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Skin microbial communities are an essential part of host health and can play a role in mitigating disease. Host and environmental factors can shape and alter these microbial communities and, therefore, we need to understand to what extent these factors influence microbial communities and how this can impact disease dynamics. Microbial communities have been studied in amphibian systems due to skin microbial communities providing some resistance to the amphibian chytrid fungus, Batrachochytrium dendrobatidis . However, we are only starting to understand how host and environmental factors shape these communities for amphibians. In this study, we examined whether amphibian skin bacterial communities differ among host species, host infection status, host developmental stage, and host habitat. We collected skin swabs from tadpoles and adults of three Ranid frog species ( Lithobates spp.) at the Mianus River Gorge Preserve in Bedford, New York, USA, and used 16S rRNA gene amplicon sequencing to determine bacterial community composition. Our analysis suggests amphibian skin bacterial communities change across host developmental stages, as has been documented previously. Additionally, we found that skin bacterial communities differed among Ranid species, with skin communities on the host species captured in streams or bogs differing from the communities of the species captured on land. Thus, habitat use of different species may drive differences in host-associated microbial communities for closely-related host species. 

Environmental temperature is a crucial abiotic factor that influences the success of ectothermic organisms, including hosts and pathogens in disease systems. One example is the amphibian chytrid fungus,Batrachochytrium dendrobatidis(Bd), which has led to widespread amphibian population declines. Understanding its thermal ecology is essential to effectively predict outbreaks. Studies that examine the impact of temperature on hosts and pathogens often do so in controlled constant temperatures. Although varying temperature experiments are becoming increasingly common, it is unrealistic to test every temperature scenario. Thus, reliable methods that use constant temperature data to predict performance in varying temperatures are needed. In this study, we tested whether we could accurately predictBdgrowth in three varying temperature regimes, using a Bayesian hierarchical model fit with constant temperatureBdgrowth data. We fit the Bayesian hierarchical model five times, each time changing the thermal performance curve (TPC) used to constrain the logistic growth rate to determine how TPCs influence the predictions. We then validated the model predictions usingBdgrowth data collected from the three tested varying temperature regimes. Although all TPCs overpredictedBdgrowth in the varying temperature regimes, some functional forms performed better than others. Varying temperature impacts on disease systems are still not well understood and improving our understanding and methodologies to predict these effects could provide insights into disease systems and help conservation efforts.