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Algorithms for producing ecological niche models and species distribution models are widely applied in biogeography and conservation biology. However, in some cases models produced by these algorithms may not represent optimal levels of complexity and, hence, likely either overestimate or underestimate the species’ ecological tolerances. Here, we evaluate a delete-one jackknife approach for tuning model settings to approximate optimal model complexity and enhance predictions for datasets with few (here, <10) occurrence records. We apply this approach to tune two settings that regulate model complexity (feature class and regularization multiplier) in the presence-background modeling program Maxent for two species of spiny pocket mice in Ecuador and southwestern Colombia. For these datasets, we identified an optimal feature class parameter that is more complex than the default. Highly complex …