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Open AccessArticle

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

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CIBIO-InBIO—Centro de Investigação em Biodiversidade e Recursos Genéticos, Laboratório Associado, Universidade do Porto, Campus Agrário Vairão, 4485-661 Vila do Conde, Portugal
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CICGE—Centro de Investigação em Ciências Geo-Espaciais, Faculdade de Ciências, Universidade do Porto, Observatório Astronómico “Prof. Manuel de Barros”, Alameda do Monte da Virgem, 4430-146 Vila Nova de Gaia, Portugal
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Departamento de Zooloxía, Xenética e Antropoloxía Física, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
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Department of Botany and Inter-University Institute for Earth System Research, University of Granada, 18071 Granada, Spain
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Andalusian Center for the Assessment and Monitoring of Global Change (CAESCG), University of Almería, 04151 Almería, Spain
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Faculdade de Ciências, Universidade do Porto, 4169-007 Porto, Portugal
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Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(18), 2086; https://doi.org/10.3390/rs11182086
Received: 7 August 2019 / Revised: 31 August 2019 / Accepted: 4 September 2019 / Published: 6 September 2019
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. View Full-Text
Keywords: ecosystem functioning attributes (EFAs); essential biodiversity variables (EBVs); Iris boissieri; rare species; satellite remote sensing; species abundance models (SAMs); species distribution models (SDMs) ecosystem functioning attributes (EFAs); essential biodiversity variables (EBVs); Iris boissieri; rare species; satellite remote sensing; species abundance models (SAMs); species distribution models (SDMs)
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MDPI and ACS Style

Arenas-Castro, S.; Regos, A.; Gonçalves, J.F.; Alcaraz-Segura, D.; Honrado, J. Remotely Sensed Variables of Ecosystem Functioning Support Robust Predictions of Abundance Patterns for Rare Species. Remote Sens. 2019, 11, 2086.

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