Validation of species-climate impact models under climate change

Validation of species-climate impact models under climate change

2005 | MIGUEL B. ARAÚJO*,†, RICHARD G. PEARSON*,‡, WILFRIED THUILLER§ and MARKUS ERHARD*
This study investigates the validation of species–climate envelope models under climate change, focusing on the predictive performance of four modeling techniques—artificial neural networks (ANN), generalized additive models (GAM), generalized linear models (GLM), and classification tree analysis (CTA)—using observed distribution shifts of 116 British breeding bird species over a 20-year period. The research aims to address the challenges of validating models for events that have not yet occurred and to assess the impact of spatial and temporal autocorrelation on model accuracy. The study found that the models generally performed well to fair on independent validation datasets, with ANN and GAM showing the highest accuracy. However, the rules used to interpret model performance are difficult to interpret and do not provide clear guidelines for decision-making. Validation using nonindependent distribution data provided optimistic estimates of model accuracy on independent data, highlighting the need for caution in interpreting these results. The research also suggests that the usefulness of models depends on the specific questions being asked and the techniques used. While species–climate envelope models can provide useful approximations of potential impacts, they may not be suitable for making predictions of future events without critical application and validation against nonindependent data. The study concludes by emphasizing the importance of improving the realism of model assumptions, algorithms, and parameters, and the need to critically evaluate the applicability of models for specific conservation and policy-making contexts.This study investigates the validation of species–climate envelope models under climate change, focusing on the predictive performance of four modeling techniques—artificial neural networks (ANN), generalized additive models (GAM), generalized linear models (GLM), and classification tree analysis (CTA)—using observed distribution shifts of 116 British breeding bird species over a 20-year period. The research aims to address the challenges of validating models for events that have not yet occurred and to assess the impact of spatial and temporal autocorrelation on model accuracy. The study found that the models generally performed well to fair on independent validation datasets, with ANN and GAM showing the highest accuracy. However, the rules used to interpret model performance are difficult to interpret and do not provide clear guidelines for decision-making. Validation using nonindependent distribution data provided optimistic estimates of model accuracy on independent data, highlighting the need for caution in interpreting these results. The research also suggests that the usefulness of models depends on the specific questions being asked and the techniques used. While species–climate envelope models can provide useful approximations of potential impacts, they may not be suitable for making predictions of future events without critical application and validation against nonindependent data. The study concludes by emphasizing the importance of improving the realism of model assumptions, algorithms, and parameters, and the need to critically evaluate the applicability of models for specific conservation and policy-making contexts.
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