4.1 The problem of ROC accuracy of the maximum entropy model for the Mamukao River
In this study, the AUC value of the maximum entropy model ROC curve for spring in the Mamukao River was only 0.571, significantly smaller than the AUC values obtained from the other three types of models. Chen et al demonstrated a correlation between the sample size of the species (feature) distribution and the AUC value, indicating that a larger sample size corresponds to a higher AUC value and better accuracy of the predictive model (Chen et al. 2012). However, if the sample size for the presence of the species (feature) is excessively large, such as in the case of presence-only samples, the ROC curve cannot be applied due to the absence of negative examples for measuring specificity at the present moment (Anderson et al. 2003,Phillips et al. 2006). The Mamukao River exhibits a significant presence of stable snow during the spring season (see Figure x). Out of the total 1682 raster samples, only 276 were found to be non-accumulating, while the remaining 85.6% represented accumulating raster samples. However, the lack of specificity in our data may have contributed to a lower accuracy in the prediction model for the spring maximum entropy of the Mamukao River.