Diversity in growth patterns among strains of the lethal fungal pathogen Batrachochytrium dendrobatidis across extended thermal optima
- 1.4k Downloads
The thermal sensitivities of organisms regulate a wide range of ecological interactions, including host–parasite dynamics. The effect of temperature on disease ecology can be remarkably complex in disease systems where the hosts are ectothermic and where thermal conditions constrain pathogen reproductive rates. Amphibian chytridiomycosis, caused by the pathogen Batrachochytrium dendrobatidis (Bd), is a lethal fungal disease that is influenced by temperature. However, recent temperature studies have produced contradictory findings, suggesting that our current understanding of thermal effects on Bd may be incomplete. We investigated how temperature affects three different Bd strains to evaluate diversity in thermal responses. We quantified growth across the entire thermal range of Bd, and beyond the known thermal limits (T max and T min). Our results show that all Bd strains remained viable and grew following 24 h freeze (−12 °C) and heat shock (28 °C) treatments. Additionally, we found that two Bd strains had higher logistic growth rates (r) and carrying capacities (K) at the upper and lower extremities of the temperature range, and especially in low temperature conditions (2–3 °C). In contrast, a third strain exhibited relatively lower growth rates and carrying capacities at these same thermal extremes. Overall, our results suggest that there is considerable variation among Bd strains in thermal tolerance, and they establish a new thermal sensitivity profile for Bd. More generally, our findings point toward important questions concerning the mechanisms that dictate fungal thermal tolerances and temperature-dependent pathogenesis in other fungal disease systems.
KeywordsBatrachochytrium dendrobatidis Amphibian chytridiomycosis Disease ecology Psychrophilic fungi Temperature
Temperature is one of the most critical abiotic factors regulating ecological processes. Owing to the rate-limiting influence of temperature on biochemical mechanisms, there is a rich and extensive literature on how the thermal sensitivities of organisms can shape their molecular and cellular biology, ecophysiology, behavior, abundance, and distribution (Johnston and Bennett 2008; Beveridge et al. 2010). In addition, there has been a great interest in establishing thermal sensitivity profiles for diverse organisms, and in investigating how thermal performance curves may have evolved (Johnston and Bennett 2008; Krenek et al. 2012). One area that has recently garnered considerable attention is how the field of thermal biology can be integrated with ecological and evolutionary studies of host–parasite interactions (Blanford and Thomas 1999; Krenek et al. 2012).
Current research is providing numerous examples of how thermal environments can shift disease dynamics and drive host–parasite coevolution (Blandford and Thomas 1999). Temperature can constrain both the ability of a host to defend itself, and a pathogen’s capacity to colonize and reproduce within a host. If the thermal sensitivity profiles of host and pathogen match, then the effects of temperature in a disease system may be relatively straightforward to understand. If, on the other hand, there is considerable diversity in thermal tolerances within and among host species, or among pathogen strains, then the effect of temperature can be remarkably complex and have important implications for the manifestation of disease. Therefore, investigating the intricacies of these interactions may provide insights into the temperature-regulated mechanisms of disease that have been difficult to resolve [e.g., disease systems that exhibit strong seasonal fluctuations (Dowell 2001; Koelle et al. 2005) or that are limited or exacerbated across altitudinal and latitudinal gradients (Guernier et al. 2004; Gilbert 2010)].
One disease that provides a compelling example of temperature-sensitive host–parasite interactions is amphibian chytridiomycosis (Berger et al. 2004; Raffel et al. 2010, 2013). Chytridiomycosis is caused by the fungal pathogen, Batrachochytrium dendrobatidis (hereafter Bd; Longcore et al. 1999). It has been suggested that Bd may have spread around the world relatively recently (although questions regarding the point of origin and the timing of spread are still debated; Rosenblum et al. 2013). In multiple geographic locations, Bd emergence in naïve host populations has caused precipitous declines in amphibian populations, including some extinctions (Skerratt et al. 2007; Schloegel et al. 2006). These declines have occurred in a wide variety of environments, including the deserts in temperate North America [e.g., Lithobates (Rana) yavapaiensis and Lithobates (Rana) chiricahuensis (Bradley et al. 2002)], high alpine, temperate regions in North America [e.g., Rana muscosa and R. sierrae (Briggs et al. 2010; Vredenburg et al. 2010)] and in tropical rainforests in Central America [e.g., Craugastor punctariolis (Ryan et al. 2008)] and Australia [e.g., Taudactylus acutirostrostris (Schloegel et al. 2006)].
Although there has been a general consensus among researchers that temperature plays an important role in this disease, the mechanisms that underpin the temperature effects on chytridiomycosis have not been fully resolved (Venesky et al. 2014). Amphibians rely on environmental heat sources to adjust their body temperatures (Richards-Zawacki 2010; Rowley and Alford 2013) and amphibian immune function is dependent on temperature (Raffel et al. 2006; Butler et al. 2013). In addition, laboratory studies suggest that Bd optimal growth and reproduction occur within a restricted thermal range of 4–25 °C (Piotrowski et al. 2004; Woodhams et al. 2008). However, field studies that have described chytridiomycosis outbreaks demonstrate that our understanding of temperature effects on Bd in the laboratory does not fully explain disease dynamics in the wild (Venesky et al. 2014).
Chytridiomycosis outbreaks in tropical regions have predominantly occurred at cooler, high-elevation sites and when temperatures are at seasonal lows (Berger et al. 2004; Woodhams and Alford 2005; Sapsford et al. 2013). In contrast, similar investigations in temperate regions have not detected seasonal, latitudinal or altitudinal patterns in infection or disease (Kriger and Hero 2008; Korfel and Hetherington 2014; Petersen et al. 2016). Because the role of temperature in chytridiomycosis in temperate regions is much less clear, some investigators have speculated that other environmental factors may outweigh temperature in determining disease outcomes (Knapp et al. 2011; Korfel and Hetherington 2014). Thus, while some studies suggest that the importance of temperature is unequivocal, other studies suggest that temperature plays virtually no role in chytridiomycosis outbreaks (Knapp et al. 2011; Korfel and Hetherington 2014). While these studies do not necessarily negate the importance of temperature, they nevertheless suggest that the effects of temperature are more nuanced than we initially appreciated (Venesky et al. 2014; Cohen et al. 2017).
One strong starting point for improving our understanding of thermal effects on Bd is to investigate its responses to temperature (1) decoupled from the confounding effects of host defenses, (2) among Bd strains from globally diverse sources, and (3) at the extremes of its thermal range.
We selected a panel of Bd strains from different geographic regions and evaluated the responses of these isolates in temperature shock treatments and across the entire known Bd temperature gradient (Piotrowski et al. 2004; Stevenson et al. 2013). We then used our empirical data to fit a mathematical model of Bd growth over our experimental temperature spectrum. This approach offered a controlled common garden experiment where we could track three Bd strains and their growth characteristics at different temperatures in real time. We hypothesized that the three Bd strains would exhibit variation in growth and viability in our experimental treatments.
Bd isolate selection
We selected three Bd isolates for our experiments from one of the largest global collections of Bd isolates available (see Rosenblum et al. 2013). Our primary aim was to choose representative strains from different clades within the Bd phylogeny. Our secondary aim was to use strains that originated from different thermal regions. Additionally, because laboratory maintenance practices can have profound effects on Bd growth patterns (Voyles et al. 2014), we selected Bd strains that were collected and cryo-archived with similar protocols (Boyle et al. 2003), and that had low passage histories.
CJB5-2 originated in the Sierra Nevada Mountains of California, and was isolated from Rana muscosa. Genomic data show that CJB5-2 is clearly nested within the global pandemic lineage (GPL; Rosenblum et al. 2013). Amphibian microhabitat temperatures in this region range from 4 to 28 °C (Knapp et al. 2011). We refer to CJB5-2 as “Temperate” throughout the paper. LFT originated in Reserva Biologica Serra do Japi in Tropical Brazil, and was isolated from Hylodes ornatus. We refer to LFT as “Tropical” throughout the paper. In this region, average annual temperatures range from 11 to 22 °C (Vieira et al. 2013). UM-142 was isolated from a bullfrog [Lithobates (Rana) catesbeiana] in an amphibian trade market in Michigan, USA. However, genetic analyses suggest that this strain may have originated in Latin America (although the location is unclear) and it was subsequently introduced to bullfrogs in the pet trade in Michigan, USA (Rosenblum et al. 2013; Schloegel et al. 2012). Although UM-142 may be more closely related to the LFT strain from Brazil (see Rosenblum et al. 2013), it was not possible to make final conclusions regarding its true point of geographic origin (or if it originated from a temperate or tropical region). Therefore, we refer to UM-142 as “Bullfrog” throughout the paper. Genomic data show that both the Tropical and the Bullfrog strains are outside of the GPL clade, and in a clade that is sometimes referred to as “BdBrazil”. The temperature profiles of the regions for the Temperate and Tropical strains likely overlap in the intermediate temperature range for Bd growth (11–22 °C), but extend beyond that range where the Temperate strain was collected.
Bd growth assays
To prepare the cultures, we revived aliquots of these Bd strains simultaneously, treated them identically, and kept passage histories low. Specifically, we cultured the revived Bd strains in TGhL broth (16 g tryptone, 4 g gelatin hydrolysate, 2 g lactose, in 1000 mL distilled water, autoclaved) in 25 cm2 flasks. We incubated the flasks at 21 °C and passaged Bd into fresh TGhL every 7–9 days (Boyle et al. 2003; Voyles 2011). To ensure that only viable zoospores were harvested for the experiment, we centrifuged each strain at 1700g for 5 min, 5–7 days prior to the beginning an experiment (Voyles 2011). This step eliminated dead zoospores from all cultures. We then added 2 mL of the supernatant to new 25 cm2 flasks with 8 mL of fresh TGhL.
On the first day of the experiment, we filtered 5–7 day old cultures through 0.22 mm filter paper to remove sporangia. For each strain, we counted zoospores using a hemocytometer and diluted the cultures with fresh TGhL to establish a standard zoospore inoculation concentration of 50 ± 3.9 × 104 zoospores per mL. We incubated one aliquot of culture at 40 °C for 10 min to generate a heat-killed culture to use as our negative control. We then pipetted the cultures into flat-bottomed, sterile 96-well plates with 50 µL culture broth and 50 µL fresh TGhL per well (n = 8 wells per three strains, plus n = 8 wells for heat-killed negative control) with 3–5 replicate plates. To prevent desiccation, we pipetted 100 µL TGhL into the perimeter wells of each plate.
We recorded an initial optical density (OD) reading at 490 nm using an E-max precision microplate reader (Molecular Devices, Softmax Pro, Sunnyvale, CA, USA). We recorded OD either once daily (T ≥ 12 °C) or every other day (T ≤ 12 °C). Prior to collecting OD measurements, we cleaned the interior surfaces of each plate with a KimWipe to remove any condensation from the plate lid. We continued to record OD daily until we saw evidence of a stationary phase, indicating the end of Bd growth.
Temperature shock experiments
Our first objective was to quantify Bd growth in temperatures beyond its previously published T min and T max (Piotrowski et al. 2004; Stevenson et al. 2013). We were particularly interested to see if Bd could survive a temporary freeze treatment or temporary exposure above the highest published temperature for Bd (27 °C; Piotrowski et al. 2004; Stevenson et al. 2013). For the freeze shock treatments, we inoculated the Bd cultures into 96-well plates (as described above) and included wells containing heat-killed Bd as a negative control. One plate was incubated in one incubator at 21 °C continuously to provide a positive control. We held a second plate in a second incubator (freezer) at −12 °C for 24 h, and we then shifted the plate to 21 °C for incubation for 7 days. Although a shift of −12–21 °C is unlikely to occur in nature, we wanted to move plates from a freezing temperature to the temperature that matched our control treatment, and one that is thought to be an ideal temperature for Bd growth (21 °C). We verified temperatures in the incubator using a non-contact infrared thermometer (Raytek ST80 Pro-Plus Non-contact thermometer, Santa Cruz, USA).
For the heat shock treatment, we used the same 96-well plate setup with heat-killed Bd as a negative control (as described above). We inoculated Bd zoospores into three plates, one plate was placed in an incubator at 21 °C continuously to provide a positive control and one plate was placed in a second incubator at 28 °C for 24 h and then subsequently moved to 21 °C, and one plate was maintained in a third incubator at 28 °C for the duration of the experiment. For both the freeze and heat shock experiments, we checked all wells for any visual signs of contamination, omitted the results if we observed contamination and then fitted the logistic growth model (described below).
Bd responses to low, high, and intermediate temperatures
In addition to our temperature shock treatments, we used three incubators to conduct growth assays at 2, 3, and 4 °C (for low temperature conditions), 26, 27, and 28 °C (for high temperature conditions) and at 5, 12, and 21 °C (for intermediate conditions). We set up multiple 96-well plates (as described above) including wells with heat-killed Bd as a negative control. Following inoculation of Bd into the wells, the plates were incubated at each temperature until growth reached a stationary phase (Fig. 1). Because we had access to a limited number of incubators, we could not test the strains in all of the temperatures simultaneously. However, we grouped the experiments by temperature ranges (low, high or intermediate temperatures), used identical protocols for plate setup, and diluted the zoospores to the same inoculating dose (50 ± 3.9 × 104 zoospores per mL) for all experiments.
Previous studies have indicated that the Bd growth cycle consists of two portions: a motile zoospore phase, and a larger, sessile sporangia state that then produces more zoospores (Longcore et al. 1999; Berger et al. 2004). Zoospores in solution must first settle and they subsequently begin to develop into sporangia, which occurs during the lag phase. These sporangia take time to mature before they produce and release zoospores. If zoospores are monitored and counted independently, many biologically important parameters can be estimated from these data (Voyles et al. 2012, 2014). However, when measuring optical densities (ODs), sporangia and zoospores are counted together. Therefore, we used a simpler model that captures key growth patterns while including important details of the Bd life cycle.
We conducted the analyses in R (R Core Team 2016) with Markov chain Monte Carlo (MCMC) implemented in rjags/JAGS (Plummer et al. 2003; Plummer 2013). We checked for convergence of the MCMC chains visually and used standard convergence metrics. Model selection was based on the Deviance Information Criterion (DIC) as implemented in rjags/JAGS (Plummer et al. 2003; Plummer 2013). We obtained posterior estimates of all the parameters in Eq. 1, and used these to obtain the posterior distributions of the growth trajectories. We used these parameters to understand the responses of the Bd strains during freeze and heat shock experiments and when they were incubated at a range of constant temperatures from 2 to 27 °C.
To assess the overall performances of the Bd strains across the different temperatures, we calculated the trajectories from the modified logistic equation based on samples from the posterior distributions of parameters. Using the posterior samples of the growth trajectories, we calculated the area under the curve (AUC) using the auc function in the flux package (Jurasinski et al. 2014) in R (R Core Team 2016).
The thermal breadth is defined as the range of temperatures over which growth of at least 75% of the maximum growth rate occurs. Although basing the breadth on the logistic parameter (r) has been done in previous studies (Raffel et al. 2013), r will not necessarily capture the overall population growth rate because our models include a delay for lag phase. Therefore, we used an additional measure, the area under the growth curve (AUC), to complement and compare to the thermal breadth measures based on r. As with r, we defined the thermal breadth for AUC to be the range of temperature across which AUC was at 75% of its maximum.
Where logistic growth occurred (i.e., r > 0), we examine alternative definitions of the thermal breadth by examining the fitted J–L curve to see what ranges could be characterized as “low” and “high” logistic growth. Specifically, we defined “low growth rate” as 0.01 > r > 0.1. That is, within this range we could be confident of at least some Bd growth. We defined “high growth rate” as r > 0.1. This approach allowed us to capture more information about the tails of the distribution of growth.
Temperature shock experiments
We used a Bayesian analysis of the probability of growth for the freeze (F) and control (C) treatments. Although the posterior samples suggested that the freeze shock treatment reduced the probability that Bd will grow, we found that all the Bd strains had at least a portion of samples that grew and produced zoospores following a freeze shock of −12 °C for 24 h.
Bd responses to a temperature gradient
We evaluated the growth patterns and determined if logistic or constant growth models were more appropriate. The DIC values indicated that, when comparing across most temperatures, the logistic model was preferred over the constant model for all the Bd strains. The only exceptions were in the Tropical and Bullfrog strains at 28 °C. However, the outcomes were equivocal because one experiment supported constant and the other supported low logistic growth. Therefore, we used the logistic model to evaluate the responses of strain to thermal treatments.
Thermal breadth of the three strains of Batrachochytrium dendrobatidis (Bd) isolates calculated 4 ways: r > 0.1; r > 0.01; width at 75% of the maximum estimated r (fit from the Johnson–Lewin curve); width at 75% of the maximum estimated area under the curve (AUC, fit from the Johnson–Lewin curve)
Tat r max
r > 0.01
r > 0.1
r = 0.75 r max
AUC = 0.75AUCmax
Previous studies (Woodhams et al. 2008; Raffel et al. 2013; Piotrowski et al. 2004) suggest that Bd should reach peak growth at an intermediate temperature of 21 °C. The pattern we observed in the Tropical strain was similar (Fig. 5a), though our results indicate a possibly higher temperature of peak growth (~23 °C). However, we found that the growth rates for the Temperate and Bullfrog strains were high and relatively flat across a broad range of temperatures (Fig. 5a), from 2 to 25 °C, although the “peaks” occurred at 24.6 °C for the Temperate strain and at 22.1 °C for the Bullfrog strain (based on the fitted J–L curves, Fig. 5a and Table 1).
The fungal pathogen Bd is known to be sensitive to temperature, and it was previously thought that its critical temperature range spanned from 4 to 27 °C, with optimal growth between 17 and 21 °C (Woodhams et al. 2008; Raffel et al. 2013; Piotrowski et al. 2004; Stevenson et al. 2013). However, these previous studies tested only a single isolate of Bd, multiple isolates from within a single Bd lineage, or only across a limited temperature range (Woodhams et al. 2008; Raffel et al. 2013; Piotrowski et al. 2004, Stevenson et al. 2013). To build on these previous studies, and to provide a fuller understanding of temperature effects on Bd, we selected three Bd strains and tested their responses in temperature shock treatments and growth assays across and beyond the previously published tolerance range for Bd (Piotrowski et al. 2004).
Our results show that the Bd strains exhibited different growth patterns across the putative thermal range of Bd. Although the three Bd strains generally had similar overall patterns in length of initial lag phase (d) and decay rate (m), we found that the three strains of Bd differed in their growth rates (r) and carrying capacities (K), which demonstrates diversity among strains in thermal tolerance and performance across a broad temperature spectrum. Additionally, we found that all three isolates continued to grow well in 2–27 °C, and following freeze shock and heat shock treatments, which establishes a new thermal sensitivity profile for Bd. Lastly, we found that two strains (the Temperate and the Bullfrog strains) differed from the Tropical strain in several intriguing ways.
The responses of the Temperate and Bullfrog strains differed from the Tropical strain in two respects. First, we found that the Temperate and Bullfrog strains had higher logistic growth rates (r) and carrying capacities (K) at the upper and lower extremities of the temperature range (especially at low temperatures, with high growth at 2–3 °C). In contrast, the Tropical strain (collected from Brazil) exhibited relatively lower growth rates and carrying capacities at these same thermal extremes. Second, our AUC analyses indicate that Temperate and Bullfrog strains had a better overall performance compared to the Tropical strain across the entire range of experimental temperatures. Because the point of origin of the Bullfrog isolate is unknown, and because we compared only three isolates, it was not possible to make general conclusions regarding adaptive responses to thermal conditions for all of our Bd strains. However, it is intriguing that the Bullfrog isolate, which is more closely related to the Tropical isolate (based on genomic data; see Rosenblum et al. 2013), exhibited striking differences from the Tropical isolate in overall performance. Additional research that includes multiple representative isolates from different thermal regions will be necessary to confirm these findings, and to better understand if variation in responses to temperature are genetically determined, or adaptive in a particular thermal environment.
While the idea of Bd adaptation to thermal conditions requires further investigation, our results indicate that there is variation among Bd strains in thermal tolerance and in overall performance, which are both important findings for different reasons. Because we know that the risk of mortality is proportional to the Bd load on an amphibian (Raffel et al. 2013), a Bd strain with better overall performance across a broad temperature breadth may be more threatening for amphibian hosts that utilize heterogeneous thermal environments. However, for amphibian hosts that occur in environments where temperatures reach the extremes of the thermal spectrum for Bd, a strain that can grow well in such conditions will likely also be problematic [e.g., if Bd survives overwintering events (e.g., Knapp et al. 2011) or persists despite host behavioral regulation of body temperatures (Rowley and Alford 2013)]. While the magnitude of these temperature-related effects will be specific to the particular environment and mediated by host biology (including life history, behavior and inherent Bd resistance/tolerance), the temperature sensitivity of Bd local strain(s) could considerably influence disease development and the propensity of Bd to cause devastating outbreaks.
Diversity in thermal tolerance among Bd isolates may help explain why amphibians have experienced severe outbreaks in regions where temperature conditions are considered sub-optimal for Bd (e.g., Knapp et al. 2011). For example, in the Sierra Nevada Mountains of California, the mountain yellow-legged frog (Rana muscosa and R. sierra species complex; Vredenburg et al. 2010) continues to experience disease-related declines despite the fact that these host species spend considerable time in temperatures that are lower than the thermal optimum of Bd (Knapp et al. 2011). Indeed, these amphibian hosts experience a wide range of temperatures (<0–30 °C) that can fluctuate dramatically on a daily and seasonal basis (Knapp et al. 2011). Our results suggest that the thermal profile for the Temperate strain (CJB5-2), which was collected from R. muscosa in this region, may contribute to severity of the chytridiomycosis outbreaks and declines in this species. The Temperate strain exhibited a better overall performance across a wider thermal breadth, had high growth rates at low temperatures, and was able to grow following a temporary freeze without cryoprotectant. However, to fully explain the severity of chytridiomycosis in these species, we need a better understanding of the diversity of strains of Bd among and within host populations (Byrne et al. 2016), the responses to a variety of stable and fluctuating thermal conditions (Raffel et al. 2013), and the species-specific host responses to temperature conditions that are important for disease development.
Beyond the implications for amphibians, it is critical to understand fungal pathogen responses to thermal conditions for a variety of reasons. First, fungi are generally known for readily adapting to their temperature conditions, but the mechanisms that dictate fungal thermal tolerance and temperature-dependent pathogenesis are not well understood (Feller and Gerday 2003). Therefore, investigations that aim to resolve these mechanisms are of central importance for multiple medically important fungal diseases (e.g., Candida; Antley and Hazen 1988). Second, fungal pathogens have been implicated in many novel fungal diseases in wildlife, including White nose syndrome in bats, colony collapse disorder in bees, and a facial fungal disease in snakes (Voyles et al. 2015; Langwig et al. 2015). Together with chytridiomycosis, these emerging fungal diseases have caused—and continue to cause—dramatic losses of biodiversity. Researchers and wildlife managers are just beginning to confront these disease threats (Voyles et al. 2015; Langwig et al. 2015) and will greatly benefit from understanding the environmental conditions that allow these pathogens to emerge, spread, and cause high levels of host mortality. Third, rapidly changing environments are predicted to shift fungal disease dynamics, but understanding complexities of fungal responses to temperature will be important for anticipating the disease impacts (Raffel et al. 2013; Rohr et al. 2013). For example, it has been suggested that mismatches in thermal tolerances of Bd and amphibian hosts could drive chytridiomycosis outbreaks (Nowakowski et al. 2016; Cohen et al. 2017). Therefore, a shifting climate, or an introduction of a Bd strain to a thermal environment that it was not adapted to, could dramatically affect the propensity of Bd to cause an outbreak (Nowakowski et al. 2016; Cohen et al. 2017).
We suggest that investigations that focus on the responses of hosts and pathogens to temperature will help advance the rapidly growing field of disease ecology. In particular, we suggest that pathogen responses to low temperatures may be currently understudied, and could be key to understanding what is driving disease dynamics in recent catastrophic fungal pandemics. The critical thermal minima of microbes have traditionally been underestimated, probably due, at least in part, to the lack of refrigerating incubators (Morita 1975; Rohr et al. 2013). It is also possible that we have yet to fully appreciate the ubiquity of psychrophilic microbes (Stokes and Redmond 1966), particularly psychrophilic and psychro-tolerant fungi. Given that many devastating fungal pandemics are occurring in ectotherms (e.g., amphibian chytridiomycosis, snake fungal facial disease; Voyles et al. 2015; Langwig et al. 2015) or in animals that undergo torpor events (e.g., White nose syndrome in bats), we suggest that an integration of thermal biology and disease ecology is timely and may prove critical for developing appropriate conservation strategies for infectious diseases in wildlife.
We thank Gabriela Rios-Sotelo, Rachel Perez, Cecelia Ogunro and Zachary Gajewski for their assistance with data collection and editing. This study was supported by the National Science Foundation (IOS-13542421 to EBR and JV) and an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health (P20GM103451 to JV).
Author contribution statement
JV and EBR designed the experiments. JV, RK, CB, DM, JM performed the experiments. LR and JR developed the models. JV and LR wrote the manuscript.
- Clark JS (2007) Models for ecological data: an introduction, vol 11. Princeton University Press Princeton, New JerseyGoogle Scholar
- Johnston IA, Bennett AF (2008) Animals and temperature: phenotypic and evolutionary adaptation. Cambridge University Press, Cambridge, UKGoogle Scholar
- Jurasinski G, Koebsch F, Guenther A, Beetz S (2014) Flux rate calculation from dynamic closed chamber measurements. R package version 0.3–0Google Scholar
- Murray PR, Rosenthal KS, Pfaller MA (2015) Medical microbiology. Elsevier Health Sciences, PhiladelphiaGoogle Scholar
- Plummer M (2013) rjags: Bayesian graphical models using mcmc. R package version 3Google Scholar
- Plummer M et al (2003) Jags: A program for analysis of bayesian graphical models using gibbs sampling. In: Proceedings of the 3rd international workshop on distributed statistical computing, vol 124, Technische Universit at Wien Wien, Austria, p 125Google Scholar
- R Core Team (2016) R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, p 2013Google Scholar
- Rosenblum EB, James TY, Zamudio KR, Poorten TJ, Ilut D, Rodriguez D, Eastman JM, Richards-Hrdlicka K, Joneson S, Jenkinson TS et al (2013) Complex history of the amphibian-killing chytrid fungus revealed with genome resequencing data. Proc Natl Acad Sci 110:9385–9390CrossRefPubMedPubMedCentralGoogle Scholar
- Voyles J, Johnson LR, Briggs CJ, Cashins SD, Alford RA, Berger L, Skerratt LF, Speare R, Rosenblum EB (2012) Temperature alters reproductive life history patterns in Batrachochytrium dendrobatidis, a lethal pathogen associated with the global loss of amphibians. Ecol Evol 2:2241–2249CrossRefPubMedPubMedCentralGoogle Scholar
- Voyles J, Johnson LR, Briggs CJ, Cashins SD, Alford RA, Berger L, Skerratt LF, Speare R, Rosenblum EB (2014) Experimental evolution alters the rate and temporal pattern of population growth in Batrachochytrium dendrobatidis, a lethal fungal pathogen of amphibians. Ecol Evol 4:3633–3641CrossRefPubMedPubMedCentralGoogle Scholar
- Voyles J, Kilpatrick AM, Collins JP, Fisher MC, Frick WF, McCallum H, Willis CK, Blehert DS, Murray KA, Puschendorf R et al (2015) Moving beyond too little, too late: managing emerging infectious diseases in wild populations requires international policy and partnerships. EcoHealth 12:404–407CrossRefPubMedGoogle Scholar
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.