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).