Spatiotemporal variation in drivers of parasitism in a wild wood mouse population

Abstract

AbstractHost-parasite interactions in nature are driven by a range of factors across several ecological scales, so observed relationships are often context-dependent. Importantly, if these factors vary across space and time, practical sampling limitations can limit or bias inferences, and the relative importance of different drivers can be hard to discern.We collected a replicated, longitudinal dataset of >1000 individual wood mice (Apodemus sylvaticus) encompassing 6 years of sampling across 5 different woodland sites to investigate how environmental, host and within-host factors determine infection intensity of a highly prevalent gastrointestinal nematode, Heligmosomoides polygyrus.We used a Bayesian modelling approach to further quantify if and how each factor varied in space and time. Finally, we examined the extent to which a lack of spatially or temporally replication (i.e., within single years or single sites) and single (cross-sectional) versus repeated (longitudinal) sampling of individuals would affect which drivers were found to predict H. polygyrus infection.Season, host body condition, and sex were the three most important determinants of infection intensity; however, the strength and even direction of these effects varied in time, but not in space. Additionally, longitudinal datasets, in which we can control for within-individual variation through repeat observations, provided more robust estimates of the drivers of parasite intensity compared to cross-sectional data.These results highlight the importance of accounting for spatiotemporal variation in drivers of disease dynamics and the need to incorporate spatiotemporal replication when designing sampling regimes. Furthermore, they suggest that embracing rather than simply controlling for spatiotemporal variation can reveal important insight into host-parasite relationships in the wild.