Optimization in Natural Resources Conservation

Chapter

Abstract

The previous three chapters of this book have been devoted to specific components of informed decision processes: objectives, potential actions, model(s) predicting system change and response to potential actions, and monitoring to provide estimates of system status. The final component of an informed decision process is a solution algorithm, providing a means for deciding which potential action to take. Optimization algorithms provide an objective and transparent approach to select the action that will do the best job of meeting objectives. Static optimization provides a solution to decision problems that are not iterative, and we provide examples for one or more decision variables (variables that are components of potential actions). Many decision problems in natural resource management are best viewed as dynamic, in that they are iterative and require decisions that are repeated through time. In dynamic decision problems, decisions made at one point in time are expected to influence system state of the next time step, thus influencing the state-dependent decision at that time. For any specific decision, dynamic optimization algorithms must thus consider all subsequent time steps for the time horizon of the decision problem. In addition to being dynamic, most decision problems in natural resource management are characterized by substantial uncertainty, and dynamic optimization algorithms have been extended to deal with several sources of uncertainty. An important source is uncertainty about how the system responds to management actions, and we may develop multiple models to characterize this uncertainty. Adaptive dynamic optimization algorithms provide solutions that deal not only with objectives, but with the anticipated reduction in uncertainty that will characterize future decisions. The output of an optimization algorithm is frequently a graph or table of recommended actions for specific values of system state variables. Decision thresholds are thus defined by the optimization algorithm and are simply locations in state space where a small change in the value of a state variable produces a change in the optimal or recommended management action.

Keywords

Adaptive management Conservation Markov decision process Optimization Uncertainty 

References

  1. Allen, C. R., K. L. Pope, and J. J. Fontaine, eds. 2011. Adaptive management for natural resources. Journal of Environmental Management 92:1339–1428.CrossRefGoogle Scholar
  2. Ben-Haim, Y. 2002. Info-gap decision theory: Decisions under severe uncertainty. 2nd ed. New York: Elsevier.Google Scholar
  3. Bertsekas, D. P. 1995. Dynamic programming and optimal control: Volumes I and II. Belmont: Athena Scientific.Google Scholar
  4. Bryson, A. E., and Y.-C. Ho. 1975. Applied optimal control: Optimization, estimation, and control. Bristol: Taylor and Francis.Google Scholar
  5. Fahey, L., and R. M. Randall. 1998. Learning from the future: Competitive foresight scenarios. New York: Wiley.Google Scholar
  6. Hillier, F. S., and G. J. Lieberman. 2001. Introduction to operations research. 7th ed. New York: McGrw-Hill.Google Scholar
  7. Holling, C. S., ed. 1978. Adaptive environmental assessment and management. New York: Wiley.Google Scholar
  8. Kaelbling, L. P., M. L. Littman, and A. R. Cassandra. 1998. Planning and acting in partially observable stochastic domains. Artificial Intelligence 101:99–134.CrossRefGoogle Scholar
  9. Lee, P. M. 1989. Bayesian statistics: An introduction. London: Edward Arnold.Google Scholar
  10. Lempert, R. J., and M. T. Collins. 2007. Managing the risk of uncertain threshold response: Comparison of robust, optimum, and precautionary approaches. Risk Analysis 27:1009–1026.CrossRefGoogle Scholar
  11. Luenberger, D. G. 1984. Linear and nonlinear programming. 2nd ed. Reading: Addison-Wesley.Google Scholar
  12. Martin J., M. C. Runge, J. D. Nichols, B. C. Lubow, and W. L. Kendall. 2009. Structured decision making as a conceptual framework to identify thresholds for conservation and management. Ecological Applications 19:1079–1090.CrossRefGoogle Scholar
  13. Miller, R. E. 2000. Optimization. New York: Wiley.Google Scholar
  14. Milly, P. C. D., J. Betancourt, M. Falkenmark, R. M. Hirsch, Z. W. Kundzewicz, D. P. Lettenmaier, and R. J. Stouffer. 2008. Stationarity is dead: Whither water management? Science 319:573–574.CrossRefGoogle Scholar
  15. Nichols, J. D., M. J. Eaton, and J. Martin. In press. Thresholds for conservation and management: Structured decision making as a conceptual framework. In Thresholds for conservation, ed. G. Gunterspergen and P. Geissler. New York: Wiley.Google Scholar
  16. Nichols, J. D., M. D. Koneff, P. J. Heglund, M. G. Knutson, M. E. Seamans, J. E. Lyons, J. M. Morton, M. T. Jones, G. S. Boomer, and B. K. Williams. 2011. Climate change, uncertainty and natural resource management. Journal of Wildlife Management 75:6–18.CrossRefGoogle Scholar
  17. Nichols, J. D., and B. K. Williams. 2012. Adaptive management. In Encyclopedia of Environmetrics 2nd ed. (pp. 1–6), New York: Wiley.Google Scholar
  18. Puterman, M. L. 1994. Markov decision processes: Discrete stochastic dynamic programming. New York: Wiley.CrossRefGoogle Scholar
  19. Runge, M. C., and T. Walshe. In press. Identifying objectives and alternative actions to frame a decision problem. In Thresholds for conservation, ed. G. Guntenspergen and P. Geissler. New York: Springer.Google Scholar
  20. Sage, A. P. and C. C. White. 1977. Optimum systems control. 2nd ed. Englewood Cliffs: Prentice-Hall.Google Scholar
  21. Stengel, R. F. 1994. Optimal control and estimation. New York: Dover Publishers.Google Scholar
  22. Walters, C. J. 1986. Adaptive management of renewable resources. Caldwell: Blackburn Press.Google Scholar
  23. Williams, B. K. 2007. Optimal management of non-Markovian biological populations. Ecological Modelling 200:234–242.CrossRefGoogle Scholar
  24. Williams, B. K. 2009. Markov decision processes in natural resources management: Observability and uncertainty. Ecological Modelling 220:830–840.CrossRefGoogle Scholar
  25. Williams, B. K. 2011. Adaptive management of natural resources: Framework and issues. Journal of Environmental Management 92:1346–1353.CrossRefGoogle Scholar
  26. Williams, B. K., and F. A. Johnson. 1995. Adaptive management and the regulation of waterfowl harvests. Wildlife Society Bulletin 23:430–436.Google Scholar
  27. Williams, B. K., J. D. Nichols, and M. J. Conroy. 2002. Analysis and management of animal populations. San Diego: Academic Press.Google Scholar
  28. Williams, B. K., R. C. Szaro, and C. D. Shapiro. 2007. Adaptive management: The U.S. department of the interior technical guide. Washington, DC: Adaptive Management Working Group (U.S. Department of the Interior).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2014

Authors and Affiliations

  1. 1.Cooperative Research UnitsUSGSRestonUSA
  2. 2.The Wildlife SocietyBethesdaUSA
  3. 3.Patuxent Wildlife Research CenterUSGSLaurelUSA

Personalised recommendations