Comparison of approaches to combine species distribution models based on different sets of predictors.

Abstract

Distribution models should take into account the different limiting factors that simultaneously influence species ranges. Species distribution models (SDM) built with different explanatory variables can be combined into more comprehensive ones, but the resulting models should maximize complementarity and avoid redundancy. We compared the different methods available for combining SD models. We modelled 19 threatened vertebrate in Spain, producing models according to three explanatory factors: spatial constraints, topography and climate, and human influence. We used five approaches for model combination: Bayesian inference, Akaike weight averaging, stepwise variable selection, updating, and fuzzy logic. We compared the performance of these approaches by assessing different aspects of their classification and discrimination capacity. We demonstrated that different approaches to model combination give rise to disparities in the outputs. Bayesian integration was affected by an error in the equations that are habitually used. Akaike weights produced models that were driven by the best single factor and therefore failed at combining the models effectively. The fuzzy-logic approach yielded models with the highest classification capacity according to Cohen’ s kappa. In conclusion: 1) Bayesian integration, employing the currently used equation, and the Akaike weight procedure should be avoided; 2) the updating and stepwise approaches can be considered minor variants of the same recalibrating approach; and 3) there is a trade-off between this recalibrating approach, which has the highest sensitivity, and fuzzy logic, which has the highest overall classifi cation capacity. Recalibration is better if unfavourable conditions in one environmental factor may be counterbalanced with favourable conditions in a different factor, otherwise fuzzy logic is better.