Determinants of haemosporidian single- and co-infection risks in western palearctic birds

Abstract

Understanding the drivers of infection risk helps us to detect the most at-risk species in a community and identify species whose intrinsic characteristics could act as potential reservoirs of pathogens. This knowledge is crucial if we are to predict the emergence and evolution of infectious diseases. To date, most studies have only focused on infections caused by a single parasite, leaving out co-infections. Yet, co-infections are of paramount importance in understanding the ecology and evolution of host-parasite interactions due to the wide range of effects they can have on host fitness and on the evolutionary trajectories of parasites. Here, we used a multinomial Bayesian phylogenetic modelling framework to explore the extent to which bird ecology and phylogeny impact the probability of being infected by one genus (hereafter single infection) or by multiple genera (hereafter co-infection) of haemosporidian parasites. We show that while nesting and migration behaviours influenced both the probability of being single- and co-infected, species position along the slow-fast life-history continuum and geographic range size were only pertinent in explaining variation in co-infection risk. We also found evidence for a phylogenetic conservatism regarding both single- and co-infections, indicating that phylogenetically related bird species tend to have similar infection patterns. This phylogenetic signal was four times stronger for co-infections than for single infections, suggesting that co-infections may act as a stronger selective pressure than single infections. Overall, our study underscores the combined influence of hosts' evolutionary history and attributes in determining infection risk in avian host communities. These results also suggest that co-infection risk might be under stronger deterministic control than single infection risk, potentially paving the way toward a better understanding of the emergence and evolution of infectious diseases.

Keywords: Bayesian inference; Climatic niche; Co-infection; Geographic range; Haemoproteus; Leucocytozoon; Phylogenetic signal; Plasmodium.