Species distribution modeling (sdm) and potential diversity loss to future climate change
To incorporate the influence of climate change and assess the potential habitats under different climate scenarios in the future, we employed a species distribution modeling (sdm) approach to construct niche models and project to current and future (in 2070) climate conditions. Given the lack of reasonable predictions for road and landcover alterations in the future, we only used 19 bioclimatic variables related to temperature and precipitation, which are suitable for predicting potential distribution areas of wild mammals (Holzmann et al., 2015; Petružela et al., 2018). The 19 bioclimatic variables were downloaded from WorldClim2 (Fick & Hijmans, 2017), with a resolution of 30 arc-sec (about 1 km2 per cell). The average of three global climate models, i.e., CCSM4, MIROC-ESM, and MIROC5, in 2070 was implemented under two contrasting representative concentration pathways (RCPs), a low-emission model (RCP2.6) and a high-emission model (RCP8.5) from CMIP5 (Taylor, Stouffer, & Meehl, 2012) for prediction. We conducted an ensemble approach that makes predictions based on the combination of six methods, including maxent, glm, svm, gam, mda, and mlp for model construction using the R package sdm (Naimi & Araújo, 2016). The method is described in detail in the SMM. Area under the receiver-operator curve (AUC) and true skill statistic (TSS) were used to evaluate model performance.
To simulate potential diversity loss associated with climate change, we calculated genetic diversity indices, including He, allelic richness, and FIS, under current habitat and climate in 2070 for individual samples overlapping with habitat suitability >0.5 and >0.75 on the prediction maps, respectively. A Kruskal Wallis test (Hollander, Wolfe, & Chicken, 2013) was performed to determine significant differences in diversity among groups using the R package stats (R Core Team, 2013).