Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes

Abstract

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 features are not typically recommended for use with small sample sizes in Maxent. However, when coupled with higher regularization, complex features (that allow more flexible responses to environmental variables) can obtain models that out-perform those built using default settings (employing less complex feature classes). Although small sample sizes remain a serious limitation to model building, this jackknife optimization approach can be used for species with few localities (<approximately 20–25) to produce models that maximize the utility of the little information available.