Model validation and model setting
Generalized Linear Models (GLM), Random Forest (RF), Generalized Additive Models (GAM), Artificial Neural Networks (ANN), Multivariate Adaptive Regression Splines (MARS), Generalized Boosted Models (GBM) , and Maximum Entropy Models (MaxEnt), are the seven modeling techniques employed in this study . SDMs were generated using the ‘biomod2 ’ package’s ensemble forecasting method found in R under the following parameters; MARS models had a highest interaction level of 2, whilst RF models were fitted by growing 750 trees with half the available predictors sampled to split at each node. The default settings and the highest iteration count of 1000 were applied to MaxEnt models. While GAMs were computed using a logistic link function, GLMs were readjusted using a binomial link function. On the other hand, GBMs were generated by performing 5000 three-fold cross-validation procedures to determine the optimal number of trees to keep and a maximum depth of variable interactions of 7. The default specifications were used to fit ANN models. This method has previously been used in other research (e.g. . We added a background set of 10,000 randomly chosen background points to the study area because our dataset only contained presence data
. As in previous research with species distribution modeling, the occurrence dataset was randomly divided into a 30% sample for evaluating the performance of the model  and a 70% sample for model calibration . We performed 175 SDMs in total (7 algorithms X 5 splitting replicates for model evaluation X 1 repetition X 5 species).