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Forest Restoration in Landscapes

Forest Restoration in Landscapes

conjunction with a set

conjunction with a set of stakeholders from the region. Similarly, GIS is being used in Madagascar to map and prioritise suitable areas for restoration within a large landscape that needs to be restored. Here, biological targets are being established for six IUCN redlisted vertebrates. Criteria are being established to map suitable habitat for each species in order to evaluate current status within the landscape. Where current habitat is insufficient for longterm viability of each population, areas will be prioritised for restoration based on connectivity, proximity to known populations, and habitat characteristics. Socioeconomic data will be used as a constraint where options exist to meet biological targets. This work is in its initial stages and is expected to continue through 2005. 3. Outline of Tools Standard vector-based GIS software—ESRI (ArcMap, ArcView, Arcinfo)—is the standard GIS virtually worldwide. It is available at low cost to conservation organisations, and it performs all types of GIS functions, from basic mapping to advanced analyses, especially when customised or linked to other programmes (e.g., statistical software, etc.). Standard raster-based GIS—IDRISI, ESRI (Spatial Analyst, GRID for Arcview, ArcMap, and Arcinfo), ERDAS. The IDRISI and ESRI products are low cost (for educational or nonprofit companies) GISs capable of doing rasterbased analyses (e.g., most analyses involving remotely sensed imagery). IDRISI includes functions for easily stepping through suitability models and MCE as part of its decision support package. ERDAS is a much more expensive software designed primarily to analyse satellite imagery and other remotely sensed data. 4. Future Needs A key need is for participatory GIS-based decision-support tools designed specifically for restoration in a biodiversity conservation context. Similarly, research is needed into tools to strengthen linkages between site-based restora- 16. Mapping and Modelling 119 tion research and spatial decision making with GIS. Recently, several new GIS models are in use that have been used extensively for spatial planning in conservation, notably C-Plan 156 and SITES/Marxan. 157 These particular applications are currently, generally speaking, spatial optimisation tools designed to meet representation targets in conservation plans. There is tremendous potential, however, especially with the simulated-annealing algorithm used by Marxan (and now SPOT among other tools) to optimise any given set of objectives (such as restoration) in a spatial model. Research is urgently needed to expand these tools to meet other objectives beyond simple reservation and representation. References Boitani, L. (coordinator), Corsi, F., De Biase, A., et al. 1999.A databank for the conservation and management of African Mammals. Institute of Applied Ecology, Rome, Italy. Dinerstein, E., Powell, G., Olson, D. et al. 2000. A Workbook for Conducting Biological Assessments and Developing Biodiversity Visions for Ecoregion-Based Conservation. Conservation Science Programme, World Wildlife Fund, Washington, DC. Eastman, J.R., Kyem, P.A.K., Toledano, J., and Jin, W. 1993. GIS and Decision Making, UNITAR. Explorations in GIS Technology, Vol. 4. UNITAR, Geneva. Eghenter, C. 2000. Mapping People’s Forests: The Role of Mapping in Planning Community-Based Management of Conservation Areas in Indonesia. Biodiversity Support Programme, Washington, DC. Ferrier, S. 2002. Mapping spatial pattern in biodiversity for regional conservation planning: where to from here? Systematic Biology 51:331–363. Halperin, J.J., Shear, T.H., Munishi, P.K.T., and Wentworth, T.R. 2004. Multiple-objective forestry planning in biodiversity hotspots of east Africa. In preparation. Herrman, S., and Osinski, E. 1999. Planning sustainable land use in rural areas at different spatial levels using GIS and modelling tools. Landscape and Urban Planning 46:93–101. 156 Pressey et al, 1995 157 Leslie et al, 2003; McDonnell et al, 2002.

120 T.F. Allnutt Lambeck, R.J. 1997. Focal species: a multi-species umbrella for nature conservation. Conservation Biology 11:849–856. Leslie, H., Ruckelshaus, R., Ball, I.R., Andelman, S., and Possingham, H.P. 2003. Using siting algorithms in the design of marine reserve networks. Ecological Applications 13:S185–S198. McDonnell, M.D., Possingham, H.P., Ball, I.R., and Cousins, E.A. 2002. Mathematical methods for spatially cohesive reserve design. Environmental Modelling and Assessment 7:107–114. Pressey, R.L., Cowling, R.M., and Rouget, M. 2003. Formulating conservation targets for biodiversity pattern and process in the Cape Floristic Region, South Africa. Biological Conservation 112:99–127. Pressey, R.L., Ferrier, S., Hutchinson, C.D., Sivertsen, D.P., and Manion, G. 1995. Planning for negotiation: using an interactive geographic information system to explore alternative protected area networks. In: Saunders, D.A., Craig, J.L., Mattiske, E.M., eds. Nature Conservation: The Role of Networks. Surrey Beatty and Sons, Sydney, pp. 23–33. Ridgely, R.S., Allnutt, T.F. Brooks, T., et al. 2003. Digital Distribution Maps of the Birds of the Western Hemisphere. Version 1.0. CD-ROM. NatureServe, Arlington, Virginia. UNEP-WCMC. 2003. Spatial analysis as a decision support tool for forest landscape restoration. Report to WWF. Additional Reading George, T.L., and Zack, S. 2001. Spatial and temporal considerations in restoring habitat for wildlife. Restoration Ecology 9:272. Huxel, G.R., and Hastings,A. 2001. Habitat loss, fragmentation, and restoration. Restoration Ecology 7:309. Jankowski, P., and Nyerges, T. 2001. Geographic Information Systems for Group Decision Making. Taylor and Francis, New York. Loiselle, B.A., Howell, C.A. Graham, C.H., et al. 2003.Avoiding pitfalls of using species distribution models in conservation planning. Conservation Biology 6:1591–1600. Wickam, J.D., Jones, B.K., Riiters, K.H., Wade, T.G., and O’Neill, R.V. 1999. Transitions in forest fragmentation: implications for restoration opportunities at regional scales. Landscape Ecology 14: 137–145.

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