2.4 Potential geographic distribution

ENM or species distribution models (SDM) can predict potential spatial distributions of species. Generalized additive models (GAM), random forests (RF), boosted regression trees (BRT, or named GBM), maximum entropy (Maxent) are widely used models (Guisan et al., 2014). Although there are many model options, no single optimal metric is widely applicable in this field (Qiao et al., 2015). The consensus algorithm can balance the performance of multiple models (Marmion et al., 2009), but results are ambiguous compared to a single model (Breiner et al., 2015; Zhu & Peterson, 2017). In order to obtain an optimal result predicting potential distribution, three individual models (RF, GBM, Maxent) and an ensemble model were implemented in the biomod2 package (Thuiller et al., 2009). Seventy percent of occurrence data was used for model training and 30% for model testing. We selected the partial receiver operating characteristic (PROC) as the model evaluation criteria; in contrast with the AUC method, PROC eliminates the misleading effects of absent data on the results and emphasizes the crucial role of the omission rate to prediction performance (Peterson, 2006). An AUC ratio of 1 implies that the niche model is no better than a random prediction, and a larger AUC ratio indicates better discrimination in the partial ROC approach (Peterson et al., 2008). In addition, given criticism of the complexity and transferability of Maxent default settings, we adjusted regularization multiplier (RM) values and feature combination (FC) settings in the ENM eval package to optimize parameters and determined the delta AICc minimum and average AUCtest maximum values to generate Maxent (see Figure S3) (Muscarella et al., 2014).
A more complete niche assessment can be obtained using all species distribution data (Broennimann & Guisan, 2008), so we used total occurrence data to model and analyze the Asian openbill potential distribution. First, we generated a calibration model using the present data within calibration range, then the potential distribution was predicted to a new projected range. Due to the potential extrapolation uncertainty of the model in the transfer, we still used ExDet to determine the novel environmental parameter (Type 1 novelty)(see Figure S2.3) (Mesgaran et al., 2014).
To determine whether the probability of two new distribution areas was higher than other areas where the Asian openbill did not yet occur, we randomly created occurrence sites outside of the existing distribution area within the projected extent. A probability value of 0-1 was generated in the optimal model with the highest PROC value to compare the occurrence probability of species in the native area, new distribution areas, and absence area.