Climatic suitability for Bsal
To understand how environmental conditions affect the distribution ofBsal , we used Generalized Linear Modeling (GLM) and Maximum Entropy Modeling (MaxEnt) to estimate its habitat suitability (Breiner et al., 2015; Phillips et al., 2006). If new positive locations ofBsal were detected in our study, they were to be combined with the previous verified presence records in native Asia to build the niche model. Previous present records of Bsal for Thailand, Vietnam, China and Japan in Asia (n=34), were attained from Basanta et al. (2019), Beukema et al. (2018), Laking et al. (2017), Lötters et al. (2020), and Yuan et al. (2018). We used the ecoregions (Dinerstein et al., 2017) of these Bsal occurrences as background to improve model calibration. Nineteen bioclimatic variables representing climatic conditions were download from WorldClim version 2.1 (Fick & Hijmans, 2017) as environmental predictors, at the spatial resolution of 30 seconds (~1 km2). To exclude the effects of high collinearity between predictors, we used the bioclimatic variables with Pearson’s r < 0.7, where the correlation was calculated in the ENMTools (Warren et al., 2010). The final predictor set included 6 variables: Mean Diurnal Range (BIO2), Maximum Temperature of Warmest Month (BIO5), Temperature Annual Range (BIO7), Annual Precipitation (BIO12), Precipitation of Warmest Quarter (BIO18), and Precipitation of Coldest Quarter (BIO19). To trim duplicate observation records, a single coordinate for each grid cell was retained in our model predictions. The model performance for Bsal was evaluated using the AUC (Lobo et al., 2008), which was calculated by splitting the training (70%) and testing (30%) observations. The final ensembled model contained ten replications for each modeling, and were run in “sdm” package (Naimi & Araújo, 2016).