ENMeval - single final model and prediction or average over folds?

[makani...@gmail.com]

I am generating some ENMs for bats of North America. I've run ENMevaluate from the ENMeval package with spatial block folds and partitioned background settings. I am selecting the optimal models using the or.10p.avg and auc.val.avg. My question is on what to do next. It appears that selecting the model through ENMeval gives a model and prediction that is then from the whole dataset run with the optimal fc and rm and not an average over the folds. The variable importances, lambdas, and other bits are also from this model that includes all the occurrences and background points with no folds. In the standalone MaxEnt java software it provides an average prediction over the various folds. I'm just seeing what would be the best way forward, or if there is one. Do I take the average prediction and variable importances from the folds? That would just mean 4 separately trained models with distinct lambdas went into one prediction. Or do I just take the single whole model and count the tuning cross-validation step as the validation for that model? That would mean one final model with just one set of lambdas. Any thoughts, help, guidance is appreciated.