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.