Incorporating model complexity and spatial sampling bias into ecological niche models of climate change risks faced by 90 California vertebrate species of concern

Dan L. Warren*, Amber N. Wright, Stephanie N. Seifert, H. Bradley Shaffer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

138 Citations (Scopus)
21 Downloads (Pure)

Abstract

Aim: Ecological niche models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex models that show high predictive performance on current distribution data may do a poor job of predicting new data due to overfitting. In addition, model performance is often evaluated using techniques that are sensitive to spatial sampling bias. Here, we explore the effects of model complexity and spatial sampling bias on niche models for 90 vertebrate taxa of conservation concern. Location: California, USA. Methods: We used Akaike information criterion (AICc) to select variables and tune Maxent's built-in regularization parameter (β) to constrain model complexity. In addition, we incorporated several estimates of spatial sampling bias based on interpolations of target group data. Ensemble forecasts were developed for future conditions from two emission scenarios and three climate change models for the year 2050. Results: Reducing the number of predictors and tuning β resulted in a reduction in the number of parameters in models built with sample sizes greater than approximately 10 occurrence points. Reducing the number of predictors had a substantially higher impact on the relative prioritization of different grid cells than did increasing regularization. There was little difference in prioritization of habitat when comparing models built using different spatial sampling bias estimates. Over half of the taxa were predicted to experience >80% reductions in environmental suitability in currently occupied cells, and this pattern was consistent across taxonomic groups. Main Conclusions: Our results demonstrate that reducing the number of correlated predictor variables tends to decrease the breadth of models, while tuning regularization using AICc tends to increase it. These two strategies may provide a reasonable bracketing strategy for assessing climate change impacts.

Original languageEnglish
Pages (from-to)334-343
Number of pages10
JournalDiversity and Distributions
Volume20
Issue number3
DOIs
Publication statusPublished - Mar 2014

Bibliographical note

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