|
1000
|
Abstract/Summary
|
-
Quantifying how the environment shapes host immune defense is important for understanding which wild populations may be more susceptible or resistant to pathogens. Spatial variation in parasite risk, food and predator abundance, and abiotic conditions can each affect immunity, and these factors can also manifest at both local and biogeographic scales. Yet identifying predictors and the spatial scale of their effects is limited by the rarity of studies that measure immunity across many populations of broadly distributed species. We analyzed leukocyte profiles from 39 wild populations of the common vampire bat (Desmodus rotundus) across its wide geographic range throughout the Neotropics. White blood cell differentials varied spatially, with proportions of neutrophils and lymphocytes varying up to six-fold across sites. Leukocyte profiles were spatially autocorrelated at small and very large distances, suggesting that local environment and large-scale biogeographic factors influence cellular immunity. Generalized additive models showed that bat populations closer to the northern and southern limits of the species range had more neutrophils, monocytes, and basophils, but fewer lymphocytes and eosinophils, than bats sampled at the core of their distribution. Habitats with access to more livestock also showed similar patterns in leukocyte profiles, but large-scale patterns were partly confounded by time between capture and sampling across sites. Our findings suggest that populations at the edge of their range experience physiologically limiting conditions that predict higher chronic stress and greater investment in cellular innate immunity. High food abundance in livestock-dense habitats may exacerbate such conditions by increasing bat density or diet homogenization, although future spatially and temporally coordinated field studies with common protocols are needed to limit sampling artifacts. Systematically assessing immune function and response over space will elucidate how environmental conditions influence traits relevant to epidemiology and help predict disease risks with anthropogenic disturbance, land conversion, and climate change.
|
