Infectious disease transmission and behavioural allometry in wild mammals.

Abstract

Animals' social and movement behaviours can impact the transmission dynamics of infectious diseases, especially for pathogens transmitted through close contact between hosts or through contact with infectious stages in the environment. Estimating pathogen transmission rates and R0 from natural systems can be challenging. Because host behavioural traits that underlie the transmission process vary predictably with body size, one of the best-studied traits among animals, body size might therefore also predict variation in parasite transmission dynamics. Here, we examine how two host behaviours, social group living and the intensity of habitat use, scale allometrically using comparative data from wild primate, carnivore and ungulate species. We use these empirical relationships to parameterize classical compartment models for infectious micro- and macroparasitic diseases, and examine how the risk of pathogen invasion changes as a function of host behaviour and body size. We then test model predictions using comparative data on parasite prevalence and richness from wild mammals. We report a general pattern suggesting that smaller-bodied mammal species utilizing home ranges more intensively experience greater risk for invasion by environmentally transmitted macroparasites. Conversely, larger-bodied hosts exhibiting a high degree of social group living could be more readily invaded by directly transmitted microparasites. These trends were supported through comparison of micro- and macroparasite species richness across a large number of carnivore, primate and ungulate species, but empirical data on carnivore macroparasite prevalence showed mixed results. Collectively, our study demonstrates that combining host behavioural traits with dynamical models of infectious disease scaled against host body size can generate testable predictions for variation in parasite risk across species; a similar approach might be useful in future work focused on predicting parasite distributions in local host communities.