Suitability index for restoration in landscapes: An alternative proposal for restoration projects |
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| Institution: | 1. Department of Environmental Engineering – São Paulo State University, Campus Sorocaba, 511 Três de Março Avenue, Sorocaba, SP 18087-180, Brazil;2. Department of Environmental Sciences – Federal University of São Carlos, Campus Sorocaba, Rodovia João Leme dos Santos (SP-264), Km 110, Sorocaba, SP 18052-780, Brazil;3. Department of Forestry and Natural Resources – Purdue University, 195 Marsteller Street, West Lafayette, IN 47907-2033, USA;4. Department of Geosciences – Mississippi State University, 108 Hilbun Hall, Starkville, MS 39762-5448, USA;1. NSW Department of Primary Industries, Fisheries Conservation Technology Unit, National Marine Science Centre, PO Box 4321, Coffs Harbour, NSW 2450, Australia;2. Department of Statistics, The University of Auckland, Private Bag 92019, Auckland, New Zealand;1. Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR, USA;2. Center of Excellence for Environmental Decisions, University of Queensland, Brisbane, Australia;3. School of Life and Environmental Science, The University of Sydney, Sydney, NSW, Australia;1. Museu Nacional, Rio de Janeiro, RJ, Brazil;2. Faculdade de Filosofia, Ciências e Letras do Alto São Francisco, Departamento de Ciências Biológicas, Luz, MG, Brazil;1. Research Group Nature and Society, Research Institute for Nature and Forest (INBO), Kliniekstraat 25, 1070Brussels, Belgium;2. University of Namur, Department of Geography, 61, Rue de Bruxelles, 5000Namur, Belgium;3. Leuphana University, Faculty of Sustainability, Institute of Ethics and Transdisciplinary Sustainability Research, Scharnhorststr. 1, 21335Lüneburg, Germany;4. Department of International Environment and Development Studies, Norwegian University of Life Sciences (NMBU), Norway;5. Norwegian Institute for Nature Research (NINA), Gaustadalleen 21, 0349Oslo, Norway;6. TERRA - BIOSE – Biodiversité et Paysages, Université de Liège, Gembloux Agro-Bio Tech, Passage des Déportés 2, 5030Gembloux, Belgium;7. Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, 117543, Singapore;8. Department of Environmental Sciences, University of Helsinki, P.O. Box 65, 00014, Finland;9. Department of Civil, Environmental and Mechanical Engineering, University of Trento, Via Mesiano 77, 38123Trento, Italy;10. ESCP Europe Business School Berlin, Chair of Environment and Economics, Heubnerweg 8-10, 14059Berlin, Germany;11. Environmental Governance Unit, Finnish Environment Institute, P.O. Box 140, 00251Helsinki, Finland;12. Department of Ecology and Environmental Sciences, Faculty of Sciences, Constantine the Philosopher University in Nitra, Slovakia;13. UFZ, Helmholtz Centre for Environmental Research, Department Computational Landscape Ecology, 04318Leipzig, Germany;14. Martin-Luther-University Halle-Wittenberg, Institute of Geoscience & Geography, 06099Halle (Saale), Germany;15. Faculty of Law of the University of Coimbra, Portugal;p. Centre for International Forestry Research (CIFOR), Bogor, Indonesia and Department of Forest and Ecosystem Science, The University of Melbourne, Parkville, 3010Victoria, Australia;q. University of Leeds, LeedsLS2 9JT, UK;r. Earth Economics, 107 N. Tacoma Avenue, Tacoma, WA98403, USA;s. NOVA IMS, Universidade Nova de Lisboa, 1070-312Lisboa, Portugal;t. Vrije Universiteit Brussel, Public Health Department, Belgium;u. European Commission Joint Research Centre (JRC), via Enrico Fermi 2749, 21027Ispra, Italy;v. Faculty of Science and Engineering, University of Waikato, Private Bag 3105, Hamilton3240, New Zealand;w. Centre for Biological Sciences, University of Southampton, University Road, SouthamptonSO17 1BJ, UK;x. Conservation Science Group, Department of Zoology, University of Cambridge, Downing Street, CambridgeCB2 3EJ, UK;y. University of Queensland, Business School, St Lucia, Queensland4072, Australia;z. Alexander von Humboldt Institute for Research on Biological Resources, Colombia;11. Center for the Environment, Plymouth State University, Plymouth, NH, USA;12. Team Nature Report and Advice Co-ordination, Research Institute for Nature and Forest INBO, Kliniekstraat 25, 1070Brussels, Belgium;13. Environmental Geography Group, Department of Earth Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1087, 1081 HVAmsterdam, The Netherlands;14. Norwegian Institute of Bioeconomy Research, P.O. Box 115, 1431Ås, Norway;15. Department of Science, Technology, Engineering and Public Policy, University College London, 36-38 Fitzroy Square, LondonW1T 6EY, UK |
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| Abstract: | Forest fragmentation constitutes one of the main consequences of land cover change worldwide. Through this process gaps in habitat coverage are created and the ability of populations in the remaining fragments to maintain themselves is put in doubt. Hence, two options need to be considered: conserving the remaining forest fragments, and restoring habitat in some deforested patches with the aim of reestablishing the connections among the fragments. We established a mathematical index (SIR) that describes the suitability of individual habitat patches for restoration within a landscape. The index considers classes of distances among fragments and categories of habitat quality in the areas surrounding the fragments to assess habitat quality in terms of probability of dispersal and survival of propagules (especially seeds and cutting). In the present study, we created detailed maps depicting SIR values for two periods (1988 and 2011) for Sorocaba region (São Paulo State, Brazil). We derived land cover maps from satellite images for the two years of our study, and then surveyed the transition of land cover categories and landscape metrics between years. A model for the SIR was created using a map of distance classes among fragments and also a map of habitat quality established according to each land cover category. For both 1988 and 2011, pasture was the predominant land cover category. The main land cover transitions were from pasture to urban (10.6%) and from pasture to forest fragments (13.4%). Although the land cover class “wood sites” increased, the data of SIR revealed that the areas of habitat categorized as excellent and good both decreased, while habitat classes categorized as poor and very poor increased. Our model has the potential to be applied to other regions where the forest is fragmented. Hence, local policy makers will be able to use this model to determine local patches of high value for conservation and also the most ideal locations for restoration projects. |
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| Keywords: | Forest fragmentation Land cover change Landscape connectivity Priority areas for ecological restoration Urban sprawl |
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