Ground-Truthing of MaxEnt Models Reveals Poor Predictive Accuracy for Lizards in the Mackenzie Basin, New Zealand.

The potential utility of Species Distribution Models (SDMs) in conservation is apparent. One application for rare or highly cryptic taxa is using model predictions to increase the efficiency of sampling effort. Though this method is potentially powerful, the accuracy of model predictions is rarely tested in the field. Further, uncertainty remains about whether validation statistics reflect true model performance, particularly for species of high conservation concern. We assessed the usefulness of SDMs for predicting the distribution of six species of lizards in the Mackenzie Basin (Te Manahuna), Aotearoa New Zealand (NZ). We built MaxEnt models using readily available occurrence data and a publicly available suite of environmental predictors. We validated model performance using both data partitioning and independent occurrence records collected in the 2022/23 austral summer. Cross-validation suggested that the top models for each species generated reasonably accurate predictions; however, for common species, predictive accuracy decreased notably when validating with independent data. Models for rare species performed more variably when validated with independent data; however, these models were overfit and based on few data, making it difficult to have confidence in the resulting abstractions. We suggest that limitations in historical occurrence data, current knowledge of species ecology and low resolution of predictor data likely restrict the relevance of predictive modelling for NZ lizard species. Whilst attractive to species managers and easy to generate, predictive models should be subject to ground-truthing with temporally relevant data prior to being used to inform sampling effort.