Optimal model selection for Maxent: a case of freshwater species distribution modelling in Bhutan, a data poor country

Maxent is commonly used species distribution modelling (SDM) program due to its better performance over other SDM programs. But model complexity and selecting optimal models are two important concerns for Maxent users. In order to help advance the field we built 44 sets of models by combining 11 regularization multipliers and four feature classes for 10 fish and 28 odonate species of Bhutan with small occurrence data. We then selected optimal models using four sequential optimal model selection approaches: two OR TEST approaches which combined threshold dependent test omission rate (OR) followed by area under receiver operating curve for test data (AUC TEST ), and two AUCDIFF approaches that combined OR followed by difference between training AUC and AUC TEST (AUC DIFF ) and then AUC TEST . We then screened for ecologically plausible binary suitable/unsuitable model for each species among the optimal models selected by the sequential approaches or from the remaining models using expert knowledge (EXP approach). We then compared different model features and the predicted binary habitat of the optimal models selected by the five approaches. Models selected by OR TEST approaches matched better with ones selected by EXP approach despite them selecting more complex models compared to AUC DIFF approaches. Further, models selected through AUCDIFF approaches overpredicted the habitat more often than the models selected through OR TEST approaches when compared to models chosen by EXP approach. We recommend use of OR TEST approaches for model selection either as the first line of model screening or by their own when less restrictive thresholds are used to produce binary habitat maps as we did here. First, this would reduce time required for expert screening of multiple models for ecologically plausible models when many species are studied. Second, when used alone, OR TEST approaches can avoid either selecting models that under predict or over predict the suitable habitat.