2.2 Environmental variables
A total of 22 variables were studied including 19 climatic variables, 3 topographical variables, and 22 initial environmental variables with a resolution of 2.5’ (Table S1). The three topographic parameters (alt, slope, and asp) were downloaded with a resolution of 2.5’ from World Climate data (http://www.worldclim.org/). Bioclimatic characteristics obtained from the WorldClim (version 2.0) database were utilized to build distribution prediction models (11). The predictions for the Geographical distributions of S. ciliatum , S. nepalense , and S. yunnanense were carried out by the BCC-CSM2-MR climate system model, developed by the National Climate Center (12). The World Climate Database (http://www.worldclim.org/ ) was used for downloading the future climate data 2050 (2041–2060) and 2070 (2061–2080). New future pathways based on socioeconomic assumptions are called Shared Socioeconomic Pathways (SSPs) that describe various levels of socioeconomic development. These were used in the study (13). The SSPs include the high-forcing scenario (SSP5-8.5), the medium-forcing scenario (SSP2-4.5), and the low-forcing scenario (SSP1-2.6). The high and low emission scenarios are represented by the SSP5-8.5 and SSP1-2.6 in the study , respectively.
It is necessary to analyze environmental variables before they can be used for niche simulation calculations to avoid the multicollinearity of the variables causing over-fitting of the model because many bioclimatic variables are spatially related (14). ArcGIS10.2 was employed to examine the relationships between these 22 bioclimatic variables (Table S1). When the correlation coefficient between two climatic variables was more than 0.80, climate variables of greater ecological relevance were kept. For the prediction of the distribution of S. ciliatum, S. nepalense, and S. yunnanense , a total of 7, 9, and 8 variables with coefficients greater than 0.8 (15) were maintained, respectively (Fig. 2).