Landscape-generated ‘super-spreaders’: How habitat composition and connectivity shape deer social networks
Sociality plays an integral role in the spread of disease in wildlife populations, and social network structure could determine population vulnerability to infectious disease. Much research has focused on how characteristics of individuals influence their roles in networks, but we were interested in the role that landscape characteristics plays in between-group sociality at both the individual (local) and the population (global) scales, using white-tailed deer (Odocoileus virginianus) as our model species.
We estimated seasonal contact rates (frequency of proximity <25 m of deer from different social groups) among 36 GPS-collared female white-tailed deer in central and southern Illinois. We used these contact rates to weight the edges of seasonal social networks. For our local-scale analysis, we used node-centrality metrics to gauge the relative sociality of individual deer and tested predictive models that included average home range overlap and both the amount and connectivity of habitat (forest, agriculture, edge). For our global-scale analysis, we used 7 independent GPS location datasets of female white-tailed deer. We used weighted network closeness as our dependent variable and assessed whether global social network structure could be predicted by habitat composition.
At the local scale, we found that both the amount and connectivity of agricultural land were the strongest predictors of node connectedness: female deer inhabiting areas with relatively high amount and connectivity of agriculture tended to be more social with their neighbors during gestation (R2 = 0.45), fawning (R2 = 0.35), and the rut (R2 = 0.39). Assuming that all deer have the same drive to be social, this implies that within a population, individuals occurring in areas with more agriculture are more likely to contact their neighbors and potentially spread communicable disease, such as chronic wasting disease. At the global scale, we found a strong relationship between network closeness and the amount of edge habitat: deer populations in areas with relatively high amounts of edge tend to be more socially connected (R2 = 0.60). This suggests that communicable disease could spread faster in deer populations occurring in areas with relatively high amounts of edge habitat. Our work represents advancement in the general understanding of animal social networks, demonstrating how landscape features can underlie both the local and global structure of social networks.