Predicting fungal infection rate and severity with skin‐associated microbial communities on amphibians

Pathogen success (risk and severity) is influenced by host‐associated microbiota, but the degree to which variation in microbial community traits predict future infection presence/absence (risk) and load (severity) for the host is unknown. We conducted a time‐series experiment by sampling the skin‐a...

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Bibliographic Details
Published in:Molecular ecology 2022-04, Vol.31 (7), p.2140-2156
Main Authors: Chen, Melissa Y., Kueneman, Jordan G., González, Antonio, Humphrey, Greg, Knight, Rob, McKenzie, Valerie J.
Format: Article
Language:English
Subjects:
Amphibians
Amphibians - microbiology
Animals
Bacteria
Bacteria - genetics
Batrachochytrium
Batrachochytrium - genetics
Chytridiomycota
Community composition
community ecology
disease biology
Fungi
Health risks
Infections
Microbial activity
Microbiomes
Microbiota
Microorganisms
Mycoses - epidemiology
Mycoses - microbiology
Mycoses - veterinary
Pathogens
Reptiles & amphibians
Risk
Skin
Skin - microbiology
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Summary:Pathogen success (risk and severity) is influenced by host‐associated microbiota, but the degree to which variation in microbial community traits predict future infection presence/absence (risk) and load (severity) for the host is unknown. We conducted a time‐series experiment by sampling the skin‐associated bacterial communities of five amphibian species before and after exposure to the fungal pathogen, Batrachochytrium dendrobaditis (Bd). We sought to determine whether microbial community traits are predictors of, or are affected by, Bd infection risk and intensity. Our results show that richness of putative Bd‐inhibitory bacteria strongly predicts infection risk, while the proportion of putative Bd‐inhibitory bacteria predicts future infection intensity. Variation in microbial community composition is high across time and individual, and bacterial prevalence is low. Our findings demonstrate how ecological community traits of host‐associated microbiota may be used to predict infection risk by pathogenic microbes.
ISSN:0962-1083
1365-294X
DOI:10.1111/mec.16372