Model evaluation metrics for the 36 MaxEnt models for each of the 103 species

Evaluation metrics of the models for each species.Explanation of the columns:rm= regulization multiplierfc=feature class. L stands for linear, H for hinge, Q for quadratic, LQ for linear-quadratic, LQH linear-quadratic-hinge and LQHP for linear-quadratic-hinge-product featurestune.args= the combination of rm and fcauc.train= Average AUC (area under the ROC curve) for the training data across partitions cbi.train= Average Boyce index for the training data across partitions auc.diff.avg= Average difference between training and validation AUC (measure of overfitting)auc.diff.sd= Standard deviation of that differenceauc.val.avg= Average validation AUC across partitionsauc.val.sd= Standard deviation of validation AUC across partitionscbi.val.avg= Average validation Boyce Index across partitionscbi.val.sd= Standard deviation of validation Boyce Index across partitionsor.10p.avg= Average omission rate at the 10th percentile training presence thresholdor.10p.sd= SD of that omission rateor.mtp.avg= Average omission rate at the minimum training presence thresholdor.mtp.sd=SD of that omission rateAICc=Akaike Information Criterion corrected for small sample sizedelta.AICc= Difference between the AICc of this model and the best model w.AIC=Akaike weight = relative likelihood of the model being the best among those compared.ncoef= number of coefficients in the MaxEnt model