All pathogens are heterogeneous in space, yet little is known about the scale and prevalence of this spatial variation, particularly in wild animal systems. Identifying how spatial patterns manifest in wild animal systems can be important for disease control, designing sampling regimes, and interpreting the findings of disease ecology studies. However, the tools to do so can be complex and unintuitive, and small-scale spatial analyses of wildlife disease are uncommon. We conducted a literature search using broad terms to identify datasets involving diseases of wild mammals in spatially distributed contexts. We found that very few studies (<10%) recorded spatial data and even fewer (<1%) shared them publicly. Through a combination of methods we obtained 31 viable datasets featuring 108 host-parasite combinations across 43 spatial locations, covering a wide range of different mammal species, pathogen groups, and sampling contexts. Only 15 (50%) of these datasets had previously been used to examine spatial variation.
Results/Conclusions
We analysed these datasets for generalised spatial patterns of disease within a standardised modelling framework using Bayesian linear models, finding strong, common within-population spatial effects. 40% of host-parasite systems across 70% of datasets exhibited detectable spatial patterns. Meta-analysis revealed that spatial patterns were more common and short-range for directly transmitted viruses and arthropods, and were rarer for vector-borne and faecal-oral parasites. These results imply strong spatial structuring of social contact networks, and demonstrate that environmental effects on transmission are not necessarily the most important contributing driver of spatial structuring. Sampling effects were important in determining the strength and extent of spatial variation, but even very small study areas (under 0.01km2) exhibited substantial spatial patterns. Our findings demonstrate that spatial effects on parasite infection are often present in wild animal systems, whether or not the aim of the sampling regime is to examine spatially varying processes. Additionally, environmental drivers on susceptibility and contact behaviours may be just as important in structuring diseases as are drivers of transmission efficiency. Disease ecology studies should take advantage of the ongoing explosion in GPS and biologging technology to more frequently record and analyse spatial data, and should more frequently share their spatial data publicly, allowing the more thorough development and testing of hypotheses in disease ecology.