Abstract

AbstractWe model Batrachochytrium dendrobatidis (Bd) infection rates in Jamaican frogs—one of the most threatened amphibian fauna in the world. The majority of species we surveyed were terrestrial direct‐developing frogs or frogs that breed in tank bromeliads, rather than those that use permanent water bodies to breed. Thus, we were able to investigate the climatic correlates of Bd infection in a frog assemblage that does not rely on permanent water bodies. We sampled frogs for Bd across all of the major habitat types on the island, used machine learning algorithms to identify climatic variables that are correlated with infection rates, and extrapolated infection rates across the island. We compared the effectiveness of the machine learning algorithms for species distribution modeling in the context of our study, and found that infection rate rose quickly with precipitation in the driest month. Infection rates also increased with mean temperature in the warmest quarter until 22 °C, and remained relatively level thereafter. Both of these results are in accordance with previous studies of the physiology of Bd. Based on our environmental results, we suggest that frogs occupying high‐precipitation habitats with cool rainy‐season temperatures, though zcurrently experiencing low frequencies of infection, may experience an increase in infection rates as global warming increases temperatures in their habitat.

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