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.