Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling
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AuthorArenas-Castro, Salvador; Gonçalves, João; Alves, Paulo; Alcaraz Segura, Domingo; Honrado, João
Public Library of Science (PLOS)
Arenas-Castro S, Gonçalves J, Alves P, Alcaraz-Segura D, Honrado JP (2018) Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling. PLoS ONE 13(6): e0199292. [https://doi. org/10.1371/journal.pone.0199292]
SponsorshipThis research was developed as part of the ECOPOTENTIAL project financed by European Union's Horizon 2020 research and innovation program under grant agreement No. 641762. SAC, DAS and JPH received funding from the ECOPOTENTIAL project. JG was supported by FCT (Portuguese Science Foundation) through PhD grant SFRH/BD/90112/2012. DAS received funding from Ministerio de Educación, Cultura y Deporte, JC2015-00316 grant, and Ministerio de Ciencia e Innovación, CGL2014-61610-EXP project.
Global environmental changes are rapidly affecting species' distributions and habitat suitability worldwide, requiring a continuous update of biodiversity status to support effective decisions on conservation policy and management. In this regard, satellite-derived Ecosystem Functional Attributes (EFAs) offer a more integrative and quicker evaluation of ecosystem responses to environmental drivers and changes than climate and structural or compositional landscape attributes. Thus, EFAs may hold advantages as predictors in Species Distribution Models (SDMs) and for implementing multi-scale species monitoring programs. Here we describe a modelling framework to assess the predictive ability of EFAs as Essential Biodiversity Variables (EBVs) against traditional datasets (climate, land-cover) at several scales. We test the framework with a multi-scale assessment of habitat suitability for two plant species of conservation concern, both protected under the EU Habitats Directive, differing in terms of life history, range and distribution pattern (Iris boissieri and Taxus baccata). We fitted four sets of SDMs for the two test species, calibrated with: interpolated climate variables; landscape variables; EFAs; and a combination of climate and landscape variables. EFAbased models performed very well at the several scales (AUCmedian from 0.881±0.072 to 0.983±0.125), and similarly to traditional climate-based models, individually or in combination with land-cover predictors (AUCmedian from 0.882±0.059 to 0.995±0.083). Moreover, EFAbased models identified additional suitable areas and provided valuable information on functional features of habitat suitability for both test species (narrowly vs. widely distributed), for both coarse and fine scales. Our results suggest a relatively small scale-dependence of the predictive ability of satellite-derived EFAs, supporting their use as meaningful EBVs in SDMs from regional and broader scales to more local and finer scales. Since the evaluation of species' conservation status and habitat quality should as far as possible be performed based on scalable indicators linking to meaningful processes, our framework may guide conservation managers in decision-making related to biodiversity monitoring and reporting schemes.