Unpacking ecosystem service bundles: Towards predictive mapping of synergies and trade-offs between ecosystem services |
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| Affiliation: | 1. Biological Sciences, University of Southampton, Southampton SO17 1BJ, UK;2. Geography and the Environment, University of Southampton, Southampton SO17 1BJ, UK;3. Laboratoire d’Ecologie Alpine, CNRS – Université Grenoble Alpes, CS 40700, 38058 Grenoble Cedex 9, France;4. NERC Centre for Ecology and Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB, UK;5. Centre for Environmental Science, Faculty of Engineering and the Environment, University of Southampton, Southampton SO17 1BJ, UK;6. Department of Natural Resource Sciences and McGill School of Environment, McGill University, Ste. Anne-de-Bellevue, QC, 21111 Lakeshore H9X3V9, Canada;7. European Commission, Joint Research Centre, Via E. Fermi, 2749, 21027 Ispra, VA, Italy;8. Environmental Dynamics Research Group, Department of Geography, King’s College London, Strand, London, WC2R 2LS, UK;9. UMR 7204 MNHN-UPMC-CNRS Centre d''Ecologie et des Sciences de la Conservation, CP135, 43 rue Buffon, 75005 Paris, France;10. Stockholm Resilience Centre, Stockholm University, Kr€aftriket 2B, SE-106 91 Stockholm, Sweden;11. Environmental Geography, Department of Earth Science, VU University Amsterdam, De Boelelaan, 1081 HV Amsterdam, The Netherlands;12. Department of Zoology, University of Wisconsin, Madison, WI 53706, USA;13. Swiss Federal Research Institute WSL, Research Unit Landscape Dynamics, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland;1. Institute of Environmental Science and Technology (ICTA), Universitat Autònoma de Barcelona (UAB), Edifici Z (ICTA-ICP), Carrer de les Columnes s/n, Campus de la UAB, 08193 Cerdanyola del Vallès, Spain;2. Hospital del Mar Medical Research Institute (IMIM), Carrer Doctor Aiguader 88, 08003 Barcelona, Spain;3. Department of International Environment and Development Studies (Noragric), Norwegian University of Life Sciences (NMBU), P.O. Box 5003, N-1432 Ås, Norway;4. Norwegian Institute for Nature Research (NINA), Gaustadalléen 21, 0349 Oslo, Norway;5. Department of Geography, Lab for Landscape Ecology, Humboldt University of Berlin, Rudower Chaussee 16, 12489 Berlin, Germany;6. Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research (UFZ), Permoser Straße 15, 04318 Leipzig, Germany;1. College of Life Sciences, Zhejiang University, Hangzhou 310058, PR China;2. Research Center for Sustainable Development, Zhejiang University, Hangzhou 310058, PR China;3. International Colleges, Qingdao University, Qingdao 266061, PR China;4. Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48823, USA;5. College of Landscape Architecture, Zhejiang A & F University, Lin’an 311300, PR China;6. Institut des Sciences de L’Environnement, Département des Sciences Biologiques, Université du Quebec à Montréal, Montréal, Quebec, Canada;1. ECOAGRASOC Research Group, University of Santiago de Compostela, Escola Politénica Superior, Campus Universitario s/n, 27002 Lugo, Spain;2. NIBIO – Norwegian Institute of Bioeconomy Research, Po. Box 115, NO-1431 Ås, Norway;1. Professorship of Ecological Services (PES), Department of Earth Science, BayCEER, University of Bayreuth, Bayreuth 95440, Germany;2. Biogeography, Department of Earth Science, BayCEER, University of Bayreuth, Bayreuth 95440, Germany;3. European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, Ispra, Varese 21027, Italy;1. UFZ – Helmholtz Centre for Environmental Research, Department of Computational Landscape Ecology, Permoserstraße 15, 04318 Leipzig, Germany;2. UFZ – Helmholtz Centre for Environmental Research, Department of Economics, Permoserstraße 15, 04318 Leipzig, Germany;3. Laboratory of Geo-information Science and Remote Sensing, Wageningen University, Wageningen, The Netherlands;4. Institute of Geoscience & Geography, Martin-Luther-University Halle-Wittenberg, 06099 Halle (Saale), Germany;5. Department of Ecology and Environmental Sciences, Faculty of Science, Palacký University Olomouc, 78371 Olomouc, Czech Republic;6. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands;1. Centre of Ecology and Conservation Sciences, UMR 7204 MNHN-CNRS-UPMC, National Museum of Natural History, CP153, 43 rue Buffon, 75005 Paris, France;2. Laboratoire d’Ecologie Alpine (LECA), UMR 5553 CNRS – Université Grenoble-Alpes, BP 53, 38041 Grenoble Cedex 9, France;3. European Commission – Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, 21027 Ispra, VA, Italy;4. Institute for Environmental Studies, VU University Amsterdam, De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands;5. European Forest Institute, Yliopistokatu 6, 80100 Joensuu, Finland |
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| Abstract: | Multiple ecosystem services (ES) can respond similarly to social and ecological factors to form bundles. Identifying key social-ecological variables and understanding how they co-vary to produce these consistent sets of ES may ultimately allow the prediction and modelling of ES bundles, and thus, help us understand critical synergies and trade-offs across landscapes. Such an understanding is essential for informing better management of multi-functional landscapes and minimising costly trade-offs. However, the relative importance of different social and biophysical drivers of ES bundles in different types of social-ecological systems remains unclear. As such, a bottom-up understanding of the determinants of ES bundles is a critical research gap in ES and sustainability science.Here, we evaluate the current methods used in ES bundle science and synthesize these into four steps that capture the plurality of methods used to examine predictors of ES bundles. We then apply these four steps to a cross-study comparison (North and South French Alps) of relationships between social-ecological variables and ES bundles, as it is widely advocated that cross-study comparisons are necessary for achieving a general understanding of predictors of ES associations. We use the results of this case study to assess the strengths and limitations of current approaches for understanding distributions of ES bundles. We conclude that inconsistency of spatial scale remains the primary barrier for understanding and predicting ES bundles. We suggest a hypothesis-driven approach is required to predict relationships between ES, and we outline the research required for such an understanding to emerge. |
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| Keywords: | Cross-study comparison Ecosystem services French Alps Land use Social-ecological systems Trade-off Natural capital Biodiversity |
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