Advertisement

Optimization of Forest Wildlife Objectives

  • John Hof
  • Robert Haight
Part of the International Series In Operations Research amp; Mana book series (ISOR, volume 99)

This chapter presents an overview of methods for optimizing wildlife-related objectives. These objectives hinge on landscape pattern, so we refer to these methods as “spatial optimization.” It is currently possible to directly capture deterministic characterizations of the most basic spatial relationships: proximity relationships (including those that lead to edge effects), habitat connectivity/ fragmentation relationships, population growth and dispersal, and patch size/ habitat amount thresholds. More complex spatial relationships and stochastic relationships are currently best captured through heuristic manipulation of simulation models. General treatment of stochastic variables in spatial optimization is in its infancy.

Keywords

Landscape Pattern Demographic Model Habitat Connectivity Habitat Protection Population Viability Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Allen, L. J. S. 1983. Persistence and extinction in single-species reaction-diffusion models. Bulletin of Mathematical Biology 45:209-227.Google Scholar
  2. Andradottir, S. 1998. A review of simulation optimization techniques. In: Proceedings of the 1998 Winter Simulation Conference, eds. D. J. Medeiros, E. F. Watson, J. S. Carson, and M. S. Manivannan. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey. pp. 151-157.Google Scholar
  3. April, J., F. Glover, J. Kelly, and M. Laguna. 2001. Simulation/optimization using “real-world” applications. In: Proceedings of the 2001 Winter Simulation Conference, eds. B. A. Peters, J. S. Smith, D. J. Medeiros, and M. W. Rohrer. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey. pp. 134-138.Google Scholar
  4. Armbruster, P. and R. Lande. 1993. A population viability analysis for African elephant (Loxodonta africana): how big should reserves be? Conservation Biology 7:602-610.CrossRefGoogle Scholar
  5. Barrett, T. M., J. K. Gilless, and L. S. Davis. 1998. Economic and fragmentation effects of clearcut restrictions. Forest Science 44:569-577.Google Scholar
  6. Beissinger, S. R. and M. I. Westphal. 1998. On the use of demographic models of population viability in endangered species management. Journal of Wildlife Management 62:821-841.CrossRefGoogle Scholar
  7. Bettinger, P., J. Sessions, and K. Boston. 1997. Using tabu search to schedule timber harvests subject to spatial wildlife goals for big game. Ecological Modelling 42:111-123.CrossRefGoogle Scholar
  8. Boyce, M. S. 1992. Population viability analysis. Annual Review of Ecology and Systematics 23:481-506.CrossRefGoogle Scholar
  9. Carroll, C., R. F. Noss, P. C. Paquet, and N. H. Schumaker. 2003. Use of population viability analysis and reserve selection algorithms in regional conservation plans. Ecological Applications 13:1773-1789.CrossRefGoogle Scholar
  10. Falcao, A. O. and J. Borges. 2001. Combining random and systematic search heuristic procedures for solving spatially constrained forest management scheduling models. Forest Science 48:608-621.Google Scholar
  11. Farmer, A. H. and J. A. Wiens. 1999. Models and reality: time-energy trade-offs in pectoral sandpiper (calidris melanotos) migration. Ecology 80:2566-2580.Google Scholar
  12. Fischer, D. T. and R. L. Church. 2003. Clustering and compactness in reserve site selection: an extension of the biodiversity management area selection model. Forest Science 49: 555-565.Google Scholar
  13. Haight, R. G., B. Cypher, P. A. Kelly, et al. 2002. Optimizing habitat protection using demographic models of population viability. Conservation Biology 16:1386-1397.CrossRefGoogle Scholar
  14. Haight, R. G., B. Cypher, P. A. Kelly, S. Phillips, K. Ralls, and H. P. Possingham. 2004. Optimizing reserve expansion for disjunct populations of San Joaquin kit fox. Biological Conservation 117:61-72.CrossRefGoogle Scholar
  15. Haight, R. G. and L. E. Travis. 1997. Wildlife conservation planning using stochastic optimization and importance sampling. Forest Science 43:129-139.Google Scholar
  16. Hanski, I. 1994. A practical model of metapopulation dynamics. Journal of Animal Ecology 63:151-162.CrossRefGoogle Scholar
  17. Hof, J. and M. Bevers. 1998. Spatial Optimization for Managed Ecosystems. Columbia University Press, New York, p. 258.Google Scholar
  18. Hof, J. and M. Bevers. 2002. Spatial Optimization in Ecological Applications. Columbia University Press, New York, p. 257.Google Scholar
  19. Hof, J. G. and M. G. Raphael. 1997. Optimization of habitat placement: a case study of the Northern Spotted Owl in the Olympic Peninsula. Ecological Applications 7:1160-1169.CrossRefGoogle Scholar
  20. Goldsman, D. and B. L. Nelson. 1998. Comparing systems via simulation. Handbook on Simulation, ed. J. Banks, Chapter 8. Wiley, New York, NY.Google Scholar
  21. Kierstead, H. and L. B. Slobodkin. 1953. The size of water masses containing plankton blooms. Journal of Marine Research 12:141-147.Google Scholar
  22. Liu, J., J. B. Dunning, and H. R. Pulliam. 1995. Potential effects of a forest management plan on Bachman's sparrow (Aimophila aestivalis): linking a spatially explicit model with GIS. Conservation Biology 9:62-75.CrossRefGoogle Scholar
  23. Loehle, C. 1999. Optimizing wildlife habitat mitigation with a habitat defragmentation algorithm. Forest Ecology and Management 120:245-251.CrossRefGoogle Scholar
  24. Maddala, G. S. 1983. Limited-dependent and Qualitative Variables in Econometrics. Cambridge University Press, New York.Google Scholar
  25. Moilanen, A. and M. Cabeza. 2002. Single-species dynamic site selection. Ecological Applications 12:913-926.CrossRefGoogle Scholar
  26. Nalle, D. J., J. L. Arthur, and J. Seesions. 2002. Designing compact and contiguous reserve networks with a hybrid heuristic algorithm. Forest Science 48:59-68.Google Scholar
  27. Nevo, A. and L. Garcia. 1996. Spatial optimization of wildlife habitat. Ecological Modelling 91:271-281.CrossRefGoogle Scholar
  28. Onal, H. and R. A. Briers. 2002. Incorporating spatial criteria in optimum reserve selection. Proceedings of the Royal Society of London B 269:2437-2441.CrossRefGoogle Scholar
  29. Onal, H. and R. A. Briers. 2003. Selection of a minimum boundary reserve network using integer programming. Proceedings of the Royal Society of London B270, 1487-1491.CrossRefGoogle Scholar
  30. Pichitlamken, J. and B. L. Nelson. 2001. Selection-of-the-best procedures for optimization via simulation. In: Proceedings of the 2001 Winter Simulation Conference, eds. B. A. Peters, J. S. Smith, D. J. Medeiros, and M. W. Rohrer. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey. pp. 401-407.Google Scholar
  31. Possingham, H., I. Ball, and S. Andelman. 2000. Mathematical methods for identifying representative reserve networks. In: Quantitative Methods for Conservation Biology, eds. S. Ferson and M. Burgman. Springer-Verlag, New York. pp. 291-306.CrossRefGoogle Scholar
  32. Saunders, D. A., R. J. Hobbs, and C. R. Margules. 1991. Biological consequences of ecosystem fragmentation: a review. Conservation Biology 5:18-32.CrossRefGoogle Scholar
  33. Skellum, J. G. 1951. Random dispersal in theoretical populations. Biometrika 38:196-218.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • John Hof
    • 1
  • Robert Haight
    • 2
  1. 1.Rocky Mountain Research StationUSDA Forest ServiceFort CollinsUSA
  2. 2.North Central Research StationUSDA Forest ServiceSt. PaulUSA

Personalised recommendations