Potential of satellite-derived ecosystem functional attributes to anticipate species range shifts |
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| Institution: | 1. Departamento de Botánica, Facultad de Ciencias, Av. Fuentenueva, Universidad de Granada, 18071, Granada, Spain;2. Andalusian Center for the Assessment and Monitoring of Global Change (CAESCG) - Universidad de Almería, Crta. San Urbano, 04120, Almería, Spain;3. iecolab. Interuniversitary Institute for Earth System Research (IISTA) - Universidad de Granada, Av. del Mediterráneo, 18006, Granada, Spain;4. InBIO/CIBIO-Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal;5. Department of Earth and Environmental Sciences, KU Leuven, BE-3001 Leuven, Belgium;6. Laboratoire d’Écologie Alpine, CNRS-UMR 5553, Université Joseph Fourier, Grenoble I, BP 53, 38041 Grenoble Cedex 9, France;7. Faculdade de Ciências, Universidade do Porto, Porto, Portugal;1. Roger Williams University, Department of Biology and Marine Biology, 1 Old Ferry Rd, Bristol, RI 02809, United States of America;2. University of Massachusetts Boston, School for the Environment, 100 Morrissey Blvd, Boston, MA 02125, United States of America;3. New England Aquarium, John H. Prescott Marine Laboratory, 1 Central Wharf, Boston, MA 02110, United States of America;4. Boston University Marine Program, Department of Biology, 1 Silber Way, Boston, MA 02215, United States of America;5. Conservation International, 2011 Crystal Dr #500, Arlington, VA 22202, United States of America;6. National Oceanic and Atmospheric Administration, National Ocean Service, National Centers for Coastal Ocean Science, 101 Pivers Island Rd, Beaufort, NC 28516, United States of America;1. InBIO-CIBIO - Rede de Investigação em Biodiversidade e Biologia Evolutiva, Centro de Investigação em Biodiversidade e Recursos Genéticos, Faculdade de Ciências da Universidade do Porto, Campus Agrário de Vairão, Rua Padre Armando Quintas, nº 7, 4485-661 Vairão, Portugal;2. InBio-CIBIO, Rede de Investigação em Biodiversidade e Biologia Evolutiva, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade de Évora, 7000-890 Évora, Portugal;3. Laboratory of Applied Ecology, CITAB - Centre for the Research and Technology of Agro-Environment and Biological Sciences, University of Trás-os-Montes e Alto Douro, 5000-911 Vila Real, Portugal;1. School of Business, Anhui University, Hefei, 230601, Nanjing, China;2. School of Geography and Ocean Science, Nanjing University, Nanjing, 210093, China;3. The Key Laboratory of the Coastal Zone Exploitation and Protection, Ministry of Land and Resource, China;4. Natural Resources Research Center, Nanjing University, China;1. School of Earth and Environmental Sciences, The University of Adelaide, Adelaide 5064, Australia;2. Science, Monitoring and Knowledge, Department of Environment, Water and Natural Resources, South Australian Government, Adelaide 5001, Australia;3. South Australia Arid Lands Region, Department of Environment, Water and Natural Resources, South Australian Government, Roxby Downs 5725, Australia |
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| Abstract: | In a world facing rapid environmental changes, anticipating their impacts on biodiversity is of utmost relevance. Remotely-sensed Ecosystem Functional Attributes (EFAs) are promising predictors for Species Distribution Models (SDMs) by offering an early and integrative response of vegetation performance to environmental drivers. Species of high conservation concern would benefit the most from a better ability to anticipate changes in habitat suitability. Here we illustrate how yearly projections from SDMs based on EFAs could reveal short-term changes in potential habitat suitability, anticipating mid-term shifts predicted by climate-change-scenario models. We fitted two sets of SDMs for 41 plant species of conservation concern in the Iberian Peninsula: one calibrated with climate variables for baseline conditions and projected under two climate-change-scenarios (future conditions); and the other calibrated with EFAs for 2001 and projected annually from 2001 to 2013. Range shifts predicted by climate-based models for future conditions were compared to the 2001–2013 trends from EFAs-based models. Projections of EFAs-based models estimated changes (mostly contractions) in habitat suitability that anticipated, for the majority (up to 64%) of species, the mid-term shifts projected by traditional climate-change-scenario forecasting, and showed greater agreement with the business-as-usual scenario than with the sustainable-development one. This study shows how satellite-derived EFAs can be used as meaningful essential biodiversity variables in SDMs to provide early-warnings of range shifts and predictions of short-term fluctuations in suitable conditions for multiple species. |
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| Keywords: | Climate change Enhanced vegetation index Essential biodiversity variables Iberian Peninsula Interannual variability Multi-species monitoring Species distribution models Threatened plants |
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