*2.4. Model Evalution*

The predicted distribution maps were compared with the currently reported areas of distribution and the locations of such records based on various local florae and the literature. The accuracy of the algorithms in prediction was assessed through three parameters, i.e., the area under the Receiver Operating Characteristic (ROC) curve (AUC) [19,29], Cohen's Kappa [34], and TSS [35]. Each of the accuracy measures was obtained based on a "confusion matrix" [33,36], while ArcGIS 10.0 was used to perform statistical analyses. The value of AUC varied from 0 to 1, among which, that of ≤0.5 suggests that the models show no predicting capability, while that of >0.7 represents that the models are acceptable [37]. The value of Cohen's Kappa was between −1 and + 1, in which + 1 suggests excellent performance, while values of ≤0 indicate that a performance was not superior to a random result [38]. TSS also varies from −1 to +1, in which +1 stands for excellent agreement, and a value of ≤0 indicates that the performance is not superior to random. A Wilcoxon signed-rank test (one-tailed) was adopted for evaluating AUC, Kappa and TSS values between GARP and Maxent for their statistical significance.

#### **3. Results**
