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.