RT Journal Article
T1 Remotely Sensed Variables of Ecosystem Functioning Support Robust Predictions of Abundance Patterns for Rare Species
A1 Arenas Castro, Salvador
A1 Regos Sanz, Adrián
A1 Gonçalves, João F.
A1 Alcaraz Segura, Domingo
A1 Honrado, Joâo P.
K1 Ecosystem functioning attributes (EFAs)
K1 Essential biodiversity variables (EBVs)
K1 Iris boissieri
K1 Rare species
K1 Satellite remote sensing
K1 Species abundance models (SAMs)
K1 Species distribution models (SDMs)
AB 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.
PB MDPI
YR 2019
FD 2019
LK http://hdl.handle.net/10347/21902
UL http://hdl.handle.net/10347/21902
LA eng
NO Arenas-Castro, S., Regos, A., Gonçalves, J. F., Alcaraz-Segura, D., & Honrado, J. (2019). Remotely Sensed Variables of Ecosystem Functioning Support Robust Predictions of Abundance Patterns for Rare Species. Remote Sensing, 11(18), 2086. MDPI AG. Retrieved from http://dx.doi.org/10.3390/rs11182086
NO 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)
DS Minerva
RD 28 abr 2026