Assessment of spatial genetic structure to identify populations at risk for infection of an emerging epizootic disease

Abstract

1. Understanding the geographic extent and connectivity of wildlife populations can provide important insights into the management of disease outbreaks but defining patterns of population structure is difficult for widely distributed species.Landscape genetic analyses are powerful methods for identifying cryptic structure and movement patterns that may be associated with spatial epizootic patterns in such cases.2. We characterized patterns of population substructure and connectivity using microsatellite genotypes from 2,222 white-tailed deer (Odocoileus virginianus) in the Mid-Atlantic region of the United States, a region where chronic wasting disease was first detected in 2009. The goal of this study was to evaluate the juxtaposition between population structure, landscape features that influence gene flow, and current disease management units.3. Clustering analyses identified four to five subpopulations in this region, the edges of which corresponded to ecophysiographic provinces. Subpopulations were further partitioned into 11 clusters with subtle (F ST ≤ 0.041), but significant genetic differentiation. Genetic differentiation was lower and migration rates were higher among neighboring genetic clusters, indicating an underlying genetic cline.Genetic discontinuities were associated with topographic barriers, however. Resistance surface modeling indicated that gene flow was diffuse in homogenouslandscapes, but the direction and extent of gene flow were influenced by forest cover, traffic volume, and elevational relief in subregions heterogeneous for these landscape features. Chronic wasting disease primarily occurred among genetic clusters within a single subpopulation and along corridors of high landscape connectivity. 5. These results may suggest a possible correlation between population substructure, landscape connectivity, and the occurrence of diseases for widespread species. Considering these factors may be useful in delineating effective management units, although only the largest features produced appreciable differences in subpopulation structure. Disease mitigation strategies implemented at the scale S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section. How to cite this article: Miller WL, Miller-Butterworth CM, Diefenbach DR, Walter WD. Assessment of spatial genetic structure to identify populations at risk for infection of an emerging epizootic disease.

Spatial analysis of chronic wasting disease in free‐ranging white‐tailed deer ( Odocoileus virginianus ) in Illinois, 2008–2019

“…Applying space–time Poisson scan statistics helps identifying emerging disease clusters from surveillance data (Kulldorff et al., 2005; Joly et al, 2006; So et al., 2013). Due to the long incubation period and high transmissibility of CWD, longitudinal analyses are critical for monitoring trends of CWD dynamics in free‐ranging cervids (Miller et al., 2020). Previous studies have provided foundations for estimating CWD prevalence in the Illinois white‐tailed deer population using recreational hunter‐harvested deer (RHH) data (Manjerovic et al., 2014).…”

Landscape Features Fail to Explain Spatial Genetic Structure in White‐Tailed Deer Across Ohio, USA

“…Moreover, spatial genetic clusters may provide biologically meaningful management units for monitoring and managing disease spread (Blanchong et al 2008). Finally, genetic approaches allow biologists to address questions at much larger spatial scales than might be possible using direct observations of movement from telemetry or mark‐recapture techniques (Miller et al 2020).…”

Spatial population genetics in heavily managed species: Separating patterns of historical translocation from contemporary gene flow in white-tailed deer

“…Recent studies at more refined spatial scales have supported large-scale geographic barriers (e.g. rivers, highways) as being semipermeable to gene flow (Kelly et al, 2014; Locher et al, 2015; Miller et al, 2020; Robinson et al, 2012). This, despite the potential complication caused by re-stocking efforts, and in accordance with radio-telemetry data (Peterson et al, 2017).…”

“…However, many of these studies relied upon (now) obsolescent molecular markers (e.g., mtDNA or reduced panels of microsatellite DNA markers) that captured substantially less polymorphism than do next-generation methods (Hodel et al, 2017;Jeffries et al, 2016;Lemopoulos et al, 2019). Subsequent studies at finer spatial scales have supported the role of large-scale geomorphic configurations (e.g., rivers, highways) as semipermeable barriers to WTD gene flow (Kelly et al, 2014;Locher et al, 2015;Miller et al, 2020;Robinson et al, 2012), despite the potential complication caused by restocking efforts, and in accordance with radio-telemetry data (Peterson et al, 2017).…”

“…Distributional patterns in white-tailed deer clearly reflect multiple factors. Those extrinsic may include: Rivers, interstate highways, suitable habitat (Robinson, Samuel, Lopez, & Shelton, 2012); agricultural land use and urbanization (Kelly et al, 2014); as well as regional eco-physiographic boundaries (Miller, Miller-Butterworth, Diefenbach, & Walter, 2020). Intrinsic factors, on the other hand, may point to: Population density (Lutz, Diefenbach, & Rosenberry, 2015); age structure/ sex ratio (Long, Diefenbach, Rosenberry, & Wallingford, 2008); and social hierarchy (Nixon & Mankin, 2016).…”