Fine-scale spatial patterns of wildlife disease are common and understudied

1. All parasites are heterogeneous in space, yet little is known about the
prevalence and scale of this spatial variation, particularly in wild
animal systems. To address this question, we sought to identify and
examine spatial dependence of wildlife disease across a wide range of
systems. 2. Conducting a broad literature search, we collated 31 such
datasets featuring 89 replicates and 71 unique host-parasite combinations,
only 51% of which had previously been used to test spatial hypotheses. We
analysed these datasets for spatial dependence within a standardised
modelling framework using Bayesian linear models, and we then
meta-analysed the results to identify generalised determinants of the
scale and magnitude of spatial autocorrelation. 3. We detected spatial
autocorrelation in 48/89 model replicates (54%) across 21/31 datasets
(68%), spread across parasites of all groups. Even some very small study
areas (under 0.01km2) exhibited substantial spatial variation. 4. Despite
the common manifestation of spatial variation, our meta-analysis was
unable to identify host-, parasite-, or sampling-level determinants of
this heterogeneity across systems. Parasites of all transmission modes had
easily detectable spatial patterns, implying that structured contact
networks and susceptibility effects are potentially as important in
spatially structuring disease as are environmental drivers of transmission
efficiency. 5. Our findings demonstrate that fine-scale spatial patterns
of infection manifest frequently and across a range of wild animal
systems, and many studies are able to investigate them – whether or not
the original aim of the study was to examine spatially varying processes.
Given the widespread nature of these findings, studies should more
frequently record and analyse spatial data, facilitating development and
testing of spatial hypotheses in disease ecology. Ultimately, this may
pave the way for an a priori predictive framework for spatial variation in
novel host-parasite systems.