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

Autor: Arenas-Castro, Salvador, Regos, Adrián, Gonçalves, João F., Alcaraz-Segura, Domingo, Honrado, João P.
Přispěvatelé: Universidade de Santiago de Compostela. Departamento de Zooloxía, Xenética e Antropoloxía Física
Jazyk: angličtina
Rok vydání: 2019
Předmět:
0106 biological sciences
010504 meteorology & atmospheric sciences
satellite remote sensing
Science
Species distribution
Rare species
Biodiversity
Species abundance models (SAMs)
essential biodiversity variables (EBVs)
010603 evolutionary biology
01 natural sciences
Essential biodiversity variables (EBVs)
Abundance (ecology)
Satellite remote sensing
media_common.cataloged_instance
Ecosystem
14. Life underwater
European union
ecosystem functioning attributes (EFAs)
Ecosystem functioning attributes (EFAs)
Relative species abundance
species distribution models (SDMs)
0105 earth and related environmental sciences
media_common
Essential biodiversity variables
Ecology
Species distribution models (SDMs)
Iris boissieri
15. Life on land
species abundance models (SAMs)
13. Climate action
Threatened species
General Earth and Planetary Sciences
Environmental science
Ecosystem functioning attributes
rare species
Zdroj: Remote Sensing, Vol 11, Iss 18, p 2086 (2019)
Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
instname
Digibug. Repositorio Institucional de la Universidad de Granada
Remote Sensing
Volume 11
Issue 18
Pages: 2086
ISSN: 2072-4292
Popis: Global environmental changes are affecting both the distribution and abundance of species at an unprecedented rate. To assess these effects, species distribution models (SDMs) have been greatly developed over the last decades, while species abundance models (SAMs) have generally received less attention even though these models provide essential information for conservation management. With population abundance defined as an essential biodiversity variable (EBV), SAMs could offer spatially explicit predictions of species abundance across space and time. Satellite-derived ecosystem functioning attributes (EFAs) are known to inform on processes controlling species distribution, but they have not been tested as predictors of species abundance. In this study, we assessed the usefulness of SAMs calibrated with EFAs (as process-related variables) to predict local abundance patterns for a rare and threatened species (the narrow Iberian endemic ‘Gerês lily’ Iris boissieri; protected under the European Union Habitats Directive), and to project inter-annual fluctuations of predicted abundance. We compared the predictive accuracy of SAMs calibrated with climate (CLI), topography (DEM), land cover (LCC), EFAs, and combinations of these. Models fitted only with EFAs explained the greatest variance in species abundance, compared to models based only on CLI, DEM, or LCC variables. The combination of EFAs and topography slightly increased model performance. Predictions of the inter-annual dynamics of species abundance were related to inter-annual fluctuations in climate, which holds important implications for tracking global change effects on species abundance. This study underlines the potential of EFAs as robust predictors of biodiversity change through population size trends. The combination of EFA-based SAMs and SDMs would provide an essential toolkit for species monitoring programs.
This work has been carried out within the H2020 project ECOPOTENTIAL: Improving Future Ecosystem Benefits Through Earth Observations (http://www.ecopotential-project.eu). The project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 641762. S.A.-C., D.A.-S., and J.H. received funding from the ECOPOTENTIAL project. A.R. was financially supported by the Xunta de Galicia, Spain (post-doctoral fellowship ED481B2016/084-0). J.F.G. was funded by the Individual Scientific Employment Stimulus Program (2017) by the Portuguese Foundation for Science and Technology (FCT CEEC-2017).
Databáze: OpenAIRE
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