Animal space use and spatial overlap can have important consequences for pathogen transmission. Identifying how environmental variability and inter-individual variation affect spatial patterns to drive transmission heterogeneity in wildlife is a priority for effective management of wildlife disease. However, there are few experimental studies investigating how food abundance and macroparasite infection affect transmission opportunities in wildlife. Wild bank voles (Myodes glareolus) are a useful study system to investigate spatial patterns of wildlife and are amenable to experimental manipulations. We conducted a replicated, factorial field experiment in which we added food and removed helminths in wild vole populations in natural forest habitat and monitored vole space use and spatial overlap using capture-mark-recapture methods. Using a combination of home range and network analyses, we quantified vole space use and spatial overlap. We then compared the effects of food addition and helminth removal and investigated the impact of season and sex on space use and spatial overlap. We found that space use was impacted by breeding season and sex but not by food addition or helminth removal. Sex-based, seasonal changes in shared space use were more notable with food addition. Food addition also increased the frequency of, and variability in, spatial overlap occurring between individual voles at trapping locations, while helminth removal had no effect. Our work provides empirical evidence quantifying the spatial effects of food abundance and macroparasite infection on wildlife populations. We demonstrate the potential for high food abundance to increase spatial overlap relevant for pathogen transmission. Our findings also suggest that individual contributions to transmission may be heterogeneous within populations and that season and sex are important covariates to consider when identifying when and with whom transmission is most likely to occur.
For free-ranging wildlife, it is often more practical to quantify interactions between individuals rather than successful transmission events; however, defining and quantifying transmission-relevant interactions is non-trivial. Researchers have choices in the technology used to collect data on animal locations in space and time as well as the methods of analysis to define network edges from those data. These choices can significantly affect network structure and subsequent inferences drawn about transmission. The chapter explores empirical and theoretical examples of network data collection and analysis to highlight important considerations for transmission inference. Since parasite–host behavior feedbacks have been understudied in network analyses, we discuss how to incorporate these feedbacks into network applications using existing and novel approaches.
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