Inhibition of Fungal Pathogens across Genotypes and Temperatures by Amphibian Skin Bacteria

Symbiotic bacteria may dampen the impacts of infectious diseases on hosts by inhibiting pathogen growth. However, our understanding of the generality of pathogen inhibition by different bacterial taxa across pathogen genotypes and environmental conditions is limited. Bacterial inhibitory properties are of particular interest for the amphibian-killing fungal pathogens (Batrachochytrium dendrobatidis and Batrachochytrium salamandrivorans), for which probiotic applications as conservation strategies have been proposed. We quantified the inhibition strength of five putatively B. dendrobatidis-inhibitory bacteria isolated from woodland salamander skin against six Batrachochytrium genotypes at two temperatures (12 and 18°C). We selected six genotypes from across the Batrachochytrium phylogeny: B. salamandrivorans, B. dendrobatidis-Brazil and four genotypes of the B. dendrobatidis Global Panzootic Lineage (GPL1: JEL647, JEL404; GPL2: SRS810, JEL423). We performed 96-well plate challenge assays in a full factorial design. We detected a Batrachochytrium genotype by temperature interaction on bacterial inhibition score for all bacteria, indicating that bacteria vary in ability to inhibit Batrachochytrium depending on pathogen genotype and temperature. Acinetobacter rhizosphaerae moderately inhibited B. salamandrivorans at both temperatures (μ = 46–53%), but not any B. dendrobatidis genotypes. Chryseobacterium sp. inhibited three Batrachochytrium genotypes at both temperatures (μ = 5–71%). Pseudomonas sp. strain 1 inhibited all Batrachochytrium genotypes at 12°C and four Batrachochytrium genotypes at 18°C (μ = 5–100%). Pseudomonas sp. strain 2 and Stenotrophomonas sp. moderately to strongly inhibited all six Batrachochytrium genotypes at both temperatures (μ = 57–100%). All bacteria consistently inhibited B. salamandrivorans. Using cluster analysis of inhibition scores, we found that more closely related Batrachochytrium genotypes grouped together, suggesting that bacterial inhibition strength may be predictable based on Batrachochytrium relatedness. We conclude that bacterial inhibition capabilities change among bacterial strains, Batrachochytrium genotypes and temperatures. A comprehensive understanding of bacterial inhibitory function, across pathogen genotypes and temperatures, is needed to better predict the role of bacterial symbionts in amphibian disease ecology. For targeted conservation applications, we recommend using bacterial strains identified as strongly inhibitory as they are most likely to produce broad-spectrum antimicrobial agents at a range of temperatures.

Symbiotic bacteria may dampen the impacts of infectious diseases on hosts by inhibiting pathogen growth. However, our understanding of the generality of pathogen inhibition by different bacterial taxa across pathogen genotypes and environmental conditions is limited. Bacterial inhibitory properties are of particular interest for the amphibiankilling fungal pathogens (Batrachochytrium dendrobatidis and Batrachochytrium salamandrivorans), for which probiotic applications as conservation strategies have been proposed. We quantified the inhibition strength of five putatively B. dendrobatidisinhibitory bacteria isolated from woodland salamander skin against six Batrachochytrium genotypes at two temperatures (12 and 18 • C). We selected six genotypes from across the Batrachochytrium phylogeny: B. salamandrivorans, B. dendrobatidis-Brazil and four genotypes of the B. dendrobatidis Global Panzootic Lineage (GPL1: JEL647, JEL404; GPL2: SRS810, JEL423). We performed 96-well plate challenge assays in a full factorial design. We detected a Batrachochytrium genotype by temperature interaction on bacterial inhibition score for all bacteria, indicating that bacteria vary in ability to inhibit Batrachochytrium depending on pathogen genotype and temperature. Acinetobacter rhizosphaerae moderately inhibited B. salamandrivorans at both temperatures (µ = 46-53%), but not any B. dendrobatidis genotypes. Chryseobacterium sp. inhibited three Batrachochytrium genotypes at both temperatures (µ = 5-71%). Pseudomonas sp. strain 1 inhibited all Batrachochytrium genotypes at 12 • C and four Batrachochytrium genotypes at 18 • C (µ = 5-100%). Pseudomonas sp. strain 2 and Stenotrophomonas sp. moderately to strongly inhibited all six Batrachochytrium genotypes at both temperatures (µ = 57-100%). All bacteria consistently inhibited B. salamandrivorans. Using cluster analysis of inhibition scores, we found that more closely related Batrachochytrium genotypes grouped together, suggesting that bacterial inhibition strength may be predictable based on Batrachochytrium relatedness. We conclude that bacterial inhibition capabilities change among bacterial strains, Batrachochytrium genotypes and temperatures. A comprehensive understanding of bacterial inhibitory function, across pathogen genotypes and temperatures, is needed to better predict

INTRODUCTION
Interactions between host microbiomes and pathogens influence the severity of host disease. The outcome of microbiome-pathogen interactions can depend on microbiome composition (Chang et al., 2008;Jani and Briggs, 2014;Rovenich et al., 2014), pathogen genotype , and environmental context (Duffy and Defago, 1999;Lokmer and Wegner, 2015). In amphibians, the skin microbiome has been implicated in variable host susceptibility to the disease chytridiomycosis (Harris et al., 2009a;Becker and Harris, 2010;Muletz et al., 2012). Chytridiomycosis has been linked to extreme loss of global amphibian biodiversity (Berger et al., 1998;Lips et al., 2006;Martel et al., 2014;Spitzen-van der Sluijs et al., 2016;Carvalho et al., 2017;Stegen et al., 2017), and is caused by skin infection by either of two congeneric chytrid fungi, Batrachochytrium dendrobatidis and B. salamandrivorans, hereafter B. dendrobatidis and B. salamandrivorans, respectively. One mechanism by which bacterial symbionts offer protection from Batrachochytrium is through production of inhibitory metabolites that can kill zoospores (Brucker et al., 2008a,b) or cause zoospores to move away from the metabolites (Lam et al., 2011). However, the impacts of amphibian skin microbiomes on fungal disease are difficult to predict because little is known about the ecological and evolutionary factors shaping microbiome functions, such as antifungal properties (e.g., Madison et al., 2017). To better predict the role of bacterial symbionts in amphibian disease ecology it is necessary to quantify antifungal properties across bacterial strains, Batrachochytrium genotypes and temperatures.
Application of antifungal bacteria has been proposed as a preventative strategy and a treatment option for chytridiomycosis in the wild (Muletz et al., 2012;Bletz et al., 2013). Of the approximately 250 bacterial operational taxonomic units (OTUs) identified as B. dendrobatidis-inhibitory Muletz-Wolz et al., 2017a), nine have been used in bioaugmentation trials (e.g., Harris et al., 2009a,b;Woodhams et al., 2012;Nebergall, 2013;Becker et al., 2015a). These trials have had mixed success in mitigating chytridiomycosis. For instance, three studies found no effect of augmenting Janthinobacterium lividum on amphibian skin in reducing B. dendrobatidis-associated disease symptoms (Becker et al., 2011;Bletz, 2013;Nebergall, 2013), even though J. lividum inhibits B. dendrobatidis growth in vitro (Harris et al., 2006) and can be effective against B. dendrobatidis on amphibian skin (Harris et al., 2009a;Muletz et al., 2012). The variation in impact of bacterial augmentation in vivo suggests that environment and/or pathogen specific factors influence antifungal activity of bacterial symbionts through regulation of metabolite production or pathogen genotype specificity.
We quantified the inhibition strength of five amphibian skin bacteria cultured from North American woodland salamanders (Plethodon cinereus and P. cylindraceus), previously shown to inhibit B. dendrobatidis (GPL1-JEL404: Muletz-Wolz et al., 2017a) across Batrachochytrium genotypes and temperatures. We had three main objectives, (i) quantify the inhibitory proprieties of putatively anti-B. dendrobatidis bacterial strains against B. salamandrivorans, (ii) quantify the effect of temperature (12 and 18 • C), Batrachochytrium genotype and their interaction on bacterial inhibition strength, and (iii) determine if Batrachochytrium relatedness predicts bacterial inhibition strength. Quantifying interactions between pathogen and bacterial symbionts in an environmental and genetic framework strengthens our understanding of disease dynamics and guides conservation measures.

MATERIALS AND METHODS
We performed in vitro challenge assays with five bacterial strains and six Batrachochytrium genotypes at two temperatures (12 and 18 • C) using 96-well plates in a full factorial experimental design. We selected five bacterial strains to represent a range of inhibition based on their previously quantified inhibition strength against B. dendrobatidis-GPL1-JEL404 at 20 • C ( Table 1; Muletz-Wolz et al., 2017a). All bacterial strains were isolated from either Plethodon cinereus or P. cylindraceus at Shenandoah National Park, Virginia, in May 2012, and were widespread in these populations (Muletz-Wolz et al., 2017b). We selected six Batrachochytrium isolates from across the Batrachochytrium phylogeny, two GPL1 isolates (JEL647 and JEL404), two GPL2 isolates (SRS810 and JEL423), a Brazilian B. dendrobatidis isolate (JEL649), and B. salamandrivorans (Table 2). Hereafter, we refer to these Batrachochytrium isolates as Batrachochytrium genotypes because genetic analyses have shown that each of these isolates represent distinct genotypes (Schloegel et al., 2012;Martel et al., 2013;James et al., 2015).

Experimental Set-up
We performed the experiment using a total of 16 96-well plates. Each plate was assigned to one of four randomly generated configurations of bacterial by Batrachochytrium combinations (Supplementary Figure S1), and housed in one of four incubators (Percival model DR-36VL; two chambers per temperature, Supplementary Figure S2). Each incubator contained a total of four plates, with one plate per configuration.
We set up challenge assays following a protocol based on Muletz-Wolz et al. (2017a), with the following modifications to accommodate the design of the experiment. We passaged cryopreserved bacteria on 1% tryptone plates three times, then inoculated each bacterial strain in 25 mL of 1% tryptone broth and grew for 3 days at room temperature (approximately 21 • C) on a shaker at 100 rpm. By 3 days of incubation, the bacterial cultures reached high densities where inhibitory metabolites are produced (Bérdy, 2005). We obtained cellfree supernatants (CFSs) from bacterial monocultures, following the centrifuging and filtering methods outlined in Muletz-Wolz et al. (2017a). By using bacterial CFS, we determined the inhibitory properties of bacterially produced extracellular factors against live Batrachochytrium zoospores, and eliminated the possibility of direct competition or priority effects between bacterial and fungal cultures. As the bacterial strains were  Each isolate represented a distinct genotype based on genetic analyses by Schloegel et al. (2012), Martel et al. (2013), and James et al. (2015). We used the combination of Batrachochytrium lineage and isolate to specify the Batrachochytrium genotype.
grown at one temperature prior to the experiment all effects of temperature on inhibition relate to temperature-dependent activity of CFS extracellular factors and/or fungal physiology. For Batrachochytrium genotypes, we passaged cryopreserved isolates (prior passage history ranged between 3 and 9 times; Table 2) on 1% tryptone plates twice, and then grew them for 1 week on multiple 1% tryptone plates at 15 • C. We harvested zoospores by flooding the plates with 1% tryptone broth, filtered out the zoosporangia using a sterile coffee filter, and homogenized the zoospore mixture.
To set up the assays, we added 50 µl of approximately 1 × 10 6 zoospores/ml of each Batrachochytrium genotype (counted with a hemocytometer; approximately 50,000 zoospores in each well) to their designated wells in a 96-well plate (Supplementary Figure S1). In sample wells, we added 50 µl of the CFS from each bacterial strain to four wells for each bacterial-Batrachochytrium combination. In total, each bacterial-Batrachochytrium combination was represented in 16 wells distributed over four plates per temperature. In each 96-well assay, we included two positive controls (PCs) and one negative control for each Batrachochytrium genotype using four wells per control. The positive controls were: 50 µl of Batrachochytrium zoospores + 50 µl 1% tryptone broth PC and 50 µl of Batrachochytrium zoospores + 50 µl of water [nutrient-depleted positive control (NDPC)]. The negative control was 50 µl of Batrachochytrium zoospores heat-killed at 60 • C for 60 min + 50 µl of 1% tryptone broth (heat-killed B. dendrobatidis: HK). We measured optical density (OD 492 nm ) of each well for 16 96-well plates using a microplate reader every other day starting on day 1, for 27 days.

B. dendrobatidis Inhibition Score Calculations
We used R version 3.2.5 for all calculations and statistical analyses (R Core Team, 2016). We visually inspected the optical density (OD) readings for each plate, and excluded data points for 32 wells (2% of wells) with unusually high densities (+0.1 or greater well OD compared to replicate wells on same plate), indicating contamination or error. We corrected for baseline zoospore OD by subtracting the average heat-killed OD of each Batrachochytrium genotype from the corresponding experimental wells in each plate. To achieve a normal distribution, we transformed the corrected OD readings using the following equation, log(OD corrected (1−OD corrected )+1). Next, we fit linear regressions to the transformed OD readings over time for each well with the intercept set at zero. We extracted the slope of the linear regression, and interpreted this as Batrachochytrium growth (i.e., optical density/time). We excluded wells in which the linear model had an r 2 less than 0.20, given the poor fit of the data. After quality filtering, we had slopes for 867 of the 960 sample wells (Supplementary Table S1). Then, we calculated Batrachochytrium growth inhibition, hereafter referred to as inhibition score, by dividing the slope of each sample well by the slope of the average NDPC wells of the corresponding Batrachochytrium genotype on the same plate, and subtracting the subsequent fraction from one, [Inhibition score = 1 -(slope sample well/average slope NDPC)]. We compared the slopes of sample wells to the NDPC wells because this accounts for the issue of nutrient depletion in PC wells and is a more conservative approach in identifying anti-Batrachochytrium bacteria Muletz-Wolz et al., 2017a). We created this standardized inhibition scoring system to be able to compare inhibition scores between the two experimental temperatures. We interpreted inhibition scores greater than zero as inhibitory, indicating that the bacterial-Batrachochytrium sample well had less growth than the NDPC wells. Values less than zero we interpreted as non-inhibitory, and to determine if any bacterial symbionts promoted Batrachochytrium growth, we compared the inhibition scores to those of the PC wells (see Statistical Analyses below; Supplementary Figure S3).

Statistical Analyses
We quantified the effects of Batrachochytrium genotype, temperature, and their interaction (explanatory variables) on inhibition scores (response variable) using a linear mixedeffects model for each bacterial strain separately, using the lmer function in the 'lme4' package (Bates et al., 2015). We included plate nested within incubator as a random effect in each model. Next, we used the Anova function in the 'car' package with type II sum of squares to determine the significance of each of the fixed-effects (Fox and Weisberg, 2011). Using the 'lsmeans' package (Lenth, 2016), we used the lsmeans function to perform post hoc analyses to determine significant difference among Batrachochytrium genotypes and between temperatures. We used the lsmip function to generate Batrachochytrium genotypes by temperature interaction plots for each bacterial strain.
We determined if any bacterial symbiont promoted Batrachochytrium growth by comparing the inhibition score of each bacterial Batrachochytrium combination at each temperature to the inhibition score of the PC well for the corresponding Batrachochytrium genotype (Supplementary Figure S3). We used a linear mixed-effects model for each Batrachochytrium genotype examining the effects of well type (i.e., bacteria or PC), temperature, and their interaction (explanatory variables) on inhibition scores (response variable). We included the same random effects, determined significance and conducted post hoc analyses as described above.
We determined if patterns of bacterial inhibition score reflected Batrachochytrium phylogenetic relatedness by conducting a cluster analysis using the mean inhibition score for each bacteria-Batrachochytrium combination at each temperature. We used two clustering methods to confirm similar clustering patterns: (i) Ward's hierarchical clustering with Euclidean distances using the pvclust function in the 'pvclust' package (Suzuki and Shimodaira, 2006), and (ii) k-means clustering using a plot of within groups sum of squares by number of clusters to determine the appropriate number of clusters (Everitt and Hothorn, 2009). We were unable to perform a full phylogenetic analysis because we could not calculate branch length due to missing genotype data for B. salamandrivorans and GPL1-JEL404 (Schloegel et al., 2012, T. James, pers. comm.).
We detected a Batrachochytrium genotype by temperature interaction effect on inhibition score for all bacterial strains (post hoc analyses: Supplementary Tables S2, S3), indicating that bacterial inhibition strength is affected by both Batrachochytrium genotype and temperature. No bacterial strain promoted the growth of any Batrachochytrium genotype (Supplementary Figure S3).
Bacterial inhibition strength may be predictable based on Batrachochytrium phylogenetic relatedness. Both clustering methods supported the same two clusters within the data, with B. dendrobatidis-Brazil and B. salamandrivorans clustering together and the B. dendrobatidis-GPL genotypes forming a separate cluster (Figure 2).

DISCUSSION
Our findings suggest that many bacteria identified as Batrachochytrium-inhibitory in vitro are unlikely to be consistently effective in mitigation of chytridiomycosis in vivo. We found that all bacterial strains consistently inhibited B. salamandrivorans growth across temperatures (12 and 18 • C), but that three of the five bacterial strains did not consistently inhibit the five B. dendrobatidis genotypes. Similarly, Antwis et al. (2015) found that 3 of 11 previously identified B. dendrobatidis-inhibitory bacterial strains did not consistently inhibit three B. dendrobatidis isolates at 18 • C. These results highlight the importance of considering Batrachochytrium genotype and bacterial strain in hostpathogen interactions, and may explain the variation in effectiveness of probiotics in experimental trials (e.g., Harris et al., 2009a;Woodhams et al., 2012;Becker et al., 2015a).
Our results also highlight the importance of considering environmental context in host-pathogen interactions. The effect of temperature on inhibition was not unidirectional, but depended on the context of the interacting Batrachochytrium genotype and bacterial strain. For example, Pseudomonas sp. strain 1 was less inhibitory of GPL1-JEL647 at 12 • C compared to 18 • C, whereas Pseudomonas sp. strain 2 and Stenotrophomonas sp. were more inhibitory of GPL1-JEL647 at 12 • C compared to 18 • C. The differences in inhibition between temperatures of the same Batrachochytrium genotype likely related to temperaturedependent changes in bacterial extracellular factor activity (Daskin et al., 2014) and/or Batrachochytrium physiology (Woodhams et al., 2008). Bacterially produced extracellular factors include bacteriocins, siderophores, organic acids, lysozymes, proteases, and secondary metabolites. However, the most likely factors contributing to inhibition are secondary metabolites (reviewed by Verschuere et al., 2000;Raaijmakers et al., 2002), and antimicrobial activity of metabolites can vary among temperatures (Humair et al., 2009;Aguilar and Klotz, 2010). In addition, Batrachochytrium isolates vary in phenotypic traits (e.g., growth rate and zoosporangium size) depending on temperature (Piotrowski et al., 2004;Martel et al., 2013; FIGURE 1 | Interaction plot showing the effect of Batrachochytrium genotype and temperature on bacterial inhibition score (percent inhibition: note different scale on y-axis). Acinetobacter rhizosphaerae (A) was moderately inhibitory of B. salamandrivorans at both temperatures, but did not inhibit any B. dendrobatidis genotypes. Chryseobacterium sp. (B) and Pseudomonas sp. strain 1 (C) were weakly to strongly inhibitory of most Batrachochytrium genotypes. Pseudomonas sp. strain 2 (D) and Stenotrophomonas sp. (E) were moderately to strongly inhibitory of all Batrachochytrium genotypes at both temperatures, and showed similar inhibition patterns. Percent inhibition is based on the parameter estimates from the linear-mixed effects models. Stevenson et al., 2013) and this may impact their susceptibility to inhibitory compounds. Investigation into the inhibition strength of Batrachochytrium-inhibitory bacterial metabolites (e.g., violacein) at different temperatures would provide insight into whether bacterial factors or Batrachochytrium physiology underpin the temperature-dependent differences in inhibition.
Our study supports the hypothesis that similarity of fungal genomes can predict strength of bacterial inhibition. Our results showed separation between two clusters: the two highly FIGURE 2 | Heatmap displaying Ward's hierarchical clustering of Batrachochytrium genotypes based on mean bacterial inhibition scores at each temperature. Approximately unbiased p-values are indicated for each cluster, with values larger than 95% being strongly supported by the data ( * ). Two clusters were supported by the data, one including B. dendrobatidis-Brazil and B. salamandrivorans, and one clustering all B. dendrobatidis-GPL genotypes. Ac = Acinetobacter rhizosphaerae, Chy = Chryseobacterium sp., Ps1 = Pseudomonas sp. strain 1, Ps2 = Pseudomonas sp. strain 2, Sten = Stenotrophomonas sp. divergent lineages (B. salamandrivorans and B. dendrobatidis-Brazil) and the GPL genotypes. Generally, B. salamandrivorans and B. dendrobatidis-Brazil were more likely to be inhibited than the GPL genotypes. The GPL genotypes have increased chromosomal copy numbers (CCNs) and loss of heterozygosity compared to the endemic B. dendrobatidis lineages, and these measures are associated with increased virulence (Schloegel et al., 2012;Farrer et al., 2013;Rosenblum et al., 2013;James et al., 2015;Becker et al., 2017). This association may relate to resistance to inhibitory compounds released by host or symbionts. For instance, Farrer et al. (2013) found that B. dendrobatidis CCN increased following exposure to a host-produced antimicrobial peptide, which then resulted in reduced inhibition. A greater sampling across Batrachochytrium genotypes would be a useful next step to confirm this lineage-based similarity in pathogen response to bacterial inhibition.
While there was a relationship between Batrachochytrium relatedness and inhibition patterns, we did not detect a relationship between bacterial relatedness and inhibition patterns. For instance, we found variation within the two Pseudomonas sp. strains tested, which were defined as the same OTU (16S rRNA sequence similarity = 99%). Pseudomonas sp. strain 1 was generally less inhibitory compared to Pseudomonas sp. strain 2, and also non-inhibitory of two pathogen genotypes at 18 • C. These findings are similar to other studies that profiled larger numbers of bacterial strains in a phylogenetic framework and found that inhibition strength was not correlated to bacterial phylogeny (Becker et al., 2015b;Muletz-Wolz et al., 2017a). While the majority of genomic information can be highly similar within a bacterial OTU, gene clusters associated with inhibitory metabolite production can differ among strains (Chen et al., 2015), potentially explaining the discrepancy in inhibition strength within an OTU. On the other hand, different OTUs can show similar inhibition patterns as homologous recombination and horizontal gene transfer of genes encoding antifungal compounds can occur between bacterial lineages with levels of DNA divergence as high as 25% (Kinashi et al., 1987;Ravel et al., 2000;Cohan, 2001). For example, we found a strong similarity in the inhibition patterns of Pseudomonas sp. strain 2 and Stenotrophomonas sp. (16S rRNA sequence similarity = 75%). Documented modes of antibiosis by Pseudomonas differ from those of Stenotrophomonas spp. (Raaijmakers et al., 2002;Compant et al., 2005); whole genome sequencing or chemical analyses of these bacterial strains may identify the specific agent(s) underlying this similarity in Batrachochytrium inhibition.
For targeted conservation applications, we suggest testing bacterial strains in vitro against multiple Batrachochytrium genotypes across a range of temperatures to identify probiotics that would be most effective at mitigating Batrachochytrium infection in situ. If probiotic-based conservation strategies are needed rapidly, previously identified Batrachochytrium-inhibitory bacterial strains that are strongly inhibitory are the most likely to be effective. For instance, we found that two bacterial strains (Pseudomonas sp. strain 2 and Stenotrophomonas sp.) were strongly inhibitory of all Batrachochytrium genotype tested across temperatures. These bacterial strains are good probiotic candidates as they likely produce antifungal compounds at a range of temperatures that inhibit a range of pathogen genotypes .

CONCLUSION
We quantified the inhibition of diverse Batrachochytrium genotypes by five bacterial strains that were isolated from woodland salamanders in the eastern United States and previously shown to inhibit an eastern US B. dendrobatidis genotype, GPL1-JEL404 (Muletz-Wolz et al., 2017a). Inhibition capabilities of the symbiotic bacteria changed as a function of bacterial strain, Batrachochytrium genotype and temperature. This has practical implications for understanding hostpathogen dynamics and developing conservation measures. Amphibians and their microbiomes will continue to be exposed to rapidly evolving Batrachochytrium genotypes, and hosts harboring higher numbers of microbial taxa and hence more potentially inhibitory species may provide greater resistance to pathogen invasion than microbiome communities with fewer taxa (Chang et al., 2008;Ling et al., 2015;Lokmer and Wegner, 2015;Longo et al., 2015). Our findings should also be considered in other systems, such as white-nose syndrome in bats and pathogens in agriculture, where probiotic application is used as a disease management strategy (Verschuere et al., 2000;Raaijmakers et al., 2002;Teplitski and Ritchie, 2009;Hoyt et al., 2015;Xue et al., 2015).

DATA ACCESSIBILITY
All experimental data and statistical analyses (R code) will be deposited upon acceptance in figshare (doi: 10.6084/m9.figshare.5297416).

AUTHOR CONTRIBUTIONS
CM-W, GD, KZ, LT, and KL designed the research. AM, FP, KZ, and LT provided the Batrachochytrium isolates. CM-W, JA, and SB conducted the lab work. CM-W and JA analyzed the data with advice from SB and GD. All authors contributed to the interpretation of the data. CM-W wrote the manuscript and all authors provided critical feedback. All authors approved the manuscript's content.

FUNDING
This research was funded by the National Science Foundation through a Research Experience for Undergraduates supplement to grant DEB #1120161 to KL and KZ.