Infection profiles in a wild rat–protozoan network are shaped by host traits and environmental factors

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. Heterogeneity in parasite infection among hosts shapes transmission dynamics and spillover risk to other host species but remains poorly understood in natural systems. We applied network-based stochastic block modeling and machine learning to a uniquely rich dataset to identify and predict protozoan infection profiles in introduced black rats (Rattus rattus) sampled along an environmental gradient in Madagascar. Three host infection profiles emerged, differing in parasite richness and composition, revealing distinct host roles in transmission. Predictive models incorporating host traits (e.g., body mass, microbiome composition) and environmental variables (e.g., population density, habitat structure) accurately classified hosts into profiles, with host traits contributing to predictions 40% more than environmental features. Our findings show how intrinsic and extrinsic factors jointly structure individual-level infection heterogeneity and underscore the value of infection profiles for understanding host–parasite dynamics. Our integrative approach offers a framework for predicting infection risk at human–animal interfaces where zoonotic pathogens circulate. https://doi.org/10.32942/X2MP9H Ecology and Evolutionary Biology, Life Sciences, Pathogenic Microbiology Host-parasite network, Infection profile, land-use change, Rat-protozoa interaction, Infection heterogeneity, Stochastic block modeling, Netwok analysis Published: 2025-08-19 00:43 Last Updated: 2025-08-19 00:43 CC-BY Attribution-NonCommercial-ShareAlike 4.0 International Data and Code Availability Statement: All data and code needed to evaluate the conclusions in the paper are present on the GitHub repository https://github.com/MadagascarEEID/rat_protozoa_infection_profiles. Language: English