Population structure and spatial genetic patterns
In terms of geographic distances and potential barriers (i.e., urban areas and mountain ridges) that may disrupt the connectivity of leopard cats in Taiwan, we defined three geographic populations (i.e., Northern, Central, and Southern groups; Fig. 2) for subsequent population-based analyses. Pairwise F ST for every population pair to characterize diversity and differentiation at the population level was calculated in Arlequin (Excoffier & Lischer, 2010).
To evaluate the consistency between geographic areas and genetic clusters among individuals, we first deployed a Bayesian-based algorithm in STRUCTURE v2.3.4 (Evanno, Regnaut, & Goudet, 2005) to investigate genetic structure and potential admixture. STRUCTURE analysis was performed from K = 1-7, with 10 replicates for each K . A total of 100,000 iterations were simulated, and 25,000 iterations were set as burn-in. Geographic populations (i.e., Northern, Central, Southern) were set as priors. All results were uploaded to the CLUMPAK server (Kopelman et al., 2015) to generate consensus plots and to evaluate the best K according to highest ΔK value (Evanno, Regnaut, & Goudet, 2005). Since frequent gene flow may homogenize population structure, we performed Discriminant Analysis of Principal Components (DAPC) for its ability to detect subtle genetic structure without any assumption on sample source (Jombart, Devillard, & Balloux, 2010). The DAPC algorithm combines PCA and DA, partitioning variance into within-group and between-group components to maximize discrimination. Based on K = 2, which presented the highest likelihood score from STRUCTURE, we performed DAPC on 2 and 3 clusters using the R package adegenet (Jombart, 2008).
We conducted a spatial Principal Component Analysis (sPCA) to determine the spatial distribution of genetic variation using the R package adegenet (Jombart et al., 2008). To construct the connection network, we employed an argument type = 5 and included the nearest 10 samples. We assessed for autocorrelation in the data using Moran’s I , global, and local tests, each with 999 replicates. The eigenvalue of each PC axis and an eigenvalue decomposition plot were used to identify spatially meaningful PC axes for interpretation.