Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling

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.

Integrating intraseasonal grassland dynamics in cross‐scale distribution modeling to support waterbird recovery plans

“…These variables are also good descriptors of suitable conditions for 2 plant species of conservation concern ( Iris boissieri and Taxus baccata ), both at coarse and fine scales; they slightly outperform SDMs based on traditional climate and land‐cover predictors (Arenas‐Castro et al. ). In fact, these RS‐derived functional variables have been suggested as essential biodiversity variables in SDMs to provide early warnings of range shifts and predictions of short‐term fluctuations in suitable conditions for multiple plant species (Alcaraz‐Segura et al.…”

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

“…In this context, the use of Sentinel-2 data for habitat suitability mapping should be viewed as an effective compromise between spatial (10 m) and temporal resolution (5-6 days), as well as its open-data policy. Regarding the statistical methods inherent to SDMs, further studies are recommended in this research field in order to understand the best robustness of approaches able to handle high dimensional data [102], as well addressed to examine the predictive performances of multiple algorithms, especially when concomitantly integrated into an ensemble modeling framework [18,43]. This would be particularly interesting when evaluating how sub-sampled group of variables (remote-sensing products, topography, landscape variables) may singularly impact on the performance of species distribution models.…”

“…To the best of our knowledge, modelling fine-scale habitat suitability for wildlife conservation, specifically with open-access remote-sensing data and with Sentinel-2 imagery, is still scarce in the literature. Besides, as Sentinel-2 derived-products mostly reflect biotic environmental attributes, the integration of these variables with abiotic descriptors (e.g., topography) into SDMs likely provide more realistic results than using each type of variables alone [28,36,43].…”

Remotely Sensed Variables of Ecosystem Functioning Support Robust Predictions of Abundance Patterns for Rare Species

“…In fact, satellite-derived EFAs are being also tested as candidate EBVs related to the carbon cycle, energy, and radiation balance capable of informing about ecosystem components linked to species conservation status [26,27]. Thus, the incorporation of EFAs into SDMs was found to increase their predictive power and transferability [28], offering the opportunity to cost-effectively monitor multiple endangered species [27,29], at different spatial and temporal scales [30]. Still, despite these advantages, the predictive ability of EFAs in abundance models as well as to assess inter-annual population dynamics of rare species remains largely untested.…”