A comparison of reserve selection algorithms using data on terrestrial vertebrates in Oregon

A comparison of reserve selection algorithms using data on terrestrial vertebrates in Oregon We compare the number of species represented and the spatial pattern of reserve networks derived using five types of reserve selection algorithms on a set of vertebrate distribution data for the State of Oregon (USA). The algorithms compared are: richness-based heuristic algorithms (four variations), weighted rarity-based heuristic algorithms (two variations), progressive rarity-based heuristic algorithms (11 variations), simulated annealing, and a linear programming-based branch-and-bound algorithm. The linear programming algorithm provided optimal solutions to the reserve selection problem, finding either the maximum number of species for a given number of sites or the minimum number of sites needed to represent all species. Where practical, we recommend the use of linear programming algorithms for reserve network selection. However, several simple heuristic algorithms provided near-optimal solutions for these data. The near-optimality, speed and simplicity of heuristic algorithms suggests that they are acceptable alternatives for many reserve selection problems, especially when dealing with large data sets or complicated analyses. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biological Conservation Elsevier

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Publisher
Elsevier
Copyright
Copyright © 1997 Elsevier Ltd
ISSN
0006-3207
D.O.I.
10.1016/S0006-3207(96)00068-7
Publisher site
See Article on Publisher Site

Abstract

We compare the number of species represented and the spatial pattern of reserve networks derived using five types of reserve selection algorithms on a set of vertebrate distribution data for the State of Oregon (USA). The algorithms compared are: richness-based heuristic algorithms (four variations), weighted rarity-based heuristic algorithms (two variations), progressive rarity-based heuristic algorithms (11 variations), simulated annealing, and a linear programming-based branch-and-bound algorithm. The linear programming algorithm provided optimal solutions to the reserve selection problem, finding either the maximum number of species for a given number of sites or the minimum number of sites needed to represent all species. Where practical, we recommend the use of linear programming algorithms for reserve network selection. However, several simple heuristic algorithms provided near-optimal solutions for these data. The near-optimality, speed and simplicity of heuristic algorithms suggests that they are acceptable alternatives for many reserve selection problems, especially when dealing with large data sets or complicated analyses.

Journal

Biological ConservationElsevier

Published: Apr 1, 1997

References

  • A note on optimal algorithms for reserve site selection
    Camm, J.D.; Polasky, S.; Solow, A.; Csuti, B.
  • Reserve selection as a maximal covering location problem
    Church, R.L.; Stoms, D.M.; Davis, F.W.
  • The virtues and shortcomings of parochialism: conserving species that are locally rare, but globally common
    Hunter, M.L.; Hutchinson, A.
  • When are peripheral populations valuable for conservation?
    Lessica, P.; Allendorf, F.W.
  • Where should nature reserves be located in South Africa?
    Lombard, A.T.; Nicholls, A.O.; August, P.V.
  • Ad hoc reservations: forward or backward steps in developing representative reserve systems
    Pressey, R.L.
  • Optimality in reserve selection algorithms: When does it matter and how much?
    Pressey, R.L.; Possingham, H.P.; Margules, C.R.
  • Effectiveness of alternative heuristic algorithms for identifying minimum requirements for conservation reserves
    Pressey, R.L.; Possingham, H.P.; Day, J.R.

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