Thermal mismatch models derived from occurrence data predict pathogen prevalence in frogs

• 成果类型: Article
• 署名作者: Duncan, Richard P.; Scheele, Ben C.; Clulow, Simon
• 署名单位: University of Canberra; Australian National University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-12880
• DOI: 10.1073/pnas.2423706122
• 发表日期: 2025-07-29
• 关键词: batrachochytrium-dendrobatidis infection; fungal pathogen; disease risk; ongoing declines; host; chytridiomycosis; DYNAMICS; patterns; amphibians; susceptibility

摘要

Emerging infectious diseases increasingly threaten many wildlife populations, yet the impacts of pathogens vary considerably both within and among host species. The environmental tolerance mismatch hypothesis (ETMH) suggests that this variability stems in part from differences in the relative performance of hosts and pathogens under varying environmental conditions. According to the ETMH, pathogen impacts should be more severe in environments where pathogen performance is high and host performance is low, and vice versa. However, testing the ETMH with field data is challenging due to the difficulty of measuring host and pathogen performance among locations and quantifying performance mismatches. Here, we demonstrate that a measure of thermal mismatch, based on species realized thermal niches derived from species occurrence data, can reliably predict variation in the prevalence of the amphibian fungal pathogen Batrachochytrium dendrobatidis (Bd-chytrid fungus) within and among 42 frog host species in Australia. Specifically, we show that 1) within species, more warm-adapted host species show a steeper decline in Bd prevalence with increasing mean annual temperature, potentially reflecting greater host advantage at warmer temperatures; and 2) among host species, mean pathogen prevalence declines as the thermal affinity of hosts diverges from that of the pathogen. Our findings strongly support the ETMH and, importantly, offer a promising approach to predicting pathogen outcomes both spatially and temporally using species occurrence data. This approach enhances our understanding of variability in pathogen impacts and could inform management actions to mitigate these effects.