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Landscape Zonation, benefit functions and target-based planning: Unifying reserve selection strategies
The most widespread reserve selection strategy is target-based planning, as specified under the framework of systematic conservation planning. Targets are given for the representation levels of biodiversity features, and site selection algorithms are employed to either meet the targets with least co...
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Published in: | Biological conservation 2007-02, Vol.134 (4), p.571-579 |
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description | The most widespread reserve selection strategy is target-based planning, as specified under the framework of systematic conservation planning. Targets are given for the representation levels of biodiversity features, and site selection algorithms are employed to either meet the targets with least cost (the minimum set formulation) or to maximize the number of targets met with a given resource (maximum coverage). Benefit functions are another recent approach to reserve selection. In the benefit function framework the objective is to maximize the value of the reserve network, however value is defined. In one benefit function formulation value is a sum over species-specific values, and species-specific value is an increasing function of representation. This benefit function approach is computationally convenient, but because it allows free tradeoffs between species, it essentially makes the assumption that species are acting as surrogates, or samples from a larger regional species pool. The Zonation algorithm is a recent computational method that produces a hierarchy of conservation priority through the landscape. This hierarchy is produced via iterative removal of selection units (cells) using the criterion of least marginal loss of conservation value to decide which cell to remove next. The first variant of Zonation, here called core-area Zonation, has a characteristic of emphasizing core-areas of all species. Here I separate the Zonation meta-algorithm from the cell removal rule, the definition of marginal loss of conservation value utilized inside the algorithm. I show how additive benefit functions and target-based planning can be implemented into the Zonation framework via the use of particular kinds of cell removal rules. The core-area, additive benefit function and targeting benefit function variants of Zonation have interesting conceptual differences in how they treat and trade off between species in the planning process. |
doi_str_mv | 10.1016/j.biocon.2006.09.008 |
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This hierarchy is produced via iterative removal of selection units (cells) using the criterion of least marginal loss of conservation value to decide which cell to remove next. The first variant of Zonation, here called core-area Zonation, has a characteristic of emphasizing core-areas of all species. Here I separate the Zonation meta-algorithm from the cell removal rule, the definition of marginal loss of conservation value utilized inside the algorithm. I show how additive benefit functions and target-based planning can be implemented into the Zonation framework via the use of particular kinds of cell removal rules. 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This hierarchy is produced via iterative removal of selection units (cells) using the criterion of least marginal loss of conservation value to decide which cell to remove next. The first variant of Zonation, here called core-area Zonation, has a characteristic of emphasizing core-areas of all species. Here I separate the Zonation meta-algorithm from the cell removal rule, the definition of marginal loss of conservation value utilized inside the algorithm. I show how additive benefit functions and target-based planning can be implemented into the Zonation framework via the use of particular kinds of cell removal rules. The core-area, additive benefit function and targeting benefit function variants of Zonation have interesting conceptual differences in how they treat and trade off between species in the planning process.</description><subject>Additive benefit function</subject><subject>Animal, plant and microbial ecology</subject><subject>Applied ecology</subject><subject>Biological and medical sciences</subject><subject>Conservation planning</subject><subject>Conservation, protection and management of environment and wildlife</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects</subject><subject>Optimization</subject><subject>Parks, reserves, wildlife conservation. 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Psychology</topic><topic>General aspects</topic><topic>Optimization</topic><topic>Parks, reserves, wildlife conservation. Endangered species: population survey and restocking</topic><topic>Site selection algorithm</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Moilanen, Atte</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><collection>Ecology Abstracts</collection><collection>Sustainability Science Abstracts</collection><jtitle>Biological conservation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Moilanen, Atte</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Landscape Zonation, benefit functions and target-based planning: Unifying reserve selection strategies</atitle><jtitle>Biological conservation</jtitle><date>2007-02-01</date><risdate>2007</risdate><volume>134</volume><issue>4</issue><spage>571</spage><epage>579</epage><pages>571-579</pages><issn>0006-3207</issn><eissn>1873-2917</eissn><coden>BICOBK</coden><abstract>The most widespread reserve selection strategy is target-based planning, as specified under the framework of systematic conservation planning. Targets are given for the representation levels of biodiversity features, and site selection algorithms are employed to either meet the targets with least cost (the minimum set formulation) or to maximize the number of targets met with a given resource (maximum coverage). Benefit functions are another recent approach to reserve selection. In the benefit function framework the objective is to maximize the value of the reserve network, however value is defined. In one benefit function formulation value is a sum over species-specific values, and species-specific value is an increasing function of representation. This benefit function approach is computationally convenient, but because it allows free tradeoffs between species, it essentially makes the assumption that species are acting as surrogates, or samples from a larger regional species pool. The Zonation algorithm is a recent computational method that produces a hierarchy of conservation priority through the landscape. This hierarchy is produced via iterative removal of selection units (cells) using the criterion of least marginal loss of conservation value to decide which cell to remove next. The first variant of Zonation, here called core-area Zonation, has a characteristic of emphasizing core-areas of all species. Here I separate the Zonation meta-algorithm from the cell removal rule, the definition of marginal loss of conservation value utilized inside the algorithm. I show how additive benefit functions and target-based planning can be implemented into the Zonation framework via the use of particular kinds of cell removal rules. The core-area, additive benefit function and targeting benefit function variants of Zonation have interesting conceptual differences in how they treat and trade off between species in the planning process.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.biocon.2006.09.008</doi><tpages>9</tpages></addata></record> |
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subjects | Additive benefit function Animal, plant and microbial ecology Applied ecology Biological and medical sciences Conservation planning Conservation, protection and management of environment and wildlife Fundamental and applied biological sciences. Psychology General aspects Optimization Parks, reserves, wildlife conservation. Endangered species: population survey and restocking Site selection algorithm |
title | Landscape Zonation, benefit functions and target-based planning: Unifying reserve selection strategies |
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