Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling
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Arenas-Castro, Salvador; Gonçalves, João; Alves, Paulo; Alcaraz Segura, Domingo; Honrado, JoãoEditorial
Public Library of Science (PLOS)
Date
2018-06-18Referencia bibliográfica
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]
Sponsorship
This 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.Abstract
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.