Mathematical Methods for Identifying Representative Reserve Networks

  • Hugh Possingham
  • Ian Ball
  • Sandy Andelman

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literature Cited

  1. Andelman SJ, Meir E (2000) Breadth is better than depth: biodiversity data requirements for adequate reserve networks. Conservation Biology (in press)Google Scholar
  2. Andelman SJ, Fagan W, Davis F, Pressey RL (2000) Tools for conservation planning in an uncertain world. BioScience (in press)Google Scholar
  3. Bailey RG (1994) Ecoregions of the US. USDA Forest Service, Washington, DCGoogle Scholar
  4. Ball IR, Smith A, Day JR, Pressey RL, Possingham H (in press) Comparison of mathematical algorithms for the design of a reserve system for nature conservation: an application of genetic algorithms and simulated annealing. Journal of Environmental ManagementGoogle Scholar
  5. Church RL, Stoms DM, Davis FW (1996) Reserve selection as a maximal covering location problem. Biological Conservation 76:105–112CrossRefGoogle Scholar
  6. Cocklin C (1989a) Mathematical programming and resources planning I: the limitations of traditional optimization. Journal of Environmental Management 28:127–141Google Scholar
  7. Cocklin C (1989b) Mathematical programming and resources planning II: new developments in methodology. Journal of Environmental Management 28:143–156Google Scholar
  8. Cocks KD, Baird IA (1989) Using mathematical programming to address the multiple reserve selection problem: an example from the Eyre Peninsula South Australia. Biological Conservation 49:113–130CrossRefGoogle Scholar
  9. Davis FW, Stoms DM, Andelman SJ (2000) Systematic reserve selection in the USA: an example from the Columbia Plateau ecoregion. Parks (in press)Google Scholar
  10. Fagan WF, Cantrell RS, Cosner C (1999) How habitat edges change species interactions. American Naturalist 153:165–182CrossRefGoogle Scholar
  11. Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. WH Freeman and Company, San Francisco, CAGoogle Scholar
  12. Golden B, Skiscim C (1986) Using simulated-annealing to solve routing and location problems. Naval Research Logistics Quarterly 33:261–279Google Scholar
  13. Kirkpatrick JB (1983) An iterative method for establishing priorities for selection of nature reserves: an example from Tasmania. Biological Conservation 25:127–134CrossRefGoogle Scholar
  14. Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680Google Scholar
  15. MacArthur RH, Wilson EO (1963) An equilibrium theory of insular zoogeography. Evolution 17:373–387CrossRefGoogle Scholar
  16. Margules CR, Nicholls AO, Pressey RL (1988) Selecting networks to maximise biological diversity. Biological Conservation 43:63–76CrossRefGoogle Scholar
  17. Margules CR, Nicholls AO, Usher MB (1994) Apparent species turnover probability of extinction and the selection of nature reserves: a case study of the Ingleborough limestone pavements. Conservation Biology 8:398–409CrossRefGoogle Scholar
  18. McNeely JA, Miller KR, Reid WV, Mittermeier RA, Werner TB (1990) Conserving the world’s biodiversity. IUCN, Gland, SwitzerlandGoogle Scholar
  19. Metropolis NA, Rosenbluth M, Rosenbluth A, Teller E (1953) Equation of state calculations by fast computing machines. Journal of Chemical Physics 21:1087–1092CrossRefGoogle Scholar
  20. Morton SR, Stafford Smith DM, Friedel MH, Griffen GF, Pickup G (1995) The stewardship of arid Australia: ecology and landscape management. Journal of Environmental Management 43:195–217CrossRefGoogle Scholar
  21. Murray AT, Church RL (1996) Applying simulated annealing to location-planning models. Journal of Heuristics 2:31–53CrossRefGoogle Scholar
  22. Nicholls AO, Margules CR (1993) An upgraded reserve selection algorithm. Biological Conservation 64:165–169CrossRefGoogle Scholar
  23. Possingham HP, Day JR, Goldfinch M, Salzborn F (1993) The mathematics of designing a network of protected areas for conservation. In: Sutton DJ, Pearce CEM, Cousins EA (eds) Decision sciences: tools for today. Proceedings of 12th National ASOR Conference, ASOR, Adelaide, Australia, pp 536–545Google Scholar
  24. Pressey RL, Humphries CJ, Margules CR, Vane-Wright RI, Williams PH (1993) Beyond opportunism: key principles for systematic reserve selection. TREE 8:124–128Google Scholar
  25. Pressey RL, Possingham HP, Margules CR (1996) Optimality in reserve selection algorithms: when does it matter and how much? Biological Conservation 76:259–267CrossRefGoogle Scholar
  26. Pressey RL, Possingham PH, Day RJ (1997) Effectiveness of alternative heuristic algorithms for identifying indicative minimum requirements for conservation reserves. Biological Conservation 80:207–219CrossRefGoogle Scholar
  27. Rebelo AG, Siegfried WR (1992) Where should nature reserves be located in the cape floristic region South Africa? Models for the spatial configuration of a reserve network aimed at maximizing the protection of floral diversity. Conservation Biology 6:243–252CrossRefGoogle Scholar
  28. Simberloff D (1998) Flagships umbrellas and keystones: is single species management passé in the landscape era? Biological Conservation 83:247–257CrossRefGoogle Scholar
  29. Soulé ME (1991) Conservation: tactics for a constant crisis. Science 253:744–750Google Scholar
  30. Underhill LG (1994) Optimal and suboptimal reserve selection algorithms. Biological Conservation 70:85–87CrossRefGoogle Scholar
  31. Willis CK, Lombard AT, Cowling RM, Heydenrych BJ, Burgers CJ (1996) Reserve systems for limestone endemic flora of the cape lowland fynbos: iterative versus linear programming. Biological Conservation 77:53–62CrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York, Inc. 2000

Authors and Affiliations

  • Hugh Possingham
  • Ian Ball
  • Sandy Andelman

There are no affiliations available

Personalised recommendations