2.2 Environmental Variables
We gathered environmental variables associated with bioclimatic, soil,
and topographic factors as potential predictors of species distribution
(Table 1). 19 climate variables spanning the last glacial, last glacial
maximum, Mid-Holocene, current periods, and future scenarios were
sourced from the World Climate Database (http://worldclim.org) (Gao et
al., 2018). Future scenarios included low-concentration emissions
(SSPs1-2.6) and high-concentration emissions (SSPs5-8.5) of greenhouse
gases. 16 soil variables pertaining to the soil surface were acquired
from the Chinese Soil Dataset within the Harmonized World Soil Database
(HWSD, http://www.fao.org/faostat/en/#data), while elevation data were
obtained from the same source. The spatial resolution was set at 2.5 m.
Map data were represented in SHP format based on a 1:1 million scale
Chinese map obtained from the National Center for Basic Geographic
Information.
To mitigate multicollinearity and potential model overfitting (Graham,
2003), we conducted Spearman’s correlation analysis within ArcGIS (Yang
et al., 2013) to examine relationships among environmental factors.
Variables demonstrating a correlation coefficient of ≥ 0.8 were
considered highly correlated, and the less influential factor was
excluded from subsequent analysis. Consequently, a total of 16
environmental factors were retained for calculation and analysis within
the MaxEnt model.
Climate and elevation variable data were clipped according to the
vectograph of a 1:1 million scale Chinese administrative map and then
converted to ASC format using ArcGIS software. Soil variable data were
integrated by importing the China soil file and HWSD DATA file into
ArcGIS software. Subsequently, the grid layer comprising the 16 soil
variables within the MU_GLOBAL layer was extracted and converted into
ASC format. Finally, all environmental variable layers underwent batch
processing using ArcGIS software, resulting in environment layers with
non-overlapping extents.