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Algoritmos de Selección de Sitios y Pérdida de Hábitat
Author(s) -
Cabeza Mar,
Moilanen Atte
Publication year - 2003
Publication title -
conservation biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.2
H-Index - 222
eISSN - 1523-1739
pISSN - 0888-8892
DOI - 10.1046/j.1523-1739.2003.01421.x
Subject(s) - metapopulation , biodiversity , habitat , population , ecology , geography , site selection , extinction (optical mineralogy) , global biodiversity , context (archaeology) , selection (genetic algorithm) , computer science , environmental resource management , biology , environmental science , biological dispersal , machine learning , demography , paleontology , archaeology , sociology , political science , law
Site‐selection algorithms are used in reserve design to select networks of sites that maximize biodiversity, given some constraints. These algorithms are based on a snapshot of species occurrence, and they typically aim to minimize the area or cost needed to represent all the species once or a few times. Most of these algorithms ignore the question of how well species are likely to persist in the set of selected sites in the long term. Furthermore, the role of the unselected habitat in biodiversity persistence has received no attention in this context. We used a theoretical approach to evaluate the long‐term performance of reserve networks in preserving biodiversity by using a model of spatiotemporal population dynamics ( a metapopulation model ). We compared extinction rates of species in reserve networks in two situations: when all sites remain suitable habitat for the species and, conversely, when the habitat in the unselected sites is lost. We made this comparison to explore the significance of unselected sites for spatial population dynamics and for the continued presence of species in the reserve network. Basic site‐selection algorithms are liable to perform badly in terms of biodiversity maintenance because the persistence of species may be strongly dependent on sites not included in the reserve network. Our results support recent calls for the integration of spatial population modeling into reserve network design. Advances in metapopulation theory provide tools that can be used for this purpose.