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Biodiversity policies in the European Union (EU) are mainly implemented through the Birds and Habitats Directives as well as the establishment of Natura 2000, a network of protected areas throughout the EU. Considerable resources must be allocated for fulfilling the Directives and the question of optimal allocation is as important as it is difficult. In general, economic evaluations of conservation targets at most consider the costs and seldom the welfare economic benefits. In the present study, we use welfare economic benefit estimates concerning the willingness-to-pay for preserving endangered species and for the aggregate area of heathland preserved in Denmark. Similarly, we obtain estimates of the welfare economic cost of habitat restoration and maintenance. Combining these welfare economic measures with expected species coverage, we are able to estimate the potential welfare economic contribution of a conservation network. We compare three simple nonprobabilistic strategies likely to be used in day-to-day policy implementation: i) a maximum selected area strategy, ii) a hotspot selection strategy, and iii) a minimizing cost strategy, and two more advanced and informed probabilistic strategies: i) a maximum expected coverage strategy and ii) a strategy for maximum expected welfare economic gain. We show that the welfare economic performance of the strategies differ considerably. The comparison between the expected coverage and expected welfare shows that for the case considered, one may identify an optimal protection level above which additional coverage only comes at increasing welfare economic loss.
Biodiversity policies in the European Union (EU) are mainly implemented through the Birds and Habitats Directives as well as the establishment of Natura 2000, a network of protected areas throughout the EU. Considerable resources must be allocated for fulfilling the Directives and the question of optimal allocation is as important as it is difficult. In general, economic evaluations of conservation targets at most consider the costs and seldom the welfare economic benefits. In the present study, we use welfare economic benefit estimates concerning the willingness-to-pay for preserving endangered species and for the aggregate area of heathland preserved in Denmark. Similarly, we obtain estimates of the welfare economic cost of habitat restoration and maintenance. Combining these welfare economic measures with expected species coverage, we are able to estimate the potential welfare economic contribution of a conservation network. We compare three simple nonprobabilistic strategies likely to be used in day-to-day policy implementation: i) a maximum selected area strategy, ii) a hotspot selection strategy, and iii) a minimizing cost strategy, and two more advanced and informed probabilistic strategies: i) a maximum expected coverage strategy and ii) a strategy for maximum expected welfare economic gain. We show that the welfare economic performance of the strategies differ considerably. The comparison between the expected coverage and expected welfare shows that for the case considered, one may identify an optimal protection level above which additional coverage only comes at increasing welfare economic loss.
Spatial optimization, Spatial association, Chance-constrained, Species distribution, C61, Q20,
A variety of decision models have been formulated for the optimal selection of nature reserve sites to represent a diversity of species or other conservation features. Unfortunately, many of these models tend to select scattered sites and do not take into account important spatial attributes such as reserve shape and connectivity. These attributes are likely to affect not only the persistence of species but also the general ecological functioning of reserves and the ability to effectively manage them. In response, researchers have begun formulating reserve design models that improve spatial coherence by controlling spatial attributes. We review the spatial attributes that are thought to be important in reserve design and also review reserve design models that incorporate one or more of these attributes. Spatial modeling issues, computational issues, and the trade-offs among competing optimization objectives are discussed. Directions for future research are identified. Ultimately, an argument is made for the development of models that capture the dynamic interdependencies among sites and species populations and thus incorporate the reasons why spatial attributes are important.
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