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Abstract Global climate change is increasing the frequency of unpredictable weather conditions; however, it remains unclear how species‐level and geographic factors, including body size and latitude, moderate impacts of unusually warm or cool temperatures on disease. Because larger and lower‐latitude hosts generally have slower acclimation times than smaller and higher‐latitude hosts, we hypothesised that their disease susceptibility increases under ‘thermal mismatches’ or differences between baseline climate and the temperature during surveying for disease. Here, we examined how thermal mismatches interact with body size, life stage, habitat, latitude, elevation, phylogeny and International Union for Conservation of Nature (IUCN) conservation status to predict infection prevalence of the chytrid fungusBatrachochytrium dendrobatidis(Bd) in a global analysis of 32 291 amphibian hosts. As hypothesised, we found that the susceptibility of larger hosts and hosts from lower latitudes toBdwas influenced by thermal mismatches. Furthermore, hosts of conservation concern were more susceptible than others following thermal mismatches, suggesting that thermal mismatches might have contributed to recent amphibian declines.
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Predicting the growth of the amphibian chytrid fungus in varying temperature environments
Abstract 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.
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- Award ID(s):
- 1750113
- PAR ID:
- 10447008
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Ecology and Evolution
- Volume:
- 11
- Issue:
- 24
- ISSN:
- 2045-7758
- Format(s):
- Medium: X Size: p. 17920-17931
- Size(s):
- p. 17920-17931
- Sponsoring Org:
- National Science Foundation
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