Predictions of Disease Risk in Space and Time Based on the Thermal Physiology of an Amphibian Host-Pathogen Interaction

Abstract

Emerging infectious diseases have been responsible for declines and extinctions in a growing number of species. Predicting disease variables like infection prevalence and mortality and how they vary in space and time will be critical to understanding how host-pathogen dynamics play out in natural environments and will help to inform management actions. The pandemic disease chytridiomycosis, caused by the fungal pathogen, Batrachochytrium dendrobatidis (Bd), has been implicated in declines in hundreds of amphibian species worldwide. We used field-collected measurements of host body temperatures and other physiological parameters to develop a mechanistic model of disease risk in a declining amphibian, the Northern cricket frog (Acris crepitans). We first used a biophysical model to predict host body temperatures across the species range in the eastern United States. We then used empirically derived relationships between host body temperature, infection prevalence and survival to predict where and when the risk of Bd-related declines is greatest. Our model predicts that pathogen prevalence is greatest, and survival of infected A. crepitans frogs is lowest, just prior to breeding when host body temperatures are low. Taken together, these results suggest that Bd poses the greatest threat to short-lived A. crepitans populations in the northern part of this host’s range and that disease-related recruitment failure may be common. Furthermore, our study demonstrates the utility of mechanistic modeling approaches for predicting disease outbreaks and dynamics in animal hosts.