Bats host a diversity of viruses, some zoonotic. Repeated emergence of diseases that jump into humans from bat reservoirs highlights a need for predictive approaches to pre-emptively identify virus-carrying species. We use a machine learning approach to examine drivers of viral diversity in bats, and differences in those drivers between RNA and DNA viruses. We find bat species with longer lifespans, broad geographic distributions in the eastern hemisphere, and large group sizes carry more viruses. Lifespan was a stronger predictor of DNA viral diversity, while group size and family were more important for RNA viruses, patterns that may reflect broad differences in infection duration. Finally, we identify 55 bat species not currently considered reservoirs that are most likely to carry viruses. Mapping these predictions highlights global regions that could be targeted for disease surveillance, including those with few bat species but a large proportion of predicted carriers.
Data were collected using primary literature searches, existing trait databases, and published field guides.