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Analysis of the Threshold and Expected Coverage Approaches to the Probabilistic Reserve Site Selection Problem

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Abstract

Two approaches to formulating the reserve site selection problem when species occurrence data is probabilistic were solved for terrestrial vertebrates in a small set of potential reserve sites in Oregon. The expected coverage approach, which maximizes the sum of the occurrence probabilities, yielded solutions that covered more species on average in Monte Carlo simulations than the threshold approach, which maximizes the number of species for which the occurrence probability exceeds some threshold.

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References

  1. R.F. Noss and A.Y. Cooperrider, Saving Nature's Legacy (Island Press, Washington, DC, 1994).

    Google Scholar 

  2. S.L. Pimm and J.H. Lawton, Planning for biodiversity, Science 279 (1998) 2068–2069.

    Google Scholar 

  3. R.L. Church and C.S. ReVelle, The maximal covering location problem, Papers of the Regional Science Association 32 (1974) 101–118.

    Google Scholar 

  4. R.L. Church, D.M. Stoms and F.W. Davis, Reserve selection as a maximal coverage location problem, Biological Conservation 76 (1996) 105–112.

    Google Scholar 

  5. A.R. Kiester, J.M. Scott, B. Csuti, R.F. Noss, B. Butterfield, K. Sahr and D. White, Conservation prioritization using GAP data, Conservation Biology 10(5) (1996) 1332–1342.

    Google Scholar 

  6. B. Csuti, S. Polasky, P.H. Williams, R.L. Pressey, J.D. Camm, M. Kershaw, A.R. Kiester, B. Downs, R. Hamilton, M. Huso and K. Sahr, A comparison of reserve selection algorithms using data on terrestrial vertebrates in Oregon, Biological Conservation 80 (1997) 83–97.

    Google Scholar 

  7. R.L. Pressey, H.P. Possingham and J.R. Day, Effectiveness of alternative heuristic algorithms for identifying minimum requirements for conservation reserves, Biological Conservation 80 (1997) 207–219.

    Google Scholar 

  8. A. Ando, J. Camm, S. Polasky and A. Solow, Species distributions, land values, and efficient conservation, Science 279 (1998) 2126–2128.

    Google Scholar 

  9. M.P. Austin, R.B. Cunningham and R.M. Fleming, New approaches to direct gradient analysis using environmental scalars and statistical curve-fitting procedures, Vegetatio 55 (1984) 11–27.

    Google Scholar 

  10. C.R. Margules and A.O. Nicholls, Assessing the conservation value of remnant habitat “islands”: mallee patches on the western Eyre Peninsula, South Australia, in: Nature Conservation: The Role of Remnants of Native Vegetation, eds. D.A. Saunders, G.W. Arnold, A.A. Burbidge and A.J.M. Hopkins (Surrey Beatty and Sons, Sydney, 1987) pp. 89–102.

    Google Scholar 

  11. C.R. Margules and J.L. Stein, Patterns in the distribution of species and the selection of nature reserves: an example from Eucalyptus forests in South-Eastern New South Wales, Biological Conservation 50 (1989) 219–238.

    Google Scholar 

  12. A.O. Nicholls, How to make biological surveys go further with generalized linear models, Biological Conservation 50 (1989) 51–75.

    Google Scholar 

  13. C.R. Margules and M.P. Austin, Biological models for monitoring species decline: the construction and use of data bases, Philosophical Transactions of the Royal Society of London Biological Sciences 345 (1994) 69–75.

    Google Scholar 

  14. S. Polasky, J.D. Camm, A.R. Solow, B. Csuti, D. White and R. Ding, Choosing reserve networks with incomplete species information, Biological Conservation 94 (2000) 1–10.

    Google Scholar 

  15. J.D. Camm, S.K. Norman, S. Polasky and A. Solow, Nature reserve selection to maximize expected species covered, Operations Research (2001), in press.

  16. M.S. Daskin, A maximum expected covering location model: formulation, properties and heuristic solution, Transportation Science 17 (1983) 48–70.

    Google Scholar 

  17. M.S. Daskin, K. Hogan and C.S. ReVelle, Integration of multiple, excess, backup and expected covering models, Environment and Planning B 15 (1988) 15–35.

    Google Scholar 

  18. C.-M. Liu and A.-H. Wang, Solving location-allocation problems with rectilinear distances by simulated annealing, Journal of the Operational Research Society 45 (1994) 1304–1315.

    Google Scholar 

  19. A.T. Murray and R.L. Church, Applying simulated annealing to location-planning models, Journal of Heuristics 2 (1996) 31–53.

    Google Scholar 

  20. G. Righini, Annealing algorithms for multisource absolute location problems on graphs, Computational Optimization and Applications 7 (1997) 325–337.

    Google Scholar 

  21. A.T. Ernst and M. Krishnamoorthy, Solution algorithms for the capacitated single allocation hub location problem, Annals of Operations Research 86 (1999) 141–159.

    Google Scholar 

  22. C.M. Hosage and M.F. Goodchild, Discrete space location-allocation solutions from genetic algorithms, Annals of Operations Research 6 (1986) 35–46.

    Google Scholar 

  23. C.R. Houck, J.A. Joines and M.G. Kay, Comparison of genetic algorithms, random restart and two-opt switching for solving large location-allocation problems, Computers & Operations Research 23 (1996) 587–596.

    Google Scholar 

  24. A. Kumar, Y. Gupta, M. Gupta and C. Sundaram, A genetic algorithmbased approach to cell composition and layout design problems, International Journal of Production Research 34 (1996) 447–482.

    Google Scholar 

  25. F.A.W. George, Hybrid genetic algorithms with immunisation to optimise networks of retail outlets, Studies in Locational Analysis 8 (1996) 59–80.

    Google Scholar 

  26. T.G. Crainic, M. Toulouse and M. Gendreau, Synchronous tabu search parallelization strategies for multicommodity location-allocation with balancing requirements, Operations Research Spektrum 17 (1995) 113–123.

    Google Scholar 

  27. E. Rolland, D.A. Schilling and J.R. Current, An efficient tabu search procedure for the p-median problem, European Journal of Operational Research 96 (1997) 329–342.

    Google Scholar 

  28. M. Ohlemuller, Tabu search for large location-allocation problems, Journal of the Operational Research Society 48 (1997) 745–750.

    Google Scholar 

  29. B. Gendron, J.-Y. Potvin and P. Soriano, Tabu search with exact neighbour evaluation for multicommodity location with balancing requirements, INFOR 37 (1999) 255–270.

    Google Scholar 

  30. R.G. Haight, C.S. ReVelle and S.A. Snyder, An integer optimization approach to the probabilistic reserve site selection problem, Operations Research 48(5) (2000) 697–708.

    Google Scholar 

  31. S.V. Ciriacy-Wantrup, Resource Conservation: Economics and Policies, 3rd ed. (University of California Press, Berkeley, CA, 1968).

    Google Scholar 

  32. R.C. Bishop, Endangered species and uncertainty: the economics of the safe minimum standard, American Journal of Agricultural Economics 60 (1978) 10–18.

    Google Scholar 

  33. L. Master, N. Clupper, E. Gaines, C. Bogert, R. Solomon and M. Ormes, Biodiversity Research Consortium Species Database Manual (The Nature Conservancy, Boston, MA, 1995).

    Google Scholar 

  34. A.M. Law and W.D. Kelton, Simulation Modeling and Analysis (McGraw-Hill, New York, 1982).

    Google Scholar 

  35. S. Polasky, J.D. Camm and B. Garber-Yonts, Selecting biological reserves cost effectively: an application to terrestrial vertebrate conservation in Oregon, Land Economics 77(1) (2001) 68–78.

    Google Scholar 

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Authors and Affiliations

  1. Department of Statistics, Oregon State University, Corvallis, OR, 97331, USA

    Jeffrey L. Arthur & Claire A. Montgomery

  2. USDA Forest Service, North Central Forest Experiment Station, 1992 Folwell Avenue, St. Paul, MN, 55108, USA

    Robert G. Haight

  3. Department of Applied Economics, University of Minnesota, St. Paul, MN, 55108, USA

    Stephen Polasky

Authors
  1. Jeffrey L. Arthur
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  2. Robert G. Haight
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  3. Claire A. Montgomery
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  4. Stephen Polasky
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Arthur, J.L., Haight, R.G., Montgomery, C.A. et al. Analysis of the Threshold and Expected Coverage Approaches to the Probabilistic Reserve Site Selection Problem. Environmental Modeling & Assessment 7, 81–89 (2002). https://doi.org/10.1023/A:1015693531132

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