2.3 Model Building, Optimization, and Evaluation
In accordance with the latest model optimization methodology proposed by Cobos et al. (2019), adjustments were made to the parameters of the MaxEnt model to assess the degree of alignment between the distribution points of T. sinense and the model, as determined by the corrected Akaike information criterion (AICc) (Yu et al., 2019). Optimal model parameters, indicated by the lowest AICc value, were selected for utilization within the MaxEnt software (Philips et al., 2017).
The distribution data of T. sinense and its corresponding environmental variables spanning the study period were inputted into the MaxEnt model. In this investigation, 25% of the 232 distribution sites of T. sinense were earmarked for model evaluation, while the remaining 75% constituted the training set for constructing a response curve. 10 replicates were generated for each training partition, and the resultant outcomes were averaged. Model results were generated in both Logistic and ASC format files, with a multi-feature combination based on optimization outcomes (Moreno et al., 2011).
To calibrate the model and verify its robustness, the receiver operating characteristic (ROC) curve was analyzed, independent of the threshold. The area under the curve (AUC) was computed to ascertain the accuracy of the model. Model performance was categorized as failure (0.5-0.6), poor (0.6-0.7), fair (0.7-0.8), good (0.8-0.9), or excellent (0.9-1.0), with higher AUC values indicating superior model performance (Fithian et al., 2015).
Classification of Fitness Levels and Area Statistics
The average output data from each period, following 10 simulations in the MaxEnt model, were imported into ArcGIS software. These data were then converted into raster layers and subsequently reclassified based on the distributed probability (P) values. Employing natural breakpoint classification, the distribution area of T. sinense was categorized into four distinct levels: Non-suitable area (P < 0.2), Low suitable zone (0.2 ≤ P < 0.4), Intermediate suitable zone (0.4 ≤ P < 0.6), High suitable zone (P ≥ 0.6) The reclassified layers were processed using an ArcGIS grid table to determine the area encompassed by each level (Zhuang et al., 2018).