Landscape genetics
Regression-based approaches are popular in landscape genetic studies for discovering the association between genetic distance and landscape features (Shirk, Landguth, & Cushman, 2018). We conducted a series of analyses to test several landscape hypotheses. To integrate various characteristics and performances from different genetic distance measures, we applied two methods to calculate individual-based genetic distance, i.e., the Nei distance (DNei) and the Euclidean distance based on the first 10 axes of a principle component analysis (DPCA). DNei and DPCA distances were calculated using the R package adegenet. The method is described in detail in the SMM. We performed the DPCA using the R package vegan (Oksanen et al., 2013).
To determine the underlying landscape features shaping the genetic variation of leopard cats in Taiwan, we constructed five resistance layers (Fig. 4)—Relevation, Rroughness, Rhuman density, Rland_cover, and—Rroad, which are speculated to be profound resistance factors for mammals (Montgelard et al., 2014). Data for the original layers were collected and processed as described in detail in the SMM.
Assigning specific resistance values to landscape features is not easy or straightforward and remains one of the greatest challenges to developing resistance surfaces (Peterman et al., 2019; Zeller, McGarigal, & Whiteley, 2012). Several methods optimize resistance layers using genetic data as a gene flow proxy, reflecting effective movement over generations (Peterman et al., 2019). We applied one of the most popular methods using a genetic algorithm (GA) implemented in the R package ResistanceGA (Peterman, 2018) for developing resistance layers. The method is described in detail in the SMM. We first employed a single surface optimization procedure for each resistance layer using the function SS_optim() in the R package ResistanceGA. All continuous and categorical layers were optimized in our analysis with commute-time distance calculated in the R package gdistance (van Etten, 2017), which is equivalent to circuit-theory-based distance. The maximum resistance of each layer was set to 500, resistance values were optimized utilizing Akaike information criterion values (AIC) as criteria, and other default parameters were adopted. To check for convergence, each resistance layer was optimized with two independent runs using one of the two genetic distances (DNei or DPCA) as a dependent variable. Aside from optimizing a single surface separately, we employed multiple surface optimization using ResistanceGA’s wrapper function all_comb(), which optimizes all possible combinations automatically.
After optimizing single and all possible multiple resistance models, we conducted model comparison by means of two independent approaches, i.e., the Maximum Likelihood Population Effects mixed effects model (Clarke, Rothery, & Raybould, 2002) and Reciprocal Causal Modeling (Cushman et al., 2013), both of which offer high accuracy for selecting true landscape features affecting genetic differentiation under different conditions (Peterman et al., 2019). The methods for performing model comparison are described in detail in the SMM. We also applied the resist_boot() function in ResistanceGA to conduct a bootstrap analysis on: (1) all possible combinations of resistance models; and (2) single-surface resistance models only.