration in P. gossypiella after treatment of the first instar larvae with Pyriproxyfen [99].
Lepidoptera belong to the most sensitive groups of insects regarding the growth regulating effects of IGRs. The inhibited growth of P. unionalis by some concentrations of Novaluron, in the current study, may be a result of the blocked release of morphogenic peptides, causing alteration in the ecdysteroid and juvenoid titers [125]. Also, Novaluron may affect the tissues and cells undergoing mitosis [126].
Recently, the developmental duration was prolonged indicating retarded development in some other insects by various IGRs, such as G. pyloalis by Lufenuron [91]; C. pipiens by Methoxyfenozide [89] and N-tertbutylphenyl thenoylhydrazide (ecdysteroid derivative) IJTSRD | May-Jun 2017 Available Online @www.ijtsrd.com [132]; C. cephalonica by Fenoxycarb [94]; P. gossypiella by Lufenuron and Pyriproxyfen [99] and Novaluron [56]; etc. In agreement with those reported results of retarded development, the present study recorded a powerful retarding effect of Novaluron on the development of P. unionalis, since the pupal duration was remarkably prolonged and the developmental rate of pupae was considerably regressed.
In the current study, retarded development of P. unionalis by Novaluron, as expressed in prolonged pupal duration and regressed developmental rate, may be attributed to the indirect interference of this CSI with neuroendocrine organs responsible for the synthesis and release of tropic hormones, like prothoracicotropic hormone (PTTH) [133]. The prolongation of larval or pupal duration may be due to the persistence of juvenile hormone (JH) in the haemolymph where it is only in the absence of JH that ecdysone could be activated and lead to the formation of the next stage [134]. Also, Novaluron may exhibit a delaying effect on the ecdysis and transformation [108]. In particular, the final step of chitin biosynthesis pathway was inhibited by this CSI and the precursor was not converted into chitin leading to a prolongation of developmental duration [112].
The effects exhibited by IGRs on insect metamorphosis may be important from the practical stand-point because they could result in various morphogenic defects as well as mortality [135]. Depending on the available literature, the major symptoms and features of the impaired metamorphosis of an insect after treatment with various IGRs (including CSIs) had been described as reduction of pupation and adult emergence, production of larval-pupal and/or pupal-adult intermediates, deformed larvae and/or pupae and the production of supernumerary larval instars (superlarvae). However, all or some of these features were observed in various insects as responses to the disruptive effects of different IGRs, such as S. littoralis by Chlorfluazuron [136], Triflumuron [72], Lufenuron [105,106], Flufenoxuron [71,72], Methoprene and Fenoxycarb [127]; Novaluron [55] and Cyromazine [78]. Also, some or all of these symptoms of the impaired metamorphosis were recorded after treatment of different insects with several IGRs, such as T. castaneum and T. confusum [137], Liriomyza trifolii [138] and Callosobruchus maculates [139] [140]; Rh. ferrugineus [121] and P. demoleus [79] by Diofenolan; Lobesia botrana by Lufenuron [141]; C. pipiens by Kinoprene [84]; etc.
In the present study on P. unionalis, Novaluron detrimentally prohibited the pupation process, since pupation % considerably decreased, especially at the higher four concentrations. This results was, to a great extent, consistent with those reported results of reduced pupation rate of some insects by various IGRs, such as P. xylostella by Hexaflumuron [142], S. littoralis by Novaluron [55] and Cyromazine [78], G. pyloalis by Lufenuron [91] and Fenoxycarb [93] as well as Encarsia formosa by Pyriproxyfen and Fenoxycarb [24].
In the present study on P. unionalis, the pupal morphogenesis was deranged, since different pupal deformities had been observed, at some concentrations of Novaluron. Some malformed pupae appeared in non-tanned segmented body or segmented body with tanned part and incompletely tanned part, depending on the concentration level of Novaluron. To some extent, similar deranged pupal morphogenesis had been reported for T. castaneum and T. confusum after treatment with Cyromazine [137], Spodoptera frugiperda after feeding of 5 th instar larvae on a diet treated with LC 10 and LC 25 of Methoxyfenozide [124], C. cephalonica after topical application of last instar larvae with Fenoxycarb [94] and P. gossypiella after treatment of the full grown larvae with Novaluron In the current investigation on P. unionalis, Novaluron exhibited a disruptive effect on the metamorphosis program, since larval-pupal intermediates had been produced, after treatment of newly moulted last instar larvae with some concentrations.
This feature of impaired metamorphosis was, also, described as abnormal or lethal pupation [124]. Our result was, to a great extent, in agreement with some of those reported results of disturbed metamorphosis of a number of insect pests by various IGRs, such as H. armigera by Hexaflumuron [101], S. littoralis by Novaluron [55] and Cyromazine [78], C. cephalonica by Fenoxycarb [94] and P. gossypiella by Novaluron [56]. Also, the larval-pupal intermediates were observed after topical treatment of last instar larvae of Spodoptera exempta, Spodoptera exigua, S. littoralis, Mamestra brassicae, Galleria mellonella, Mythimna unipuncta and Spodoptera frugiperda with RH-5849, Tebufenozide or Methoxyfenozide [143,113,116]. Moreover, some pupal-adult intermediates of P. unionalis had been produced only at 0.01 ppm of Novaluron, in the current investigation, as a feature of impaired metamorphosis program. As far as our literature survey could ascertain, no information was available on the production of pupal-adult intermediates.
The production of larval-pupal and pupal-adult intermediates, in the present study on P. unionalis, can be explicated by an inhibitory effect of Novaluron on the DNA synthesis [145] or the chitin biosynthesis and chitin synthase [146]. (4) The molt induction had lethal consequences because the induction of a rapid molt did not provide enough time for the completion of larval-pupal transformation. Thus, the insects molted to nonviable forms between the life stages [147]. Molts induced during the early phase of the last instar produce larval-like individuals, while those formed in the late phase generate pupal-like individuals [148].
CONCLUSION
Depending on results of the present study, it can be concluded that Novaluron exhibited various degrees of toxicity against all developmental stages of P. unionalis, as well as it displayed some disruptive effects on development, metamorphosis and pupal morphogenesis. Therefore, Novaluron may be considered as a promising control agent against this economic pest of the commercial olive groves in Egypt and other olive producing countries as a potential alternative to the conventional pesticides. [55] K. Ghoneim
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Domain: Environmental Science Biology
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Mollusca , Gastropoda , Succineidae , Omalonyx unguis ( d ’ Orbigny , 1835 ) : Distribution extension and new records for Brazil
central western Brazil. In fact, this is the only area in Brazil that was not sampled by Tillier (1981). Furthermore, Travassos (1928) used specimens of Omalonyx from Mato Grosso state as experimental hosts for a bird trematode, strongly suggesting that Omalonyx occurs in that area. The present investigation uses comparative morphology to establish the first record of O. unguis in western Brazil. The occurrence of the genus in this region is thus confirmed, and the known geographical distribution of the species is expanded. The material studied consists of slugs collected (permits granted by IBAMA [SISBIO] #12113-3) from four localities in western Brazil: Foz do Iguaçu, Paraná state; Campo Grande and Miranda, Mato Grosso do Sul state; and Poconé, Mato Grosso state. All samples were collected from aquatic vegetation on the margins of freshwater systems. One specimen from Miranda is shown in Figure 1. The animals were taken to the laboratory, where they were kept alive until sexual maturity was ascertained by the extrusion of eggs. Mature animals were relaxed overnight in water at 4°C and then killed by immersion in hot water (80°C) for 45 seconds. Shells were removed and stored in dry receptacles, and samples of foot tissue were frozen in an ultra-low temperature freezer (-80°C) for further molecular studies. Bodies were then preserved in RallietHenry solution (5% formaldehyde, 2% acetic acid, 0.6% sodium chloride). All specimens were deposited in the malacological collection of the Laboratório de Malacologia e Sistemática Molecular (LMSM) in the zoology department of the Universidade Federal de Minas Gerais, Brazil. Reproductive systems were dissected from the preserved bodies and compared with descriptions of anatomical details that are widely used to identify O. unguis (Hylton-Scott and Lapuente 1968; Arruda et al. 2006; Arruda and Thomé 2008) and other species of the genus (Hylton-Scott and Lapuente 1968; Hylton-Scott 1971; Tillier 1980; 1981). We examined the following Brazilian material: Paraná Omalonyx unguis (d’Orbigny, 1835) is the type species of the genus Omalonyx d’Orbigny, 1837, which comprises neotropical succineid slugs. Although this species has been reported from Brazil on different occasions (Moricand 1836; Hidalgo 1870; 1872; Lange de Morretes 1949; Salgado and Coelho 2003; Simone 2006), information from a recent study (Arruda and Thomé 2008) suggests that those reports may be incorrect. Tillier (1981) conducted an extensive taxonomic study of the genus using samples from most of the South American countries and the Lesser Antilles. In Brazil, he studied animals from the southern, southeastern, northeastern and northern regions, but did not include specimens from the central western region. Tillier (1981) established O. unguis as the senior synonym of most of the southern species of Omalonyx (O. convexus Martens, 1868; O. patera Doring, 1873; O. gallardoi Hylton-Scott and Lapuente, 1968, O. weyrauchi Hylton-Scott, 1970), which resulted in a distribution of O. unguis that included Argentina, Paraguay and the southern region of Brazil. Arruda and Thomé (2008) revalidated the species O. convexus and explained that all of the specimens identified as O. unguis by Tillier (1981) were, in fact, O. convexus, with the exception of the paratype from Paraguay (shell only). The map provided by Arruda and Thomé (2008) shows overlapping distributions of the two species in the Argentinean region of the Parana River Basin and adjacent localities in Uruguay. That map also shows that O. convexus extends to Rio Grande do Sul state in southern Brazil; while O. unguis extends to the Paraguay River sub-basin in Paraguay. This distributional pattern excluded Brazil from the range of O. unguis. Hylton-Scott and Lapuente (1968) also reported O. unguis in eight localities, most in Argentina and one in Paraguay. The previously known occurrences of O. unguis are summarized in Table 1. Although there are no consistent records of O. unguis occurrence in Brazil, there is no reason to doubt its occurrence in the upper Paraguay River sub-basin of Abstract: Omalonyx unguis was previously known to occur in Argentina, Paraguay and Uruguay. We report the first conclusive record of occurrence of this species in Brazil, based on specimens collected in three localities in the Paraguay River sub-basin (Mato Grosso do Sul state – Campo Grande and Miranda; Mato Grosso state – Poconé), and in the Brazilian margin of the Paraná River (Foz do Iguaçu, Paraná State). The species was identified by comparative morphology of the reproductive system, and a map that synthesizes the literature and reports new records is presented. 1 Universidade Federal de Minas Gerais, Instituto de Ciências Biológicas, Departamento de Parasitologia. Avenida Antônio Carlos 6627. CEP 31270901. Belo Horizonte, MG, Brasil. 2 Universidade Federal de Minas Gerais, Instituto de Ciências Biológicas, Departamento de Zoologia, Laboratório de Malacologia e Sistemática Molecular. Avenida Antônio Carlos 6627. CEP 31270-901. Belo Horizonte, MG, Brasil. * Corresponding author. E-mailDaniel Coscarelli 1,2 and Teofânia H. D. A. Vidigal 2* Mollusca, Gastropoda, Succineidae, Omalonyx unguis (d’Orbigny, 1835): Distribution extension and new records for Brazil
central western Brazil. In fact, this is the only area in Brazil that was not sampled by Tillier (1981). Furthermore, Travassos (1928) used specimens of Omalonyx from Mato Grosso state as experimental hosts for a bird trematode, strongly suggesting that Omalonyx occurs in that area.
The present investigation uses comparative morphology to establish the first record of O. unguis in western Brazil. The occurrence of the genus in this region is thus confirmed, and the known geographical distribution of the species is expanded.
The material studied consists of slugs collected (permits granted by IBAMA [SISBIO] #12113-3) from four localities in western Brazil: Foz do Iguaçu, Paraná state; Campo Grande and Miranda, Mato Grosso do Sul state; and Poconé, Mato Grosso state. All samples were collected from aquatic vegetation on the margins of freshwater systems. One specimen from Miranda is shown in Figure 1. The animals were taken to the laboratory, where they were kept alive until sexual maturity was ascertained by the extrusion of eggs. Mature animals were relaxed overnight in water at 4°C and then killed by immersion in hot water (80°C) for 45 seconds. Shells were removed and stored in dry receptacles, and samples of foot tissue were frozen in an ultra-low temperature freezer (-80°C) for further molecular studies. Bodies were then preserved in Ralliet-Henry solution (5% formaldehyde, 2% acetic acid, 0.6% sodium chloride). All specimens were deposited in the malacological collection of the Laboratório de Malacologia e Sistemática Molecular (LMSM) in the zoology department of the Universidade Federal de Minas Gerais, Brazil.
Reproductive systems were dissected from the preserved bodies and compared with descriptions of anatomical details that are widely used to identify O. unguis (Hylton-Scott and Lapuente 1968;Arruda et al. 2006;Arruda and Thomé 2008) and other species of the genus (Hylton-Scott and Lapuente 1968;Hylton-Scott 1971;Tillier 1980;1981).
The taxonomic characters that we examined matched the diagnosis proposed by Arruda and Thomé (2008), confirming the species identification of the Brazilian specimens as O. unguis. That identification was also consistent with the taxonomic information provided by Hylton-Scott and Lapuente (1968) concerning the Argentinean populations of O. unguis. The reproductive system, which provides the anatomical features that allow specific identification, is shown in Figure 2. The presence of a serpent-like fold in the surface of the epiphallus (Figure 2D), is the most robust and evident character that is used for the diagnosis of this species.
The presence of O. unguis in the material that we examined confirms that the geographical distribution of this species encompasses two hydrological systems in Brazil. The first is the Paraguay River sub-basin, which drains the Pantanal region of western Brazil. The second is the Paraná River where it forms the border between Brazil and Paraguay. These new records extend the range of O. unguis northward, and the map in Figure 3 shows the new profile of its distribution. Mato Grosso do Sul Miranda LMSM 2705-08, 2739-42, 2769, 2780-81, 2788, 2795-99, 2898-99, 2900-08, 2911-13, 2917 36 Mato Grosso do Sul Previously known occurrences of Omalonyx unguis, based on reports in the literature.
That map also shows that O. convexus extends to Rio Grande do Sul state in southern Brazil; while O. unguis extends to the Paraguay River sub-basin in Paraguay. This distributional pattern excluded Brazil from the range of O. unguis. Hylton-Scott and Lapuente (1968) also reported O. unguis in eight localities, most in Argentina and one in Paraguay. The previously known occurrences of O. unguis are summarized in Table 1. Although there are no consistent records of O. unguis occurrence in Brazil, there is no reason to doubt its occurrence in the upper Paraguay River sub-basin of Coscarelli and Vidigal | New records of Omalonyx unguis for Brazil Check List | Volume 7 | Issue 4 | 2011 state -Foz do Iguaçu, Refúgio Ecológico Bella Vista, by the margins of the Itaipu Dam, Paraná River, 25°26'49" S, 54°32'58" W (LMSM 3260, 3269; Coscarelli, D. coll.);Mato Grosso do Sul state -Campo Grande, on the campus of the Universidade Federal do Mato Grosso do Sul, 20°30'19" S, 54°36'57" W (LMSM 2747; Coscarelli, D. coll.) and Miranda, Fazenda São Francisco, 20°05'56" S, 56°42'34" W (LMSM
Table 2 .
New records of Omalonyx unguis occurrence in Brazil based on recent field collections. LMSM= Laboratório de Malacologia e Sistemática Molecular, Zoology Department of the Universidade Federal de Minas Gerais, Brazil.
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Domain: Environmental Science Biology
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Inducible morphological defense in Daphnia pulex: food quantity effects revised
In aquatic systems, organisms largely rely on chemical cues to perceive information about the presence of predators or prey. Daphnia recognize the presence of the predatory larvae of Chaoborus via a chemical cue, emitted by the larvae, a so-called kairomone. Upon recognition, neckteeth, an alteration of the carapace, are induced in Daphnia that reduce predation rates of Chaoborus. Neckteeth induction was often reported to entail costs. In a previous study, food quantity affected the level of neckteeth induction, with stronger neckteeth induction at low food concentrations and weak induction at high food concentrations. However, reducing neckteeth induction at high food quantities seems to be maladaptive and not in accordance with the concept that inducible defenses are associated with costs. Here, we hypothesized that weaker neckteeth induction at high food concentrations is caused by increased bacterial degradation of the kairomone. More specifically, we assume that higher algal food concentration is associated with higher bacterial abundances, which degrade the kairomone during the experiment. We tested our hypothesis by treating food algae with antibiotics before providing them as food to Daphnia. Antibiotics reduced bacterial abundances at high and low food concentrations. Reduced bacterial abundances at high food concentrations led to the same level of neckteeth induction as at low food concentrations. A linear regression revealed a significant correlation of neckteeth induction to bacterial abundances. We therefore conclude that differences in neckteeth induction at different food concentrations are not caused by the food quantity effects but by differences in bacterial degradation of the kairomone.
Introduction
In freshwater systems, communication is mostly reduced to chemical cues due to conditions like high turbidity or poor light transmission, which make it difficult for animals to rely on visual cues (Brönmark and Hansson 2000). Chemical cues may transmit information about the presence of prey or predators, Handling Editor: Télesphore Sime-Ngando Electronic supplementary material The online version of this article ( [URL]10452-020-09809-2) contains supplementary material, which is available to authorized users. food or mating partners (Brönmark and Hansson 2000). One well investigated model organism in this field of research is Daphnia sp., a herbivorous filterfeeder and important link between trophic levels (Lampert 2006). Daphnia recognizes the presence of predators via chemical cues that are emitted by the predator, so-called kairomones (Diel et al. 2020;Krueger and Dodson 1981;Stibor 1992). Kairomones are semiochemicals that induce responses which are advantageous for the receiver and disadvantageous for the emitter. Although many cases of information conveyance by kairomones are known, the chemical identity of kairomones themselves has been uncovered in only a few of those cases (Hahn et al. 2019;Weiss et al. 2018). Upon recognition of the kairomone, a wide range of inducible defenses can be expressed, which comprise physiological, behavioral, or morphological changes (Lass and Spaak 2003). The expression of those inducible defenses is associated with costs, which trade-off the benefits of a defense in terms of fitness (Hammill et al. 2008). The associated costs are regarded as the cause why those defenses are inducible, because constitutive expression would lead to reduced fitness in the absence of the predator.
In many cases physiological changes in response to kairomones lead to changes in life history (Dodson and Havel 1988;Tollrian 1995b). The effect of food quantity on kairomone-mediated physiological changes might be the most difficult to investigate, because changes in food quantity per se can lead to life-history changes (Vanni and Lampert 1992). Variability introduced by genetic variance (Reznick et al. 2000) as well as the dependence of reaction norms on intrinsic values of traits (Wolinska et al. 2007) further complicates this relation (Cressler et al. 2010). This might be the explanation for the fact that in the presence of predators that select for big prey, like fish or Notonecta sp., some Daphnia only respond to the kairomone at high food concentrations (Dodson 1988;Dodson and Havel 1988), whereas others show enhanced responses under food stress (Pauwels et al. 2010). For predators that select for small prey, like Chaoborus sp., this relation with food quantity gets even more complex, and responses of Daphnia are highly variable (Dodson 1988;Tollrian 1995b). Due to the fact that costs of inducible defenses are measured as life history traits, it is hardly possible to differentiate life-history change as part of the defense from those that represent the cost of the inducible defense.
Behavioral changes as inducible defenses can, amongst others (e.g. Langer et al. (2019)), result in diel vertical or horizontal migration (Dawidowicz and Loose 1992;Kvam and Kleiven 1995;von Elert and Loose 1996). Vertically migrating Daphnia experience lower temperatures in deeper strata, so that the associated costs of migration are reduced somatic growth and reproduction rates (Dawidowicz and Loose 1992). As in nature food availability is decreasing in deeper strata, lower food quantity in the epilimnion leads to a slight decrease in diel vertical migration of D. magna in the presence of fish (Loose and Dawidowicz 1994) or even to a suppression of migration when food quantity is limiting (Beklioglu et al. 2008;Johnsen and Jakobsen 1987).
Morphological defenses comprise a huge variety of alterations in carapace shape like the elongation of head or tail spine, formation of head crests, neckteeth, or helmets (Diel et al. 2020;Dodson 1988;Lass and Spaak 2003;Spaak and Boersma 1997;Tollrian 1993). The effect of food quantity on morphological defenses was investigated in various predator-prey-systems of Daphnia. D. ambigua increased its helmet size with increasing food concentration in the presence of Chaoborus (Hanazato 1991). The same response of increased defense at high food concentrations was observed in D. retrocurva for its increase in head length in the presence of Chaoborus or Notonecta (Dodson 1988). Furthermore, the relative tail spine length of individuals of the D. galeata 9 cucullata 9 hyalina complex increased significantly at high food concentrations compared to low ones in the presence of fish (Spaak and Boersma 1997).
In our study, we focused on the morphological defense of D. pulex against larvae of Chaoborus sp. D. pulex changes its morphology during those juvenile instars, which are most vulnerable to Chaoborus predation (Pastorok 1981;Tollrian 1995a). The formation of neckteeth, which are small protuberances in the neck region of D. pulex, reduces predation rates to a great extent (Tollrian 1995a). Interestingly, neckteeth induction is, so far, the only morphological defense reported to be decreasing at high food concentrations (Parejko and Dodson 1991). Associated costs of neckteeth induction were often detected as a delay of reproduction, shorter body lengths at first reproduction or reduced fitness measured as the intrinsic rate of increase (Black and Dodson 1990;Havel and Dodson 1987;Riessen and Sprules 1990).
Under the assumption that costs of neckteeth induction per se are independent of food availability, neckteeth induction should not be suppressed when food quantity is saturating. Hence the observation of a reduced neckteeth induction at high food levels (Parejko and Dodson 1991) seems not consistent with the concept of costs associated with the induction of these morphological changes.
Considering this conceptual discrepancy, we hypothesized that the effect of food quantity on neckteeth induction is actually caused by differing bacterial abundances: we assumed that with increasing food concentrations higher abundances of (accompanying) bacteria were added, which lead to higher degradation rates of the Chaoborus kairomone at high food concentrations. We tested this hypothesis by treating the food alga Chlamydomonas klinobasis with antibiotics before adding the alga as food for Daphnia pulex. We counted bacterial abundances in the experimental jars, scored neckteeth, which were induced by the Chaoborus kairomone, during the second instar of D. pulex, and performed a linear regression with the two parameters.
Cultivation of animals
Prior to the experiments D. pulex clone TCO (Colbourne et al. 2011) was kept in aged and aerated tap water at a density of 10-12 individuals per 800 mL at 19.2 ± 0.3°C and a 16:8 light:dark cycle. Every second day the animals were transferred into fresh medium containing at least 1 mg C/L of Chlamydomonas klinobasis strain #56 (Limnological Institute, University of Constance).
Cultivation of food C. klinobasis was grown in 5 L semi-continuous batch cultures in cyanophycea medium (von Elert and Jüttner 1997) modified with vitamins. For the experiment Chlamydomonas was incubated for different treatments. The control treatment ('Control') contained Chlamydomonas suspension without any further treatment. For the antibiotics treatment ('Antibiotics') 2 times 45 mL of Chlamydomonas suspension were centrifuged at 3214 g for 5 min and the supernatant was discarded. The pellets were each resuspended in 100 mL cyanophycea medium containing 500 lg/mL ampicillin and 50 lg/mL tetracycline and incubated in sterile Erlenmeyer flasks for 22 h on a rotary shaker set to 80 rpm at constant light. After incubation, the cultures were centrifuged as above. The pellets were resuspended in fresh cyanophycea medium without antibiotics and centrifuged again to wash the cells. Afterwards, the pellets were resuspended in 100 mL fresh cyanophycea medium and stored in Erlenmeyer flasks for subsequent use. In order to account for effects of processing the cultures, an additional control treatment for centrifugation and incubation on the rotary shaker was established ('Shaker'). All food suspensions were screened using a 30 lm gauze, which had been inserted in the cap of a bottomless Nalgene bottle, that was used as a funnel. The volume of the food suspension that was needed was determined photometrically at a wavelength of 470 nm by using a calibration curve relating the carbon content to the optical density.
Preparation of Chaoborus incubation water extract
The extract of Chaoborus incubation water was prepared as according to Klintworth and von Elert (2020). Approximately 300-350 fourth instar larvae (ordered from www.interaquaristik.de) of Chaoborus flavicans were fed with 1-2 neonates of D. pulex clone TCO per larva. After 1-2 h of feeding, the larvae were transferred into 1 L of fresh aged and aerated tap water without any food. After 24 h the larvae were removed from the water using a 250 lm gauze, and the water was filtered through a glass fiber filter (Whatman, MN 85/220, 0.4 lm). Subsequently, the kairomone was enriched by solid phase extraction (VARIAN, Bond Elut-C18, 10 g of sorbent, volume 60 mL, Agilent Technologies) as according to Christjani et al. (2016). The eluates were pooled and evaporated to dryness in a rotary evaporator and a vacuum centrifuge. The dried residues were dissolved in 58 lL methanol and stored at -20°C until use. A control extract was prepared in exactly the same way but without any animals in the water.
Experimental setup
A cohort of synchronized animals that had just released their first eggs into the brood chamber was distributed to the different food level treatments, containing either 0.5 mg C/L or 1.5 mg C/L of C. klinobasis. Subsequently, the treatments are referred to as 'low food' and 'high food'. When the animals had deposited their second clutch into the brood chamber, they were divided into the food treatments control ('Control'), control of the rotary shaker ('Shaker') and the antibiotics treatment ('Antibiotics'). For details on the preparation of those treatments see 'Cultivation of food'. For each of these food treatments, there was a control treatment and a kairomone treatment. The kairomone treatment contained 1.5 lL of the Chaoborus incubation water extract per 150 mL. This volume of extract had induced an intermediate degree of neckteeth induction during a dose response experiment of D. pulex clone TCO (same conditions as for the cultivation). The control treatment contained the same volume of control extract. Each treatment was replicated fivefold.
The animals carrying their second clutch in the brood chamber were kept individually in 150 mL aged and aerated tap water containing either 0.5 mg C/L or 1.5 mg C/L of C. klinobasis of the respective food treatment combined with either the control extract or the extract of the Chaoborus incubation water extract. After the second clutch had hatched, mothers were removed from the jars, and neonates were removed so that no more than 6 experimental neonates remained in the jars, as neckteeth induction was shown to be affected by the density of conspecifics (Tollrian et al. 2015). Neonates that were removed were pooled per treatment, and their dry mass was determined in subsamples of 2 times 10 neonates per treatment. Those dry masses were later on used as w 0 for the calculation of the somatic growth rates. The experimental animals were transferred daily to freshly prepared jars. After transferring the animals, a sample of 8 mL was taken from each jar that had contained animals in their first instar to quantify the abundance of bacteria. For details on fixing, staining, and counting the bacteria see 'Bacteria counting'. Neckteeth induction of 5 experimental animals per jar was determined during their second juvenile instar using the method developed by Tollrian (1993) with the slight modification that each tooth was scored with 10% and no differentiation was made between big and small teeth as described in Schwarzenberger et al. (2014). After the animals had deposited their first clutch into their brood chambers, the clutch size was determined under a binocular microscope and three egg-bearing animals per jar were taken for the determination of their dry mass (w t ). The time until maturity was determined and the somatic growth rates (g) were calculated according to the following formula: g = (ln(w t )-ln (w 0 ))/t, with w t being the individual weight at day t and w 0 being the individual weight at day 0 (Rothhaupt and Lampert 1992). Since we were not interested in any resource allocation effects, but only in any physiological effects of the antibiotics on the animals, we did not dissect the eggs from the mothers. Furthermore, including the eggs during the dry weight determination makes g a better predictor for fitness than growth rates calculated from dry weights excluding the egg masses (Lampert and Trubetskova 1996).
Bacteria counting
The samples were immediately fixed in 4% sugarformol. The bacteria in the samples were stained using DAPI (4 0 ,6-Diamidin-2-phenylindol). The filtration device was rinsed with deionized water and the required volume of the sample was filtered (0.2 lm pore size, 25 mm ø, Whatman Nuclepore Polycarbonate Membrane) to reach the appropriate density of bacteria on the filter. The filter with the sample on it was rinsed with deionized water and 300 lL of 5 lg/ mL DAPI were put on the filter. During the incubation time of 5 min the filters were covered. Subsequently, the DAPI was removed and the filters were rinsed with deionized water again. Bacteria were counted immediately after staining under a fluorescence microscope (ZEISS Axioskop equipped with Shutter HXP 120) using 100 9 magnification. The abundance of bacteria on the filter was calculated by counting 20 randomly selected fields per filter (1 field e 15,625 lm 2 ). One filter per experimental replicate was prepared (n = 5 per treatment).
Statistical analyses
All statistical analyses were performed in RStudio version 1.1.423 (R version 9 64 3.4.3). In case a parameter was determined on more than 1 animal per replicate, the average of those values per replicate was calculated and used for statistics. Data were checked for homoscedasticity, and, if given, an ANOVA type 2 followed by Tukeys HSD test was performed. If not given, a Kruskal-Wallis rank sum test was performed. Data were checked for normal distribution, but an ANOVA was performed either way, as the ANOVA remains a valid statistical procedure under nonnormality, as long as the assumption of homoscedasticity is met (Blanca et al. 2017). In cases of unequal sample sizes, an ANOVA type 3 followed by a Games-Howell test was performed. Spearmans rank correlation was used for the correlation of neckteeth induction and bacteria abundances. The significance level for all analyses was p \ 0.05.
Results
All Daphnia that grew in the chemical absence of Chaoborus did not express any neckteeth and were excluded from statistical analyses. In the chemical presence of Chaoborus neckteeth induction was affected by the food concentration and the treatment of the food (Table 1). No interaction between treatment of the food and food concentration was found. Neckteeth induction in the Control food treatment was significantly different at the two food concentrations (Fig. 1). On low food neckteeth induction was 63 (± 4) % (mean ± SD), whereas on high food neckteeth induction was only 42 (± 11) %. This reduction in neckteeth induction on high food was not found in the Shaker or the Antibiotics treatment. In the Shaker treatment neckteeth induction was 62 (± 9) % on low food and 52 (± 7) % on high food. In the Antibiotics treatment neckteeth induction was 75 (± 6) % on low food and 71 (± 6) % on high food. Neckteeth induction on high food in the Antibiotics treatment was significantly higher than in the Control and the Shaker treatment and was not significantly different from the neckteeth induction on low food.
Bacterial abundances were also affected by both the food concentration and the treatment of the food (Table 2). A significant difference in bacterial abundances between food concentrations was only found in the Control treatment (Fig. 2). At low food only 2.2 Table 1 Results of a two-way ANOVA type 2 for the analysis of the neckteeth induction (see Fig. 1 Only data of the treatments containing Chaoborus incubation water extract were included in the analysis. Significant p-values are indicated in bold Neckteeth induction in the chemical absence of Chaoborus was 0% in all treatments. Asterisks associated to brackets represent significant differences within the same food concentration, whereas single asterisks indicate significant differences within the same food treatment (two way ANOVA type 2 followed by Tukeys HSD test). Details on the statistical analysis can be found in Table 1 (± 0.8) Á10 5 cells/mL were counted, whereas at high food 3.5 (± 0.3) Á10 5 cells/mL were counted. In the Shaker treatment bacterial abundances were slightly but not significantly reduced to 1.8 (± 0.6)Á10 5 cells/ mL at low food compared to 2.6 (± 0.7) Á10 5 cells/mL at high food. Antibiotics significantly decreased the abundance of bacteria to 0.5 (± 0.1) Á10 5 cells/mL and 0.8 (± 0.2) Á10 5 cells/mL at low food and high food, respectively. In order to test if neckteeth induction would decrease with the abundance of bacteria, we correlated the neckteeth induction to the bacterial abundances and performed a linear regression (Fig. 3). Spearmans rank correlation revealed a significant negative correlation (rho = -0.78; p = 3.6 * 10 -7 ) with neckteeth induction decreasing with increasing bacterial abundances.
The analysis of clutch size and somatic growth rates revealed that a lower food concentration reduced both parameters, whereas the presence neither of antibiotics nor of the kairomone had an effect (Fig. S1, S2, Table 2 Results of a two-way ANOVA type 2 for the analysis of the bacterial abundance (see Fig. 2 Only data of the treatments containing Chaoborus incubation water extract were included in the analysis. Significant p-values are indicated in bold Only data from jars containing D. pulex grown in the chemical presence of Chaoborus are presented. Asterisks associated to brackets represent significant differences within the same food concentration, whereas single asterisks indicate significant differences within the same food treatment (two way ANOVA type 2 followed by Tukeys HSD test). Details on the statistical analysis can be found in Table 2 Fig Table S1, S2). The analysis of the time to maturity revealed that there were no significant differences between treatments (Fig. S3, Table S3).
Discussion
We found that neckteeth induction decreased with increasing abundances of bacteria. Bacterial abundances were reduced by treating the algae suspensions with antibiotics before the algae were provided as food for Daphnia. The reduction of bacteria was significant both at low and high food concentrations, and reduced bacterial abundances at high food led to the same level of neckteeth induction as at low food. A linear regression of neckteeth induction to bacterial abundances revealed a significant negative correlation: neckteeth induction decreased with increasing bacterial abundances. Parejko and Dodson (1991) reported high neckteeth induction at low food and less neckteeth induction at high food, and thus, concluded that neckteeth induction is influenced by food quantity. Although they had chosen more extreme food concentrations (0.31 mg C/L and 15.3 mg C/L for low and high food, respectively), we as well found less neckteeth induction at high food in the Control food treatment, and therewith corroborate the findings of Parejko and Dodson (1991). Due to an additional treatment of the food suspension with antibiotics we here revealed that this decrease in neckteeth induction at high food is related to the high abundance of bacteria. In our experiments increasing experimental food concentrations were obtained by diluting increasing aliquots of the same stock food suspension. Since the food suspension was not bacteria free, addition of different aliquots of this suspension did not only result in low and high algal concentrations but as well in low and high bacterial abundances. Although no bacterial abundances are reported by Parejko and Dodson (1991) it is reasonable to assume that in their experiments high and low food concentrations as well were related to high and low bacterial abundances, since their food alga was not explicitly cultured axenically and since the different food concentrations supposedly were as well prepared from the same stock suspension.
We hypothesized that higher bacterial abundances result in higher degradation rates. By treating the food suspensions with antibiotics, this treatment contained reduced bacterial abundances and initially identical kairomone concentrations. Due to the fact that at high food concentrations the neckteeth induction was significantly lower in the Antibiotics than in the Control treatment, we conclude that kairomone concentrations, in line with higher bacterial abundances, most probably decreased at higher rates in the Control than in the Antibiotics treatment. Therefore, our results clearly demonstrate that food quantity does not affect neckteeth induction as long as food concentrations are not limiting. This conclusion is in line with a study, in which a flow-through system was used to determine food quantity effects on neckteeth induction (Tollrian 1995b). In that setup a continuous input of the kairomone, and thus, a constant concentration of the kairomone, was assured. Therefore, no difference in neckteeth induction on different food concentrations was observed.
The kairomone produced by Chaoborus was identified as a group of fatty acid-amino acid conjugates, which are made up of a glutamine head group and long chain fatty acids conjugated to the a-amino group of glutamine (Weiss et al. 2018). The fact that glutamine lipids themselves can be incorporated in bacterial cell membranes (Zhang et al. 2009) makes it very likely that the kairomone is degradable by bacteria and that bacteria use such conjugates as sources for those molecules.
The expression of inducible defenses can be considered adaptive as long as the degree of expression of the defense is linked to the real predation risk. Since inducible defenses are associated with costs, expression of such defenses in the absence of predators would bear costs without any benefits. It is therefore highly adaptive to link the expression of inducible defenses to chemical cues that reliably indicate the risk of predation. Hence, from an evolutionary perspective, kairomones must reliably signal not only the presence of a predator but as well predator abundances or predator activities. Accordingly, higher predator densities result in stronger expression of defenses, for example in increased amplitudes of diel vertical migration (von Elert and Pohnert 2000), in a decreased size at first reproduction (von Elert and Stibor 2006) or stronger expression of a morphological defense (Tollrian 1993).
In order to be considered reliable cues for predator densities, kairomones dissolved in water should be subjected to a certain turnover, so prey can also perceive declining predation risk. One way of turnover or removal is bacterial degradation. The effect of bacterial degradation of a kairomone on an inducible defense of Daphnia was already shown for the kairomone produced by fish (Loose et al. 1993). After incubating the non-sterile fish incubation water for 24 h at 37°C, D. magna did not respond to the fish incubation water by performing diel vertical migration, whereas fish incubation water that was incubated in the absence of bacteria remained active. Furthermore, incubation of fish incubation water at 4°C under otherwise identical conditions did not lead to a loss of activity and it still induced diel vertical migration in D. magna. The kairomone produced by fish is the bile salt 5a-cyprinol-sulphate (Hahn et al. 2019). Bacteria are known to be involved in synthesis pathways of bile salts (Hofmann and Hagey 1998), and also in the degradation of bile salts (Philipp 2011). Another study on the bacterial degradation of the fish kairomone provided further evidence that the amplitude of diel vertical migration of D. magna is dependent on bacterial abundances (Beklioglu et al. 2006).
We here assured that Daphnia would not be affected by the antibiotics treatment of the food algae by washing the algae with medium free of antibiotics prior to feeding them to the daphnids. It has been reported that antibiotics may negatively affect growth rates or clutch sizes in Daphnia (Kim et al. 2012;Wollenberger et al. 2000). However, we detected neither of those effects, which makes it highly improbable that in the antibiotics treatment any direct effect of the antibiotics on Daphnia occurred. Accordingly, the effects of food quantity on somatic growth rate or clutch size that we observed here are in accordance with the well-known effects of food quantity on these parameters (Lampert and Trubetskova 1996;Vanni and Lampert 1992).
Furthermore, we detected no apparent costs of neckteeth induction in our study. Clutch sizes, somatic growth rates, and the time until maturity were not affected by the kairomone treatment. Especially the time until maturity was considered a cost of neckteeth induction (Black and Dodson 1990;Havel and Dodson 1987), which could be also caused by changes in lifehistory. More recent studies, however, found no costs of neckteeth induction (Riessen 2012;Tollrian 1995b). Tollrian (1995b) disentangled the effects of life-history change and costs of morphological defense by restricting the kairomone exposure of Daphnia to the developmental times when the respective defense is expressed and found no life-history shifts in the treatments, in which neckteeth were induced. Riessen (2012) performed life-history experiments with 2 clones that differed in their degree of neckteeth formation, but found the same degree of life-history shifts, which, assuming neckteeth induction would entail costs, should have also differed. As neckteeth induction does not seem to have any apparent costs that would affect life-history or fitness of Daphnia, it might be maladaptive that this morphological defense would be affected by the availability of resources.
From our study we conclude that the food quantity effects on neckteeth induction that have been reported before (Parejko and Dodson 1991) were most probably caused by differences in bacterial abundances and bacterial degradation rates of the kairomone in the experimental setup. We largely excluded the effect of bacterial degradation of the kairomone in our study, and we were able to show that differences in neckteeth induction between food concentrations, as they were detected in the Control, were not detected in the treatment, in which bacterial degradation was reduced. The inducible morphological defense of D. pulex against Chaoborus does not only include the formation of neckteeth, but comprises further morphological changes as a stiffening (Laforsch et al. 2004) and thickening (Kruppert et al. 2017) of the carapace. This has been corroborated by the finding that Chaoborus incubation water extract induces an upregulation of chitin deacetylase genes in D. pulex (Christjani et al. 2016). Although we focused solely on neckteeth induction in this study, we assume that further morphological changes like the thickening of the carapace are as well not affected by food concentration in D. pulex.
Funding Open Access funding enabled and organized by Projekt DEAL.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of interest.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [URL]/4.0/.
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Domain: Environmental Science Biology
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Distribution patterns of Syllidae ( Annelida : Polychaeta ) from seagrass ( Zostera marina and Z . noltei ) meadows in the Ensenada de O Grove ( Galicia , NW Spain )
This paper describes the distribution and composition of the syllid fauna inhabiting seagrass meadows in the Ensenada de O Grove (NW Spain). Samples were collected on muddy sediments colonized by either Zostera marina L., Zostera noltei Hornemann or by a mixed meadow with both species. Syllids were dominant (13340 individuals; 37% of total polychaete abundance), including 22 species (12 genera). The mixed meadows housed the highest number of species and the Z. noltei meadow had practically no syllids. The dominant species were Exogone naidina, Parapionosyllis elegans, Parexogone hebes and Prosphaerosyllis campoyi (>80% of total abundance). Carnivores (mainly species of Parapionosyllis, Amblyosyllis, and Streptosyllis) were dominant, especially in muddy sand with either Z. marina or Z. noltei and sandy mud with a mixed meadow. The most important abiotic variables for explaining the composition and distribution of the syllid fauna were bottom water salinity, sorting coefficient and carbonate content. The highest number of species was recorded at sites with a high salinity and carbonate content and the lowest at sites with a high sorting coefficient.
INTRODUCTION
Seagrass meadows are of great ecological importance in shallow-water environments, as their structures (leaves, rhizomes and roots) increase the habitat complexity. They harbour numerous epiphytic and epifaunal taxa (Orth and Heck 1980, Webster et al. 1998, Attrill et al. 2000), providing shelter and protection from predators (Heck and Thoman 1981) and a variety of food resources (e.g.seagrass, detritus and epiphytes) (Kitting et al. 1984, Hily et al. 2004, Fredriksen et al. 2005) to the associated faunal assemblages. Among them, the most diverse taxa are generally polychaetes, molluscs and crustaceans (Gambi et al. 1998, Nakaoka et al. 2001, Arroyo et al. 2006), which are often represented by small-sized, interstitial species that are also usually present in bare soft sediments (San Martín et al. 1985, Sardá 1985, Brito et al. 2005). In Zostera meadows, as in other seagrass meadows, these species may be favoured by the sediment retained by the seagrasses (Parapar et al. 1994, Fredriksen et al. 2010) and by the rhizomes and roots, which create spatial complexity within sediment and enable oxygenation (Tu Do et al. 2011 and references therein).
Meadows of the seagrasses Zostera marina L. and Zostera noltei Hornemann are typical of estuaries and shallow coastal areas in the northern hemisphere (Duffy and Harvilicz 2001), which are protected through the Habitat directive 92/43/EEC. On the Atlantic coast of the Iberian Peninsula, Z. marina and Z. noltei occur as extensive meadows in intertidal and shallow subtidal areas, particularly in the Galician rias (Laborda et al. 1997). In the sheltered, inner parts of the Ensenada de O Grove, highly dense meadows extend from the intertidal to the shallow subtidal (<20 m depth), providing macrofauna with protection against desiccation during low tide.
Studies exclusively focusing on the Syllidae from the Galician coasts are scarce (but see San Martin et al. 1985) and are most often included in wider benthic ecology studies (Moreira et al. 2006, Lourido et al. 2008) of both hard and soft substrata (San Martín et al. 1985, Parapar et al. 1994, Parapar et al. 1996a,b, Cacabelos et al. 2010).
As part of a broader project devoted to characterizing the soft-bottom benthic fauna of the Ensenada de O Grove (NW Spain) (Project XUGA30101A98), the main objective of this paper is to describe the syllid fauna inhabiting the seagrass meadows of the inlet in terms of composition, abundance, number of species and trophic structure.
Study area
The Ensenada de O Grove is located in the inner part of the Ría de Arousa (Galicia, NW Spain) between 42º41'N-42º28'N and 9º01'W-8º44'W (Fig. 1). The inlet has an area of 15 km 2 and is sheltered from wave action and dominant winds by the O Grove Peninsula. It receives freshwater inputs from rivers, both at the mouth and in the inner part. The inner and intertidal and shallow subtidal areas (<20 m) are soft bottoms largely colonized by Z. marina and Z. noltei. This inlet is of great socio-economic importance, especially with regard to mussel culture on rafts, bivalve collection (intertidal harvesting by hand and boat trawling) and fishing. Furthermore, the inlet is protected because of the seagrass meadows (Habitat Directive 92/43/CEE) and as a habitat for birds (ZEPAS, 1979 andRAM-SAR Convention, 1990). It is also a natural space of importance for the European Community, listed in the European Natura 2000 network.
Sample collection
The present study focuses on the inner soft bottoms of the Ensenada de O Grove colonized by Z. marina and Z. noltei. Ten sites were selected as representative of the different meadows (i.e. Z. marina, Z. noltei and mixed) and tidal conditions (intertidal vs subtidal). Sampling was done during October and November 1996 following the standard methodology for the XU-GA30101A98 project. We used a Van Veen grab with a sampling surface of 0.056 m 2 to collect five replicates per site (total area: 0.28 m 2 ). Samples were then sieved through a 0.5 mm mesh and all retained material was fixed in a 10% buffered formalin-sea water mixture. Additional sediment samples were used to determine particle-size composition and carbonate and organic matter contents, and single measurements of water temperature (°C), pH and salinity (practical salinity units, psu) and sediment pH and temperature (°C) were obtained in situ.
Laboratory analyses
Syllids were sorted out from the sediment under a stereomicroscope, identified to species level whenever possible, counted, and preserved in 70% ethanol. The names of species and higher taxonomic levels used follow the classification by Aguado and San Martín (2009), the MarBEF Data System (ww.marbef.org)and the WoRMS database (www.marinespecies.org).
Data analyses
The structure of the syllid assemblage was analysed using the PRIMER v 6.0 software package (Clarke and Warwick 1994). For each site, total abundance (N), total number of species (S), the Shannon-Wiener diversity index (H', log 2 ) and Pielou evenness (J') were determined using the DIVERSE routine. Affinities among sites were determined through non-parametric multivariate techniques (Field et al. 1982). Abundance data were fourth-root transformed (Currie and Small 2005, Bremec and Giberto 2006, Rueda et al. 2009) prior to constructing a matrix of similarities using the Bray-Curtis coefficient and calculating the centroids. Based on this matrix, the sampling sites were classified by cluster analysis (which was tested by the Simprof) and ordered through a non-metric multidimensional scaling (nMDS). These two analyses are complementary, so the graphic representation of the nMDS ordination includes the similarity levels derived from the cluster analysis. The SIMPER routine was used to identify the species most contributing to the dissimilarity among assemblages. Site 37 was excluded from the multivariate analyses (there was only one syllid). Also, the species in each group were classified according to the constancy and fidelity indexes (Glémarec 1964, Cabioch 1968), and those representing more than 4% of the total abundance per site or group were considered as dominant (Junoy 1996). The frequency × dominance (F×D) index was calculated to determine the numerical importance of species. The syllid species were assigned to one of the following guilds: carnivores, herbivores, detritivores, and omnivores (Rasmussen 1973, Fauchald and Jumars 1979, Gambi and Giangrande 1985a,b, Tena et al. 1993, 2000, Giangrande et al. 2000) (Table 1), and the importance of these guilds in the whole inlet and within the groups identified in the nMDS was analysed.
Correlations between assemblage descriptors and all measured environmental variables were determined through the non-parametric Spearman rank coefficient (SPSS 15 software package). Co-linearity (r>0.7) was also detected between some environmental variables and therefore only some of them were selected for the BIO-ENV routine (see Table 2). The rationale was the following: when two variables were highly correlated, that offering the most relevant information was selected for the BIO-ENV. For example, carbonate content was highly correlated with fine sand content (CARB-FS: 0.939). In this case, the latter was non-selected because other granulometric fractions that also provide information of sediment had already been included in the analysis. Variables expressed in percentages were previously log (x+1) transformed (Lourido et al. 2008, Sánchez Moyano andGarcía-Asencio 2009).
Environmental variables
Sampling sites were characterized by moderate to high silt/clay contents (6%-62%). Sand content was generally greater at subtidal sites and sediment ranged from muddy sand to mud (Table 2). Water salinity was lower than 33 psu, particularly at sites 34 and 37 (close to the river, 20 psu). Carbonate content ranged from 5% to 10% and organic matter content ranged from low at subtidal sites (1.3%) to high (10.7-15.5%)at intertidal inner sites. Site 37 also showed the highest organic matter content and the lowest carbonate content.
Description of faunal assemblages
Subgroup A1 included subtidal sites with Z. marina, mostly with muddy sand, a moderate selection, and a low organic matter content. N was high (mean ± sd: 5018±2657 ind.m -2 ) and total S was the highest (18), ranging from 7 to 14 per site. The group was dominated by E. naidina, P. elegans, P. hebes, P. minuta, P. campoyi and S. clavata. H' ranged from 1.29 to 2.30 and J' from 0.54 to 0.78. Carnivores and omnivores were dominant in abundance (36% and 28%, respectively) and number of species (33% and 33%) (Table 7).
As stated above, group B only included site 34 (an intertidal sandy mud area with a mixed meadow), and was characterized by low salinity, carbonate content, S (6), N (86 ind.m -2 ) and H' (1.34). The syllid assemblage, characterized by X. scabra, B. pusilla, P. tetralix, O. gibba and S. limbata, was clearly different from that in A (Table 7).
DISCUSSION
Syllids are common members of benthic assemblages associated with seagrass meadows (Çinar 2003), including those formed by Zosteraceae (Hutchings 1981), and the meadows at the Ensenada de O Grove were no exception (37% of total abundance and 24% of polychaete species richness; Quintas 2005). This contrasts with lower abundances found in other quantitative studies using the same sampling methodology on soft bottoms (coarse sand to mud; see Moreira 2003, Moreira et al. 2006, Cacabelos et al. 2008, Lourido et al. 2008, Lourido 2009, Cacabelos et al. 2010), which may be partially explained by the presence of a dense seagrass meadow in the present study rather than differences in granulometric composition or organic matter content (Table 8). In general, seagrass meadows reduce physical stress, trap sediment, reduce suspension, protect small invertebrates from predators, and enhance food availability, also adding complexity to the habitat (Orth et al. 1984). In the case of Cymodocea nodosa and Zostera noltei, syllids are among the most abundant polychaete taxa in the foliar and rhizome layers (Giangrande and Gambi 1986, Gambi et al. 1998, Brito et al. 2005). In fact, the tridimensional structure provided by those seagrasses and especially the rhizome structure make available a variety of microhabitats for small-sized taxa. Syllids are mostly interstitial animals and therefore the availability of small spaces along the rhizomes could favour their presence (Giangrande 1985, Somaschini and Gravina 1994, Brito et al. 2005). In fact, syllids require spatial structures at microhabitat rather than at macrohabitat level (Abbiati et al. 1987, Giangrande 1988), while the interactions among syllids and with other macrofaunal species have also been suggested as factors controlling the abundance and, partially, the variability of syllid assemblages (Musco 2012).
In the studied seagrass meadows, abundance, number of species, and diversity differed among sites, resulting in two distinct faunal assemblages: (1) the muddy sand with Z. marina and the intertidal muddy sand or sandy mud with Z. noltei or mixed meadows (with high values of S and H'), and (2) the intertidal sandy mud with mixed meadows and the intertidal mud flat with Z. noltei (low values of S and H'). These differences may be partially explained by the sediment characteristics (carbonate and silt/clay content), the proximity of a river, and the dominance of Z. marina, Z. noltei or both seagrass species; the latter determines, in turn, the availability of microhabitats (size and shape of the leaves and rhizomes), food and amount of sediment retained by the rhizomes. The overall composition of the syllid assemblage in the meadows from the Ensenada de O Grove is similar to those reported from other seagrass meadows. Streptosyllis websteri occurred in mud, muddy sand and shallow muddy gravel bottoms with Zostera in the Ría de Ferrol (Parapar et al. 1994). At the island of Ischia (Tyrrhenian Sea, Italy) 33 syllid species (mostly Exogoninae: Exogone spp., Sphaerosyllis spp., Parapionosyllis spp.) were associated with C. nodosa and Z. noltei meadows, with E. naidina and P. elegans being positively correlated with the foliar substrate (Gambi et al. 1998). On the other hand, E. verugera, E. naidina, S. hystrix, B. pusilla, and S. clavata are cosmopolitan and ubiquitous species that are common in other habitats including bare bottoms (soft and hard substrata) (Sardá 1985). Exogone naidina, P. hebes, P. tetralix, P. campoyi, P. elegans, S. websteri were previously recorded in intertidal bare and soft sediments near to the seagrass meadows studied here (San Martín et al. 1985). Fredriksen et al. (2010) reported higher abundance of P. hebes and S. hystrix in Z. marina meadows than in bare soft sediments in Norway. Prosphaerosyllis campoyi is also abundant in C. nodosa and P. oceanica meadows (San Martín 2003) and bare intertidal (San Martín et al. 1985) and subtidal sedimentary substrata (Parapar et al. 1994). Similarly, Syllis garciai is common in C. nodosa and Z. noltei meadows (Gambi et al. 1998) and bare muddy sand (Parapar et al. 1996b, Lourido et al. 2008). In the Ensenada de O Grove, E. belizensis showed a noteworthy presence in subtidal muddy sand with Z. marina and intertidal sandy mud with a mixed seagrass meadow. This species has been reported from warm and tropical seas, including those of the Iberian Peninsula (López and San Martín 1997, Olano et al. 1998, San Martín 2003) and also in low densities in muddy sediments of the Ensenada de San Simón, Galicia (Cacabelos et al. 2010). In the Ensenada de O Grove, specimens were similar to those from the western Atlantic according to morphological characters. However, it has not yet been elucidated whether they have been accidentally introduced by human activities or have a true amphiatlantic distribution. Some warm-water species (mainly Mediterranean molluscs) have been previously collected in O Grove (Rolán et al. 1985, Rolán 1992, Quintas 2005, Quintas et al. 2005). The introduction of these species in this area has been attributed to commercial activities such as oyster importation (Rolán et al. 1985). In some cases, these accidental introductions may result in significant alterations in the composition of assemblages and biotic interactions (Grall and Hall-Spencer 2003). There is, however, no evidence of oyster importation being the cause of the presence of S. belizensis in the study area and direct dispersion should not be discarded as an alternative hypothesis.
Syllids were less abundant in the intertidal sandy mud with a mixed meadow at site 34, and, particularly, they were nearly absent in the nearby muddy sediment with Z. noltei at site 37, in the inner part of the inlet. This absence can be explained by the vicinity of the river mouth, which is associated with regular freshwater inputs, high silt/clay content and low salinity. In particular, the salinity and/or resulting horizontal stratification of waters is a key factor structuring the macrozoobenthic communities (mainly infauna and small, slowly motile epifauna) of Z. noltei meadows in the inner part of Arcachon Bay, France (Blanchet et al. 2004). Our results also agree with those of Cacabelos et al. (2010), who found that syllids were also scarce in Z. noltei meadows subjected to environmental conditions similar to those in the Ensenada de O Grove.
The wide spectrum of feeding habitats among syllids allows them to find a variety of suitable feeding resources on seagrass meadows. In the Ensenada de O Grove, carnivorous syllids were mostly represented by species of Parapionosyllis, Amblyosyllis and Streptosyllis. Omnivores such as P. hebes have been reported in other studies of leaf development of the plant (Gambi et al. 1995). S. hystrix and S. garciai have been considered as herbivorous and omnivorous, respectively (Giangrande et al. 2000, Sánchez Moyano andGarcía-Asencio 2009). In the studied meadows, surface and sub-surface deposit feeding polychaetes were numerically dominant (Quintas 2005), as has been found in other seagrass meadows (Jacobs et al. 1983, Kiting et al. 1984, Thayer et al. 1984, Junoy 1996). However, in this study, the collection of a high number of syllids, including carnivores, herbivores, detritivores and omnivores, shows the importance of syllids for understanding the trophic structure of these habitats.
In conclusion, the present study shows that Syllidae were well represented and had a high diversity in the Z. marina and Z. noltei meadows in the Ensenada de O Grove compared with bare soft bottoms of the inlet (Quintas 2005). This information suggests that seagrass meadows are biodiversity preservation hot spots. This is the first quantitative and systematic study based on the Syllidae family associated with the seagrass meadows of the inlet. Therefore, this paper can be considered as a baseline study for future monitoring and environmental management studies aimed at increasing the protection of seagrass meadows of the inlet. However, detailed long-term studies considering separately different spatial scales or microhabitats in the plant (blades, rhizomes) and the sediment are needed to better understand the temporal dynamics of syllid assemblages and the environmental factors governing them in the studied meadows.
Fig. 1 .
Fig. 1. -Location of the Ensenada de O Grove (Galicia, Spain) showing the distribution of sampling sites with seagrass meadows.
Fig. 2 .
Fig. 2. -Spatial distribution and density of the numerically dominant syllid species in the seagrass meadows at the Ensenada de O Grove.
Table 1 .
-Systematic list of syllid species identified in the study. Sampling sites, trophic guild (TG; C: carnivores, H: herbivores, D: detritivores, O: omnivores) and abundance values (Abund.)per m 2 (considering all sites) are indicated. The species acronym used in the nMDS ordination is also listed.
Table 3 .
-Total abundance (N, individuals per m 2 ), number of species (S), Shannon-Wiener's diversity index (H', log 2 ) and Pielou evenness (J') for each sampling site in the Ensenada de O Grove. Values: mean±standard deviation.
Table 5 .
-Results of SIMPER analysis showing the main taxa contributing to the dissimilarity among subgroups determined from cluster analysis. Average abundance (Av. Ab.), average dissimilarity (Av. Disim.),ratio value (dissimilarity/standard deviation, Dis./SD) and percentage of cumulative dissimilarity (Cum. Disim.)were also included.
Table 6 .
-Best combinations of variables obtained by the BIO-ENV routine. SBW, salinity of bottom water; GR, gravel; CS, coarse sand content; MS, medium sand content; CARB, carbonate content; S o , sorting coefficient.ρW, Spearman's rank correlation.
Fig.4.-Non-metric multidimensional scaling (nMDS) ordination plot showing the syllid species ordination with a numerical dominance >4% at any given site in the study area. See Table1for species acronyms.
Table 7 .
-Summary of biotic and physical characteristics of the three assemblages derived from multivariate analysis (values: mean ± standard deviation). First ten constant species are listed including their fidelity (ELE, elective; PRE, preferential; ACE, accessory) and frequency x dominance values (in brackets). N, total number of individuals per m 2 ; S, total number of species; H', Shannon-Wiener diversity; J', Pielou evenness; C, carnivores; H, herbivores; D, detritivores; O, omnivores; TC, tidal condition; SSW, salinity of surface water; SBW, salinity of bottom water; OM, organic matter content; CARB, carbonate content; Q 50 , median grain size. N & S of trophic categories expressed in %.
Table 8 .
-Summary of biotic and physical characteristics of three studies carried out on Galician Rias with similar sampling methodology (values: mean ± standard deviation). S. type, sedimentary type (GR, gravel; CS, coarse sand; MeS, medium sand; FS, fine sand; MS, muddy sand, SM, sandy mud, M, mud); OM, Organic matter content (%); CARB, carbonate content (%); TC, tidal condition (S, Subtidal; I, Intertidal); N polychaetes, total number of polychaetes; N syllids, total number of syllids; S syllids, total number of species; Dom Species of Syllids, dominant species of syllids for each groups of sites.
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Domain: Environmental Science Biology
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The sensory ecology of fear: African elephants show aversion to olfactory predator signals
Human–elephant conflict is a persistent problem across elephant home ranges, that results in economic damage to commercial and subsistence farmers, and physical harm and death to humans and elephants. This problem is likely to intensify with increased development, dwindling of natural habitats, and climate change‐driven environmental shifts. Various methods to mitigate human–elephant conflict have been employed, but to date these have been hampered by financial and logistical considerations. Based on the fact that African elephants are predated by lions and possess a remarkable sense of smell, we hypothesize that elephants are strongly averse to olfactory signals of lion presence, and that this can be utilized to create invisible barriers which elephants will not cross. We conducted a series of tests that show that lion dung is an effective deterrent of elephants. We conducted chemical analyses of lion dung and identified the main compounds. We then used synthetic mixtures containing these compounds, and show that they successfully elicit the deterrence effect, even in miniscule concentrations. These results indicate that elephants can be deterred using simple and low‐concentration mixtures based on available commercial products, that can be developed into products that offer a safe, sustainable, and cost‐effective method to mitigate human–elephant conflict.
| INTRODUCTION
Human-elephant conflict occurs everywhere these taxa co-exist, and is a significant problem for both (Sitati, Walpole, Smith, & Leader-Williams, 2003). While difficult to quantify globally, regional analyses have documented that elephants are responsible for an average of 30% of large-mammal caused human injury and fatalities (Acharya, Paudel, Neupane, & Köhl, 2016), with fatalities often followed by retaliatory elephant killings (Riddle, Melissa Schmitt and Kim Valenta contributed equally to this study. Schulte, Desai, & van der Meer, 2010). Additional to direct mortality, elephants cause massive damage to human livelihoods and food security, primarily via crop raiding and trampling (Naughton-Treves, 1998). While crop raiding occurs frequently in commercial plantations, subsistence agriculturalists are also affected, with devastating outcomes for financially vulnerable, food-insecure populations (Mackenzie & Ahabyona, 2012). In subsistence agricultural regions surrounding protected areas, communities frequently cite elephants as a critical factor in their lack of support for conservation, and noncompliance with conservation efforts (Woodroffe, Thirgood, & Rabinowitz, 2005).
Several schemes have been deployed throughout Africa and Asia to mitigate human-elephant conflict, through creative and diverse means. Beehive fences and the playing of bee sounds have shown some success in deterring elephants from crops (King, Lawrence, Douglas-Hamilton, & Vollrath, 2009), fencing and trenching have been useful in some contexts (Hoare, 2012), and scare shooting, or other loud noises during crop raiding events can also be locally effective (Taylor, 1999). Unfortunately, the vast majority of deterrent methods are expensive to implement, particularly for small-hold farmers, and require intensive maintenance (Sarkar, Chapman, Kagoro, & Sengupta, 2016). Most importantly, they frequently lose effectiveness over time as elephants habituate to them (Enukwa, 2017).
Given that elephants are olfactorily-oriented animals (Nevo, Schmitt, Ayasse, & Valenta, 2020), with the highest number of intact olfactory receptor genes of any known animal (Niimura & Nei, 2007), the key to elephant deterrence probably lies in the nose. Multiple elephant-deterrent endeavors have focused on deterrent odors, from motor oil to chili peppers to bee pheromones (Wright et al., 2018). Most olfactory schemes have focused on compounds that are present as naturally occurring deterrent secondary compounds in foods that are unpalatable to elephants-particularly capsaicinoids, the compound class that results in the human perception of the "hotness" of chilis (Lawless, Rozin, & Shenker, 1985). However, while olfactory signals of unpalatable secondary compounds in plants may deter elephants from actively consuming items broadcasting that signal (McArthur, Finnerty, Schmitt, Shuttleworth, & Shrader, 2019;Schmitt, Shuttleworth, Shrader, & Ward, 2020), the ubiquity of olfactory plant deterrents in an elephant's landscape are unlikely to result in deterrence from a particular area: elephants may not eat something that smells like a chili, but will probably not avoid an area, or consuming adjacent plants, simply because chilis are present.
While a great deal of research has focused on plantbased olfactory deterrence of elephants (Ernest & Robertson, 2019;Hedges & Gunaryadi, 2010;Schmitt, Shuttleworth, Ward, & Shrader, 2018), there has been a dearth of research on predator-based odors. This is despite the fact that fear of predation is a universal and ancient mammalian motivator (Parsons et al., 2018), and scent strongly modulates memory and emotion (Takahashi, 2014). Moreover, fear of predators and their cues is continuously reinforced wherever predators are present.
Predators can impact prey both directly via a predation event that ultimately removes the prey individual from the environment, as well as indirectly (Creel & Christianson, 2008;Lima, 1998). The indirect or nonlethal effects of predation can have a large impact on the behaviors and habitat use of prey species (Creel & Christianson, 2008;Lima, 1998). Many studies have found that the non-lethal effects of predation are longlasting and have far-reaching consequences for the behavior, habitat use, and demography of prey species (Brown, 1988;Creel & Christianson, 2008;Lima, 1998). For example, many prey species will avoid areas where they perceive the risk of predation to be high (Brown, 1988), even when there is a larger food opportunity available in riskier areas compared to less risky areas, thus demonstrating that the fear of predation can override the temptation of food (Stears & Shrader, 2015). In the case of African and Asian elephants, despite their size, both species are at risk of predation by wild apex predators in their systems: lions (Panthera leo) in Africa, and tigers (Panthera tigris) in Asia. Although it is siteand season-specific, African elephants can make up a significant portion of lion diets (Creel et al., 2018;Davidson, Valeix, Kesteren, Loveridge, & Hunt, 2013;Loveridge, Hunt, Murindagomo, & Macdonald, 2006;Power & Shem Compion, 2009;Wittemyer, Daballen, Rasmussen, Kahindi, & Douglas-Hamilton, 2005). In fact, in some areas-such as Botswana-lion prides specialize in hunting elephants, with the majority of hunting success occurring when lions focused on elephants aged between 4 and 15 years old (Joubert, 2006;Power & Compion, 2009). Adult elephants are still often targeted by lions, though the attacks are not as successful as those on younger individuals (Joubert, 2006). In other parts of Africa, though the rate that lions successfully hunt elephants is lower than it is in Botswana, there is still a chance that elephants could be attacked by lions. Thus, while the removal of individual elephants from the system due to predation is unlikely to dramatically alter elephant population sizes, the long-lasting indirect effects (i.e., the fear of being attacked) of lions will likely have dramatic impacts to elephant behavior, regardless of age.
Thus it is not surprising that both African and Asian elephants respond to environmental cues that suggest lions or tigers are present; previous research has found that both African and Asian elephants exhibit aversive behavioral responses to playbacks of predator sounds (McComb et al., 2011;Thuppil & Coss, 2013). Some predator scents cause animals to substantially modify their range use and activity patterns (Parsons et al., 2018), and in some cases prey animals become more responsive to predator signals over time (Cox, Murray, Hall, & Li, 2012). Given elephant reliance on olfaction (Miller et al., 2015;Plotnik et al., 2019;Plotnik, Shaw, Brubaker, Tiller, & Clayton, 2014;von Dürckheim et al., 2018), and the importance of predator avoidance, it is possible that predator odors can be used as effective elephant deterrents.
Here, we report an experiment in applied evolutionary ecology that utilizes elephant predator avoidance instincts to mitigate human-elephant conflict. We hypothesize that given the superior elephant sense of smell, elephants perceive predation risk from scents associated with their main predators, and that this can be applied to create "molecular barriers" which elephants will not cross. We tested whether African elephants (Loxodonta africana) show aversion to predator-related odors (carcasses, cheetah feces, dog feces, African lion feces), odorless controls (water), and control stimuli (herbivore feces), and confirmed elephant aversion to lion fecal odor. We then conducted chemical analyses of lion fecal material and identified its main constituents. Finally, we show that application of a mixture of two of the main chemical compounds in lion feces reproduces the aversion effect. This approach may yield a safe and cost-effective method to mitigate human-elephant conflict.
| Chemical analysis
Because we hypothesized that elephants show aversion to predator-related odors, we first completed odorant sampling and analysis of fresh lion feces to qualify their odorant compounds. We collected four fresh (within 24 hr) fecal samples from a mated pair of lions fed raw meat at the Carson Springs Wildlife Conservation Foundation, Gainesville, Florida, USA, in November, 2019. We pooled feces together, placed 0.5 kg of feces in inert sampling bags (Reynolds), and left them to incubate for 20 min. Air within each bag was then pulled through selfproduced odorant traps at a rate of 0.3 L/min for 10 min, until complete bag deflation, using a lab pump (GilAir Plus, Sensidyne). Odorant traps were made of 3 cm quartz tubes with three adsorbent media (Tenax TA 60-80 mesh; Carbotrap B 20-40 mesh; Carbosieve S-III 60-80 mesh, Sigma; 1.5 mg each), trapped between layers of glass wool. We collected eight replicas of the 0.5 kg fecal samples to ensure identification of all components, and five blank control samples.
We analyzed samples on an Agilent 7890B gas chromatograph with an Agilent DB-Wax polar capillary column (30 m, 0.25 mm diameter), using thermal desorption and a cold injection system, and an Agilent 5977A mass spectrometer in EI mode. Samples were introduced to the thermal desorption unit (TDU) at split mode (95:5) at 30 C. After 1 min, the TDU began heating at 100 C/min until it reached 310 C, where it was held for 8 min. The liner was kept at −100 C. Following desorption, the liner heated up at 12 C/s until 250 C, and held for 8 min. Initial oven temperature was 30 C. After 1 min, the oven began heating at 10 C/min until it reached 240 C, and held for 30 min. MSD transfer line was set to 250 C, MS source to 230 C, and MS quad to 150 C.
Samples were analyzed using Amdis 2.71. Components were identified based on their retention indices (using a ladder of n-alkane standards) and mass spectra using NIST11. We identified likely contaminants by comparing the runs to blank control samples that were taken in identical conditions but using an empty sampling bag. We also excluded known contaminants like siloxanes.
| Behavioral trials
To understand how African elephants perceive odors in their system, and determine whether these odors act as deterrents, we conducted behavioral experiments in March 2019 and January 2020 at the Adventures with Elephants facility, Limpopo Province, South Africa. Initially, we used five semi-tame, adult elephants ranging between 15 and 20 years old (three females, two males). Semi-tame African elephants are extremely uncommon and, given the nature of our experiment, this is an exceptionally high sample size for such a study. After a trial with lion odors, one individual male chose to no longer participate, thus our full experiment included four individuals. The herd of elephants currently live in a 500 ha game reserve that has leopards (Panthera pardus) and spotted hyena (Crocuta crocuta), but no lions or cheetah (Acinonyx jubatus), however, the elephants previously lived in an area that included a wide range of large predators (African lion, cheetah, spotted and brown hyena [Hyaena brunnea]) for 10 years. Thus, although there is no current predation risk to the elephants from lions, the elephants are familiar with the threats and odors associated with these different predators. All research was approved by the Duke Institutional Animal Care and Use Committee (IACUC #A248-18-10), and adhered to the laws of South Africa where the behavioral trials took place.
We conducted two experiments. The first experiment was conducted to determine whether predator-related odors acted as deterrents relative to controls. Predator related odors included (a) indirect predator cues (rotting impala meat); (b) predator dung of animals not known to prey on elephants (domestic dog, cheetah); and (c) African lion (Leo panthera) feces. We additionally tested several controls: (a) odorless control (water), and (b) herbivore dung (giraffe, wildebeest), to examine the possibility that elephants dislike feces of any source. Feces and carcasses were collected in the field.
The second experiment was conducted to test if the effect of lion dung can be replicated using mixtures of the main chemical compounds found in lion feces. We tried three combinations: (a) indole; (b) phenol; (c) phenol and indole, all at 1 ppm (molar). These two chemicals were identified in our and previous studies as major lion dung volatiles, and important constituents of other obligate carnivore feces (Frank, Brückner, Blüthgen, & Schmitt, 2018;Mansourian et al., 2016).
The sampling for the two experiments was split between 2019 and 2020. For both experiments and sampling periods, to present the odors to the elephants, we soaked 2 mm cotton twine in each of the 10 odorants. For the 5 mammal species-related odorants for behavioral trials, we collected fresh dung from each species from surrounding farms. We added 200 mL water to each dung sample to make it slightly runny and soaked the cotton twine in the dung mixture. For synthetic mixtures, we mixed 1 ppm solutions of phenol and indole in water. To absorb the odor of rotting meat (carcass), we soaked the cotton twine in the liquid from the carcass of a recently-killed impala. In 2019, we presented the elephants with the odors on the cotton twine (treatments: string control, wildebeest dung, lion dung, and carcass odors), which was suspended just above the ground (<3 cm) across a 3.5 m wide dirt road path between two hedges. However, for the 2020 trials (treatments: control string, giraffe, dung, indole and phenol, phenol, indole, and cheetah), we threaded the twine through a 4 m long 25 mm diameter PVC pipe to limit the elephant's ability to handle the cotton twine saturated with the odorants. Each pipe had 300, 1.5 cm holes drilled into them to allow odors to pass through. The pipe was laid across the same 3.5 m wide dirt road flanked by hedges, limiting the ability of the elephants to bypass the pipe by walking around it ( Figure S1). For each trial, each elephant was instructed by a handler to walk down the road while the herd foraged 100 m away. We measured how long it took for each elephant to walk from 5 m away from the string or pipe until they stepped over the string or pipe with both front feet. Additionally, if elephants tried to divert their route or refused to step over the string or pipe with verbal commands alone, we offered oranges-a reward highly favored by the elephants. A single orange was tossed 5 m on the opposite side of the string or pipe-just outside the reach of an African elephant's trunk. Oranges were offered when an elephant would refuse to continue or would begin to turn away to avoid the area, or retreat. We increased the number of oranges incrementally (either when an elephant showed signs of retreating or every 10 s) until elephants were willing to cross the string or pipe, or until the trial ended. Each trial took 60 s on average, however the maximum time was 180 s and the minimum was <30 s if the elephants were unphased by the odorant provided. Importantly, the herd of semi-tame elephants are very well-trained and respond to handler instructions closely, and they have been trained to respond to over 80 verbal commands. Thus, any hesitation in crossing through the odor wall after instruction suggests a clear deterrent response. Moreover, we quantified the number of verbal commands given to the elephants for all treatments. The order in which we presented the odors was random, but consistent across all individuals (see Table S1 for the order of treatments). Due to technical limitations in the field with respect to allowing any potential residual odors to clear the area between trials, we could not make the order in which we presented treatments random between individuals.
| Statistical analysis
The results of the two experiments were combined for analysis. First, however, we examined whether there was any significant difference in the amount of time it took the elephants to cross the control twine presented alone versus the control string presented inside the pipe as well as the number of commands given to complete these actions. We found no significant differences in the amount of time (GEE: χ 2 = 0.683, p = .408) nor with the number of commands required (GEE: χ 2 = 1.660, p = .198) or between the two types of control treatments. Thus, we assume that the presentation of the odorants in 2019 and 2020 did not influence the elephants' responses.
To determine whether the time it took for the elephants to walk 5 m and step over odor walls with two front feet was significantly different, we used Generalized Estimating Equations (GEEs) using the geepack package in R (Halekoh, Højsgaard, & Yan, 2006). GEEs account for potential non-independence of our data (i.e., the same individuals were repeatedly used in the experiments), which could stem from an individual possibly remembering previous trials. GEEs use a population-level approach based on a quasilikelihood function and deliver population-averaged estimates of the parameters. The coefficients of GEE regressions are marginal effects (Wang, 2014), and model the average time it took to walk 5 m and step over the odor wall with both front feet. The model incorporated an exchangeable correlation matrix and gamma distribution with a log link function. We used an exchangeable correlation matrix and elephant ID as the unit of repeated measurements. Upon finding that two elephants could not be enticed to step over the lion odor string (Experiment 1) no matter the number of oranges presented to them (see Section 3), we coded their time-to-cross as 180 s, which was the maximum trial duration. Similarly, we used a GEE to determine whether the average number of commands given to each elephant for each treatment significantly varied. The model used an exchangeable correlation matrix and Poisson distribution with a log link function. We used elephant ID as the unit of repeated measurements.
| RESULTS
We identified 15 compounds that were present exclusively or significantly more in lion feces compared to controls ( Figure 1 and Table S2). Of these, we focused behavioral trials on two compounds in particular, phenol and indole, because these matched compounds detected in previous studies of predator fecal odorants, and are not central to non-predator fecal compounds (Mansourian et al., 2016).
Behavioral experiments showed that elephants responded significantly differently to the various odor treatments (GEE: χ 2 = 130.567, p < .001). Treatment effects fell into three distinct categories (Figure 2): (a) not significantly different from the water control trial, (b) intermediate hesitation (i.e., higher than the control but lower than lion feces), and (c) responses similar to those elicited by the feces of African elephant's main predator-the lion. On average, it took (mean ± SD) 10.5 ± 5.7 s for elephants to walk 5 m and step over the odors of: (a) carcass, (b) giraffe, and (c) wildebeest, suggesting that the odors of both herbivores and odors related to death (carrion) have no significant effects in deterring elephants. It took an average of 22.45 ± 11.48 s for elephants to walk and step over (d) indole, and (e) dog feces. Finally, it took an average of 45.51 ± 49.64 s for elephants to step over the (f) cheetah feces, (g) phenol, (h) indole and phenol combined, and (i) lion feces. While these high risk odors all elicit similar responses from the elephants, the odor of lion dung had by far the most significant effect, which cannot be assessed via a statistical model: three elephants refused to step over the pipe, no matter how many oranges were offered, and one of these elephants chose not to participate in further trials after a single exposure to lion dung during trials. Moreover, no other trials required all of the elephants to be offered oranges (lion odor: two elephants required one orange, and two elephants were offered 50 oranges but would not cross, carcass odor: one elephant required one orange, cheetah odor: one elephant required one orange). The odor treatment that resulted in the second-longest delay (i.e., aversion) was the combination of 1 ppm indole and 1 ppm phenol, which took elephants an average of 52.46 ± 9.49 s to walk 5 m and step over, and numerous verbal commands from handlers. We also found that the number of commands F I G U R E 1 Chromatogram of lion fecal volatiles. TIC is the result of thermal desorption of volatile trap and separation on an Agilent Technologies GC 7890B equipped with a DB-WAX polar column. X axis-retention time (min). Y axis-signal intensity (units are systemunique). Main constituents are identified based on the retention index and NIST11 mass spectral library. Numbers refer to compounds listed in Table S2. Red: compounds suspected as contaminants; Blue: compounds present here and in only a handful of other samples (i.e., less representative). Unannotated peaks are contaminants significantly varied across treatments (GEE: χ 2 = 951.468, p < .001, Figure S2), with the felid predator odor treatments (i.e., cheetah, phenol, indole and phenol, and lion) requiring significantly more commands than the other treatments.
| DISCUSSION
Our results show that African elephants show strong aversion to the scent of lion feces-their main predator. We do, however, also find a significant aversion towards cheetah feces as well, which is likely due to the fact that the odor signature from cheetah feces likely carries similar odor signatures to lion dung, given that they are both felids. The elephants' aversion is highly specific-they are not deterred by fecal materials of herbivores, indirect predator scents (carcass), or non-felid carnivores (dog). Further, our results show that the deterrent effect can be replicated using a combination of two major components of lion dung scent-phenol and indole, used in miniscule concentrations. The fact that both are water soluble in these concentrations, commercially available, inexpensive, and effective even in extremely low concentrations (1 ppm), means that their application can be implemented safely and cheaply.
Lion dung had a stronger deterrent effect on elephants in our study than phenol and indole, though this may result from the use of very low concentrations of these compounds (1 ppm). Additionally, the difference in response to lion dung and phenol and indole was statistically non-significant, indicating that the deterring effect may be triggered by miniscule amounts. This would allow minimizing potential negative effects of widespread application of these chemicals during elephant encounters. Further, we used an equal concentration of the chemicals, whereas our chemical analyses indicate that phenol is more common in lion dung headspace by a ratio of 3:1. Synthesizing phenol: indole ratios more accurately may elicit a stronger response. In addition, we chose to exclude one compound (dimethyl disulfide) due to its strong and foul smell, which would render the mixture less useful for widespread application, though future studies may test whether its addition increases behavioral aversion.
The presence of bees, bee sounds, and bee pheromones have been shown to successfully deter elephants from crop raiding or incursion into a given area (King, Douglas-Hamilton, & Vollrath, 2007;King, Lala, Nzumu, Mwambingu, & Douglas-Hamilton, 2017;Wright et al., 2018). While elephants have thick skins, it is hypothesized that their aversion to bees results from aversion to bee stings, particularly in the delicate tissues inside of their noses (Vollrath & Douglas-Hamilton, 2002). While proximity to bees may result in pain to elephants, proximity to predators may result in F I G U R E 2 Deterrent effect of lion dung, control stimuli, and synthetic mixtures mimicking lion dung Y axis-amount of time (s) it took for the elephants to walk 5 m and step over the pipe emitting odor cues for each treatment. (a) Natural odors: odorless control, herbivore feces (giraffe, wildebeest), carcass, feces of less non-elephant-predator carnivores (dog, cheetah), and feces of the main predatorlion. (b) Synthetic mixtures mimicking lion fecal material. Letters denote statistical differences (e.g., two groups marked by the same letter were not statistically distinguishable with α = .05). Colors denote the three response levels. Yellow: odorants that elicited responses that are non-significantly different from the odorless control; Green: odorants that elicited responses that are significantly different from both control and lion; Red: odorants that elicited hesitation responses that are non-significantly different from the response to lion dung. In two cases elephants refused to cross the line after 180 s, and their time was set to 180 s pain and death, and thus highly effective elephant deterrence is likely situated in the context of predator avoidance.
Our approach harnesses the long-lasting, indirect (non-lethal) effects of predators, which can shape prey behavior and habitat use, even in the absence of the predator themselves (Brown, 1988;Lima, 1998). African elephants are only at risk of predation from lions, and in some areas, elephants can contribute to a significant portion of lion diets (Creel et al., 2018;Davidson et al., 2013;Loveridge et al., 2006;Power & Compion, 2009;Wittemyer et al., 2005). While lions are unlikely to remove enough elephants from the population via predation events to impact their population sizes and demography, the fear of being attacked by lions will likely have dramatic impacts on elephant behavior, regardless of age. Our results support this notion, whereby we find that African elephants show a strong aversion to the odor of felid predators (i.e., lion and cheetah feces), even when they have not directly interacted with these two predators in over decade. Moreover, our results also suggest that elephants respond to predation risk (i.e., the landscape of fear), which is the psychological topography of the landscape in the mind of prey that is characterized by areas that are considered to be safe while other areas are considered to be risky due to differences in perceived predation risk (Laundré, Hernández, & Altendorf, 2001;Laundré, Hernández, & Ripple, 2010). Much like many other prey species, the elephants in our study weighed up the potential food reward offered to them in an area that had lion odor (signaling danger), which the elephants ultimately considered to be a risky area, and altered their behavior to reflect the trade-off between food and fear. It is likely that in wild situations, elephants would avoid areas where they detect lions, regardless of food availability (thus living in a landscape of fear), however, this requires further investigation.
A further strength of this study is that it likely overrides behavioral-response variation resulting from variation in elephant age-sex classes. It is hypothesized that male-only elephant groups are more risk-tolerant compared with matriarch-led family groups, however, evidence for variation in predator avoidance behavior based on elephant age-sex classes is mixed (Sitati et al., 2003), and extant studies have found that elephants across herd size and composition demonstrate aversion to predator auditory signals (Thuppil & Coss, 2013). While calves are thought to be disproportionately targeted by felid predators, larger elephants are not immune (Power & Shem Compion, 2009).
While our study focused on African elephants, there is reason to expect that this approach could be useful in closely related Asian elephants (Elephas maximus).
Analogous to African elephants, Asian elephants are at risk of predation from tigers (Panthera tigris). One study found that when Asian elephants detected auditory cues of tigers, they quietly retreated from the area (Thuppil & Coss, 2013). Interestingly, chemical analyses have revealed that tiger feces, like lion feces, are characterized by the presence of indole and phenol (Vester, Burke, Dikeman, Simmons, & Swanson, 2008), and like their African counterparts, Asian elephants are highly olfactorily-oriented (Plotnik et al., 2014;Rasmussen, 1998).
While communities living amongst elephants may be acutely aware of the risk posed by elephants to their lives and livelihoods, expensive and labor-intensive methods like bee fencing, or trench digging, tend not to be successful in the long term, as continued maintenance stagnates, or funds run dry (Sarkar et al., 2016). Familiarization is a concern with all human-elephant conflict mitigation strategies, however, by harnessing the indirect effects that lions have on elephants, it is likely that this strategy will have longer-lasting impacts on elephant behavior than other techniques. This is because the consequences of becoming complacent around predator cues could be lethal. While we did not see familiarization in our trials, we suggest that this be explored on a larger-sale with additional field testing. It is also possible that our molecular barrier could be used in conjunction with other human-elephant mitigation strategies to create a dynamic deterrent fence that elephants do not become easily familiarized with.
Ideally, elephant-deterrence would also be useful outside of the context of crop raiding, including sudden elephant encounters. A more immediately relevant and effective solution to reducing human-elephant conflict is likely one which requires no maintenance, and which can be deployed in the moment of need, for example, bear spray (Floyd, 1999), as opposed to current deterrent efforts which require planning and maintenance just in case. We hope that these results can be used for the development of products that would prove effective ad hoc repellents of elephants, thus protecting farmers from economic damage, humans from fatality, and elephants from retaliatory killings.
A. Shuttleworth, and Y. D. Jacques for their invaluable help with logistics. Funding: K. V. was funded by the University of Florida. O. N. was funded by Deutsche Forschungsgemeinschaft #NE2156/1-1. M. H. S. was funded by NRF #120670.
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Domain: Environmental Science Biology
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Preference behavior of silver catfish , Rhamdia quelen , juveniles in waters with pH gradients : laboratory experiments
The aim of this study was to determine the preferred pH in silver catfish Rhamdia quelen juveniles acclimated to different water hardness and the effect of shelters and infection by Ichthyophthirius multifiliis. Fish were acclimated for two weeks at different water hardness levels (4, 24, 50, or 100 mg CaCO3 L -1) and then transferred to a polyethylene tube with a pH gradient ranging from 3.5 to 11.7 and maintaining the same hardness. The position of the fish in the pH gradient was observed at 1, 2, 4, 6, 8, 10, and 12 h after transfer. Acclimation to different water hardness did not change pH preference of uninfected silver catfish (pH 7.30-7.83), and the presence of a shelter at the preferred pH or outside this preferred pH did not change the chosen pH range, either. Consequently silver catfish favored the acid-base regulation over shelter seeking tendency. Juveniles infected with I. multifiliis acclimated to water hardness of 24 mg CaCO3 L -1 preferred alkaline pH (9.08-9.79). This choice is not explained by the higher Na+ levels at alkaline pH compared to neutral pH because infected and uninfected fish choose the same waterborne Na+ levels in a Na+ gradient with the same pH.
Introduction
Water pH plays an important role in fish homeostasis, development, and survival. Alterations of pH may cause disturbances in acid-base balance, ion regulation and ammonia excretion (Baldisserotto, 2011). The usual pH range for fish growth is 6.0 to 9.0; lower pH can occur due to the presence of acidic cations, humic and fulvic acids, and more alkaline pH can be due to high levels of carbonate and other ions (Parra & Baldisserotto, 2007). Water quality may elicit a preference or avoidance response in fish (Kroon & Housefield, 2003), and several studies have demonstrated that fish preferred a specific pH (Jones et al., 1985;Nakamura, 1986;Peterson et al. 1989;Åtland & Barlaup 1996;Åtland, 1998;Ikuta et al., 2003;Kroon & Housefield, 2003, Kroon, 2005, Scott et al., 2005, Riffel et al., 2012).
Water pH and hardness also affects infection of silver catfish by the ciliate protozoan Ichthyophthirius multifiliis (Garcia et al., 2011), which causes ichthyophthiriosis, also known as "white spot disease" or "ich", damages gill epithelium and skin, and can cause the death of the host (Miron et al., 2003;Carneiro et al., 2005;Garcia et al., 2007Garcia et al., , 2011)). Therefore, if available in the fish culture pond, it is possible that fish might choose a specific pH and/or hardness to reduce the intensity of ich infection. However, there are no studies regarding the preferred pH in fish acclimated to different water hardness levels or infected by I. multifiliis. Consequently, the objective of this study was to determine the preferred pH in silver catfish acclimated to different water hardness or infected by I. multifiliis. In addition, as silver catfish is less stressed when in a shelter (Barcellos et al., 2009), an analysis was also made of whether or not the presence of shelters can change the preferred pH.
Material and Methods
Silver catfish juveniles (12.40 ± 1.33g and 11.00 ± 0.29cmtotal length) were obtained from a fish culture farm near the city of Santa Maria, southern Brazil, and transferred to the Fish Physiology Laboratory at the Universidade Federal de Santa Maria. These juveniles were maintained in continuously aerated (two air pumps of 12 W each) 250 L tanks for 15 days for acclimation (temperature: 23 ± 0.1ºC, pH: 7.1-7.7,dissolved oxygen levels: 6.9 ± 0.1 mg L -1 ). Juveniles were then divided in the following treatments (in mg CaCO 3 L -1 ): 4, 24, 50, and 100, and kept for 20 days in continuously aerated 250 L tanks with the same temperature, pH and dissolved oxygen conditions than acclimation. A water hardness of 4 mg CaCO 3 L -1 was obtained using distilled water, and waterborne Na + , Cl -, and K + levels were adjusted to identical levels of the water with 24 mg CaCO 3 L -1 . A water hardness of 50 or 100 mg CaCO 3 L -1 was reached by adding CaCl 2 .2H 2 O. The photoperiod was 12h light -12h darkness, and the luminosity of the laboratory was 0.6 lux (measured with a LI-COR photometer model LI-185B). Juveniles were fed once a day at 8:00 am with a commercial diet (Supra 42% crude protein, Alisul Alimentos S. A., Carazinho, Brazil) at 5.0% of their body mass. Residues and feces were siphoned 30 min after finishing the food, and consequently at least 20% of the water was replaced with water previously adjusted to the appropriate water hardness. Fish were fasted for 24 h prior to any experiment.
After acclimation to experimental water hardness each group was transferred to a 6m long polyethylene tube containing 50 L of water, which had been added at one end 0.5 N sulfuric acid to generate pH around 3.5 and at the other end 1N sodium hydroxide (NaOH) to obtain pH around 11.7.
The solutions added at the extremities diffused through the water along the tube, creating the pH gradient, which was maintained by adding the same solutions at the extremities every two hours.
Each group (six replicates per treatment, N = 5 each) was placed in the polyethylene tube closest to its acclimation pH. Fish location at the pH gradient was visually observed at 1, 2, 4, 6, 8, 10, and 12 h after the transfer, in order to identify their preferred pH. The pH was always measured at the location at the moment of the observation. After 12 h observation the water of the tube was replaced and a new replicate was placed in the tube. Aerators were placed at the tube, and dissolved oxygen levels were maintained at 6.0 -6.5 mg L -1 . Dissolved oxygen was monitored with an oxygen meter YSI (Y5512, YSI Inc., Yellow Springs, Ohio, USA) every 4 h. The pH was verified with a DMPH-2 pH meter (Digimed, São Paulo, Brazil) and water hardness by the EDTA titrimetric method (Eaton et al., 2005).
The same experiment was repeated, but with the tube containing a shelter (25 mm diameter tube) placed at the preferred pH determined in the first experiment. In another series, two shelters were placed at pH other than preferred one (pH 6.5 and 8.5) In the third experiment, silver catfish infected with I. multifiliis were separated into different groups according to their level of infection: 1 -20, 21 -50, 51 -100, and 101 or more trophonts/fish. The white spots (trophonts) were counted with the aid of a stereomicroscope (Garcia et al., 2007) in fish anesthetized with eugenol (20 µL L -1 ) (Cunha et al., 2010). The infected fish were transferred to the polyethylene tube containing the pH gradient and water hardness of 24 mg CaCO 3 L -1 . The pH preference was observed for 12h.
Silver catfish infected by I. multifiliis preferred an alkaline pH (see results), and as the alkaline pH was obtained by adding NaOH, waterborne Na + levels were 50-70% higher at alkaline pH. Therefore an additional experiment with infected and uninfected fish was carried out in polyethylene tubes with a sodium chloride gradient (adding NaCl 0.5N at one extremity). Waterborne Na + levels were checked every 2 h (up to 8 h) at the site preferred by the fish. The Na + range in the tube was 1.14±0.0 to 7.78±0.0mmol L -1 , water hardness of 24 mg CaCO 3 L -1 (pH: 7.76±0.02). Waterborne Na + levels were determined with a B262 flame spectrophotometer (Micronal, São Paulo, Brazil).
Statistical Analysis.
The homogeneity of variances between groups was tested with the Levene test. The comparisons between different treatments were performed by one-way analysis of variance (ANOVA) followed by the Tukey's test using the Software Statistica version 7.0. Data were expressed as mean ± S. E. M. The minimum significance level was set at P < 0.05.
Results
After the silver catfish (infected with I. multifiliis or not) were placed in the polyethylene tubes, they swam along the entire length of the tube (along the pH gradient), but after about 10 minutes they remained as a shoal at their preferred pH range up to 12 h of exposure.
Acclimation to different water hardness levels did not significantly affect the preferred pH, which was within the 7.30-7.83range. When the shelter was within the preferred pH range, fish remained all 12h in the shelters. The presence of shelter at the preferred pH or outside this preferred pH did not change the chosen pH range (Table 1), i.e., if the shelter was outside the preferred pH, silver catfish did not use the shelters.
Mortality of silver catfish infected with I. multifiliis was 10%, irrespective of the number of trophonts. Infected fish choose a more alkaline pH than uninfected fish and those with 21-50 trophonts/fish preferred the highest pH between the infected fish (Table 2). In the experiment with the Na + gradient in the tube, silver catfish preferred the 1.14 -5.94 mmol L -1 range, and there was no significant difference between uninfected and infected fish.
Discussion
Fish in the wild may respond to several environmental factors (Gunn & Noakes, 1986), and laboratory experiments may separate and clarify these responses (Peterson et al., 1989). Previous study demonstrated that uninfected silver catfish juveniles at water hardness of 24 mg CaCO 3 L -1 preferred the 7.0-7.6pH range (Riffel et al., 2012). These results are within the same range observed in the present study for this water hardness, with is in agreement with the fact that juveniles of this species presented better growth at pH 7.0-7.5 than at pH 5.5 and 9.0 (Baldisserotto, 2011). Survival of silver catfish juveniles in acidic and alkaline water is improved by the addition of Ca 2+ to the water (Townsend & Baldisserotto, 2001), and high water hardness reduced the deleterious effects of acidity (pH 5.5) on growth in soft waters (Copatti et al., 2011a). In addition, growth of juveniles maintained at water hardness close to zero was higher at pH 6.0 than 7.0 and 8.0 (Copatti et al., 2011b). Therefore, as silver catfish growth at different pH is altered by water hardness, it was expected that the pH preference could also be altered by adaptation to different water hardness. However, water hardness did not change pH preference (pH 7.30-7.83) in this species.
Several others species also showed preference to a specific pH (Peterson et al., 1989). Japanese fat minnows (Phoxinus lagowski) presented avoidance behavior and their swimming region shifted from pH 6.0 to 7.0 immediately after decreasing pH and during the exposure to acidic water their swimming activity clearly decreased (Nakamura, 1986). Sockeye salmon (Oncorhynchus nerka), brown trout (Salmo trutta) and Table 1. Preferred pH of silver catfish acclimated to different water hardness. Values are reported as mean ± SEM. Water hardness or the presence of shelter in the tube did not significantly change the preferred pH. Japanese trout (Salvelinus leucomaenis) showed inhibition of digging and swimming behavior in slightly acidic (5.8-6.4) compared to neutral water (6.8-7.1)(Ikuta et al., 2003). Juvenile brook trout (Salvelinus fontinalis) avoided pH 4.0, 5.0 and 5.5 and these acidic pH values affected social interactions (Pedder & Maly, 1986). Common carp (Cyprinus carpio) and goldfish (Carassius auratus) avoided pH values within the 5.5-7.0 range with preference to pH 8.4 and 7.2, respectively (Ishio, 1965). Silver catfish remained in the shelters when they were within the preferred pH. This result is in accordance with the fact that the presence of a shelter in the tank reduced whole body plasma cortisol peak values and their duration in previously stressed silver catfish (Barcellos et al., 2009). Shelter seeking tendencies were also observed in channel catfish Ictalurus punctatus (Brown et al., 1970). The presence of the shelter provided a darkened refuge that, most likely, created a more comfortable environment and allowed a fast recovery from stress (Britz & Piennar, 1992) and better growth rates in African catfish, Clarias gariepinus (Hossain et al., 1998). However, when the shelters were outside the preferred pH range, silver catfish did not use them. Therefore, silver catfish prefer a certain pH over shelter seeking tendency.
Silver catfish infected by I. multifiliis showed preference for a more alkaline pH (9.08 -9.79) than uninfected fish (pH 7.70). This result is unexpected, since infected silver catfish presented higher mortality at this water hardness when maintained at pH 9.0 than at pH 5.0 (Garcia et al., 2011). As there was a gradual reduction of "white spots" in silver catfish infected by I. multifiliis using NaCl (786 mmol L -1 Na + ) (Miron et al.,2003), it was hypothesized that infected fish preferred the alkaline pH due to the higher Na + levels at this pH, compared with neutral pH. Fish would choose the site where the infection would be reduced due to the higher salt concentration. However, there was no difference in the range of Na + levels chosen by infected and uninfected fish. Therefore, the reason for infected silver catfish to choose alkaline pH remained to be studied.
In conclusion, the results indicated that acclimation to different water hardness did not change pH preference of uninfected silver catfish (pH 7.30-7.83),and the presence of shelter at the preferred pH or outside this preferred pH did not change the chosen pH range. Consequently silver catfish prefer a certain pH over shelter seeking tendency. Juveniles infected with I. multifiliis acclimated to water hardness of 24 mg CaCO 3 L -1 preferred alkaline pH (9.03-9.79). This choice is not explained by the higher Na + levels at alkaline pH compared to neutral pH because infected and uninfected fish chose the same waterborne Na + levels in a Na + gradient with the same pH.
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Domain: Environmental Science Biology
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Abundance and Diversity of the Phototrophic Microbial Mat Communities of Sulphur Mountain Banff Springs and Their Significance to the Endangered Snail , Physella johnsoni
Seasonal population fluctuations and diversity of anoxygenic phototrophs and cyanobacteria at the Sulphur Mountain thermal springs, Banff, Canada were investigated and compared to drastic population changes of the endangered snail Physella johnsoni. The microbial community revealed new species of anoxygenic phototrophic bacteria with novel spectral and morphological characteristics. Major mat-forming organisms included densely growing Thiothrix-like species, oxygenic phototrophs of the genera Spirulina, Oscillatoria, and Phormidium and purple nonsulfur bacteria Rhodobacter, Rhodopseudomonas and Rhodomicrobium. Aerobic anoxygenic phototrophs comprised a significant portion, upwards of 9.6 × 104 CFU/cm2 of mat or 18.9% of total aerobic heterotrophic isolates, while PNSB and purple sulfur bacteria were quantified at maximum abundance of 3.2 × 105 and 2.0 × 106 CFU/cm2 of mat, respectively. Photosynthetic activity revealed incredibly productive carbon fixation rates, averaging 40.5 mg C/cm2/day at one studied spring system. A temporal mismatch was observed for mat area and available organics to the fluctuation of P. johnsoni population in a tracking inertia manner. Mat chlorophyll a content appeared directly proportional to snail numbers making it an appropriate indicator of population. This survey of the Sulphur springs microbial communities suggests that phototrophic species are among the main determinants to the proliferation of P. johnsoni. Corresponding author.
Introduction
Banff National Park, Alberta lies in the southern Canadian Rocky Mountains. The area includes Sulphur Mountain, which contains thermal springs discovered in 1883. These serve as the landmark catalyzing the development of Canada's National Park System. There are three groups of sulfur springs along the Mountain thrust at progressively lower elevations towards the Bow Valley River: the highest includes the Upper Hot Spring (1584m); next, the Middle Springs (1500 m); and the lowest at the Cave and Basin (C & B) (1400 m) within the National Historic Site [1] (Figure 1). The springs are hosted by carbonate rock, and therefore have a CaHCO 3 SO 4 chemistry [2] with high Ca (240 to 414 mg/l), low O 2 (0.0 to 1.6 mg/l), and high H 2 S (14.7 to 45.7 mg/l) levels and minimal variation in total dissolved solids (1070 to 2030 mg/l) [1]. Surface water temperature averages 33˚C ± 3˚C in all seasons, despite Banff's cool annual average of -4˚C [3] and pH remains near-neutral, ranging from 6.8 to 7.3 [3].
Biological components include thick bacterial mats (periphyton) on rocks, branches and sediment within the outflow stream; and, the endemic snail Physella johnsoni [4] which was designated threatened by the Committee on the Status of Endangered Wildlife in Canada in April 1997 and later uplisted to endangered in May 2000 [5]. Presently, P. johnsoni is found at the Kidney Springs, the Upper and Lower of the Middle Springs group and at the C & B Historic Tourist Site, predominantly atop cyanobacterial mats. Snail population has been studied for over a decade showing trends in seasonal fluctuation, often >10 in magnitude, with the highest numbers typically observed January to February and lows May to July. Lepitzki (2007) [3] enumerated snail numbers as high as 14,000 and dropping as low as 3000 at the Upper Middle Springs (UMS) over a period of less than three months. Influences include a combination of human interference, water chemistry, possible predation and the, as yet uninvestigated, nutritive and microbiological aspects of the niche.
The importance of phototrophic microbes as primary producers of fixed carbon in aquatic environments has been demonstrated [6] [7], as dense metabolically diverse mats can attain production rates comparable to those of tropical rain forests [8]. Excreted photosynthates, extracellular polymeric substances and cell lysates [9]- [11] are then available to support anabolism of heterotrophs. Oxygenic photosynthetic cyanobacteria predominate in illuminated aquatic environments [12], producing a variety of organic compounds such as glycolate, acetate and lactate, capsular and extracellular polymers, proteins and nucleic acids [13]- [16]. These give integrity to the biofilm structure and serve to sequester and retain dissolved organic carbon, minerals and ions [17] [18]. As such, mats act as an essential trophic link to higher levels within the ecosystem. Anoxygenic phototrophic prokaryotes, including purple and green sulfur (PSB), purple and green nonsulfur bacteria (PNSB) and aerobic anoxygenic phototrophs (AAP) are well-established as co-dominant cyanobacterial mat inhabitants [19] [20], contributing in biomass, biofilm polymer production and an abundance of nutritive carotenoids.
While it has been reported that filamentous Thiothrix-like sulfur bacteria are among the dominant chemotrophic members of the mat, no work has been done on the resident phototrophs. This study, complimentary to Parks Canada's Recovery Initiative for P. johnsoni, focuses on elucidating the composition and dynamics of the above mentioned groups of anoxygenic phototrophs and cyanobacteria within the spring mat community in search of correlation with the drastic changes in snail population.
Collection of Samples
Samples were first collected in August 2003 from 13 sites including both the UMS (N51˚09'50", W115˚34'53") and the C & B Historic Site (N51˚10'05", W115˚35'24"), which are separated by a 2.7 km drive along Sulphur Mountain (~1 km straight-line). Representative 1 cm 2 mat specimens were cut from each site, measured for thickness, placed into 1.5 ml Eppendorf tubes containing 1 ml of spring water and put on ice to be used for enumeration. A second set of samples was stored on ice in 50 ml plastic tubes (Falcon) for community spectrophotometry and microscopy. All sites, except Site 2 (a submerged mat not colonized by P. johnsoni), were sampled in subsequent fieldtrips during May 2004; February, May and October 2005; March 2006; and May, August and November 2007. Sampling also occurred in December 2005 for community spectral analysis only.
Agar deeps were incubated in the light at 27˚C for 14 days before enumeration. RO plates were grown in the dark at 28˚C for 10 days before first and 14 days for second counts. Colonies were categorized by morphotype by microscopy using a Axioscop 2 Light Microscope (Zeiss) at 1000x magnification; and color; and were screened for bacteriochlorophyll (Bchl) with a U2010 spectrophotometer (Hitachi, Tokyo, Japan) with absorbance spectra recorded between 350 and 1100 nm . Estimation of chlorophylls a, b and c was calculated as reported [23].
Study Site Description
Sites (Figure 1) were chosen for proximity to snail aggregations (Sites 3, 5, 6 and 10 -12), microbiological interest (Sites 1, 2, 4, 7, 8 and 9) or as a snail-free control (Site 13). Site 1, within the cave of the C & B, contains an 8 -10 m wide and 0.5 -1.5 m deep pool with a 0.5 m wide outflow stream. A flocculent cream-white bacterial mat grew attached to the surface of the underlying rocks within the turbulent outflow. Dim illumination came from sunlight through a 1 m grating 6 m above the pool surface and from an incandescent lamp approximately 4 m from the mat. Site 2 was an approximately 1 m deep mat lining the floor of the Basin pool and was discontinued after August 2003 due to difficulties in winter sampling and lack of snails at such depths. Site 3 was the deep green microbial mat on the surface of the Basin spring outflow pool. The expanse of the growth was determined largely by the supporting floating detritus. Site 4, samples were taken from the dominant orange formation just subsurface of an open 10 -12 m diameter, 0.1 -0.5 m deep pool (known as Billy's Pool), dense with aquatic plants and the small "mosquito fish" Gambusia affinis [30]. Site 5 was within the stream of the Lower Spring of the C & B that feeds Billy's Pool and typically had a lush, white midstream microbial development attached to rocks and branches source-side of a boardwalk crossing over the stream. The deep green component of the microbial mat, adjacent to the white layer of Site 5, was distinguished as Site 6. At the Upper C & B spring, Site 7 was sampled from the thin pink-purple mat (Figure 2(A)) and Site 8 was the green subsurface portion of the mat next to Site 7.
The remaining sites were located at the UMS. Site 9, the thin dark olive green mat within the cave was approximately 20 cm underwater near the spring source. Sunlight was limited due to cave structure (midday intensity recorded at ~1.3•µE•m −2 •s −1 ). Average water temperature was highest of all sampling locations at 35.2˚C. Site 10 was approximately 5 m from the opening of the source cave and sampling was from the white flocculent growth on the surface of rocks within the spring stream. Site 11, located near Site 10, was the deep green portion of the microbial mat (Figure 2(B)). Site 12 developed approximately 10 m from Site 11 on the downstream side of a wooden weir. Samples were taken from the white and pale green mat present within the turbulent outflow caused by the weir. Site 13, located 10 m laterally from the UMS stream, grew at the mouth of the West Cave (WC) [5], was chosen as a control for comparison of bacterial populations. The samples were collected from a thin purple and green mat 1 -4 cm subsurface along the edge of the cooler rainwater-fed pool (average temperature 25.2˚C).
Macro-and Microscopic Analysis of Natural Samples
Table 1 summarizes the data of several seasonal observations. Micrographs revealed Thiothrix-like filaments to be the dominant component at Site 1. Large, amorphous refractile intracellular particles were common, indicating sulfide oxidation and accumulation of elemental sulfur typical of Thiothrix (Figure 2(J)). Ovoid cells were visible in significant proportions only in May and August 2007, corresponding to the thickest mat development of 7 mm, from 4 mm observed October 2005. The mat with the greatest and most variable thickness was Site 3 (4 mm in March 2006 and over 20 mm in August 2007) (Table 1). Giant, straight, un-branched green filaments, hundreds of micrometers in length were noted only at this site, predominant in May and August 2007.20 -26 µm in diameter (Figure 2(C)), they were sheathed with near-rectangular cells separated by deep constrictions, distinguishing them from most species of Oscillatoria and Lyngbya in size and frequency of cell division [31]. In colder sampling months (February, March, October and November), the population majority fluctuated between Phormidium, Microcoleus, Spirulina (Figure 2(F)) and Oscillatoria-like (Figure 2(E)) species. Site 4 was available for analysis only five of nine samplings. Aerobic growth of a Chloroflexus-like organism observed in August 2003 contributed to the orange color (Figure 2(K)) which, in future samplings, was apparent due to coccoid AAP related to Erythromicrobium and Porphyrobacter (Figure 2(P)). Strains of these two genera were subsequently cultured in large proportion at multiple sites (see Enumeration of Phototrophs). Unicellular Aphanothece-and Dermocarpa-like (Figure 2(I)) and pennate diatoms similar to Amphora (Figure 2 (3 -4 mm thickness) had a significant presence of Phormidium-/Microcoleus-like species, Spirulina and Dermocarpa-like cells with consistent dominance by Oscillatoria-like filaments in all seasons. This mat was also lost in 2005 after a summer and autumn of unusually high precipitation.
Site 7 was composed predominantly of large ovoid cells resembling PSB Chromatium (Figure 2(T)) and rounded Thiocapsa (Figure 2(U)), each containing multiple, highly-refractive sulfur inclusions. Filaments of Phormidium were consistently also part of the cellular matrix. Site 8, dominated alternately by filamentous species of Spirulina and Phormidium, attained maximum thickness in autumn and winter (October and February) (Table 1).
Phormidium sp.filaments consistently populated Site 9 with a co-dominant green coccoid chain-forming bacterium similar to Chlorobium limicola observed throughout the year (Figure 2(V)). Oscillatoria and Spirulina were visible in February, March, May and October. Small white tufts atop the mat were Beggiatoa-like filaments with multiple refractive inclusions. The Thiothrix-like mat at Site 10 showed no co-dominant organisms and little fluctuation in thickness (3 -6 mm). In summer and autumn 2007, a layer of Phormidium and Microcoleus developed beneath top layers of Thiothrix, creating an interesting structural arrangement. Site 11 was dominated by Spirulina in all seasons with Phormidium sp.filaments in February, March, May and October. Site 12 had the most turbulent water flow and supported a Thiothrix-dominated mat that varied in thickness from 3 -4 mm in 2005 to over 1 cm in August 2007. During warmer months the mat was interwoven with light green filaments of Oscillatoria (Figure 2(E)), while Phormidium, Microcoleus, Spirulina (Figure 2(F)), Anabaena (Figure 2(D)) and single cells resembling Aphanothece occurred in cold months. Site 13 lacked Thiothrix, though morphotypes from all other sites were observed: Beggiatoa as a minor component in all seasons along with cyanobacterial representatives Phormidium (major morphotype in all seasons), Anabaena (minor in November), Oscillatoria (significant in all seasons), Spirulina (minor in all seasons) and sheathed Lyngbya (major in February) (Table 1).
Seasonal Community Spectrum Analysis
Pigment abundance was ascertained spectrophotometrically to reflect phototrophic organism dominance and availability for P. johnsoni grazing. Chl a was the main photosynthetic pigment (absorption peak maxima at 661 -664 nm) throughout all seasons for Sites 3,6,8,11,12 Low Chl a values at these sites reinforced stratification, separating oxygenic phototrophs from Thiothrix-dominant portions of the mat expanse. As revealed by enumeration of pigmented strains (following section), cohabitation with Thiothrix was generally reserved for PNSB. Generally, higher Chl a concentrations were obtained in cooler months (October, November and December). This was consistent with the indirect relationship between light intensity/availability and production of photosynthetic pigments, to allow for greater light harvesting [32] [33]. However, disparate patterns of fluctuation between Bchl and Chl concentrations emerge at different sites. Figure 3(A 3(C)-(F)) exhibited a more correlated change. For spectral analysis, samples 10 and 11 were taken from an area that P. johnsoni did not access and thus, like Sites 8 and 13, could experience no grazer impact, while Sites 3 and 12 were the most populated with the snail. Granted limited sampling trials, the possibility exists that a dense population of P. johnsoni could influence the fluctuations of pigments by impacting the changes in bacterial populations. Intense grazing could cause physical damage to oxygenic phototrophs, thus affecting mat Chl content. Snails may also serve as an input of organics through fecal matter and slime trail excreta and, in concert with grazing upon cyanobacteria, may offer increased nutrients and illumination to spur proliferation of PNSB deeper in the mat. Low detection of Bchl at the height of mat thickness and development (May or August) also suggests an increased reliance on purely heterotrophic metabolism when covered by a thicker layer of oxygenic phototrophs, especially for PNSB at light-limited depths of greater than 9 mm [34]. However, no definitive conclusion can be made without more trials.
In vivo Bchl a peaks were obtained from Site 3 in each May sampling, with the greatest amount in 2007 (0.061 µg Bchl a/g wet mass) and in August 2007; Site 4 in August 2003 (concordant with the presence of Chloroflexus); and Site 6 in August and November 2007. Estimation of Bchl a content per mass of mat are presented in Table 3. Bchl a peaks were observed most consistently from Site 7, reaching maximal µg Bchl a/g wet weight in October (0.192 µg Bchl a/g wet mass) and December (0.217 µg Bchl a/g wet mass) 2005. In all seasons, Light Harvesting (LH) Complex I peaks were visible in the range of 797 -803 nm with shoulders at 881 -883 nm and LHII peaks at 851 -857 nm due to abundant Rhodomicrobium-like species (see Diversity section). Site 9 in vivo spectral samples consistently exhibited a peak at 749 -757 nm indicative of Bchl c, typical of green sulfur bacteria, and an expected Chl a peak near 680 nm from cyanobacteria. There was also a strong presence of Bchl a in winter months (0.103 µg Bchl a/g wet mass in October and 0.159 µg Bchl a/g wet mass in December) (Table 3).
Enumeration of Anoxygenic Phototrophs
Table 4 describes the numbers and proportions of Bchl a-containing organisms compared to total bacterial enumeration. The highest counts of AAP were obtained August 2003 at Site 3 (2.3 × 10 5 CFU/cm 2 of mat, or 4% of pigmented bacteria or 2.3% of total heterotrophs) and was over 200 times greater than August 2007 values (1.1% of pigmented isolates and 0.4% of total heterotrophs), over 17 times more CFU/cm 2 than obtained in May 2005 (23% of pigmented colonies, 1% of total heterotrophs) and over 23 times more numerous than in November 2007 (4.4% of pigmented isolates, 3.1% of all heterotrophs). Increase in mat thickness might suggest increase in habitat for AAP from season to season (Table 1), though a typical cyanobacterial mat will become anoxic below depths of around 3 mm at night [35] inhibiting obligate aerobes. While the May 2005 (1.32 × 10 4 CFU/cm 2 ) and November (1.00 × 10 4 CFU/cm 2 ) figures (Table 5) are only ~9 times that of August 2007 (1.43 × 10 3 CFU/cm 2 ), the 20 times larger Aug. 2003 value of 2.33 × 10 5 CFU/cm 2 might be explained by P. johnsoni populations. At 2104 snails, the August 2003 count was more than double that of August 2007 (Lepitzki unpublished), therefore, as described above for PNSB, the potential for simultaneous input of organics (e.g.fecal matter) and the removal of surface cyanobacteria by grazing P. johnsoni could spur heterotrophic growth of AAP. Rhodomicrobium-like isolates, exhibiting characteristic ovoid to elongate cells and production of exospores were one of the most frequently enumerated PNSB from all Sites excluding 1, 5 and 10. Site 7 consistently yielded highest counts from 1.3 × 10 3 to over 1.0 × 10 4 CFU/cm 2 (February and October, respectively). Sites 12 and 13 proved the next most abundant in fall to early spring from 1.2 × 10 2 to 1.0 × 10 3 CFU/cm 2 (Site 12 for October and February, respectively) and 7.0 × 10 2 to 1.0 × 10 4 CFU/cm 2 (Site 13 in October and May 2007, respectively).
Numerous single-celled, chlorophyll a-containing algae were isolated in PNS and PS anaerobic deeps from all sites, reaching 1.8 × 10 4 CFU/cm 2 at Site 8 (October). One species of pennate diatom (30 CFU/cm 2 in February) was isolated at the outflow basin.
Overall, the majority of strains cultured from PS deeps were found to be PNSB able to tolerate the chosen H 2 S concentration of 0.35 g/l. Aside from two isolates enriched from Sites 7 and 9 (Thiocapsa-and Chlorobiumlike organisms; see following section), no other true purple or green sulfur bacteria were obtained.
Diversity of Isolated Strains
Morphologically, AAP isolated on RO included coccoid to ovoid (strains BF3, BF6, BF8), short straight rods (BF7), curved or bent rods (BF10, BF15, BF60) and long, filament-like rods (BF61, BF62). Colony color was most frequently medium to dark orange, accounting for most Bchl a-containing isolates found at every sampling site, excluding Site 9, and accounted for 2.6% to 52.4% (February 2005 and March 2006, respectively) of total Bchl-containing colonies. Low numbers of pale pink strains were BF60 (Figure 2 Isolated AAP were divided into three groups based on absorption spectrum features. The first major group was represented by strains BF6 and BF8, closely related to Porphyrobacter tepidarius (97% 16S rRNA sequence similarity) and Erythromicrobium ramosum (98% 16S rRNA sequence similarity), respectively. In vivo spectra indicated LH1 Bchl a absorption peaks at 802 -806 and 868 -871 nm [36] (Figure 4(E)). No LHII was observed in either strain, though it has been found in Porphyrobacter neustonensis [37] and in E. ramosum, and is a genus-defining trait in the latter [38]. Bchl to carotenoid ratios ranged from 2.9 to 3.5 with major carotenoid Table 5. Phenotypic and phylogenetic diversity of select strains isolated from the Sulphur Mountain thermal spring microbial mats.*RO, rich organic medium; PNS, medium for purple/green nonsulfur bacteria; PS, medium for purple/green sulfur bacteria; PMS, pyruvate minimal salts medium; PE, Chloroflexus medium, n/a-not applicable.peaks at 462 and 483 -90 nm, as seen in Por.tepidarius (Figure 4(E)). The second cluster included pink strains BF15 and BF60, related to Paracraurococcus ruber (93.7% 16S rRNA sequence similarity). The LHI complex absorbed in vivo at 868 nm with the RC near 801 nm, and spheroidene predominant near 482, 511 and 540 nm (Figure 4(F)). Carotenoic acids and spirilloxanthin are found in BF15's closest relative, Pcr.ruber [39]. The third grouping was represented by strain BF4 in which LHI absorbs at 802 and 858-9 nm (not shown). Carotenoid peaks were observed at roughly 30 times the absorption values of Bchl a peaks, with maxima at 412, 478, 538, 584 and 635 nm. Interestingly, BF4's closest phylogenetic relative is the non-phototrophic, non carotenoid-producing Brevundimonas diminuta [40]. Also, spectrophotometrically, an orange ovoid strain, BF62, exhibits incredibly low amounts of Bchl (LHI at 870 nm), typical of most AAP (Figure 4(G)). A second interesting feature is the sharp peak at 421 nm. While certain carotenoids can absorb near 420 nm e.g.ζ-carotene at 422 nm or neurosporene at 414 nm [41], it is also possible the peak is the Bchl precursor Mg-protoporphyrin (normally absorbing near 416 nm). This may lend insight to the small amount of Bchl a produced [42]. Further study will be required to indentify the compound and learn what conditions either consistently induce carotenoid synthesis or interrupt Bchl synthesis in such a manner. PNSB were the dominant phototrophic isolates at up to 100% of pigmented Bchl a-containing cells, nearly all of which were red, purple, pink or olive green in appearance. Morphologies observed included ovoid (strain BF49; 99.9% 16S rRNA sequence similarity to Rubrivivax gelatinosus); short, curved to long rods (BF9, BF37, BF38); teardrop-shaped (BF44); and pleomorphic rods, often with tapered ends (BF23, BF33). The most abundant and widespread isolates (BF9, BF12, BF39) were morphologically and spectrally akin to Rhodobacter with absorption peaks at 871, 855 and 800 nm and carotenoids absorbing near 590, 509 and 477 nm, indicating spheroidene. Within agar deeps, colonies grew purple-pink in the upper, oxygenated zone and olive-brown in the lower anoxic area as is typical of Rba.sphaeroides in which spheroidene and hydroxyspheroidene are converted to their corresponding ketocarotenoids under oxic conditions changing from brown to red [43]. Subsequent 16S rRNA sequencing indicated the presence of Rhodopseudomonas species (BF5, BF30 and BF34, each of 99.6% 16S rRNA similarity to Rps. palustris) (Figure 2(M)). Along with other pink-red community members of similar morphology (BF39, BF51), these organisms were ubiquitous at sampling sites (from 10 to 3.1 × 10 3 CFU/ml over the year) with greatest proportions observed at Site 13 (3.9 × 10 4 CFU/ml in February) and Site 7 (from 3.0 × 10 3 to 1.0 × 10 4 CFU/ml in winter). Unique strains BF1, BF13, BF14 and BF16, phylogenetically related to Rhodomicrobium vannielii (99.6%, 98.0%, 98.6% and 99.6% 16S rRNA sequence similarity, respectively), were isolated from Sites 4, 6, 7, 8, 9, 10 and 13. As seen in BF1 (Figure 2(Q)) the strains exhibited typical ovoid to elongate-ovoid cell morphology and produced exospores, though budding cell length was often up to 0.3 μm longer than the characteristic 2.0 -2.8 µm range. BF14 was spectrally unique, exhibiting a LHI (or possible novel pigment-protein environment) absorption maxima at 802 and 885 nm (Figure 4(K)), distinguishing it from the LHI in Rmi.vannielii which absorbs at 800 -807 and 869 -872 nm [44].
One of the most dominant morphotypes and representative of a second PNSB group were the short rod to spirilloid strains (BF18, 99% 16S sequence similarity to Rps. palustris; Figure 2(M)) isolated from both PS agar deeps and PMS-containing Balch tubes. On PMS plates, these strains anaerobically produce large amounts of Bchl relative to carotenoids (ratio 1:1.7) where, as previously observed by Evans et al. [45], the photosynthetic RC was the dominant complex with Bchl a peak maxima near 808 nm at absorbance values over four times those of the LHI peak at 878 nm (Figure 4(J)). The LHII peak appeared as a subtle shoulder to the left of LHI near 860 nm.
Another prevalent morphotype was that of strain BF9, an ovoid to short curved rod (Figure 2(L)) that grows colorless in the presence of oxygen, purple-pink microaerophilically and olive-brown in anoxically. Spectroscopy showed the LHII absorbing at 860 nm, RC at 802 nm and LHI at 874 nm. Spheroidene series carotenoids were predominant and, unlike the previous group, were produced only in a 1:2 ratio to Bchl a. 16S rRNA sequencing confirmed 98.0% relatedness to Rhodobacter capsulatus. This morphotype was found at nearly all C & B and UMS sites in every season.
Green and purple sulfur bacterial isolates included only Chlorobium-like (BF27; 4(H)) strains from Sites 9 and 7, respectively. BF27 was a deep grass green, strictly anaerobic, chain-forming coccoid to ovoid organism in liquid medium. Bchl c and the major carotenoid, chlorobactene (750 and 457 nm, respectively) were produced in a nearly 1:1 ratio (Figure 4(I)) and are both typical of the green sulfur bacterium Chlorobium limicola [46]. Cell size ranged from 0.4 -0.8 µm in diameter and up to 1.3 µm in length, also similar to Chl. limicola. Phototaxis was exhibited with visibly denser growth along the illuminated side of the Balch tube in conjunction with buoyancy regulation, growing throughout the medium column. Only two of eight known Chlorobium species contain gas vesicles, Chl.clathratiforme and Chl.luteolum [47]. Also typical of the family was its ability to grow at low sunlight intensities (average of less than 1.3 µE•m −2 •s −1 at Site 9). BF28 represented a fifth spectral group. In vivo carotenoid peaks were at 460, 487 and 516 nm, similar to Thiocapsa imhoffii [48], suggesting spirilloxanthin as the major pigment (Figure 4(H)). RC and LHII peaks were akin to those of Thc.roseopersicina at 800 and 854 nm with a LHI shoulder at 880 nm, blue-shifted compared to that of Thc.litoralis [49]. Morphologically, BF28 most resembles Thc.imhoffii, though it did not form the characteristic tetrads (Figure 2(U)).
Cyanobacterial strains of Phormidium, Oscillatoria and Anabaena were obtained on BG11 plates. Dark green, single-celled, oxygenic phototrophs were isolated from both the aerobic and anaerobic zones of agar deeps from Site 3, 4, 6, 8 and 12 samples. These dominant organisms were morphologically similar to Aphanothece, Synechocystis, Chlorococcum (Figure 2(G)) and Dermocarpa (Figure 2(I)). The dominant cyanobacteria resembling Spirulina (Figure 3(F)) proved to be atypical regarding culturing. Contrary to approaches and media published for Spirulina and Arthrospira cultivation [50]- [52], the species at Banff springs preferred neutral over basic pH and perished immediately in widely used Zarrouk-based medium.
Spectral analysis of oxygenic phototrophs yielded three basic absorption profiles, each of which displayed characteristic peaks of Chl a near 420 and 440 nm and in the 670 -683 nm range [53]. The first group, obtained from single-celled isolates (Figure 4(A)), included in vivo cytochrome peaks at 414 -416 nm, fucoxanthin within 470 -482 nm, phycobilin at 581 -597 nm, phycocyanin near 624 nm, Chl b or allophycocyanin at 650 -653 nm and a disproportionately large Chl a peak at 682 -683 nm [54]. The second group (Figure 4(B)) was obtained for Spirulina-like strains and included large, near-equal peaks of Chl a at 680 and of phycocyanin at 624 nm [55]. Third, Anabaena-like morphotypes included first group characteristics with an additional strong absorption near 569 nm in the phycobilin range [53]. The single diatom strain exhibited peaks at 491 and 581 nm, indicative of fucoxanthin; near 636 nm, suggesting phycocyanin or Chl c and a large Chl a peak at 682 nm.
Microbial Carbon Fixation Rates
Fixation of C i was measured during five sampling trips to estimate the microbial community organic input. Five components to the quantitative measure of autotrophy were assessed in the mat: fixation by chemolithoautotrophs measured in a dark vial, fixation by all community members excluding photosystem II-containing oxygenic phototrophic organisms (via a light-exposed vial containing the photosystem II inhibitor diuron), organic production by Bchl-containing phototrophs (diuron vial minus dark vial values), production solely by photosystem II-containing oxygenic phototrophic organisms (light vial less the sum of dark and diuron vial fixation) and total community fixation (light-exposed vial) [26] (Table 6).
At each sampling trip, total community fixation was measured to be consistently greatest at Site 3 (Figure 5) with highest productivity occurring in May 2007 (177 mg C/cm 2 /day). The May 2007 experiment yielded highest rates at all sites except for the May 2005 samplings at combined Site "5/6" (27.47mgC/cm 2 /day) and Site "10/11" (41.71 mg C/cm 2 /day), with production values dominated by oxygenic phototrophs. Primary production was driven by Chl-containing organisms at most sites, accounting for 20% -92% of total production except for Site 12 in November 2007 with over 40% of measured productivity by non-light-driven fixation (8.72 of 19.75 mg C/cm 2 /day). A possible outlier at Site 12 was the anoxygenic fixation value obtained in October of 18.9 mg C/cm 2 /day, accounting for 97% of the total. Site 9 showed insignificant contributions (0.12 to 2.14 mg C/cm 2 /day) by oxygenic phototrophic community members in all seasons except May 2007, comprising roughly 15% of a low total fixation. Productivity in the dark accounted for 40% to over 98% of the total activity at this dimly lit site, though anoxygenic phototrophic fraction values did not fluctuate in any correlative manner. This suggests that chemoautotrophy was the dominant metabolic scheme of the Phormidium-and Chlorobiaceae-like organisms at this site. Overall, the values obtained (Table 3) generally reflected the relationship of higher average temperatures and greater light availability to productivity. The seasonal pattern for all sites was similar with higher values in each May, facilitated by the increase in daylight and temperature. Change in dark fixation was insignificant at all sites except 7/8 in May and October 2005 and March.
Also evident were signs of microbial succession at Site 5/6 which vanished during the late summer of 2005. The predominance of dark fixation in the following months, accounting for ~63% of total fixation in May, could have reflected re-establishment of the mat by Thiothrix and other chemoautotrophs, as oxygenic fixation was not measured at significant rates. Colonization was followed by an increasing oxygenic phototrophic presence accounting for almost 11 mg C/cm 2 /day of the total 23.7 mg C/cm 2 /day measured, compared to 8.3 mg C/cm 2 /day obtained by dark fixation.
Compared to other studies of spring mat systems, the results obtained for the Banff communities indicated exceptional productivity. A study of the Microcoleus and Lyngbya-dominated marine mats of Shark Bay, Western Australia measured fixation rates as high as 1.47 mg C/cm 2 /day [56], while results here comparatively range 3.7 to 120 times larger. Namsaraev et al. studied the Phormidium-, algae-and Chloroflexus-dominated mats of the alkaline Bol'sherechenskii hot (>60˚C) springs and found a maximum total community production of 1.3 g C/m 2 /day and a highest dark fixation rate of 0.806 g C/m 2 /day [57]. Converting units of area, even the lowest total community results at Banff springs exceeded the Bol'sherechenskii maximum by 9 times while the Site 3, May 2007 result of 177 mg C/cm 2 /day was over 1300 times greater. Maximum dark fixation at Banff occurred in May 2007 at 13.8 mg C/cm 2 /day, or 17 times greater. Compared to pelagic microbial dark production in an estuary of Ebro River [58], our results ranged from 217 to over 3200 times the marine values. Oxygenic phototroph production rates from the Urinskii alkaline hot springs were found to be an average of 2.1 g C/m 2 /day (0.21 mg C/cm 2 /day) at the 45˚C -50˚C zone where Oscillatoria limosa and diatoms were predominant [59]. A maximum anoxygenic phototroph fixation value of 0.42 g C/m 2 /day was in the 35˚C -40˚C zones where Chloroflexus was dominant, though this value is roughly 70 times less than that observed at the PNS-and PSBdominated Banff Site 7.
Microbial Dynamics and Physella johnsoni Trends
Our working hypotheses included that 1) there would exist a correlation between cyanobacterial mat develop- ment and P. johnsoni population. As primary producers of organic C, microbial photoautotrophs should form the base trophic layer in the springs. Number of anoxygenic phototrophic bacteria per cm 2 of mat was only tentatively expected to be correlated to snail fluctuations as it was uncertain whether they would be directly proportional to expanse of the mat. A phototroph-snail relationship would have potentially manifested as either simultaneous high counts in each or a high development of phototrophs preceding an increase in P. johnsoni.2) There may not be a correlation between P. johnsoni and colorless bacteria unless one is determined to exist between heterotrophs, sulfate reducing bacteria, etc. and Chl-and Bchl a-containing strains. It has been shown that proliferation of heterotrophic bacteria often depends upon excretion of exoplymeric substances by algae for use as carbon sources [60] and heterotrophs may also benefit from the phosphatase activity of neighboring photosynthetic organisms to uptake phosphorous [61]. If such is the case, a similar fluctuation between populations of strict organotrophs and phototrophic bacteria should occur. Conversely, opposite population trends may be expected if grazer consumption of Chl-and Bchl-containing organisms improved the competitive capabilities of other bacteria. Grazer impact on periphyton biomass and composition has been well-documented [6] [62] [63].
If such an impact occurred at Banff, it may be due to increased access to detrital or water column-based nutrients and snail mucous trails as carbon sources and fecal matter as phosphorous reservoirs [64], benefitting anoxygenic phototrophs, which are primarily heterotrophic. Selection of pigmented over colorless bacteria is impossible at the scale of snail radulas, therefore it is assumed that carbon inputs from grazers that would benefit colorless organotrophs could also support photoheterotrophs. 3) Photosynthetic activity, a direct quantification of the organics available to higher trophic levels, should correlate to the fluctuation in P. johnsoni numbers [63]. Note, that while grazers tend to have impacts on periphyton biomass and taxonomic structure (over 75% of the time), they do not significantly affect overall productivity of the mat (less than 30% of the 89 experiments as reviewed by Feminella (1995) [65]). That said, while general and significant trends appear throughout the literature, many questions remain. Studies of interactions between prokaryotes and eukaryotic microbes have often proved to be unique to the niche and species under observation. The limited direct observations suggested a 30% -60% difference in area covered by the mat at each of the C & B and UMS spring systems from winter to late spring. Mat thickness was variable throughout all seasons and rose dramatically in August at select sites, tripling in some areas of Site 3 between May and August 2007. The microbial mat, along with rocks, twigs and other detrital matter provides structural support for P. johnsoni. Larger mat expanses in the summer often coincided with the trough of snail fluctuations, while larger snail populations and smaller mat area co-occurred in the winter. Common in ecology, the temporal mismatch between predator/grazer population responses to their variable resources has been referred to as "tracking inertia" [66]. From mid-October, when mat productivity and area decreased, snail population continued to increase at a similar rate, resulting in overconsumption of resources causing the beginnings of the sharp decline witnessed around February or March. Recovery of the mat in spring and typical maximum development in summer would facilitate the response in P. johnsoni by providing increased organics in the form of exopolymers and bacterial cells. Large mat areas also provide greater space for reproduction and avoidance of energy expenditures on interference behaviors, e.g.shaking shells after contact with another individual, as found in Physella virgata [63]. It has also been observed in P. virgata and others, that high snail density depresses grazing rates and, in turn, growth rates [63] [65]. A dense population can result in a crash, as many specimens would not attain sufficient growth/maturity to reproduce. Those snails that succeed at reproduction would also decline as P. johnsoni are semelparous, rearing offspring only once before dying. The hatching of young molluscs then coincides with a seasonal decline/sloughing phase of the mat, thus creating the temporal mismatch suggested by Figure 6.
The near complete loss of several microbial sampling sites at the LCB in mid 2005 to early 2006 corresponded to record breaking rainfall in August 2004 and June and September 2005. An effect was also witnessed on the LCB snail population: from 2852 snails counted in February 2005, numbers plummeted to only 40 specimens in 5 months and took 8 months to recover to 1592 [3]. Mat expanse and snail numbers at the UMS Sites 10, 11 and 12 decreased drastically, suggesting a connection to the excessive rainfall, the decrease in mat area or, likely, both.
Areas for the snail censuses that were comparable in size and held consistent qualitative bacterial dominance included Site 5/6 (Oscillatoria-dominated), 10/11 (Spirulina-dominated) and 12 (Oscillatoria, Phormidium and Thiothrix co-dominant). Highest values in 2007 at these three sites were 2401, 1149 and 7264 snails, respectively [3]. For comparison, populations in 2006 reached 1592 at Site 5/6 in April, 1569 at Site 10/11 in January and only 1260 at Site 12. The Site 12 "peak" was in August 2007, but occurred in the midst of an 11-month decline that began the previous November from a population of 3054 snails. Previous to 2005, the maxima were 2852 snails at Site 5/6, 2151 at Site 10/11 and 5052 at Site 12. As the same morphotypes were dominant at these sites at each peak of the snail population cycle, the lack of significant difference between Site 5/6 and 10/11 values suggests there was no advantage for P. johnsoni survival grazing on Oscillatoria or Spirulina-dominated mats.
Calculating the average number of months between maximum and minimum snail census values at the LCB and UMS sites (from 2001 to 2007) showed approximately nine month cycles at Site 5/6, just over seven months at Site 10/11, and about six and a half months at Site 12. June most frequently had lowest snail enumeration values. The longer average rise at 5/6 was influenced more by the extended recovery period necessary after the 2005 microbial mat habitat loss than by the difference in dominant species at each site, but Site 12 Thiothrix could be suggested as influential in the quicker recovery of P. johnsoni. This was also suggested in 2007 by Lepitzki [3] when an offshoot of the UCB stream was observed to harbor thick Thiothrix mats that were quickly colonized by migrant snails, suggesting a less selective and more opportunistic approach to their food choices. Unfortunately, the mat was not observed at its infancy to identify the founding component of the microbial community. At the UMS system it was often seen that below the layers of Thiothrix there was a hidden layer of Phormidium, suggesting that oxygenic phototrophs are the pioneers, providing a scaffold that Thiothrix can overtake.
Figure 7 compares chemolithotrophic and oxygenic photosynthesis-driven carbon fixation at Sites 3, 10/11 and 12 to select months of P. johnsoni enumeration. As observed for area of mat, maximal total community fixation at Sites 3 (Figure 7(A)) and 10/11 (Figure 7(B)) appeared to occur during the periods of lowest snail pop- nearly equally dominated by Thiothrix and oxygenic phototrophs. While Thiothrix does have chemoautotrophic capabilities (which may explain the large November 2007 dark-fixation result), the fixation at this site was driven by oxygenic and anoxygenic phototrophs. If large numbers of snails were present and consuming Thiothrix more often, whether by accident or not, the result could then be to exert less grazing pressure/damage on phototrophic and photosynthetic bacteria.
A comparison of Chl a content extracted from Sites 3, 10/11 and 12 to enumeration of P. johnsoni is shown in Figures 8(A)-(C), respectively. While lacking many replicates, the graphs may indicate a rough correlation. Density of nutritive photopigments in prokaryotes is typically highest when day length is shortest and light intensity is lowest (late fall and winter), and snail population fluctuated in a similar manner with higher numbers at low-light periods of the year. An increase in the concentration or density of nutrients could result in a decrease in the grazing distance and energy expenditures necessary for snails to feed to attain reproductive capabilities. A larger sample set would be required to clearly identify mat Chl concentration as a predictor of snail numbers changes.
In summary, observations of the Sulphur Mountain thermal spring microbial mats showed a diverse matrix of prokaryotic and eukaryotic phototrophs that did not significantly vary in membership over the year, while mat area fluctuated greatly by season. Consistent major mat-forming oxygenic phototrophs included Phormidium-, Spirulina-, and Oscillatoria-like cyanobacteria and single-celled algal species resembling genera Chlorocococcum, Dermocarpa and Aphanothece. PNSB Rhodobacter, Rhodopseudomonas, Rhodomicrobium and Rubrivivax were the most commonly isolated photoheterotrophs, followed by AAP Erythromicrobium and Porphyrobacter species.
This investigation of Sulphur Mountain springs focused on six microbiological components compared to the fluctuation of P. johnsoni: observed microbial mat expanse, dominant morphotypes in various seasons, chlorophyll content, photosynthetic activity and enumeration of Bchl a-containing strains and colorless, heterotrophic bacteria. Regarding our first working hypothesis, positive correlations between density of Bchl-containing strains and P. johnsoni were found at three of five sampling locations, while no relationship was found in the vicinity of the UCB purple mat and a weak negative slope resulted at Site 5/6, likely influenced by dramatic precipitation and sloughing events. The most consistent indicator of snail numbers at the time of sampling appeared to be Chl content per unit mass of the mat. This is logical as photosynthetic activity was at its greatest in warmer months when snails were at their lowest numbers resulting in abundance of exopolymeric substance production (thus decreasing pigment proportion per unit weight) and a decrease in cellular Chl compared to the levels required to harvest fall and winter season luminosity.
Qualitative assessments of microbial proliferation were made visually by examining the area spanned by the microbial mat at each site relative to the maximum expanse observed in August of 2003. Juxtaposed to P. johnsoni population counts from Lepitzki (2002) [5], the trend was that lowest mat development correlated with highest snail counts. This was followed by a severe snail population decrease during mat rebuilding to maximum expanse then an approximate 7-month decline in development while snail numbers grew to their peak. Snail fecundity therefore appeared to be dependent on mat components, allowing continued reproduction of P. johnsoni during its decline. An important note to make regarding the analysis is that what was significant from a statistical perspective may not reflect biological significance. An approximate 20% difference in grazing area may beg the question of significance, but much of the mat expanse was already unavailable to P. johnsoni due to turbulent water, rocks or branches and other obstacles. Any further limit in territory may have contributed to the influences of increased mollusc density that can include physical interference between specimens.
Another physid, P. virgata tended to reproduce only after attaining a certain shell diameter and that threshold size increased in the presence of an environmental stress such as a predator [67]. This trait, possibly shared by P. johnsoni, extends the risk of fatality by other means before rearing offspring, thus emphasizing the importance of suitable grazing area.
Cyanobacteria serve as primary producers of organics such as glycolate during photorespiration, or fermentation products of acetate, ethanol, lactate and other simple compounds which facilitate growth of heterotrophic and other bacteria that may be nutritionally significant to P. johnsoni. Studies have provided examples of filamentous cyanobacteria being integral to mollusc diets in some cases, while a preference towards phototrophic eukaryotes, heterotrophic bacteria and detritus occur in others [68]. The case for P. johnsoni is uncertain. If detritus was a major source of carbon for P. johnsoni as in many Physa species [62], it still follows that a larger mat area traps more material thus increasing accessibility [10]. As mats at Sites 3, 5/6 and 8 were built upon forest debris, it is likely some detritus consumption occurred, however, snails tended to be positioned directly upon bacterial formations rather than forest litter. Animal byproducts were not likely available in any significant amounts as few terrestrial species have been observed at the springs [5]. Due to strict controls on the availability of test specimens of P. johnsoni, an aspect lacking in the study was direct experimentation with the snail in controlled grazing environments as other studies have performed [69] [70] or gut content analysis. These analyses may provide definite conclusions as to which of the myriad of available organics are foundational in P. johnsoni nutrition.
Figure 1 .
Figure 1. Map of the Banff Sulphur Mountain thermal springs area (A) including the Cave and Basin Historic Tourist Site (B) and the Upper Middle springs restricted area (C). Sampling sites are indicated by numbers 1 -13. Black dots in (A) indicate individual springs; lined area is Town of Banff, UCB, Upper Cave and Basin spring; LCB, Lower Cave and Basin spring.
) and Figure 3(B) display somewhat opposing trends in the seasonal measures of Chl and Bchl at sites 3 and 12, respectively, while in contrast Sites 8, 10, 11, and 13 (Figures
Figure 3 .
Figure 3. Comparison of mat Chl a (dark squares) and Bchl a (bars) content (in µg of pigment per gram wet mass of mat) obtained in each sampling season at (A) Site 3, (B) Site 12, (C) Site 10, (D) Site 11, (E) Site 8 and (F) Site 13. AAP also declined to anywhere from 100 CFU/cm 2 in March 2006 to non-detection (<1 CFU/cm 2 ) in February and October 2005 and May 2007. The second highest AAP enumeration occurred at Site 4, likely reflecting the higher oxygenation of this shallow, plant-laden pool. August 2007 sampling yielded 3.0 × 10 4 CFU of orange ovoid AAP per cm 2 of mat, equal to 35.4% of all AAP isolates, and 9.6 × 10 5 CFU/cm 2 (18.9% of total heterotrophs) in May 2005. Third was Site 8, also in May 2005 at 9.3 × 10 4 CFU/cm 2 or 9.5% of all heterotrophic isolates, followed by 1.9 × 10 4 CFU/cm 2 enumerated in May 2007. The Site 1 Cave yielded AAP only in October 2005 (2.2 × 10 3 CFU/cm 2 ) and May 2007 (2.4 × 10 2 CFU/cm 2 ). It is possible that excessive runoff, due to abnormally high precipitation in 2005, combined with any sloughing of Lower C & B mats, led to bacterial transfer higher than typically observed spring streams. The water may have transfered phototrophic bacteria from higher elevated sampling locations to Site 1 through the Cave's overhead opening. A low maximum of 8.2 × 10 2 CFU/cm 2 was observed at Site 10 in May 2005 and the seasonal mean for AAP at the three Thiothrix mat sites (1, 5 and 10) was a mere 90 CFU/cm 2 . PNSB were abundant at Site 3 in August 2003 at 4.8 × 10 5 CFU/cm 2 with variance from as low as 90 CFU/ cm 2 (February 2005) to 6.6 × 10 3 and 8.1 × 10 3 CFU/cm 2 (May and August 2007, respectively). Unexpectedly, the next highest density was discovered within the turbulent Site 12 UMS stream at 3.15 × 10 5 CFU/cm 2 in (R)) at 10 CFU/cm 2 from the basin floating mat (February 2005) and BF15 (Figure 2(S)) at 100 CFU/cm 2 from Site 4 (February 2005).
Figure 5 .
Figure 5. Seasonal comparison of total community productivity values (mg C fixed per cm 2 per 24 hr) for all sampling sites. Black column, May 2005; lined, October 2005; white, March 2006; textured, May 2007 and grey, November 2007. The Site 3 May 2007 value of 176.9 mg C/cm 2 /24h is truncated for graph clarity. No sampling done at Sites 5 or 6 in October or March.
Figure 6(A) shows typical P. johnsoni orientation on the green portions of the mat; snails clinging to the epilithon to avoid being swept away by the outflow stream. An increase in mat expanse should provide increased habitat, though there was a clear lag effect as mat development and snail counts follow inverse trends. Monthly P. johnsoni counts compared to development of Sites 10 -12 (Figure 6(C)) and 3, 5 and 6 (Figure 6(D)) expressed as a relative percentage of the maximum expanse observed in August 2003 displayed this incongruence. Highest snail counts were generally obtained February/March at Sites 10 -12 and November/December for 3, 5 and 6, and were compared to observations of mat development. Sampling months are shown color-coded in Figure 6(E) and Figure 6(F) and correspond to the color indicating the same month of P. johnsoni enumeration in Figure 6(C) and Figure 6(D).
Figure 6 .
Figure 6. Physella johnsoni and changes in mat area.(A) Photograph of P. johnsoni atop cyanobacterial growth at the UMS. Inset: Close-up of the snail.(B) Relative percent development of select sites compared to the maximum expanse (i.e.100%) observed in August 2003.(C) Sampling months color-coded atop a graph of monthly snail counts at Sites 10-12. Colors correspond to sampling months on the X-axis of Panel E. (D) Sampling months color-coded atop on a graph of monthly snail counts at Sites 3, 5 and 6 combined. Colors correspond to sampling months on the X-axis of Panel F. (E) Bar chart of the averaged relative percent development for Sites 10-12.(F) Bar chart of the averaged relative percent development for Sites 3, 5 and 6 (due to loss of locations 5 and 6, October and March values are Site 3 only).ulations. Site 3 May 2005 and 2007 values were a clear example, in that over twice the productivity (77 to 177 mg C/cm 2 /24h) occurred at times when 1/3 less snails (721 down to 526) were present. That large May 2007 value occurred just as the slow increase in P. johnsoni numbers began until the last C-fixation measurement in November 2007 revealed incredibly low photosynthetic activity. While intense grazing pressures could have physically damaged cyanobacteria and the autotrophic capabilities of other mat surface members, C-fixation rates fluctuate in the same way as mat expanse and snail numbers. The temporal lag of a population catching up to available resources, the aforementioned tracking inertia, may be inferred by the rudimentary patterns obtained at Sites 3 and 10/11. Visually, Site 12 (Figure 7(C)) does not suggest the mismatch between P. johnsoni numbers and production of organics as well as the other two locations. This might have been a result of the mat composition as Site 12 is
Figure 7 .
Figure 7. Photosynthetic activity reflected in mg of carbon fixed per cm 2 of mat over 24 h (primary axis) compared to monthly P. johnsoni counts (secondary axis) for (A) Site 3, (B) Site 10, 11 and (C) Site 12. Black bars, protein production in the dark; white bars, fixation by oxygenic phototrophs; grey bars (in C only), anoxygenic phototrophic fixation; line, P. johnsoni enumeration.
Figure 8 .
Figure 8. Chlorophyll a and b extracted from mat samples in select months compared to monthly enumeration of P. johnsoni.(A) Data from Site 3, C & B floating mat; (B) combined data from Sites 10 and 11 at the UMS and (C) Site 12. Black bars, Chl a; white bars, Chl b; line, P. johnsoni enumeration.
(H)) were ob
served in spring and summer. The Site 4 mat was thickest in May 2005 (4 mm), but was virtually undetectable in all subsequent seasons except August 2007 (<1 mm). During its maximal development (1 cm), Site 5 May and August 2007 samples contained single-celled oxygenic phototrophs of Synechocystis morphology and motile ovoid cells as minor members. Minimal thickness was measured in February and May 2005 before the mat disappeared in October 2005 until summer 2006. Site 6 of the Lower C & B spring
Table 1 .
and 13. Concentrations of Chl a, b and c were calculated in October and December 2005, March 2006, May, August and November 2007 for wet sample mass (Table2). Samples 3 and 11, from the thickest and greenest photosynthetic mats, had the highest Chl a concentra-Summary of seasonal site observations including mat development (expressed as a percentage of the maximum development observed in August 2003), thickness of the mat sample, maximum peaks from whole-community spectral analysis, organisms/morphotypes that were dominant in each sample as viewed under a light microscope and select spring water physicochemical parameters.
Temp 1 pH -HCO 3 -CO 3 HS -Dissolved In vivo Community Relative % Organism(s) (mm) ˚C (mg/l) (mg/l) (mg/l) oxygen Spectral Peaks 3 (nm) Development 2
tions (max of 3043 and 3785 µg Chl a/g wet mass respectively, in October), though Site 13 was also consistently Chl-rich (212 to 2231 µg Chl a/g wet mass). Thiothrix-dominant samples contained the least; typically <50 µg Chl a/g wet mass at Sites 1 and 5 and <130 µg Chl a/g wet mass at Site 10 in all experiments.
Table 2 .
Chl a, b and c values in µg Chl/g microbial mat for Banff Springs sampling sites in select seasons. Proportions were calculated from wet samples and are therefore an underestimate of actual Chl contained per mass of mat.
Chl a Chl b Chl c Chl a Chl b Chl c Chl a Chl b Chl c Chl a Chl b Chl c Chl a Chl b Chl c Chl a Chl b Chl c
Table 3 .
Bchl a values expressed as µg Bchl/g microbial mat for Banff Springs sampling sites in select seasons. As for Chl, data were obtained from wet samples and are an underestimate of actual Bchl contained per mass of mat. February followed by 2.0 × 10 5 CFU/cm 2 in October 2005. Site 11 showed low PNSB counts (<200 CFU/cm 2 ) in all seasons except autumn samplings: 1.7 × 10 3 and 8.0 × 10 4 CFU/cm 2 in October 2005 and November 2007, respectively. Lowest average PNSB counts arose from the dimly lit Site 1 at an average <1 × 10 2 CFU/cm 2 , with the exception of 1.1 × 10 4 CFU/cm 2 detected in August 2007.
Table 6 .
Productivity of different fractions of the microbial mat community and total carbon fixation for sampling locations at the Sulphur Mountain springs in late spring, autumn and winter.
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Domain: Environmental Science Biology
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On Evidence of Absence
When ACE-ÉCO received the submission by Hill et al., Evidence suggesting that Ivory-billed Woodpeckers (Campephilus principalis) exist in Florida, we faced a challenge. The Arkansas case is widely familiar (Fitzpatrick et al. 2005, Walters and Crist 2005, Fitzpatrick et al. 2006, Jackson 2006, Sibley et al. 2006) and highly politicized. One does not need to be a member of the ornithological community to appreciate that there might be further controversy generated by news of more putative observations of Ivory-billed Woodpeckers in Florida.
On Evidence of Absence De la certitude de l'absence
Thomas D. Nudds1 , Jeffrey R. Walters2 , and Marc- André Villard 3 When ACE-ÉCO received the submission by Hill et al., Evidence suggesting that Ivory-billed Woodpeckers (Campephilus principalis) exist in Florida, we faced a challenge. The Arkansas case is widely familiar (Fitzpatrick et al. 2005, Walters and Crist 2005, Fitzpatrick et al. 2006, Jackson 2006, Sibley et al. 2006) and highly politicized. One does not need to be a member of the ornithological community to appreciate that there might be further controversy generated by news of more putative observations of Ivory-billed Woodpeckers in Florida.
Neither did it escape our attention that the notoriety associated with the paper might be good for a fledgling journal. On the other hand, especially because of its political volatility, negative fallout from publishing the article might have dire consequences. Indeed, it is legitimate to ask whether the scientific bar has been adjusted for publicity, and whether it is productive-from a scientific perspective-to publish further papers claiming to have rediscovered Ivory-billed Woodpeckers without direct, physical evidence, such as clear photographs, videos, or feathers.
We promote ACE-ÉCO as intermediate in scope to journals with traditional emphases on basic ecology or management and conservation (Nudds and Villard 2005), and are careful nevertheless to put good science first; this implies embracing uncertainty (Villard and Nudds 2006). To deny publication of a controversial paper simply because it did not present a definitive conclusion to an ongoing debate with political consequences would only mean that we abrogated our responsibility. The subject matter is first and foremost consistent with our vision for ACE-ÉCO.
What is that responsibility, more specifically?Consider the scientific method: observations about nature generate hypotheses and predictions that are subjected to further scrutiny. This leads, through strong inference (Platt 1964, Chamberlin 1965), either to falsification of the hypotheses, or an increase in our confidence that the hypotheses can account for the observations. In this case, the null hypothesis seems clear: Ivory-billed Woodpeckers are not present-in Arkansas, Florida, or anywhere else for that matter. Some advocates may treat the alternative hypothesis-that Ivory-billed Woodpeckers are present, at least somewhere-as an article of faith, and skeptics will rightly point out that the evidence for this alternative hypothesis may be weak. From a scientific perspective, it seems safe to state that the observations do not allow rejection of the alternative hypothesis out of hand. Regardless, as Carl Sagan pointed out, absence of evidence is not evidence of absence. Hill et al. conclude that their evidence at least warrants an expanded search in space and time. We agree."Harder" physical evidence, such as photographs, would enable an unequivocal rejection of the null hypothesis. If no such evidence ever materializes, despite an expanded search effort, the alternative hypothesis is assessed just the same. Furthermore, Hill et al. offer new forms of evidence (cavity size distributions, putative foraging sites) that can be [URL] in other areas, including those in which Ivory-billed Woodpeckers clearly are absent. Thus, they provide both evidence consistent with the alternative hypothesis, and means to increase confidence in our inability to reject the null hypothesis. Science is a way of knowing, and knowing occurs either way.
Another responsibility of the journal is to provide an efficient medium for communication among those who must scrutinize the evidence. For the first time, sounds are directly appended to a paper. On the other hand, making this evidence quickly and widely accessible might also have dire consequences for the putative remnant population if it leads to uncoordinated and unregulated search efforts. Therefore, we asked the authors to take steps to guard against this.
By bringing this paper to the attention of avian ecologists and conservationists, ACE-ÉCO is participating in the scientific process of hypothesis generation and evaluation. As is the nature of our business, readers will decide for themselves. The online, open access format of the journal readily permits dialogue on this topic, and we invite readers to submit comments. For now, we are prepared to embrace the uncertainty presented by the evidence in Hill et al. Time, and rigorous testing, will be the ultimate judge.
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Domain: Environmental Science Biology
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NEST OCCUPATION AND PREY GRABBING BY SAKER FALCON (FALCO CHERRUG) ON POWER LINES IN ThE PROvINCE OF vOJvODINA (SERBIA)
Research on nest occupation and prey grabbing by saker falcon (Falco cherrug) on power lines in Vojvodina (Serbia) was done in the period from 1986 to 2004. During three specially analyzed periods, saker falcon took the nests of raven (Corvus corax) in 91% of a total of 22 cases of nest occupation, and those of hooded crow (Corvus corone cornix) in only 9%. Saker falcon regularly grabs prey from different birds that occasionally or constantly spend time around power lines [Kestrel (Falco tinnunculus), hobby (Falco subbuteo), hooded crow (Corvus corone cornix), jackdaw (Corvus monedula), marsh harrier (Circus aeruginosus), hen harrier (Circus cyaneus), buzzard (Buteo buteo), and raven (Corvus corax)]. One year a studied pair of saker falcons on a power line in Donji Srem, Serbia grabbed prey from five different species of birds. Out of a total of 40 cases of prey grabbing in the period from January to December, as much 70% of the grabbed prey was taken from kestrel (Falco tinnunculus). During the winter and early spring, prey was grabbed predominantly by males; after May, prey was sometimes grabbed by females as well. Most of the grabbed prey was common vole (Microtus arvalis).
INTRODUCTION
Power lines represent a new structural element in the habitat of birds. Owing to their special characteristics and significant alteration of the areas where they are erected, power lines have forced birds to adapt to new living conditions (F e r r e r and J a n s s , 1999; P u z o v i ć and K r n a j s k i , 2007). The adaptation of birds to using power lines has led to changes in seasonal or permanent living, behavior, nesting biology, size and shape of nesting territory, ecology, and kinds of prey (J a l k o t z y et al., 1997; N e g r o , 1999; J a n s s , 2001).
Several previous studies treat the reproduction of saker falcon (Falco cherrug) on power poles in agricultural areas, occupation of vacant or taking of inhabited nests from corvids (birds of the Corvidae family), and the phenomenon of prey grabbing from other birds of prey (C r a m p and S i m m o n s , 1980; S n o w and P e r r i n s , 1998; B a g y u r a et al., 2003, 2003a).
Before the appearance of power lines, saker falcon (Falco cherrug) lived in steppe and forreststeppe habitats, where it nested in lonely trees or on the edges of forests, as well as on rocks and loess outcrops (S n o w and P e r r i n s , 1998; G a l u s h i n , 2004). This species has significantly changed its nesting places and nourishment in the second half of the 20 th century in countries of the Pannonian Plain, especially in Hungary, Slovakia, and Serbia (O b u c h and C h a v k o , 1997; B a g y u r a et al., 2003a; P u z o v i ć , 2000). Because of evironmental changes (plowing of steppe land, deforestation, disappearance of traditional prey), this species in many countries of Central Europe and Ukraine has begun to frequent agricultural areas near smaller settlements (D e m e t e r and N a g y, 2005; K a r y a k i n , 2005). Saker falcon is one of the most endangered birds of prey in the world: in 2006 BirdLife International put it in category EN (endangered). In Serbia it was proclaimed a natural rarity in 1993 (Official Gazette of the Republic of Serbia, 50/93). Abstract -Research on nest occupation and prey grabbing by saker falcon (Falco cherrug) on power lines in Vojvodina (Serbia) was done in the period from 1986 to 2004. During three specially analyzed periods, saker falcon took the nests of raven (Corvus corax) in 91% of a total of 22 cases of nest occupation, and those of hooded crow (Corvus corone cornix) in only 9%. Saker falcon regularly grabs prey from different birds that occasionally or constantly spend time around power lines [Kestrel (Falco tinnunculus), hobby (Falco subbuteo), hooded crow (Corvus corone cornix), jackdaw (Corvus monedula), marsh harrier (Circus aeruginosus), hen harrier (Circus cyaneus), buzzard (Buteo buteo), and raven (Corvus corax)]. One year a studied pair of saker falcons on a power line in Donji Srem, Serbia grabbed prey from five different species of birds. Out of a total of 40 cases of prey grabbing in the period from January to December, as much 70% of the grabbed prey was taken from kestrel (Falco tinnunculus). During the winter and early spring, prey was grabbed predominantly by males; after May, prey was sometimes grabbed by females as well. Most of the grabbed prey was common vole (Microtus arvalis).
NEST OCCUPATION AND PREY GRABBING BY SAKER FALCON (FALCO CHERRUG) ON POWER LINES IN
Key words: Falco cherrug, nest occupation, prey grabbing, power lines, Vojvodina, Serbia UDC 598.24(497.113):591.5The nesting habitats of saker falcon in Serbia are mostly in Vojvodina (95% of the total number), and almost all pairs nest on power poles (P u z o v i ć , 2000, 2007). It is most numerous in South Banat, East Srem, and Southeast Bačka. Nesting usually begins in the second half of February. Its food predominantly consists of mammals and middle-sized birds. The given species mostly hunts small rodents, ground squirrels, and hamsters among mammals, and pigeons and starlings among birds. It attacks prey both on the ground and in the air (B a g y u r a et al., 2003).
The total world population is estimated to be 5,000 to 8,000 nesting pairs, while in Europe there are 450 to 600 pairs distributed in 16 countries, which represents approximately 7% of its total in the world (B u r f i e l d and B o m m e l , 2004; D i x o n , 2006; D e m e t e r and N a g y, 2005). The most important nesting places are in Ukraine, Hungary, Serbia, and Slovakia. The total population in Serbia was estimated to be 55-60 nesting pairs in 1994-1996 (P u z o v i ć et al., 2003). In the period of 1994-2006, the total was estimated to be 52-64 pairs, which means that it has stagnated in the last decade, although locally it has increased in some places and decreased in others.
MATERIAL AND METHODS
Facts about the content, size/density of the nesting population, and the distribution and kinds of nesters that nest on power poles in Vojvodina were collected in the field during the period of 1986-2004. Observations were conducted along power lines partly modified versions of the minimal and limited transect methods and on-the-spot censuses (M a t v e j e v, 1988; H a m , 1986; B i b b y et al., 1992) in conjunction with detailed mapping of the birds' nests at the beginning of reproduction and their regular checking. The work covered all highvoltage power lines in Vojvodina, and special attention was paid to the region of Srem, where there are about 730 km of power lines (110, 220, and 400kV) with a total of 2,450 metal power poles.
For mapping of nesting pairs of birds of prey on power lines, two methods were used according to Vo r i š e k (1995): 1) entering of nests on a map (the nest-mapping method); and 2) census of territories where pairs were spotted on their wedding flight (the displaying pairs method). In more than 90% of cases, the first method was chosen because of terrain clarity. Census-taking was done by walking under power poles in the direction of the conducting cables regardless of the type of soil below. An all-terrain vehicle was sometimes used for this purpose. Both the species which occupied a nest and the species which made it were recorded. The years of detailed census-taking were 1986, 1994, and 2004.
Facts about prey grabbing from other birds by saker falcon were collected throughout the whole research period. Detailed monitoring of a saker falcon pair was conducted on a chosen power line near the locality of Preka Kaldrma in Donji Srem throughout a calendar year (1994) and involved 28 equally distributed all-day recordings/censuses on the spot. Appropriate photographs were taken, and samples of food under the nests were collected.
RESULTS AND DISCUSSION
Changes in the lifestyle of saker falcon (Falco cherrug) as a consequence of permanent or temporary residence on power lines in the middle of intensely cultivated agricultural areas and near settlements and traffic arteries in Vojvodina include changes of behavior, nesting biology, and feeding ecology.
Nest occupation by saker falcon
One of the especially important specificities of nesting on power lines by saker falcon is its dependence on other species of birds, which is manifested in occupation of nests made by corvids (birds of the Corvidae family). Different species of falcon usurp/ occupy nests on power lines from other birds, above all from raven (Corvus corax), but also from hooded crow (Corvus corone cornix) and sometimes even from the European magpie (Pica pica).
In Vojvodina saker falcon (Falco cherrug) predominantly occupies the nests of raven (Corvus corax), which is the best nest builder on power lines, and rarely those of hooded crow (C.corone cornix).
Crow's nests are also occupied by hobby (Falco subbuteo) and kestrel (Falco tinnunculus). However, these species usurp fewer nests and often occupy ones that are abandoned or unoccupied.
Falcons are forced to usurp nests in deforested agricultural areas for several reasons, above all because environmental conditions favorable for nesting or alternative nesting places do not exist. More than 70% of all nests on power poles from the previous building season do not survive until the next season, so falcons directly depend on whether ravens and crows build their nests on power lines again.
During three specially analyzed periods (1986, 1994, and 2004), there were no significant changes in the phenomenon of usurpation/occupation of raven's and crow's nests by saker falcon. In all three periods, saker falcon took the nests of raven (Corvus corax) in 91% of a total of 22 cases of nest occupation, and those of hooded crow (Corvus corone cornix) in only 9%. This shows a stable tie of this large bird of prey with raven's nests. There were recorded only two cases involving occupation of the nests of hooded crow in 1994. In one of those cases, it was the first nesting of a young saker falcon pair near the Voika settlement.
Saker falcon (Falco cherrug) usually does not occupy a raven's nest while the ravens are forming a brood, but after nest building. This is significant from the aspect of maintenance of the raven population on power lines because this species then builds a new nest and forms a brood faster and more successfully. During research in Poland, when some nests of raven (Corvus corax) with eggs and a brood in them were destroyed and predation was simulated, pairs exposed to such circumstances often left the nesting location (Tr y j a n o w s k i et al., 2004).
Installing artificial nesting platforms and boxes on high-voltage power poles and development of cooperation with power companies can help saker falcon become significantly independent of the nedd to occupy or usurp the nests of other birds. In Hungary and Slovakia, saker falcon nowadays nests mainly on installed nesting platforms and boxes (B a g y u r a et al., 2003, 2003a).
Prey grabbing by saker falcon from other bird species
On power lines saker falcon (Falco cherrug) regularly grabs prey from different birds that occasionally or constantlly spend time on the lines. In Vojvodina saker falcon grabs prey from other species of falcon which nest on power poles or close to them (kestrel Falco tinnunculus and hobby Falco subbuteo); from nesters of the crow family (hooded crow Corvus corone cornix and jackdaw Corvus monedula); and from species which migrate over the area (marsh harrier Circus aeruginosus and hen harrier Circus cyaneus) (P u z o v i c , 2007a).
Although group hunting is known for some other large falcons, there is very little information about such behavior in saker falcon (B a g y u r a et al., 2003a). B a u m g a r t (1991) mentions that, like other birds of prey, saker falcon has 'the will for cooperation' when it hunts and that will is not reduced in nesting pairs. C r a m p and S i m m o n s (1980) characterized saker falcon as an unsocial and solitary species that most of the year hunts prey on its own. There is very little information about parasitism of saker falcon on other birds that hunt their prey, usually small rodents ('joined parasitism'). One described case involved grabbing of prey from a kestrel (Falco tinnunculus) that had caught a mouse, and another was when falcon chased a sparrowhawk (Accipiter nisus) that had caught a blackbird (Turdus merula) (B a u m g a r t , 1991, 1994). B a g y u r a et al. (2003a) were the first to consider the propensity of this species to grab prey from other birds during the non-nesting period. In Hungary it took prey from buzzard (Buteo buteo) (53%), hen harrier (Circus cyaneus) (20%), kestrel (Falco tinnunculus) (16%), rough-legged hawk (Buteo lagopus) (5%), goshawk (Accipiter gentilis) (4%), and sparrowhawk (Accipiter nisus) (2%). The authors emphasized that the male has the main role in more than 90% cases of prey grabbing, and that cases of prey grabbing were few during the nesting period. C r a m p and S i m m o n s s (1980) and S n o w and P e r r i n s (1998) do not mention such a phenomenon in the behavior of saker falcon.
As can be seen from Table 2 and Fig. 1, a saker falcon (Falco cherrug) pair grabbed prey from five different kinds of birds on a power line near Preka Kaldrma (Karlovčić) in Donji Srem in the course of a year (1994). Out of a total of 40 recorded cases of prey grabbing in the period from January to December, most of them (70%) were from kestrel (Falco tinnunculus). Prey grabbing in the period of winter and early spring was done predominantly by the male, while after May it began to be done by the female, too. At the beginning of the reproduction period, the saker falcon pair did almost no hunting of living prey, but concentrated on prey grabbing from other birds of prey.
Success in prey grabbing is significantly greater than when the falcon hunts living prey on a wider area. To conserve energy, the pair focused on prey grabbing from species that frequent power lines. Prey was grabbed from migrating hen harriers (Circus cyaneus) during February and March, and from marsh harriers (Circus aeruginosus) in March and April. The male falcon successfully grabbed prey from buzzards (Buteo buteo) throughout the winter and early spring, but it tried to do so from a raven (Corvus corax) only once, the raven being one that nested on a nearby power pole. On the basis of results of research on feeding ecology of saker falcon in Donji Srem and in other parts of Central Europe, it can be aserted that common vole (Microtus arvalis) represents a large part of the grabbed prey.
The female saker falcon behaved in a rather reserved and peaceful manner: it seldom flew away from the male, hunted less, and mainly stayed on power lines around the nesting place. It prepared for incubation in that way. In most cases, the female was given food by the male, food which it had grabbed or hunted by itself. During that period, the prey consisted mostly of small rodents and much less of birds. It was not before the middle of April that the pair's diet started to includ many more domestic pigeons, which were hunted by the falcons in the nearby villages and over cultivated land. Then prey grabbing from other species of birds became much less present. From the beginning of May to the end of June, the falcons brought a average of two pigeons a day back to the nest, sometimes even three. There is a special ritual of prey giving and feeding. During the early spring, falcons eat their prey mainly on clods of nearby plowed fields or on bare soil, but from May to June they eat on power poles because then the vegetation below them is much higher.
The phenomenon of prey grabbing by saker falcon is probably present throughout most of the year, yet it is extremely seasonal. Of all cases of prey grabbing during the year by the couple analyzed in Srem, 15% were recorded in February, 17% in March, and 23% in April. Then the phenomenon decreases in
Species robbed
Month Total May and June to 12% each month, seems to stop during the summer, gradually intensifies again and in late autumn. However, it is possible that even during the late summer and early autumn months there were occasional prey grabbings, as noted by B a g y u r a et al. ( 2003a), especially from kestrel (Falco tinnunculus) and buzzard (Buteo buteo), but they were not recorded because the falcons moved around a much wider area which could not be controlled adequately. In keeping with prey grabbing and occupation of the nests of other species, saker falcon (Falco cherrug) has significantly reduced its vocal communication on open areas with power lines. The birds have become quieter because of better terrain visibility, to be less noticed by man, and to be able to surprise other hunting species and grab prey from them.
CONCLUSION
Power lines offer birds certain benefits by offering them power poles for nesting and at the same time satisfying several needs related to reproduction, retention of territory, resting, and hunting. Saker falcon significantly modified its behavior and feeding ecology in countries of the Pannonian Plain during the second half of the 20 th century, largely as a result of life on power lines.
During three analyzed periods (1986, 1994, and 2004), saker falcon occupied raven's nests in 91% of a total of 22 cases and the nests of hooded crow in only 9%. This indicates a close connection of this bird of prey with raven's nests on power lines. In 1994 only two cases of occupation of hooded crow were recorded.
Saker falcon occupies the nest of a raven after the nest has been built, which is significant from the aspect of preserving the population of raven's on power lines, where raven is the main nest builder. By installing artificial nesting platforms and boxes on high-voltage power poles and by developing cooperation with power companies, saker falcon can be weaned from exclusive dependence on occupation of raven's nests.
Saker falcon regularly grabs prey from different birds that occasionally or constantly frequent power lines. Prey is grabbed from other species of falcon which nest on power poles or nearby (kestrel Falco tinnunculus and hobby Falco subbuteo), from nesters of the crow family (hooded crow Corvus corone cornix and jackdaw Corvus monedula), and from species which migrate over the area (marsh harrier Circus aeruginosus and hen harrier Circus cyaneus). The male saker falcon grabs prey from buzzard (Buteo buteo) during winter and early spring, and very occasionally tries to do so from ravens (Corvus corax) as well.
A pair of saker falcons grabbed prey from five different species of birds on a power line in Donji Srem in the course of a year. Of a total of 40 cases of prey grabbing during the period of January-December, as many as 70% were from kestrel. At the beginning of the reproduction period, the observed saker falcon pair did not hunt living prey very much, but rather focused on prey grabbing. In winter and early spring, grabbing was performed predominantly by the male, while after May it was sometimes done by the female, too. Results of research on the feeding ecology of saker falcon in Srem and Central Europe indicate that a large portion of the grabbed prey consists of common vole (Microtus arvalis).Пар степског сокола (Falco cherrug) на далеководу у Доњем Срему у току једне године отимао је плен од 5 различитих врста птица.Од укупно 40 случајева преотимања плена у периоду јануардецембар, чак 70% се односило на ветрушку (Falco tinnunculus).Преотимање је у периоду зиме и раног пролећа вршио пре свега мужјак, а од маја повремено и женка.Знатан удео у преотетом плену чини пољска волухарица (Microtus arvalis).
Fig. 1 .
Fig. 1. Yearly dynamics of prey grabbing from other species of birds and period of significant participation of domestic pigeons in the feeding ecology of saker falcon (Falco cherrug) on a power line near Karlovčić, Srem area, 1994.
ThE PROvINCE OF vOJvODINA (SERBIA)
Provincial Secretariat for Environmental Protection and Sustainable Development, 21000 Novi Sad, Serbia .
Table 2 .
Analysis of seasonal prey grabbing by a saker falcon (Falco cherrug) pair from other species of birds on power lines nearKarlovčić, Srem area, 1994.
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Domain: Environmental Science Biology
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Susceptibility to Melampsora Leaf Rust of Poplar Clones From Diverse Genetic Backgrounds: Effects on Photochemistry and Water Relations
The selection of resistant genotypes is the most appropriate approach in the prevention of the reduction of biomass and mortality caused by rust infection in poplar plantations. Thus, it is pertinent that we improve our understanding of the consequences that this fungal disease has on leaf physiology. Here, we studied the susceptibility to Melampsora rust in three different poplar clones of commercial interest: Lux clone – Populus deltoides Batr. (cottonwood) and Luisa Avanzo and Adige clones – both Populus × canadensis Mönch. The most susceptible clone to the infection was L. Avanzo whereas Lux and especially Adige were only slightly affected. The propagation of the disease was very rapid in L. Avanzo; their leaves showed a high incidence and severity of the disease in early and advanced stages of infection as was clearly evidenced by the degree of infection. Infected leaves of L. Avanzo were shown to have drought impaired water relations during summer as reflected by the marked decline in the relative water content (RWC). Chlorophyll fluorescence imaging revealed heterogeneity of the effect of the pathogen in the leaves, and areas with pustules showed low maximum quantum yield (Fv/Fm) and PSII quantum yield (ΦPSII) values, indicative of strong photoinhibition. In L. Avanzo, with a greater pustule density, rust provoked a decline in whole leaf photochemistry as indicated by Fv/Fm and photochemical reflectance index (PRI) results. Leaf structural parameters were not affected by the disease but results in L. Avanzo and Lux showed higher leaf mass per area (LMA) and higher leaf density (D) indicating an adaptation to increasing summer drought. In all clones, the effect of the pathogen was reflected in lower leaf chlorophyll content.
Melampsora spp.depends on the host tissues for their development, proliferation and reproduction. The fungus invades host cells, depleting their sugars and nutrients, thereby producing a decrease in stem growth, premature defoliation and predisposition to attack by insects and secondary pathogens, such as Cytospora or Dothichiza, and in some cases, it may cause plant death (Newcombe et al., 2001;Wang & van der Kamp, 1992). The life cycle of some Melampsora species is completed by infection of poplars and coniferous hosts (Feau et al., 2007). During the summer, urediniospores are produced in yellow-orange pustules (uredinia) on the underside of Populus spp.leaves. These spores serve as the inoculum to spread the disease across Populus spp. Teliospores are produced in late summer and overwinter on the underside of dead poplar leaves on the ground (Torres, 1998). The intensive cultivation of poplars due to their high growth potential, their use in renewable energy systems (Scholz & Ellerbrock, 2002), their capacity to capture atmospheric CO 2 (Isebrand & Karnosky, 2001) and to remediate metal pollution in soils and water (Fernàndez et al., 2012) has made them susceptible to many pathogens (Gérard et al., 2006). Moreover, stands are even-aged and monoclonal and the number of cultivars planted in a given region is limited. Simplistic monoclonal and intensive breeding, combined with the obviously high adaptive potential of Melampsora spp., have led to successive resistance breakdowns. As they do not have a secondary line of defence, most cultivars have been exposed to the epidemic spread of newly emerged virulent strains of the fungus (Frey et al., 2005).
In order to prevent the harmful effects on plant productivity brought about by infection by Melampsora spp., the cultivation of hybrids for their resistance to pathogens and for their wood quality highly regarded by growers is a standard practice (Newcombe et al., 1996). Lux clone -Populus deltoides Batr.(cottonwood) and Luisa Avanzo and Adige clones -both Populus × canadensis Mönch.were selected on the basis of their relevance as crops and because they respond differently to biotic and abiotic constraints. L. Avanzo and Adige are highly resistant to mosaic virus infections, whereas Lux is sensitive; the latter is tolerant to infection by the fungus Marsonnina brunnea, whereas Adige has a low tolerance and L. Avanzo is sensitive. L. Avanzo can withstand windy conditions, while Adige is less capable of doing so and Lux is sensitive to such conditions (Facciotto & Frison, 1999).
The functional characteristics of leaves can be modified by environmental and biotic stresses (Niinemets, 2001). Direct measurements of photosynthetic parameters and other aspects of primary metabolism have not yet been reported for poplars with pathogen infections (Major et al., 2010). Foliar pathogens can reduce net CO 2 assimilation (Hajji et al., 2009) by affecting stomatal conductance (Pinkard & Mohammed, 2006) and chlorophyll decay (Holloway et al., 1992). Furthermore, the effects of foliar pathogens on plant water relations and the consequences for the crop water-use efficiency (WUE) have recently been reviewed (Grimmer et al., 2012).
The aim of this study was to determine the susceptibility to Melampsora infection in the selected poplar clones. The analysis of rust incidence (number of infected leaves per tree and number of infected trees per genotype) and severity (expressed as the degree of infection in an individual in function of the percentage of affected leaf area) allowed for the observation of the propagation of the disease in the different clones.
Another aim was to characterize the effect of the disease on physiological parameters related to photosynthetic processes and to leaf water relations in early and advanced stages of the infection during the summer. The use of chlorophyll fluorescence imaging and leaf reflectance indices allowed for a non-destructive evaluation. Chlorophyll fluorescence imaging has been used during biotic stress (Pineda et al., 2011) and provides information on electron transport characteristics both in whole leaves and in specific leaf areas affected by fungal infection.
Determination of the susceptibility to leaf rust and its effect on physiology can help to identify the most resistant clones to this infection and the most suitable ones for multiple ecological services.
Experimental Site and Plant Material
Poplar clones were selected from a collection of fifty clones established since 2001 in the IBAF-Institute experimental field near Rome in the Tevere valley. This area has an alluvial soil type. The climate is Mediterranean with cold winters, cool wet springs and autumns, and hot dry summers. The area registers a mean annual temperature of 13-14 ºC, and an annual rainfall of 500-700 mm. Three poplar clones from different genetic backgrounds were used: Lux clone -Populus deltoides Batr.(cottonwood), which was successfully introduced into Europe from North America; and Luisa Avanzo and Adige clones -both Populus × canadensis Mönch., a hybrid from Populus deltoides and Populus nigra. Populus nigra, is native European taxon. All clones were female. Each clone was represented by 10-30 individuals in single plots.
Sampling
Sampling for the purpose of physiological measurements was performed in mid-July (a period when the Melampsora spp.uredinia are produced on the Populus leaves) and mid-September (after the disease has been able to develop over the summer). Climatic parameters during the study are shown in Table 1. June was relatively humid with elevated temperatures with respect to the period of sampling in July which was in contrast dry with even higher temperatures. Conditions in August were similar to those of July. In September, just before sampling, the temperatures decreased while precipitation increased markedly. All measurements were recorded on 3 randomly selected 8-years old individuals per clone in healthy leaves (control) and leaves that showed Melampsora sp.pustules (infected leaves). In both cases, leaves were fully expanded, South-oriented and collected at 2 to 4 m height from the base of the tree. Taking into account the different degrees of infection in each leaf throughout the whole plant, measurements on the infected leaves were recorded on those showing the average degree of severity of the measured clone. Total chlorophyll content and radiometric measurements were obtained at the IBAF-Institute experimental fields at 13.30 -16.30h local time. Leaf structural, hydric and imaging fluorescence measurements were analysed in the IBAF laboratories.
Incidence and Severity of Melampsora rust
We evaluated the incidence of the pathogen first on the basis of the percentage of individuals of each clone showing visual symptoms of the disease. Then the incidence in each individual was evaluated as a percentage of the number of infected leaves (presence of pustules). Fifteen leaves were selected at random in an individual and in total 3 individuals per clone were evaluated. Severity was considered as the degree of the infection in an individual. This parameter was determined in 3 plants per clone. For each individual, we examined a total of 24 leaves from different orientations and plant height to make sampling as representative as possible. Three leaves each were taken from 8 different points on the tree: facing North, South, East and West, and from the base of the tree (2 m from the soil surface) and from the top of the tree. The degree of severity in each leaf was established from the percentage of leaf area covered by Melampsora rust, as shown in Table 2. For each clone, different severity degrees were established by calculating the percentage of leaves that exhibited each degree of severity in each individual. The different percentages of affected area correspond to a degree of severity.
Chlorophyll Fluorescence Imaging
Chlorophyll fluorescence was recorded with the pulse-amplitude-modulated chlorophyll fluorometer Imaging-PAM (MICRO-version (Walz, Effeltrich, Germany)) operated using the Imaging Win v.2.21d (Heinz Walz) software. Chlorophyll parameters were obtained for one squared image area of 26 x 34 mm 2 per leaf. After 40 minutes dark-adaptation, minimum (F o ) and maximum fluorescence (Fm), and maximum quantum yield of PSII photochemistry (F v /F m ) (equivalent to (F m -F o )/F m ) were obtained. Subsequently, the light-adapted components of chlorophyll fluorescence (minimum fluorescence yield (F' o ), maximum fluorescence yield (F' m ) and quantum yield of photosystem II (Φ PSII ; equivalent to (F' m -F)/F' m )) (Genty et al., 1989) were obtained after five minutes of light adaptation with an incident actinic light of 300 μmol m -2 s -1 (Pietrini et al., 2010). Measurements were performed in 3 controls and in 3 infected leaves for 3 individuals of each clone. They were also performed on pustules and in non-affected areas of infected leaves.
Relative Water Content and Leaf Structural Parameters
For all analyses, relative water content (RWC) was measured at midday on 3 infected and 3 control leaves of the 3 selected plants per clone. RWC was calculated as [(M f -M d )/(M fs -M d )•100], with M f being plant fresh mass; M fs , plant fresh saturated mass (after rehydrating samples for 24h in the dark at 4 ºC); and M d , plant dry mass (after oven-drying samples at 65 ºC until a constant weight was achieved). Leaf area (LA) was determined with a CI 2003 Laser Leaf Area Meter (CI-203) (CID, Inc., Camas, WA 98607, USA). Leaf mass per area (LMA) was calculated as M d /LA and its components Thickness and Density were calculated as M f /LA and [(M d /M f )•100] respectively (Dijkstra, 1989).
Leaf reflectance Indices and Total Chlorophyll Content
Leaf reflectance was measured at midday in situ on 3 infected and 3 control South-oriented leaves for 3 individuals of each clone (the same plants for all analyses) with a portable spectral analysis system with artificial light (USB4000, Oceanoptics), operated with the Spectrasuite (Oceanoptics) software. One measurement was calculated as an integration of ten scans (integration time 50 ms). The photochemical reflectance index (PRI) and the water index (WI) were derived from the spectra. PRI was calculated as [(R 531 -R 570 )/(R 531 +R 570 )], where R n is the reflectance at n nm (Peñuelas et al., 1995). WI was calculated as (R 900 /R 970 ), where reflectance at 970 nm is associated with water absorption, and 900 nm is a reference wavelength (Peñuelas et al., 1993). For methodological reasons PRI and WI results obtained in July or September could not be compared (Peñuelas et al., 1995). Total chlorophyll content was measured with a leaf chlorophyll meter (SPAD, Minolta, Osaka, Japan) on 8 infected and 8 control leaves of 3 plants per clone.
Statistical Analysis
All statistical procedures were performed using Statgraphics for Windows (Statgraphics v. 15.2.14, Statpoint Inc., Virginia, USA). Analysis of variance (ANOVA) was used to test the main effects against appropriate error terms between treatments (leaf types: control, infected), clones and time (July, September) on the measured parameters.
A multiple comparison test of the means was carried out using the Tukey HSD post-hoc test. Statistical significance was set at p ≤ 0.05.
Incidence and Severity of the Infection by Melampsora sp.
In L. Avanzo the incidence of the infection originated by Melampsora sp.expressed as a percentage of individuals showing symptoms of infection was approximately twice as much as in Lux and Adige individuals during July (Table 3). In September, all individuals of L. Avanzo and Lux were infected while only 20% of the individuals of Adige showed symptoms. In July, the incidence of infection expressed as percentage of affected leaves was more than double in L. Avanzo that of Lux and Adige. In September, all leaves of L. Avanzo were infected; in Lux most leaves showed signs of the disease and in Adige only around a 16% of leaves showed symptoms. In July, 61% of L. Avanzo leaves were infected and presented a degree of severity between 2 and 3 while in September, 86% of leaves were infected and showed a degree of severity between 4 and 5 (Table 4). In July, 83% of Lux leaves were infected, which decreased to 68% in September. The degree of severity ranged from between 1 and 1.5. Most Adige leaves were infected during the study period with a degree of severity of 1. The incidence is expressed as a percentage of infected individuals and as a percentage of the number of infected leaves of 45 randomly selected leaves of 3 infected individuals (15 per individual) per clone expressed as mean ± SE. The degree of severity was determined for each clone in 3 plants and in 24 leaves (three leaves each facing North, South, East and West, and at two different heights: one at the base (2m from the soil surface) and the other at the top of the individuals). Values are mean ± SE.
Relative Water Content (RWC) and Water Index (WI)
No differences in RWC between the control and infected leaves were observed throughout the study except in the infected leaves of Lux, which showed a significant decrease in July (Figure 1
Leaf Structural Parameters
Leaf area (LA) in Lux was higher than that of the other clones (Table 5) and no differences between the control and infected leaves were observed in any of the measurements. L. Avanzo showed the greatest reduction in LA throughout the summer. Control leaves of Lux showed the highest leaf mass per area (LMA) in July, and control leaves of L. Avanzo showed the lowest LMA. For each clone, no differences in LMA were observed between the control and infected leaves. Control leaves of Lux showed the highest leaf thickness (T) in July, followed by the control leaves of Adige and then by those of L. Avanzo. Infected leaves of Lux had a lower T than did the control leaves. In September, similar values were observed in the control leaves of all three clones, whereas infected leaves in Lux and Adige showed a higher T than those of L. Avanzo. In July, leaf density (D) was highest in Lux and lowest in L. Avanzo. For each clone, the control and infected leaves showed similar D values. Comparing September with July, increases in D were observed in both kinds of leaf except for infected leaves in Adige. The number of replicates was 3 leaves per types of leaf, clone and time. Significant differences (p < 0.05) between infected and control leaves for each clone are expressed by different lower-case letters (a,b). Differences between the controls or between infected leaves of the different clones are expressed by different upper-case letters (A, B). Differences between times are expressed by different Greek letters (α, β). Values are mean ± SE.
Chlorophyll Fluorescence, Photochemical Reflectance Index (PRI) and Total Chlorophyll Content
In all studied clones, the chlorophyll fluorescence images obtained for Fv/Fm and Φ PSII (Figure 2) revealed lower values on the areas containing fungal pustules than in areas free of the pathogen fructification. Values obtained for Fv/Fm for each clone were between 0.75-0.85(Figure 3 a, b). In L. Avanzo, infected leaves showed a significant decline in Fv/Fm values from July to September with lower values than the control leaves, while Lux showed low values in September both in control and infected leaves. L. Avanzo showed the lowest PRI values in both July and September compared to the other clones, especially in the infected leaves (Figure 3c, d). Lux and Adige did not show differences between the control and infected leaves but showed lower values in September. In July, the total chlorophyll content of the infected leaves was lower than in the control leaves in L. Avanzo and Adige (Figure 3 e, f). L. Avanzo showed lower values than Lux and Adige. In September, Lux and L. Avanzo showed a decline in total chlorophyll content in infected leaves compared to the control leaves. In Adige, values were higher in comparison to those of L. Avanzo and Lux.
Discussion
The evaluation of the effects of leaf rust on the physiological responses of poplar clones such as monitoring changes in the photochemistry processes and water relations affecting whole leaf photosynthesis can help to determine their potential in productivity programs (Widin & Schipper, 1980).
In our study, we detected significant differences in the susceptibility to rust infection and its effect on the physiology of the selected clones during the development of their leaves.
Plants did not show signs of infection until July, thereafter L. Avanzo showed the highest level of incidence of Melampsora rust. In July, most of their individuals were infected and the percentage of affected leaves was more than double with respect to Lux and Adige. In July, the degree of severity in L. Avanzo was mild to moderate, while in September, this clone was severely affected. Other studies reported differences in the susceptibility to rust of L. Avanzo. Artificially inoculated leaf discs of two strains of M. larici-populina showed that this clone was highly resistant to the E1 strain and highly susceptible to E3 (Giorcelli et al., 1996). Frey and Pinon (1997) also observed a wide range of reactions in L. Avanzo, ranging from susceptibility to resistance, depending on the isolate. Lux, a clone considered to be resistant to the E1 and E3 strains of M. larici-populina (Giorcelli et al., 1996) showed moderate susceptibility in this study. In July, 18% of leaves presented traces of the infection or were not infected. In September, the percentage of infection had increased to 73%, while the severity remained at the same degree. In contrast to Facciotto and Frison (1999), Adige was the most resistant clone with a low incidence and degree of severity in July, and registering decreases in both parameters in September.
Productivity in fast growing species like poplars depends on water availability (Tschaplinski et al., 1994).
Relative water content (RWC) and water index (WI) determinations provided information about poplar water status. In L. Avanzo, the higher RWC and WI of infected leaves in July with respect to the infected leaves of the other clones would favour the proliferation of Melampsora sp.infection in its early stages since high humidity promotes rust spore germination (Agrios, 2005), as reported for poplars (Coyle et al., 2006). Most leaves, both healthy and infected, showed elevated RWC indicating a sustained capacity to retain water during the summer. However, in L. Avanzo, RWC declined below 80% in infected leaves at the end of the summer which could be related to a possible increase in respiration and a higher limitation in photosynthetic activity (González & González-Vilar, 2001). In Lux, infected leaves also showed lower RWC than control leaves in July but values did not fall markedly during summer drought. Foliar disease has been reported to impair stomatal closure in the dark and stomatal opening in the light thereby affecting plant CO 2 assimilation and the ability to conserve water (Grimmer et al., 2012). In Adige, results for RWC and WI showed that both in July and September there was a higher susceptibility to water stress with respect to the other clones, however, no direct effects on these parameters as a result of infection were observed.
Leaf structural characteristics such as higher total and individual leaf area are related to higher productivity rates in several Populus clones (Marron, 2005). The leaf mass per area (LMA) and their two components -density (D) and thickness (T) -have been used to indicate poplar response to water stress and photosynthetic capacity.
An increase in D is associated with thicker cell walls while an increase in T accounts for additional mesophyll layers (Niinemets, 2001). In spite of disease infection, no differences in D, LMA and LA were observed between control and infected clones. In September, increased sclerophylly (increases in D and LMA) and reductions in leaf transpiratory area (LA) (mainly in L. Avanzo) indicate that structural adaptations occur during summer drought.
Rust infection was not observed as having an effect on morphological parameters which contrasts with studies on defence mechanisms during pathogen infection in Populus spp. These report the strengthening of cell walls through lignin deposition (Duplessis et al., 2009) which would imply an increase in cell density (D). We only observed a decline in T in the early stages of Melampsora rust in Lux.
Chlorophyll fluorescence imaging provides a sensitive method for determining the impact of fungal pathogens on the photosynthetic metabolism of their hosts (Scholes & Rolfe, 2009). Our results showed differences between pustule (rust-affected) and nearby non-affected areas of infected leaves for each clone. The leaf zones where the fungus developed fructification had lower potential efficiency of PSII photochemistry (Fv/Fm) and PSII quantum yield (Φ PSII ). In fact, Fv/Fm values lower than 0.75 indicated a strong photoinhibition (Demmig-Adams & Adams, 1993). The distribution of the mycelium of this pathogen is heterogeneous in leaves and as demonstrated by Alves et al. (2011) in eucalyptus infected by the rust fungus Puccinia psidii, photosynthesis was only reduced in diseased leaf area. The effect of infection on whole leaves depends on the ratio between infected and non-infected leaf areas. Highest pustule density was observed for L. Avanzo, followed by Lux and Adige. By September, when the disease had progressed strongly in L. Avanzo (all individuals and 100% of leaves were infected mostly with a high-severity degree of infection where leaf global Fv/Fm values were significantly lower than those of the other clones. Lux showed lower global Fv/Fm values in September but no effects from the infection. However, whole-leaf Fv/Fm values (between 0.75 and 0.85) in all cases indicate that the clones were not subjected to a strong photoinhibition as a result of infection. In fact, although it can limit carbon assimilation, this disease is seldom responsible for tree death (Feau et al., 2007). The effect of the pathogen on L. Avanzo photochemistry was also apparent at high incident light intensities in the field (about 1100 μmol•m -2 •s -1 ) with the lowest PRI values in both July and September compared to the other clones, PRI is related to PSII efficiency (Peñuelas et al., 1995). In general, a decline in total chlorophyll content was observed in September, especially in infected leaves of L. Avanzo and Lux in accordance with Bertamini et al. (2005). In Adige, the effect of the pathogen was not detected at the end of the summer which is in accordance with the observed reduction in the proliferation of the fungus.
Determination of susceptibility to Melampsora rust infection can help to identify the most resistant Populus clones. In this study, L. Avanzo was the most susceptible clone to rust showing a high incidence and increased severity during the summer, affecting photochemical processes, chlorophyll content and water relations. Lux and especially Adige were more tolerant to the pathogen and showed only traces of infection and slight physiological Figure 1 a, b) Relative water content (RWC) and c,d) the radiometric index WI during the study in infected and control leaves of the different Populus spp.clones. The number of replicates was 3 leaves per types of leaf, clone and time. Significant differences (p < 0.05) between infected and control leaves for each clone are expressed by different lower-case letters (a, b). Differences between controls or between infected leaves of the different clones are expressed by different upper-case letters (A, B). Differences between times are expressed by different Greek letters (α, β). Values are mean ± SE
Figure 2 .
Figure 2. Chlorophyll fluorescence images of maximum quantum yield (Fv/Fm) in a dark-adapted state and quantum yield of photosystem II photochemistry (Φ PSII ), at steady-state with actinic illumination of 300 µE•m -2 •s -1 . Measurements were recorded in pustules (red circles) or not-affected areas (white circles) of infected leaves of the studied clones. The false colour code displayed at the top of the image ranges from 0 (black) to 1 (pink)
Table 1 .
Climatological data from the meteorological station closest to the site of the study. The first sampling corresponds to July 11 th and the second sampling to the Sepember 11 th
Table 2 .
Degrees of severity of the foliar infection caused by Melampsora sp.
Table 3 .
Incidence of Melampsora infection in July and September
Table 4 .
Percentage of infected leaves showing the different degrees of severity in L. Avanzo, Lux and Adige clones
Table 5 .
Variations in structural parameters during the study in infected and control leaves of the different Populus spp.clones: leaf area (LA, cm -2 ), leaf mass per area (LMA, gM d •m -2 ), leaf thickness (T, g•m -2 ) and leaf density (D, %)
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Domain: Environmental Science Biology
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Composition of bat assemblages ( Mammalia : Chiroptera ) in tropical riparian forests
Various studies have focused on the richness and abundance of bats in tropical forests and how the composition of these forests affects bat assemblages, but there are few studies on the relationship of bats with riparian forests. The aim of this study was to ascertain the differences among bat assemblages of three riparian forest areas of the Tinguá region, state of Rio de Janeiro, Brazil. These areas were: I) an agricultural area (Takume); II) a reforested area in primary succession (Canavarro); and III) an area with late secondary vegetation (Tinguá Biological Reserve). Assessments of bat species composition in these areas may shed light on how anthropogenic modifications in riparian forests can influence local bat assemblages. Bats were captured with mist nets during 72 sampling nights. Total bat abundance was 1,511 individuals in 26 species. The three areas differed in their species composition. The Tinguá Biological Reserve was the richest area, Canavarro presented the lowest diversity and the highest abundance of individuals, and the evenness index was highest in Takume. The differences found in the composition and ecological indices indicate that bat assemblages have distinct characteristics in the three areas studied, with varied degrees of transformation and anthropization.
Various studies have focused on the richness and abundance of bats in tropical forests and how the composition of these forests affect bat assemblages (ESTRADA et al. 1993, ZANON & REIS 2007, BOBROWIEC & GRIBEL 2010). However, there are few studies on the relationship of bats with riparian forests. The latter are relevant ecosystems in several respects, particularly for the conservation of water resources and biotic communities. They are found bordering rivers, streams and ponds, serving primarily as filters of pollutants and sediments that would otherwise reach the water (MARTINS 2001, LIMA & ZAKIA 2001). Riparian forests also provide shelter and food for animals, and serve as ecological corridors, thus enabling maintenance of biological diversity (MARTINS 2001, LIMA & ZAKIA 2001, ARRIAGA-FLORES et al. 2012). However, continued intervention and modifications of riparian forests and nearby water resources cause environmental damage, which in turn threaten the health and continuity of watercourses (MARTINS 2001, LIMA & ZAKIA 2001, ARRIAGA-FLORES et al. 2012).
The preservation and recuperation of riparian forests has been a matter of concern to the government and nongovernmental organizations in Brazil, because riparian forest strips are so important for the conservation and restoration of the biodiversity of many groups of animals and plants. In the same sense, trying to protect these forest areas from human intervention, the Brazilian government has made it a federal offense to tamper with them (Law 12,651 of 2012). In the state of Rio de Janeiro, a region of Atlantic forest remnant was delimited and converted into a protected area known as Tinguá Biological Reserve with the purpose of preserving the local water resources. This conservation unit has a surrounding buffer zone, where human activities are also subject to specific restrictions to minimize impact on the reserve (MMA 2006). Despite the legal restrictions on the buffer zone and riparian forests as a whole, riparian forests are still impacted, for instance when natural vegetation is removed for the cultivation of crops and establishment of pastures.
Several factors can contribute to the relevance of riparian forests in structuring bat assemblages. The floristic composition of riparian forests and their conservation status are directly related to the fauna of an area (HOLLOWAY & BARCLAY 2000, LIMA & ZAKIA 2001, CARDOSO-LEITE et al. 2004, CAMARGO et al. 2009, LOURENÇO et al. 2010a, b, COSTA et al. 2011, 2012). The different uses of riparian forests can partly determine their biodiversity and thus their local bat fauna (FENTON 1997, WILLIAMS et al. 2006). An assessment of the bat species composition in different types of riparian forest sheds light on how anthropogenic modifications ZOOLOGIA 31 (4): 361-369, August, 2014 can influence the local bat assemblage. Therefore, the aim of this study was to analyze whether there are differences in the bat assemblages of three riparian forest areas in the Tinguá region, state of Rio de Janeiro, Brazil: two areas inside the buffer zone of the Tinguá Biological Reserve (an agricultural and a reforested area in primary succession stage), and a third area inside the reserve characterized by late secondary vegetation. We expected to find the greatest richness, abundance and diversity of bats in the late secondary vegetation area, the least impacted area, followed by the reforested area. This hypothesis is based on prior knowledge that in environments where human pressure is stronger, species richness and diversity are lower (ODUM & BARRETT 2006, REIS et al. 2006).
MATERIAL AND METHODS
This research was carried out in the Tinguá region, in the extreme northeast of the municipality of Nova Iguaçu, Rio de Janeiro, Brazil. Three areas within the limits of the Tinguá Biological Reserve and its buffer zone were investigated, with distances between areas varying from 3.10 km to 4.39 km (Fig. 1) and elevation ranging from 65 to 140 m above sea level.
Area 2. This area is located along the Ana Felícia River and was designated Canavarro. It is characterized as a reforested area in primary succession and is also located in the reserve's buffer zone (22°36'50.69"S,043°24'47.17"W). The vegetation in Canavarro consists mainly of Guinea grass, though Solanum paniculatum L., Solanum lycorcapum St. Hil., Cecropia sp. and Trema micanthra (L.) Blume are also present.
Area 3. This area, characterized by late secondary vegetation, was designated Bioreserve. It is within the reserve (22°34'57.4"S,043°26'15.9"W)and contains vegetation resulting from natural succession after total or partial suppression of the primary vegetation by human intervention or natural causes. It contains an arboreal stratum with Ficus spp.and an understory with predominance of Piper spp. The sampling points were located near the banks of Tinguá River and nearby watercourses, just outside the reserve's administrative building, on a trail starting at a workers' lodging (CEDAE's, the state water and sewer company) at the Macuco Dam, where there are banana, cajá and lemon (Citrus limonum Risso) trees.
Sampling was conducted monthly, one night in each of the areas, on consecutive days, totaling three nights per month and 72 nights in two years, from May 2011 to April 2013. Bats were captured with mist nets (usually ten mist nets measuring 12 x 3 m and 20 mm mesh) extended at ground level, which were opened at dusk and closed just after dawn. Sampling was performed regardless of the weather. The nets were placed in open areas, in the understory, on river banks and over water.
Bats were identified in the field using the descriptions of VIZOTTO & TADDEI (1973), GARDNER (2007) and DIAS & PERACCHI (2008), tagged with collars and then released. Some specimens that could not be definitely identified in the field were taken to the laboratory for confirmation and deposited in the Adriano Lucio Peracchi collection located at the Biology Institute of Federal Rural University of Rio de Janeiro, Seropédica, state of Rio de Janeiro (Appendix 1). These specimens were collected under license from SISBIO/ICMBio number 28064-2. The sampling effort was calculated according to STRAUBE & BIANCONI (2002) and the Kruskal-Wallis test was used to assess possible differences in sampling effort between areas. The Margalef richness and Simpson diversity indices (1-D) and Pielou's evenness index were calculated for each area (MAGURRAN 2004). For pairwise comparison of the areas' ecological indices, randomized bootstrap was used.
The species accumulation curves (SOBERÓN & LLORENTE 1993) for each area were plotted by randomization (100 iterations) based on the total number of captures and the first-order jackknife (Jackknife-1) estimator (COWELL & CODDINGTON 1994, HELLMANN & FOWLER 1999) was calculated, both with the EstimateS software (COLWELL 2009).
To assess whether there are differences in the composition of bat species between areas, the Kruskal-Wallis test was used. One-way analysis of similarity (ANOSIM) was also used to assess possible differences among the three bat assemblages studied. ANOSIM was based on the use of Bray-Curtis dissimilarity measures between samples (areas) and within samples (nights). This test produces an R-statistic that varies from -1 to 1. The groups are considered to differ more as the statistic becomes more positive (CLARKE 1993). Species turnover was represented using Whittaker's beta-diversity index and based on the presence/absence of each species in each area. The Chisquare test was applied to compare species abundance among the areas. Ecological indices were calculated and statistical analyses were performed with the PAST version 1.44 software (HAMMER et al. 2001).
RESULTS
The sampling effort was similar in the three areas (H = 1.139, p = 0.566) and comprised a total of 268,473 m 2* h. Twentysix bat species were captured (Table I), with a significant difference in species richness between Takume and Bioreserve. The Bioreserve was the richest area, also presenting the greatest Margalef richness. The Simpson diversity index was significantly lower in the Canavarro area than in the other areas. In turn, Pielou's evenness index was higher in the Takume area than in the other two (Table II).
Species accumulation curves and Jackknife-1 estimator of species richness showed that richness was the highest in Takume. In Canavarro, the curve showed signs of leveling, while in the Bioreserve area the curve was ascending (Figs 2-5). In Canavarro, the richness found represented 85% of its estimated richness while in the Bioreserve this value was 73% (Table II). The addition of species occurred until the 10th sampling night (36,150 m²*h -141 individuals captured) in the Takume area, until the 19 th in Canavarro (71,337 m²*h -527 individuals) and until the 18 th in the Bioreserve (70,353 m²*h -301 individuals).
The three areas differed in their composition of assemblages (H = 6.206, p = 0.045) due to differentiation between the Bioreserve and Takume (p = 0.017). The analysis of similar-ity showed a significant difference among the three areas (R = 0.292, p <0.001), although R was low, indicating low similarity between areas. Canavarro and Bioreserve differed the most (R = 0.427), followed by Takume and Bioreserve (R = 0.313) and lastly Canavarro and Takume (R = 0.144). Whittaker's beta di- versity index was highest between Canavarro and Bioreserve (0.33), followed by the Takume and Bioreserve (0.30) and lastly Takume and Canavarro (0.12). The Bioreserve contributed 84.61% to the regional richness. The total number of specimens captured was 1,511, ranging from 399 to 623 specimens in each area, with significant differences among areas. The most abundant species in the entire region was Artibeus lituratus (Olfers, 1818) (27.53%), followed by Carollia perspicillata (Linnaeus, 1758) (25.41%) and Sturnira lilium (É. Geoffroy, 1810) (15.95%). The dominant spe-cies in each area were S. lilium (35.95%) in Canavarro and C. perspicillata in Takume (29.45%) and Bioreserve (32.83%). The Bioreserve had eight exclusive species, Canavarro had two and Takume had none. Seven species were represented by only one individual in the samples, five of them in the Bioreserve and two in Canavarro. With one exception, Myotis riparius Handley, 1960, (Vespertilionidae) all captured species belong to the family Phyllostomidae. There was a difference in the abundance of some species among the three areas (Table I). This difference was significant for nine species.
DISCUSSION
The riparian forests studied showed high richness and abundance of bats, as reported in some other studies (FLEMING et al. 1972, BORDIGNON 2006, CRUZ et al. 2007, CAMARGO et al. 2009, ARRIAGA-FLORES et al. 2012). Likewise, the composition of bat species followed the same pattern found in other Neotropical areas, with the predominance of Phyllostomidae, a few dominant species and many rare species (ESTRADA et al. 1993, STEVENS & WILLIG 2002, ZANON & REIS 2007, BOBROWIEC & GRIBEL 2010, ARRIAGA-FLORES et al. 2012).
The results of this study emphasize the relevance of these environments to bats of the family Phyllostomidae. This family has the largest number of species in the Neotropics, and includes species with varied feeding habits and many frugivorous species (GARDNER 2007), which were prevalent in our samples (see KALKO et al. 1996). The few studies focusing on riparian forest areas in the Neotropics have found high abundance and richness of frugivorous and insectivorous bats (CAMARGO et al. 2009, LOURENÇO et al. 2010a, b, GALINDO-GONZALES & SOSA 2003, ARRIAGA-FLORES et al. 2012). The high richness and abundance of insectivorous bats in these environments is mainly related to the availability of water resources and the abundance of insects (LOURENÇO et al. 2010a, b, COSTA et al. 2012). The richness of insectivores can vary according to the presence or absence of vegetation cover (COSTA et al. 2012), while frugivorous species are more associated with the surrounding vegetation (GALINDO-GONZALES & SOSA 2003, AVILA-CABADILLA et al. 2012). Although our sampling method favored the capture of Phyllostomidae bats (SIMMONS & VOSS 1998), open nets over the water were not efficient in capturing insectivores of other families, such as Mollossidae and Vespertilionidae, as also demonstrated by other studies (LOURENÇO et al. 2010a,b, COSTA et al. 2012).
Our sampling strategy, including three areas of riparian forest that have different vegetation, serves to increase knowledge of the diversity of the region. The greater the variety of environments sampled, the greater the chances of finding species not previously recorded in a particular region (STEVENS & WILLIG 2002, BERGALLO et al. 2003). Although Takume and Canavarro are in permanent preservation areas of riparian forest, and also in the buffer zone of the reserve, the local richness of these areas contributed little to the regional richness, with the Bioreserve contributing the most.
This study added six new records to the list of bats found in the reserve: Artibeus planirostris (Spix, 1823), Micronycteris hirsuta (Peters, 1869), Micronycteris minuta (Gervais, 1856), Phylloderma stenops Peters, 1865, Phyllostomus hastatus (Pallas, 1767) and Chiroderma villosum Peters, 1960. Additionally, two other species, captured in the buffer zone of the Tinguá Biological Reserve, can be added to the list, Chiroderma doriae Thomas, 1891 and Chrotopterus auritus (Peters, 1856). This increases the number of bat species recorded for the conservation unit from 28 (DIAS & PERACCHI 2008) to 36. Since bats tend to be better represented in riparian forest than in the surrounding areas, (SHERWIN et al. 2000, ROGERS et al. 2006, CAMARGO et al. 2009), our sampling strategy played a decisive role in finding the new records.
However, even though species richness in the Bioreserve was high, the highest diversity index obtained in this study was not for this area. This results from a number of species being represented by a few individuals and the high dominance of C. perspicillata. The highest diversity index was calculated for the Takume area, although it presented the lowest species richness. In Canavarro, dominance was also high, with most individuals belonging to only three species.
It is noteworthy that among the three areas, Takume was the only one that was detected in the accumulation curve stabilization and reached the maximum number of species according to the Jackknife-1 estimator. This probably results from the agricultural use of this area, which does not attract some bat species and mainly favors those that are adapted to anthropogenic environments, such as A. lituratus and C. perspicillata (BONNACORSO & GUSH 1987, ESTRADA & COATES-ESTRADA 2002, GALLO et al. 2008). In the Bioreserve, the Jackknife-1 estimated 30 species, but this number is underestimated in relation to the richness now known, 34 species according to DIAS & PERACCHI ( 2008) and this study.
The differences in the assemblages of bats between areas were expected, especially between the agricultural area and the Bioreserve. The beta diversity between the areas reveals the specificity of habitats in the Bioreserve and a possible limitation on the dispersion of some species. When we analyzed the species composition between this and the other areas, it appeared that some species found exclusively in the Bioreserve are more strongly associated with preserved areas, as it is the case of species in the subfamily Phyllostominae (FENTON et al. 1992, MEDELLÍN et al. 2000, BOBROWIEC & GRIBEL 2010). Species such as M. hirsuta and P. stenops have low density in Atlantic Forest areas (REIS et al. 2007, SAMPAIO et al. 2008a, b, PERACCHI & NOGUEIRA 2010), which explains their low abundance in our data. These two species had only been previously recorded twice in the state (PERACCHI & ALBUQUERQUE 1993, ESBÉRARD 2004, ESBÉRARD & FARIA 2006).
In Canavarro, we highlight the presence of C. doriae and C. auritus, both represented by a single individual. The occurrence of C. auritus in Canavarro was not expected, because it is more common in primary and secondary forests (PERACCHI & ALBUQUERQUE 1993, BAPTISTA & MELLO 2001, BIANCONI et al. 2004), although it has also been recorded in open areas (EMMONS & FEER 1990, GONÇALVES & GREGORIN 2004).
In Canavarro, the high abundance and dominance of A. lituratus and S. lilium can be explained by the presence of S. lycocarpum, a solanaceous species (Solanaceae) that is one of the first to colonize anthropogenic environments. It bears fruit almost throughout the year (MOURA et al. 2010), and has been listed as a food resource for these bats (FABIÁN et al. 2008, per-sonal observation). ZANON & REIS (2007) observed large consumption of solanaceous species by A. lituratus and S. lilium in the state of Paraná. The presence of these bat species attracted to this key species can bring benefits to the successional process, as it can lead to dispersal of seeds to other areas, aiding in their restoration (KUNZ et al. 2011).
In Takume, the presence of guava fruits throughout the year may have influenced the dominance of A. lituratus and C. perspicillata. Both species are found in abundance in different biomes of the Neotropics (REIS et al. 2000, SIMMONS 2005, ESBÉRARD et al. 2006, GARDNER 2007, ZANON & REIS 2007, BOBROWIEC & GRIBEL 2010) and have more generalist feeding habits, explaining their occurrence in a variety of environments. These species are considered to be well adapted to human disturbances (BONNACORSO & GUSH 1987, ESTRADA & COATES-ESTRADA 2002, GALLO et al. 2008) and cultivated fruit trees (BERNARD et al. 2001, KALKO & HANDLEY 2001, REX et al. 2008), such as guava in this study. Another species captured in abundance in Takume was D. rotundus. This may be associated with the proximity of cattle and horses, which are food resources for these bats, and a forest fragment that can provide shelter (GOMES & UIEDA 2004, COSTA & ESBÉRARD 2011). This result is consistent with the literature citing D. rotundus as a species commonly found in disturbed habitats (FENTON et al. 1992, MEDELLÍN et al. 2000, COSTA & ESBÉRARD 2011). Among the species that were found in these three areas, C. perspicillata also stands out for its high abundance in all areas, demonstrating its plasticity (BOBROWIEC & GRIBEL 2010).
The variations in the assemblages of bats, such as richness, abundance, composition and diversity of species, in the three areas sampled demonstrate the impact of human disturbance and transformation of the riparian forest buffer zone of the Tinguá Biological Reserve. The different structures of riparian forests in the Tinguá region affected bat assemblages, although the expectation that the Bioreserve would have the highest ecological indices was not borne out. The Bioreserve did not have the greatest abundance of bats or the highest diversity index, but did have the highest richness and highest value of the Margalef richness index, as expected. Analysis of the composition and structure of its assemblage shows the importance of this area for some species and their relationship with the more preserved areas, emphasizing the importance of the area.
The higher rates of beta diversity of the Bioreserve in comparison with the other areas are indicative of two issues: the possibility of isolation of some populations of bats in the reserve and the ineffective protection of riparian forests. The areas studied are among those with the highest anthropogenic pressure in the reserve's buffer zone (MMA 2006). Therefore, measures are needed to minimize the impacts of this anthropization and allow riparian forests to exercise their role as ecological corridors for greater dispersion of species. Since riparian forests are already degraded, the commitment to reforestation and restoration is important and bats can accelerate this process through their ecological role as seed dispersers.
Figure 1 .
Figure 1. Location of the buffer zone and Tinguá Biological Reserve, state of Rio de Janeiro, with satellite image (Google Earth: April 22, 2011) indication of the sampling areas.
Table I .
Number of bats captured in the buffer zone and Tinguá Biological Reserve, state of Rio de Janeiro
Table II .
Sampling effort and ecological index in the areas of the buffer zone and Tinguá Biological Reserve, state of Rio de Janeiro.
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Domain: Environmental Science Biology
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Reproductive potential of the lithodids Lithodes santolla and Paralomis granulosa (Anomura, Decapoda) in the Beagle Channel, Argentina*
Lithodidae is the only group of reptant decapods that occurs in the Antarctic Convergence waters and has been particularly abundant in the Beagle Channel, the Straits of Magellan of the Straits of Magellan and south to 50o S. Because of their abundance in coastal waters, the sympatric Lithodes santolla and Paralomis granulosa have constituted a mixed fishery since the 1930s. The two species differ markedly in their reproductive potential. Lithodes santolla is large (maximum size of 190 mm carapace length, CL, and 8 kg weight), has a generation time of 6 yrs., the reproductive cycle is annual and females carry between 5,000-60,000 eggs per female per clutch. In their life span, L. santolla females produce 6 times more eggs than P. granulosa females. Paralomis granulosa is smaller than its relative (maximum 115 mm CL and 1.5 kg weight), and has a slower growth rate, resulting in a generation time of 12 yrs. The reproductive cycle is biennial and females carry between 800-10,000 eggs per female per clutch. Moreover, the reproductive potential of P. granulosa is reduced because an important proportion of the largest and more prolific females of the population do not carry eggs. In other terms, in one generation time of P. granulosa, two complete generations of L. santolla are produced, and compared to other Subantarctic lithodids L. santolla is the most prolific species. The higher reproductive potential of L. santolla probably confers to this species the ability to recover more rapidly from an overfishing situation.
INTRODUCTION
Lithodidae is the only group of reptant decapods that occurs in the Antarctic Convergence waters. Only one species, Lithodes murrayi Henderson, 1888 was found in the Bellingshausen Sea (Klages et al., 1995) and in the Subantarctic waters off the Crozet Islands (Arnaud, 1971). By contrast, lithodids -stone or king crabswere very abundant in the Subantarctic benthos, especially in the the Straits of Magellan region. Among the 11 species of lithodid crabs that occur on the Antarctic Convergence and Subantarctic waters, 7 species occur in the southern tip of South America. Lithodes santolla (Molina, 1782) and Paralomis granulosa Jacquinot, 1847 occurred at high densities between 2 and 50 m deep in the Straits of Magellan, Beagle Channel, and the Pacific channels south to 50º S. Such high densities have encouraged the development of a fishery since the 1930s, which affected negatively the populations (Campodonico et al., 1983;Campodonico and Hernández, 1983;Bertuche et al., 1990;Lovrich, 1997a and references therein). Lithodes turkayi Macpherson, 1988 is the third species that rarely occurs in the Magellan Straits and the Beagle Channel, at > 70 m depth (identified as L. murrayi by Campodonico and Guzmán, 1972;Vinuesa et al., this issue). Lithodes confundens, Macpherson 1988 occurs on the Atlantic continental shelf near the eastern entrance of the Straits of Magellan (Lovrich et al., 1998b) and off the Islas Malvinas (Falkland Is.), and in the Straits of Magellan near Punta Arenas (Macpherson, 1988). Paralomis spinosissima Birstein and Vinogradov, 1972, P. formosa Henderson, 1888, and Neolithodes diomedeae (Benedict, 1894) occur south of the Antarctic Convergence, off South America Georgia the Crozet Islands (Macpherson, 1988;Otto and MacIntosh, 1996). However, N. diomedae is the only one of these species that occurs further north off the Pacific coast up to 5º S, and has been landed and sold in Puerto Montt at about 42º S (Vinuesa pers. obs.).
Knowledge on the biology of lithodids has arisen from the interest in their fishing and with the purpose of an efficient management of the populations. Therefore, most of the available information is related with the reproduction and life history of the current or potentially exploited species. By contrast, only the occurrences of less abundant or smaller species with no commercial interest were reported, as for example the other Subantarctic lithodids Paralomis birsteini Macpherson, 1988, P. aculeata Henderson, 1888or P. anamerae Macpherson, 1988. In this article, we summarize the available information on the life history and reproductive potential of Lithodes santolla and Paralomis granulosa in the Beagle Channel, Argentina.
The biology of the lithodids in the Beagle Channel
Both species are very different in their morphology. The body of Lithodes santolla is covered with spines and males may attain a maximum size of 190 mm of carapace length (CL) and 8 kg weight (Vinuesa, 1990). By contrast, P. granulosa is smaller, its body is covered by clusters of granules and males reach a maximum size of 115 mm CL and 1.5 kg weight (unpublished data). In the following, we summarize the main life history traits for both species. Literature sources from where the information proceeds are detailed in Table 1.
The biology of Lithodes santolla
In late November-early December, the reproductive cycle of L. santolla begins with female molting (Vinuesa, 1990). The precopulatory embrace and mating occur between a male with an old shell and a female recently molted and slightly smaller than her couple. Mating pairs may be found in the population for approximately a month (unpubl.data). As in other lithodids (Powell and Nickerson, 1965), fertilization is external and occurs immediately after female oviposition. Eggs are carried by females and the embryogenesis lasts approximately 9-10 mo. Fecundity (number of eggs per brood) increases with female size, between 5,500 and 60,000 eggs ( Table 1). Larval hatching occurs between mid September and October, without significant annual variation (unpubl.data). Larvae are lecithotrophic (Oyarzún, 1992) or facultative lecithotrophic (Comoglio and Vinuesa, 1991), pass through three zoeal and one glaucothoe -or megalopal-stages (Campodonico, 1971), and metamorphose to the first benthic crab stage, which is about 1.5 mm CL (Oyarzún, 1992). The entire larval period lasts about 35-55 days at 7-8ºC (Vinuesa et al., 1985;Oyarzún, 1992). At temperatures <7ºC, the glaucothoe does not succeed in molting to the juvenile stage (Oyarzún, 1992).
Molt frequency of L. santolla decreases with age: during the first year, crabs molt 6-7 times, during the second year, 4-5 times, and during the third year, 3 times. Three year old crabs are about 50 mm CL (Vinuesa et al., 1990). Thereafter, males molt twice a year until they reach morphometric maturity (defined as the change in the allometric relationship between carapace and claw size), i.e., at 5 yrs.age, and 90-99 mm CL ( Table 1). In the fourth year, females begin the ovarian maturation and thus, molt annually (Vinuesa et al., 1991). Gonadal maturity (in males defined as the presence of spermatozoa in the deferent ducts, and in females as the presence of embryos attached to pleopods) is reached at 60-75 mm CL in males, and at 66-87 mm CL in females ( Table 1; Vinuesa, 1984). In females, oogenesis lasts ca.24 mo (Vinuesa and Labal, in press). After gonadal maturity, females continue to molt annually (Vinuesa et al., 1991), and after morphometric maturity males apparently continue molting biannually until they reach 110 mm CL (pers.obs.). Males enter the fishery at 110 mm CL and males >1 50 mm CL probably molt biennially (Geaghan, 1973).
The biology of Paralomis granulosa
During October-November, the reproductive cycle begins with courtship and mating, between an old-shelled male and a recently molted female that is smaller than her couple (unpubl.data). The fertilization is external and the female broods the embryos attached to pleopods between 18 and 22 mo ( Table 1). Fecundity varies between 800 and 10,000 eggs, increasing with female size. However, ca.50% of the females > 80 mm CL do not carry eggs although their ovaries are well developed (Hoggarth, 1993;Lovrich, 1997a). In the Beagle Channel, larval hatching occurs mainly during winter (June to August), almost two year after mating (Lovrich and Vinuesa, 1993). Larval development seems to be shorter than in L. santolla, since the 2 zoeal stages last 18-21 days at 8 or 5ºC, respectively ( Table 1). However, there is no information about the duration of the glaucothoe stage, the entire larval development in the natural environment, and growth from the first crab stage to the stage of ca. 10 mm CL. During the immature phase, growth is slow. The smaller crabs ( 10-40 mm CL) molt twice a year: in winter and summer, while crabs > 40 mm CL molt only in summer. During the immature phase, percentage of growth per molt is constant and 12.4 % irrespective of crab size (Lovrich and Vinuesa, 1995). At this rate of growth, we suspect that gonadal maturity would be reached at ca. 10 yrs age (Lovrich, 1997a).
Several indirect evidences indicate that fishing has been the main factor of mortality and the cause of the density reduction in the populations of lithodid crabs of the Beagle Channel (Campodonico and Hernández, 1983;Bertuche et al., 1990;Vinuesa, 1990;Lovrich, 1997a). Since L. santolla has been continuously fished, the effects of the fishery on its population were more evident than on P. granulosa. Between 1975 and 1996 the population of L. santolla underwent significant decreases in (1) the relative abundance ( Table 1), ( 2) the proportion of legal males, (3) the proportion of ovigerous females, and (4) the mean size of the size frequency distributions of males and females. The only stock assessment of the population of the Beagle Channel was done in 5130 after 14 yrs of annual landings of ≥ 200 t (Boschi et al., 1984), and was roughly coincident with the start of the decline of landings. Hence in 1980-81, crabs were probably less abundant than before the fishery developed, i.e., in the 1950s. On average, the density of L. santolla ≥ 60 mm CL was 3.1 crab • 100 m -2 and the relative abundance was 9.3 crabs per trap (Boschi et al., 1984).
Density estimations of P. granulosa are still needed, and reported relative abundances are scarce. In 1970, the relative abundance in the Beagle Channel was 38 crabs per trap (González, 1971), whereas in 1996 and 1997 it was 66 and 105 crabs ≥55 mm CL per trap, and 19 and 16 legal crabs per trap, respectively ( Table 1; Lovrich 1997b;Lovrich et al., 1998a). However, there is some evidence of the negative influence of the fishery on the population. In the Straits of Magellan between 1979 and 1984-86, after intensive fishing, there were decreases in (1) the ~60% of the biomass of legal sized-crabs, (2) the relative abundance of landed crabs (probably > 75 cm CL) from 9.5 to 4 kg per trap, and (3) the mode of male size distributions from 92 to 74 mm CL (Campodonico et al., 1983;Díaz and Alvarado, 1986).
Reproductive potential
In crustaceans, the reproductive potential has been quantified in terms of fecundity, age at maturity, fishing mortality, proportion of females in each size class, and growth of individuals in a population (Campbell and Robinson, 1983;Shields, 1991). In this study the reproductive potential was calculated as the cumulative fecundity of a given female along her reproductive life-span (see Shields, 1991 p. 207). The size of each molt stage was derived from data of L. santolla female growth (Vinuesa and Lombardo, 1982), and the size-fecundity relationship was calculated from data of Vinuesa (1982).
The size of each molt stage of P. granulosa was assumed to be 5 mm CL (Campodonico et al., 1983 and unpubl. data), and the fecundity at size was calculated according to the function reported by Lovrich 1997b;Lovrich and Vinuesa (1993).
For an individual female and along her life span, L. santolla produces 6 times more eggs than P. granulosa (Fig 2). This is determined by two factors: first, L. santolla is larger and thus may carry more eggs than P. granulosa. Second, the embryogenesis of L. santolla lasts 9-10 mo, which allows females to have an annual reproductive cycle. Thus, a single female molts every year, increases her size and the total productivity. By contrast, the biennial reproductive cycle constrains P. granulosa females to molt once every two years, and thus prevents the increase of productivity. Moreover, if the female becomes large, e.g., > 85 mm CL, she probably does not find a male of the appropriate size to mate, does not carry eggs, and thus the reproductive potential will not increase ( Lovrich 1997a;1997b and unpubl. data). Finally, in population terms, in one generation time of P. granulosa (i.e., when one egg produces another egg), two generations of L. santolla are already produced, and the third one begins to produce eggs (Fig 2).
From the preceding observations, we advance two different hypotheses. First, life history traits of L. santolla, such as having more eggs that are pro-duced annually, annual molt, and reaching larger sizes, suggest that this species has more energetic requirements than its sympatric P. granulosa. Hence, L. santolla has probably occupied grounds of better quality than those used by P. granulosa, as occurs with king crabs of the Bering Sea Paralithodes camtschaticus and P. platypus (Jensen and Armstrong, 1989). So far, only anecdotal observations sustain that grounds formerly occupied by L. santolla were colonized by P. granulosa, once L. santolla was removed by fishing. Second, we suggest that the higher reproductive potential of L. santolla confers to this species the ability to recover more rapidly from an overfishing situation. By contrast, the longer generation time, the lower fecundity and the correspondingly lower reproductive potential of P. granulosa suggest that this species cannot support heavy rates of fishing for many years, as has occurred in the Islas Malvinas-Falkland Is. (Hoggarth, 1991). In the case of overfishing, the population recovery to pre-fishery levels will be relatively slow. However, much work is needed to estimate pre-fishery abundances, interactions between both species and niche overlapping, and effects of fishing on the competition between the two populations. Therefore, data acquisition from virgin stocks should be a priority for lithodid research in the the Straits of Magellan region.
FIG. 1. -Landings of Lithodes santolla (u) and Paralomis granulosa (m ) in Ushuaia, Argentina. Landings for 1998 were forecasted with the available data until July 1998, i.e., 6 out of 10 mo of the fishing season. Source: Dirección de Recursos Naturales, Province of Tierra del Fuego.
TABLE 1 .
-Summary of life-history traits of the sympatric Lithodes santolla and Paralomis granulosa mainly in the Beagle Channel, Argentina. Numbers in superscript indicate bibliographic references: 1:
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Domain: Environmental Science Biology
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