Continental-scale climatic gradients of pathogenic microbial taxa in birds and bats

Abstract

<p>The connections of climatic variables to zoonotic and wildlife diseases remain uncertain. Here, we compiled a systematic database for the prevalence of 121 pathogenic microbial taxa in birds (ca 376 species) and bats (ca 39 species), including 11 939 observations from over 450 000 individuals across Europe and surrounding regions. We modelled the potential connection of climatic variables with the prevalence of 75 pathogenic microbial taxa at a multi-pathogenic-taxa level and of 17 most-studied pathogenic taxa at a single-pathogenic-taxon level. According to the multi-taxa model, the prevalence of bacterial taxa was positively associated with temperature, while this association was significantly weaker for eukaryotes and viruses. The prevalence of bacterial taxa was negatively associated with rainfall, while viruses showed a positive association with rainfall. These associations between climatic variables and prevalence of pathogenic taxa were not different between bird and bat hosts. According to the single-taxon models, the prevalence of influenza A viruses, <em>Plasmodium</em>, and several bacterial taxa in birds and bats was positively associated with temperature. Rainfall showed positive associations with the prevalence of Usutu, Sindbis and Influenza A viruses but the directions of significant associations varied among bacterial taxa. Strikingly, this was evidenced also between bacterial taxa that share hosts and transmission mechanism hinting towards hitherto unknown features on pathogen ecology, e.g. <em>Salmonella</em> versus <em>Campylobacter </em>and <em>Anaplasma</em> versus <em>Borrelia</em>. Our results suggest that rising temperature and increasing precipitation will accelerate the threat of bird- and bat-associated bacterial and viral pathogens to wildlife, domesticated animals and humans, respectively. However, the idiosyncratic relationships with climatic conditions among pathogenic taxa highlight the need for pathogen-specific predictive models to understand future pathogen distributions.<br></p>