*2.2. Environmental Parameters*

A total of 28 environmental parameters were selected to be the candidate predicting factors for the distribution of *E. japonica* habitat according to other SDMs studies and the biological relevance to distribution [2,3,6,19,26]. Notably, altogether 19 bioclimatic parameters that showed relatively high biological significance for defining the species tolerance to eco-physiological stresses [27] were acquired based on the WorldClim dataset (http://www.worldclim.org/bioclim.htm). Three topographic parameters, i.e., slope degree, aspect, and elevation, were extracted from digital elevation model acquired via the Geospatial Data Cloud (http://www.gscloud.cn) with 30 × 30 m resolution. Three soil variables, i.e., soil organic carbon, soil pH, and soil type, were acquired from the Center for Sustainability and the Global Environment (SAGE) database (http://www.sage.wisc.edu/atlas/index.php); in addition, Normalized Difference Vegetation Index (NDVI), relative humidity, and sunshine duration in growing season were acquired based on China Meteorological Data Sharing Service System (http://data.cma.cn/ site/index.html).

The future climate data adopted for simulation were the BCC-CSM 1.1 modeling data under Representative Concentration Pathway (RCP) 2.6 and 8.5 for 2050 and 2070 issued via the IPCC Accessment Report 5 (AR5). The BCC-CSM 1.1 data have been recommended for research on climate change across China [28]. RCP 2.6 reflects potential radiative forcing by 2100, compared with the pre-industrial values of +2.6 W/m<sup>2</sup> which is optimistic, while RCP 8.5, the more pessimistic situation, represents the great emission levels of greenhouse gases, and leads to radiative forcing of 8.5 W/m<sup>2</sup> by 2100.

A 1 km spatial resolution was employed to resample all environmental variables; in addition, all variables were clipped in the study area. Next, all layers were processed using ArcGIS 10.0 along with the same cell size, spatial extent, and a WGS84 projection system. The variables were next tested by Pearson correlation coefficient and principal component analyses. Only one parameter was selected

for those with high cross-correlation (r2 > 0.90) based on the biological significance to *E. japonica* distribution [26]. Eventually, the number of predicting factors was decreased to 19 (Table 1).


**Table 1.** Selected environmental variables and their percent contribution for *Euscaphis japonica* tree species in China.
