Predicting Suitable Habitats of Melia Azedarach L. Using Data Mining

Background: Melia azedarach L. is a globally distributed tree species of economic importance; however, it is unclear how the species distribution will respond to future climate changes.Methods: We aimed to select the most accurate one among seven data mining models to predict the species suitable contemporary and future habitats. These models include: maximum entropy (MaxEnt), support vector machine (SVM), generalized linear model (GLM), random forest (RF), naive bayesian model (NBM), extreme gradient boosting (XGBoost), and gradient boosting machine (GBM). A total of 906 M. azedarach locations were identified, and sixteen climate predictors were used for model building. The models’ validity was assessed using three measures (Area Under the Curves (AUC), kappa, and accuracy). Results: We found that the RF provided the most outstanding performance in prediction power and generalization capacity. The top climate factors affecting the species distribution were mean coldest month temperature (MCMT), followed by the number of frost-free days (NFFD), degree-days above 18°C (DD>18), temperature difference between MWMT and MCMT, or continentality (TD), mean annual precipitation (MAP), and degree-days below 18°C (DD<18). We projected that future suitable habitat of this species would increase under both the RCP4.5 and RCP8.5 scenarios for the 2020s, 2050s, and 2080s.Conclusion: Our findings are expected to assist in better understanding the impact of climate change on the species and provide scientific basis for its planting and conservation.