Novel methods for spatial prioritization with applications in conservation, land use planning and ecological impact avoidance
| Autor: | Atte Moilanen, Pauli Lehtinen, Ilmari Kohonen, Joel Jalkanen, Elina A. Virtanen, Heini Kujala |
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| Přispěvatelé: | Suomen ympäristökeskus, The Finnish Environment Institute, Department of Geosciences and Geography, Unit of Biodiversity Informatics, Biosciences, Helsinki Institute of Sustainability Science (HELSUS), Helsinki Institute of Urban and Regional Studies (Urbaria), Finnish Museum of Natural History |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
maankäytön suunnittelu
algorithm Ecological Modeling land use planning site selection luonnon monimuotoisuus priorisointi zonation 5 release candidate 1181 Ecology evolutionary biology algoritmit spatial conservation prioritization ecological impact avoidance spatial priority ranking luonnonsuojelu systematic conservation planning Ecology Evolution Behavior and Systematics 1172 Environmental sciences arvottaminen |
| Popis: | 1. Spatial (conservation) prioritization integrates data on the distributions of bio-diversity, costs and threats. It produces spatial priority maps that can support ecologically well-informed land use planning in general, including applications in environmental impact avoidance outside protected areas. Here we describe novel methods that significantly increase the utility of spatial priority ranking in large analyses and with interactive planning. 2. Methodologically, we describe a novel algorithm for implementing spatial prior-ity ranking, novel alternatives for balancing between biodiversity features, fast tiled FFT transforms for connectivity calculations based on dispersal kernels, and a novel analysis output, the flexibility map. 3. Marking by N the number of landscape elements with data, the new prioriti-zation algorithm has time scaling of less than Nlog2N instead of the N2 of its predecessor. We illustrate feasible computation times with data up to billions of elements in size, implying capacity for global analysis at a resolution higher than 0.25 km2, or close to 1- ha resolution for a continent. 4. The algorithmic improvements described here bring about improved capacity to implement decision support for real-world spatial conservation planning problems. The methods described here will be at the technical core of forthcoming software releases. |
| Databáze: | OpenAIRE |
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