Differing sensitivity of species distribution modelling algorithms to climate data source

Abstract

The increasing availability of contemporary climate databases calls for comparative studies testing their performance in ecological modelling. We evaluated how contemporary climate data sources interact with the modelling algorithm and affect the predictive accuracy of Species Distribution Models (SDMs). We modelled the distribution of 37 woody plants in the Iberian Peninsula using three global climate databases: WorldClim (WC), CHELSA (CH) and MERRAclim (MER); a regional climate database (the Iberian Climate Atlas, ICA), and three modelling algorithms: Generalized Linear Models (GLMs), Generalized Additive Models (GAMs), and Partial Least Squared Regressions (PLSRs). The predictive capacity of the models was estimated by performing an external evaluation while controlling for extrapolation issues. The climate database source variable and its interaction with the modelling algorithm variable showed significant effects on a model's predictive performance. Post hoc tests revealed that MER produced the lowest discrimination capacity scores, especially using GLMs as algorithm. PLSR models were significantly better than GLMs when MER was used. GAMs and PLSR models with MER were significantly worse than some ICA and CH models but not worse than any of the WC models. These results indicate that contemporary climate data sources need to be considered as a seedbed of uncertainty in SDMs and that poorly flexible algorithms are unable to deal with suboptimal data. CH is a reliable global climate database and PLSR is a technique worth considering in SDMs. The best practice is to select the most accurate climate data available and to choose the algorithm based on the purpose of the study and on context-dependent technical details.