Performance of our sampling design, genetic distance and model
comparison
In landscape genetic studies, identifying the true landscape features
that affect movements between fragmented habitats can be challenging
(Peterman et al., 2019). There are three critical aspects of this topic,
including sampling collection, genetic distance measures, and model
comparisons.
Firstly, our study collected samples from road-killed individuals,
representing an uncommon strategy in landscape genetic research.
Sampling design has a significant impact on how the impacts of landscape
patterns on gene flow are detected, with different strategies having
varying probabilities of correctly identifying landscape processes
(Landguth et al., 2012; Oyler-McCance, Fedy, & Landguth, 2013). Our
data collection approach is equivalent to linear sampling, which is a
recommended sampling strategy for landscape genetics (Oyler-McCance,
Fedy, & Landguth, 2013). We collected more than a hundred samples along
the transportation networks that fragment leopard cat habitats. The
strong signals of autocorrelation and genetic discontinuity we observed
in our dataset indicate that road-killed samples can provide insights
into ecological processes and landscape connectivity, offered a
sufficient sample size gathered over an appropriate study area.
Secondly, we used two genetic distance measures to validate and test the
congruency of identifying the most important landscape features. The
accuracy of the metrics used in landscape genetic studies can be
affected by various factors, such as sampling sizes, dispersal rates,
generation after introduction, and strength of landscape features
(Landguth et al., 2012; Shirk, Landguth, & Cushman, 2018). Shirk,
Landguth, and Cushman (2017) reported that genetic distance based on
multivariate ordination techniques is among the most accurate distance
measure under most conditions. Our study considered 10 axes in the PCA
as a compromise between diagnostic power and noise reduction, since
using a select few top eigenvectors may have limited power in detecting
subtle variation in highly dispersive species. Although the results
between DNei and DPCA distances were
inconsistent, we obtained comparable marginal R2values from the top models using both distances, indicating similar
accuracies. Inconsistencies between different genetic metrics are not
uncommon in studies comparing multiple metrics (Kimmig et al., 2020;
Velo-Antón et al., 2021), emphasizing the importance of selecting
suitable genetic metrics and validating the sensitivity and robustness
of results.
Lastly, we conducted model comparisons using two methods, i.e., MLPE for
both single and multivariate layers and RCM for multivariate layers. The
accuracy of model selection in landscape genetic studies is influenced
by factors such as correlation among the hypotheses being compared and
the strength of resistance (Shirk, Landguth, & Cushman, 2018). A
simulation study by Shirk, Landguth, and Cushman (2018) indicated that
linear mixed-effects models (LME) display the highest accuracy in model
selection among most conditions, with RCM performing poorly in
single-variable landscapes, though this latter improved when the number
of variables increased to three. In our study, using the MLPE method, we
consistently identified elevation and roads as the top factors affecting
gene flow, regardless of the genetic distances utilized, supporting that
movements of leopard cats in Taiwan are influenced significantly by only
one or two variables. The divergent results for model comparisons using
RCM may be attributed to its poor performance when only a few landscape
variables affecting population connectivity are considered (Shirk,
Landguth, & Cushman, 2018). Therefore, the results inferred from RCM in
our study should be interpreted with caution due to potentially low
accuracy.