Methods for designing regional reserve networks mostly concentrate on providing maximal representation of species occurring in the region. Representation-based methods, however, typically consider a static snapshot of species incidences, and the spatial dynamics of the species are ignored. It has been empirically demonstrated that reserves designed using representation do not guarantee another important goal of reserve design: long-term persistence. The question studied here is the following: Which subset of sites do you select to maximize the long-term persistence of a species living in a metapopulation, given that each site has a cost and the amount of resource (e.g., money) available is limited? We present an optimization method, which uses a combination of evolutionary optimization (a genetic algorithm) and local search to find the optimal selection of sites. The quality of each candidate solution is evaluated using a spatially realistic metapopulation model, the incidence function model. The proposed method is applied to a metapopulation of the false heath fritillary butterfly, an endangered species in Finland. With this data set, the proposed estimation method produces intuitively acceptable and consistent results within minutes of computation time. Sites favored by the algorithm are located in three patch clusters, and they tend to be inexpensive and initially occupied. Expensive and/or very isolated patches are rarely selected into the optimal site selection.
Ecological Applications – Ecological Society of America
Published: Jun 1, 2002
Keywords: economic constraint ; genetic algorithm ; incidence function model ; local search ; long-term persistence ; Melitaea diamina ; metapopulation ; objective function ; reserve network design ; site selection algorithm
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