Data from: Forest cover mediates genetic connectivity of northwestern cougars

Population structure, connectivity, and dispersal success of individuals can be challenging to demonstrate for solitary carnivores with low population densities. Though the cougar (Puma concolor) is widely distributed throughout North America and is capable of dispersing long distances, populations can be geographically structured and genetic isolation has been documented in some small populations. We described genetic structure and explored the relationship between landscape resistance and genetic variation in cougars in Washington and southern British Columbia using allele frequencies of 17 microsatellite loci for felids. We evaluated population structure of cougars using the Geneland clustering algorithm and spatial principal components analysis. We then used Circuitscape to estimate the landscape resistance between pairs of individuals based on rescaled GIS layers for forest canopy cover, elevation, human population density and highways. We quantified the effect of landscape resistance on genetic distance using multiple regression on distance matrices and boosted regression tree analysis. Cluster analysis identified four populations in the study area. Multiple regression on distance matrices and boosted regression tree models indicated that only forest canopy cover and geographic distance between individuals had an effect on genetic distance. The boundaries between genetic clusters largely corresponded with breaks in forest cover, showing agreement between population structure and genetic gradient analyses. Our data indicate that forest cover promotes gene flow for cougars in the Pacific Northwest, which provides insight managers can use to preserve or enhance genetic connectivity.

对于种群密度较低的独居食肉动物而言，验证其种群结构、连通性以及个体扩散成功率往往颇具挑战。尽管美洲狮（Puma concolor）在北美全域广泛分布，且具备长距离扩散能力，但其种群仍可能呈现地理结构特征，部分小型种群的遗传隔离现象已有文献记载。本研究基于猫科动物17个微卫星位点（microsatellite loci）的等位基因频率，对华盛顿州与不列颠哥伦比亚省南部地区美洲狮的遗传结构展开描述，并探讨了景观阻力与遗传变异之间的关联。我们采用Geneland聚类算法（Geneland clustering algorithm）与空间主成分分析（spatial principal components analysis）对美洲狮的种群结构进行评估。随后，我们借助Circuitscape，基于森林冠层覆盖度、海拔、人口密度与公路的标准化GIS图层（rescaled GIS layers）估算个体间的景观阻力。我们通过距离矩阵多元回归（multiple regression on distance matrices）与提升回归树分析（boosted regression tree analysis），量化了景观阻力对遗传距离的影响。聚类分析在研究区域内识别出4个美洲狮种群。距离矩阵多元回归与提升回归树模型结果显示，仅森林冠层覆盖度与个体间的地理距离会对遗传距离产生显著影响。遗传聚类簇之间的边界与森林覆盖的断点大体吻合，表明种群结构与遗传梯度分析的结果具有一致性。本研究数据表明，森林覆盖可促进太平洋西北地区（Pacific Northwest）美洲狮的基因流（gene flow），这一发现可为管理者保护或提升遗传连通性（genetic connectivity）提供参考依据。