3.1 Species richness patterns of all species of rodents
Rodents are widely distributed in China and are recorded in almost every grid; however, their spatial distribution is uneven. All species (n = 237) of rodents occurred in 1665 grids, and the species richness in each grid was between 0 and 84 (mean: 31.39 ± 16.51 SD) species (Figure 1a; Table S5). The highest abundance of rodent species in China was found in the subtropical and tropical regions of the oriental realm, with the Hengduan Mountains being the most abundant region, followed by the Qilian Mountains and Tianshan Mountains regions. In addition, Taiwan and Hainan Island also have high species richness. The species richness was the lowest in the Qinghai-Tibet Plateau and Tarim Basin regions, with only a few species in most grids.
The simple regression results showed that, when considering the effect of individual factors, all species richness was highly significantly correlated with each environmental factor (p < 0.001). The two most relevant variables were VEG and AET (Figure 2a, b; Table S6).
Regarding the relationship between the predictor set and all species richness, the best model was explained by a set of five variables (EW1+CS1+HH1+HE1+HE2). The multiple regression model (OLS) explained 71% of the total variation in rodent species richness. Due to the removal of the effect of spatial autocorrelation by the spatial autoregressive model (SAR), the degree of explanation was reduced to 64% (Table 1). As shown in Table 1, the importance of each variable in explaining the species richness pattern varied slightly across the regression models, but the difference was not significant. Both the regression models showed that HH1 and HE1 were the most important predictors. The variance partitioning results showed that the four predictor sets explained 71.7% of the variance in the total species richness. The habitat heterogeneity predictor set explained 48.94% of the variation in all species richness patterns, followed by human factors (42.30%), energy-water (28.79%), and climatic seasonality (18.30%) (Figure 3a, b; Table S7)