2.2 Selection and comparison of climate variables
Table 1. 19 Bioclimatic variables in the study.
A total of 19 environmental variables for the current (1971–2000s) and future (2041–2060s, 2061–2080s) were downloaded from WorldClim (http://www.worldclim.org/) with a resolution of 2.5’ (Table 1). Data of BCC - CSM2 - MR from Coupled Model Intercomparison Project phase 6 were selected for future climate data, including three scenarios in shared socioeconomic pathways: sustainable development SSP1-2.6, moderate development SSP2-4.5, and conventional development SSP5-8.5 (Riahi et al. , 2017). The multilinearity of the 19 environmental factor variables related to PWD can affect the accuracy of MaxEnt model prediction results, so Pearson correlation analysis was used to test the correlation of variables. First, 19 climate variables and 647 pine wood nematode distribution points were loaded into the MaxEnt model. The importance of 19 climate factors was determined using the knife-cutting method and ranked according to the contribution rate of the factors. Additionally, correlation analysis of 19 climate factors was conducted. If the correlation coefficient of two related variables is greater than 0.8, the lower contribution rate of the variables should be deleted to avoid the impact of multicollinearity on the model results (Zhanget al. , 2016). Finally, seven variables were obtained through screening: isothermality (bio2/bio7) (*100) (bio3), temperature seasonality of warmest month (bio4), max temperature of warmest month (bio5), mean temperature of driest quarter (bio9), coefficient of variation of precipitation seasonality (bio14), precipitation seasonality (bio15), and precipitation of wettest quarter (bio16). China’s administrative division vector map was obtained from the national basic geographic information system (http://mail.nsdi.gov.cn).