Predictors of malaria infection in a wild bird population: landscape-level analyses reveal climatic and anthropogenic factors.

How the environment influences the transmission and prevalence of disease in a population of hosts is a key aspect of disease ecology. The role that environmental factors play in host-pathogen systems has been well studied at large scales, that is, differences in pathogen pressures among separate populations of hosts or across land masses. However, despite considerable understanding of how environmental conditions vary at fine spatial scales, the effect of these parameters on host-pathogen dynamics at such scales has been largely overlooked. Here, we used a combination of molecular screening and GIS-based analysis to investigate how environmental factors determine the distribution of malaria across the landscape in a population of Berthelot's pipit (Anthus berthelotii, Bolle 1862) on the island of Tenerife (Canary Islands, Spain) using spatially explicit models that account for spatial autocorrelation. Minimum temperature of the coldest month was found to be the most important predictor of malaria infection at the landscape scale across this population. Additionally, anthropogenic factors such as distance to artificial water reservoirs and distance to poultry farms were important predictors of malaria. A model including these factors, and the interaction between distance to artificial water reservoirs and minimum temperature, best explained the distribution of malaria infection in this system. These results suggest that levels of malaria infection in this endemic species may be artificially elevated by the impact of humans. Studies such as the one described here improve our understanding of how environmental factors, and their heterogeneity, affect the distribution of pathogens within wild populations. The results demonstrate the importance of measuring fine-scale variation - and not just regional effects - to understand how environmental variation can influence wildlife diseases. Such understanding is important for predicting the future spread and impact of disease and may help inform disease management programmes as well as the conservation of specific host species.