Effects of species traits and environmental predictors on performance and transferability of ecological niche models
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Regos, A., Gagne, L., Alcaraz-Segura, D. et al. Effects of species traits and environmental predictors on performance and transferability of ecological niche models. Sci Rep 9, 4221 (2019). [https://doi.org/10.1038/s41598-019-40766-5]
SponsorshipThis research was developed as part of the project ECOPOTENTIAL, which received funding from the European Union’s Horizon 2020 Research and Innovation Programme under agreement No. 641762. We thank everyone who contributed to the fieldwork: Xosé Pardavila, Adrián Lamosa (Sorex, Ecoloxía e Medio Ambiente SL), Marta Arenas, Alberto Toupa and Fernando Martínez-Freiría. Field surveys were funded by the project INTERREG-POCTEC (‘NATURA Xurés-Gerês’). A.R. was financially supported by the Xunta de Galicia (post-doctoral fellowship ED481B2016/084-0). Miquel Ninyerola and Meritxell Batalla (UAB) generated climate variables from data provided by the Spanish Meteorological Agency and the Spanish Ministry of Marine and Rural Environment within the MONTES-Consolider project (CSD2008-00040).
The ability of ecological niche models (ENMs) to produce robust predictions for different time frames (i.e. temporal transferability) may be hindered by a lack of ecologically relevant predictors. Model performance may also be affected by species traits, which may reflect different responses to processes controlling species distribution. In this study, we tested four primary hypotheses involving the role of species traits and environmental predictors in ENM performance and transferability. We compared the predictive accuracy of ENMs based upon (1) climate, (2) land-use/cover (LULC) and (3) ecosystem functional attributes (EFAs), and (4) the combination of these factors for 27 bird species within and beyond the time frame of model calibration. The combination of these factors significantly increased both model performance and transferability, highlighting the need to integrate climate, LULC and EFAs to improve biodiversity projections. However, the overall model transferability was low (being only acceptable for less than 25% of species), even under a hierarchical modelling approach, which calls for great caution in the use of ENMs to predict bird distributions under global change scenarios. Our findings also indicate that positive effects of species traits on predictive accuracy within model calibration are not necessarily translated into higher temporal transferability.