Range area and the fast–slow continuum of life history traits predict pathogen richness in wild mammals

Abstract

Surveillance of pathogen richness in wildlife is needed to identify host species with a high risk of zoonotic disease spillover. While several predictors of pathogen richness in wildlife hosts have been proposed, their relative importance has not been formally examined. This hampers our ability to identify potential disease reservoirs, particularly in remote areas with limited surveillance efforts. Here we analyzed 14 proposed predictors of pathogen richness using ensemble modeling and a dataset of 1040 host species to identify the most important predictors of pathogen richness in wild mammal species. After controlling for research effort, larger species geographic range area was identified to be associated with higher pathogen richness. We found evidence of duality in the relationship between the fast–slow continuum of life-history traits and pathogen richness, where pathogen richness increases near the extremities. Taxonomic orders Carnivora, Proboscidea, Artiodactyla, and Perissodactyla were predicted to host high pathogen richness. The top three species with the highest pathogen richness predicted by our ensemble model were Canis lupus, Sus scrofa, and Alces alces. Our results can help support evidence-informed pathogen surveillance and disease reservoir management to prevent the emergence of future zoonotic diseases.

Identifying life-history patterns along the fast-slow continuum of mammalian viral carriers

“…The eco-evolutionary peculiarities of each viral group drive patterns of compatibility between mammals and viruses, as we partially captured by separately assessing different eco-evolutionary groups of viruses. By including multiple sides of viral diversity, we were able to pick up on trends that went undetected in previous studies where pathogen richness was analysed as a whole [ 4 , 65 ]. We found that larger mammals are carriers for several viral groups, but both fast- and slow-living species are exploited by multiple viral groups with different ecological, evolutionary and structural features.…”

A framework to predict zoonotic reservoirs under data uncertainty: a case study on betacoronaviruses

“…We ran a Wilcoxon test to assess whether our selection of pseudo-negatives was biased by species' range size, and found that the median range size of pseudo-negatives was signi cantly larger than the median range size of a random sample of mammals (p < 0.001) but also signi cantly smaller than the median range size of positive species (p < 0.001). While this comparison highlighted that neither positive species nor pseudo-negative species are a representative sample of the average mammal in terms of range size (Figure S1), largerthan-random range size of pseudo-negatives is actually desirable as it may counterbalance the exceptionally large range size of positive species, which is probably due to a combination of sampling bias and exposure to pathogens (Choo et al, 2023). Our pseudo-negative protocol identi ed 1,117 pseudo-negative species that met the criteria.…”