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Representational success: A new paradigm for achieving species protection by reserve site selection

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Models for designing habitat reserve networks have focused on minimizing the number of sites necessary to cover each species one or more times. A solution to this problem is usually one from among a large number of alternative optimal configurations of sites. This paper develops an iterative method for building reserve networks that produces an optimal solution to the species set covering problem (SSCP) and also maximizes the number of species covered two or more times, three or more times, and so on, conditional on the solution to the previous iteration. We refer to this as representational success. Thus, a pareto optimal species set covering is achieved that is preferable to an arbitrary optimal solution to the SSCP.

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References

  1. J. Kirkpatrick, An iterative method for establishing priorities for the selection of natural reserves: an example from Tasmania. Biol. Conserv. 25 (1983) 127–134.

    Article  Google Scholar 

  2. M. Game and G.F. Peterken, Nature reserve selection strategies in the woodlands of central Lincolnshire, Engl. Biol. Conserv. 29 (1984) 157–181.

    Article  Google Scholar 

  3. C. Margules, A. Nichols and R. Pressey, Selecting networks of reserves to maximize biological diversity. Biol. Conserv. 43 (1988) 63–76.

    Article  Google Scholar 

  4. A.O. Nicholls and C.R. Margules, An upgraded reserve selection algorithm. Biol. Conserv. 41 (1993) 11–37.

    Google Scholar 

  5. L. Belbin, Environmental representativeness, regional partitioning and reserve selection. Biol. Conserv. 66 (1993) 223–230.

    Article  Google Scholar 

  6. L. Underhill, Optimal and suboptimal reserve selection algorithms. Biol. Conserv. 35 (1994) 85–87.

    Article  Google Scholar 

  7. H. Possingham, J. Day, M. Goldfinch and F. Salzborn, The mathematics of designing a network of protected areas for conservation, in: Decision Sciences, Tools for Today, Proceedings of the ASOR Conference, Adelaide, Australia, eds. D. Sutton, E. Cousins and C. Pierce (1993).

  8. R. Church, D. Stoms and F. Davis, Reserve selection as maximal covering location problem. Biol. Conserv. 76 (1996) 105–112.

    Article  Google Scholar 

  9. 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. Biol. Conserv. 80 (1) (1997) 83–97.

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  12. R. Haight, C. ReVelle and S. Snyder, An integer optimization approach to a probabilistic reserve site selection problem. Oper. Res. 48 (2000) 697–708.

    Article  Google Scholar 

  13. J. Camm, S. Norman, S. Polasky and A. Solow, Nature reserve site selection to maximize expected species covered. Oper. Res. 50 (2002) 946–955.

    Article  Google Scholar 

  14. M.I. Westphal, M. Pickett, W.M. Getz and H.P. Possingham, The use of stochastic dynamic programming in optimal landscape reconstruction for metapopulations. Ecol. Appl. 13 (2003) 543–555.

    Article  Google Scholar 

  15. C. Costello and S. Polasky, Dynamic reserve site selection, Resour. Energy Econ. 26 (2004) 157–174.

    Article  Google Scholar 

  16. C. ReVelle, J. Williams and J. Boland, Counterpart models in facility location science and reserve selection science. Environ. Model. Assess. 7 (2002) 71–80.

    Article  Google Scholar 

  17. J. Williams, C. ReVelle and S. Levin, Spatial attributes and reserve design models: a review. Environ. Model. Assess. (2004) in press.

  18. S. Malcolm and C. ReVelle, Models for preserving species diversity with backup coverage. Environ. Model. Assess. (2004) in press.

  19. B. Hamadie, C. ReVelle and S. Malcolm, Biological reserves, rare species and the opportunity cost of diversity, Ecol Econ. in press.

  20. P.R. Kleindorfer, H.C. Kunruether and P.J.H. Schoemaker, Decision Sciences: An Integrative Approach (Cambridge, New York, 1993).

    Google Scholar 

  21. 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).

  22. GAMS Development Corporation, General Algebraic Modeling System. Version 2.25.090 (Washington, DC, 1990).

  23. V. Marianov and C. ReVelle, Siting emergency services, Chapter 10, in: Facility Location, ed. Z. Drezner (Springer, Berlin Heidelberg New York, 1995) pp. 203–227.

  24. J. Cohon, Multi-Objective Programming and Planning (Academic, 1978).

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Correspondence to Scott A. Malcolma.

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Malcolma, S.A., ReVelle, C. Representational success: A new paradigm for achieving species protection by reserve site selection. Environ Model Assess 10, 341–348 (2005). https://doi.org/10.1007/s10666-005-9015-5

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  • DOI: https://doi.org/10.1007/s10666-005-9015-5

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