Miller William L, Miller-Butterworth Cassandra M, Diefenbach Duane R, Walter W David
Pennsylvania Cooperative Fish and Wildlife Research Unit Department of Ecosystem Science and Management Intercollege Graduate Degree Program in Ecology The Pennsylvania State University University Park PA USA.
Penn State Beaver Monaca PA USA.
Ecol Evol. 2020 Apr 22;10(9):3977-3990. doi: 10.1002/ece3.6161. eCollection 2020 May.
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.We characterized patterns of population substructure and connectivity using microsatellite genotypes from 2,222 white-tailed deer () 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.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 ( ≤ 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 homogenous landscapes, 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.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 of ecophysiographic provinces are likely to be more effective than those implemented at finer scales.
了解野生动物种群的地理范围和连通性可为疾病暴发的管理提供重要见解，但对于分布广泛的物种而言，确定种群结构模式却颇具难度。景观遗传学分析是识别可能与此类情况下空间动物流行病模式相关的隐秘结构和移动模式的有力方法。我们利用美国中大西洋地区2222只白尾鹿的微卫星基因型对种群亚结构和连通性模式进行了特征描述，该地区于2009年首次检测到慢性消耗病。本研究的目的是评估种群结构、影响基因流动的景观特征与当前疾病管理单元之间的并置关系。聚类分析确定了该地区的四到五个亚种群，其边缘与生态地理省份相对应。亚种群进一步被划分为11个聚类，它们之间存在细微（FST≤0.041）但显著的遗传分化。相邻遗传聚类之间的遗传分化较低，迁移率较高，表明存在潜在的遗传渐变群。然而，遗传间断与地形障碍有关。抗性表面模型表明，在同质景观中基因流动是扩散的，但在这些景观特征各异的子区域中，基因流动的方向和范围受森林覆盖、交通流量和海拔起伏的影响。慢性消耗病主要发生在单个亚种群内的遗传聚类之间以及景观连通性高的走廊沿线。这些结果可能表明，对于分布广泛的物种，种群亚结构、景观连通性与疾病发生之间可能存在相关性。考虑这些因素可能有助于划定有效的管理单元，尽管只有最大的特征在亚种群结构上产生了明显差异。在生态地理省份尺度上实施的疾病缓解策略可能比在更精细尺度上实施的策略更有效。
