The fine-scale landscape of immunity and parasitism in a wild ungulate population

Abstract

AbstractSpatial heterogeneity in parasite susceptibility and exposure is a common source of confounding variation in disease ecology studies. However, it is not known whether spatial autocorrelation acts on immunity in particular at small scales, within wild animal populations, and whether this predicts spatial patterns in infection. Here we used a well-mixed wild population of individually recognised red deer (Cervus elaphus) inhabiting a heterogeneous landscape to investigate fine-scale spatial patterns of immunity and parasitism. We noninvasively collected 842 faecal samples from 141 females with known ranging behaviour over two years. We quantified total and helminth-specific mucosal antibodies and counted propagules of three gastrointestinal helminth taxa. These data were analysed with linear mixed models using the Integrated Nested Laplace Approximation (INLA), using a Stochastic Partial Differentiation Equation approach (SPDE) to control for and quantify spatial autocorrelation. We also investigated whether spatial patterns of immunity and parasitism changed seasonally. We discovered substantial spatial heterogeneity in general and helminth-specific antibody levels and parasitism with two helminth taxa, all of which exhibited contrasting seasonal variation in their spatial patterns. Notably, strongyle nematode intensity did not align with density hotspots, while Fasciola hepatica intensity appeared to be strongly influenced by the presence of wet grazing. In addition, antibody hotspots did not correlate with distributions of any parasites. Our results suggest spatial heterogeneity may be an important factor affecting immunity and parasitism in a wide range of study systems. We discuss these findings with regards to the design of sampling regimes and public health interventions, and suggest that disease ecology studies investigate spatial heterogeneity more regularly to enhance their results, even when examining small geographic areas.