Combining probabilities of occurrence with spatial reserve design

Combining probabilities of occurrence with spatial reserve design Summary 1 There is a great concern about the loss of biodiversity that calls for more nature protection. Unfortunately, the funding available for conservation is limited, and often a compromise is needed between nature protection and economic interests. Reserve selection algorithms are optimization techniques that concentrate on identifying a set of reserves that represents biodiversity efficiently. Simple approaches to reserve selection use presence–absence data assuming that if a species occurs in a selected reserve, it will persist there indefinitely. 2 A refinement of this technique is the selection of reserves according to the local probabilities of occurrence of the given species. These can be estimated from habitat models fitted by empirical modelling techniques. However, local probabilities of occurrence are not static, and are influenced by the changing threats to biodiversity in and around the reserves. 3 The effects of landscape change can be minimized when compact reserves are favoured. Compact reserves that represent all species with a given target probability of occurrence can be achieved by combining the probability approach with spatial reserve design. 4 In this study we brought together two reserve selection approaches that implicitly deal with biodiversity persistence, by combining habitat models and spatial reserve design in a single algorithm. We applied the combined algorithm to a data set of 26 species of butterflies from north Wales. 5 For the particular case study addressed in this paper, clustered reserve networks could be identified at no or a small increase in cost. 6 A new, backwards, heuristic algorithm performed better and more consistently than the regular forwards heuristic approach. 7 Synthesis and applications. The reserve selection approach presented considers habitat quality (via probabilities of occurrence) and the spatial configuration of the reserves during the selection process. Emphasis on reserve networks that include high‐quality sites in an aggregated manner increases the potential for long‐term persistence of species in the reserve network. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Ecology Wiley

Combining probabilities of occurrence with spatial reserve design

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Publisher
Wiley
Copyright
Copyright © 2004 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0021-8901
eISSN
1365-2664
DOI
10.1111/j.0021-8901.2004.00905.x
Publisher site
See Article on Publisher Site

Abstract

Summary 1 There is a great concern about the loss of biodiversity that calls for more nature protection. Unfortunately, the funding available for conservation is limited, and often a compromise is needed between nature protection and economic interests. Reserve selection algorithms are optimization techniques that concentrate on identifying a set of reserves that represents biodiversity efficiently. Simple approaches to reserve selection use presence–absence data assuming that if a species occurs in a selected reserve, it will persist there indefinitely. 2 A refinement of this technique is the selection of reserves according to the local probabilities of occurrence of the given species. These can be estimated from habitat models fitted by empirical modelling techniques. However, local probabilities of occurrence are not static, and are influenced by the changing threats to biodiversity in and around the reserves. 3 The effects of landscape change can be minimized when compact reserves are favoured. Compact reserves that represent all species with a given target probability of occurrence can be achieved by combining the probability approach with spatial reserve design. 4 In this study we brought together two reserve selection approaches that implicitly deal with biodiversity persistence, by combining habitat models and spatial reserve design in a single algorithm. We applied the combined algorithm to a data set of 26 species of butterflies from north Wales. 5 For the particular case study addressed in this paper, clustered reserve networks could be identified at no or a small increase in cost. 6 A new, backwards, heuristic algorithm performed better and more consistently than the regular forwards heuristic approach. 7 Synthesis and applications. The reserve selection approach presented considers habitat quality (via probabilities of occurrence) and the spatial configuration of the reserves during the selection process. Emphasis on reserve networks that include high‐quality sites in an aggregated manner increases the potential for long‐term persistence of species in the reserve network.

Journal

Journal of Applied EcologyWiley

Published: Apr 1, 2004

References

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