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

Gregory F. Albery*, Amy R. Sweeny, Daniel J. Becker, Shweta Bansal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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. Conducting a broad literature search, we collated 31 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. 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 0.01 km2) exhibited substantial spatial variation. 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. 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. A free Plain Language Summary can be found within the Supporting Information of this article.

Original languageEnglish
Pages (from-to)214-225
Number of pages12
JournalFunctional Ecology
Issue number1
Early online date15 Oct 2021
Publication statusPublished - 5 Jan 2022

Keywords / Materials (for Non-textual outputs)

  • meta analysis
  • parasite transmission
  • spatial analysis
  • wildlife disease


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