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. EFA-based 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, EFA-based 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.
Highlights
Global environmental changes are affecting species distributions and ecosystem functioning worldwide, with profound effects in terms of loss and relocation of biodiversity [1,2]
The Euro-Siberian (Atlantic) region holds vegetation types such as alpine natural and seminatural grasslands and heathlands, and forest ecosystems with alpine needleleaf coniferous and temperate broadleaf deciduous and semideciduous species; whereas the Mediterranean region is primarily represented by evergreen broadleaf and conifer canopy species in Ecosystem functional attributes as predictors in species distribution models forest ecosystems, and a huge representation of shrub and herbaceous species able to resist the adverse conditions of long summer-drought periods
Building on previous studies that explored the predictive value of satellite-derived ecosystem functional attributes (EFAs) [35], here we tested additional variables such as Albedo and Land Surface Temperature
Summary
Global environmental changes are affecting species distributions and ecosystem functioning worldwide, with profound effects in terms of loss and relocation of biodiversity [1,2]. Different approaches combining statistical modelling tools and biodiversity monitoring have allowed to quantify and assess biodiversity distribution and change across scales [5,6]. Species Distribution Models (SDMs) can be defined as associative models for quantifying species-environment relationships, and are based on assessing the species’ ecological niche [10,11]. Once the ecological niche of a species has been defined through statistical functions, these can be applied to scenarios of climate or landscape conditions to project the future variation of the species’ distribution. The application of SDMs in conservation and management is still hampered by significant spatial and temporal biases (e.g. taxonomy errors, sampling overlapping, interpolations with insufficient data, inaccuracies in geo-referencing, etc.), both in species occurrence data, and in the set of predictive variables that represent the environmental variability [16]
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