Making better M axent models of species distributions: complexity, overfitting and evaluation

作者：Aleksandar Radosavljević, Robert P. Anderson · 发表于：Journal of Biogeography · 年份：2013 · DOI：10.1111/jbi.12227 · 被引用次数：1713 · 研究领域：Species Distribution and Climate Change、Wildlife Ecology and Conservation、Ecology and Vegetation Dynamics Studies

Abstract Aim Models of species niches and distributions have become invaluable to biogeographers over the past decade, yet several outstanding methodological issues remain. Here we address three critical ones: selecting appropriate evaluation data, detecting overfitting, and tuning program settings to approximate optimal model complexity. We integrate solutions to these issues for Maxent models, using the Caribbean spiny pocket mouse, H eteromys anomalus , as an example. Location N orth‐western S outh A merica. Methods We partitioned data into calibration and evaluation datasets via three variations of k ‐fold cross‐validation: randomly partitioned, geographically structured and masked geographically structured (which restricts background data to regions corresponding to calibration localities). Then, we carried out tuning experiments by varying the level of regularization, which controls model complexity. Finally, we gauged performance by quantifying discriminatory ability and overfitting, as well as via visual inspections of maps of the predictions in geography. Results Performance varied among data‐partitioning approaches and among regularization multipliers. The randomly partitioned approach inflated estimates of model performance and the geographically structured approach showed high overfitting. In contrast, the masked geographically structured approach allowed selection of high‐performing models based on all criteria. Discriminatory ability showed a slight peak in perf...