Thresholds for Conservation and Management: Structured Decision Making as a Conceptual Framework

  • James D. Nichols
  • Mitchell J. Eaton
  • Julien Martin


A conceptual framework is provided for considering the threshold concept in natural resource management and conservation. We define three kinds of thresholds relevant to management and conservation. Ecological thresholds are values of system state variables at which small changes bring about substantial or specified changes in system dynamics. They are frequently incorporated into ecological models used to project system responses to management actions. Utility thresholds are components of management objectives and are values of state or performance variables at which small changes yield substantial changes in the value of the management outcome. Decision thresholds are values of system state variables at which small changes prompt changes in management actions in order to reach specified management objectives. Decision thresholds are derived from the other components of the decision process. We advocate a structured decision making (SDM) approach within which the following components are identified: objectives (possibly including utility thresholds), potential actions, models (possibly including ecological thresholds), monitoring program, and a solution algorithm (which produces decision thresholds). Adaptive resource management (ARM) is described as a special case of SDM developed for recurrent decision problems that are characterized by uncertainty. We believe that SDM, in general, and ARM, in particular, provide good approaches to conservation and management. Use of SDM and ARM also clarifies the distinct roles of ecological thresholds, utility thresholds, and decision thresholds in informed decision processes.


Adaptive management Decision threshold Ecological threshold Structured decision making Utility threshold 


  1. Bellman, R. 1957. Dynamic programming. Princeton: Princeton University Press.Google Scholar
  2. Bennetts, R. E., J. E. Gross, K. Cahill, C. McIntyre, B. B. Bingham, A. Hubbard, L. Cameron, and S. L. Carter. 2007. Linking monitoring to management and planning: Assessment points as a generalized approach. George Wright Forum 24:59–77.Google Scholar
  3. Benton, T. G. 2003. Understanding the ecology of extinction: Are we close to the critical threshold? Annales Zoologici Fennici 40:71–80.Google Scholar
  4. Bestelmeyer, B. T. 2006. Threshold concepts and their use in rangeland management and restoration: The good, the bad, and the insidious. Restoration Ecology 14:325–329.CrossRefGoogle Scholar
  5. Bodin, P., and B. L. B. Wiman. 2007. The usefulness of stability concepts in forest management when coping with increasing climate uncertainties. Forest Ecology and Management 242:541–552.CrossRefGoogle Scholar
  6. Borchers, D. L., S. T. Buckland, and W. Zucchini. 2003. Estimating animal abundance. New York: Springer-Verlag.Google Scholar
  7. Brown, J. H., T. J. Valone, and C. G. Curtin. 1997. Reorganization of an arid ecosystem in response to recent climate change. Proceedings of the National Academy of Sciences of the United States of America 94:9729–9733.Google Scholar
  8. Burgman, M. 2005. Risks and decisions for conservation and environmental management. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  9. Caughley, G. 1994. Directions in conservation biology. The Journal of Animal Ecology 63:215–244.CrossRefGoogle Scholar
  10. Clemen, R. T., and T. Reilly. 2001. Making hard decisions with decision tools. Pacific Grove: Duxbury.Google Scholar
  11. Conroy, M. J., and C. T. Moore. 2001. Simulation models and optimal decision making in natural resource management. In Modeling in natural resource management: valid development, interpretation and application, ed. T. M. Shenk and A. B. Franklin, 91–104. Washington, D.C.: Island Press.Google Scholar
  12. Conroy, M. J., C. R. Allen, J. T. Peterson, L. Pritchard, and C. T. Moore. 2003. Landscape change in the southern Piedmont: Challenges, solutions, and uncertainty across scales. Conservation Ecology 8:3.Google Scholar
  13. Dorazio, R. M., and F. A. Johnson. 2003. Bayesian inference and decision theory—A framework for decision making in natural resource management. Ecological Applications 13:556–563.CrossRefGoogle Scholar
  14. Fahrig, L. 2001. How much habitat is enough? Biological Conservation 100:65–74.CrossRefGoogle Scholar
  15. Field, S. A., A. J. Tyre, N. Jonzen, J. R. Rhodes, and H. P. Possingham. 2004. Minimizing the cost of environmental management decisions by optimizing statistical thresholds. Ecology Letters 7:669–675.CrossRefGoogle Scholar
  16. Fonnesbeck, C. J. 2005. Solving dynamic wildlife resource optimization problems using reinforcement learning. Natural Resource Modeling 18:1–39.CrossRefGoogle Scholar
  17. Groffman, P., J. Baron, T. Blett, A. Gold, I. Goodman, L. Gunderson, B. Levinson, M. Palmer, H. Paerl, G. Peterson, N. Poff, D. Rejeski, J. Reynolds, M. Turner, K. Weathers, and J. Wiens. 2006. Ecological thresholds: The key to successful environmental management or an important concept with no practical application? Ecosystems 9:1–13.CrossRefGoogle Scholar
  18. Gunderson, L. H. 2000. Ecological resilience—in theory and application. Annual Review of Ecology and Systematics 31:425–439.CrossRefGoogle Scholar
  19. Halpern, B. S., H. M. Regan, H. P. Possingham, and M. A. McCarthy. 2006. Accounting for uncertainty in marine reserve design. Ecology Letters 9:2–11.CrossRefGoogle Scholar
  20. Holling, C. S. 1973. Resilience and stability of ecological systems. Annual Review of Ecology and Systematics 4:1–23.CrossRefGoogle Scholar
  21. Holling, C. S. 1978. Adaptive environmental assessment and management. New York: Wiley.Google Scholar
  22. Huggett, A. J. 2005. The concept and utility of ‘ecological thresholds’ in biodiversity conservation. Biological Conservation 124:301–310.CrossRefGoogle Scholar
  23. Intriligator, M. D. 1971. Mathematical optimization and economic theory. Englewood Cliffs: Prentice-Hall.Google Scholar
  24. Johnson, F. A., C. T. Moore, W. L. Kendall, J. A. Dubovsky, D. F. Caithamer, J. R. Kelley, and B. K. Williams. 1997. Uncertainty and the management of mallard harvests. Journal of Wildlife Management 61:202–216.CrossRefGoogle Scholar
  25. Kendall, W. L. 2001. Using models to facilitate complex decisions. In Modeling in natural resource management: valid development, interpretation and application, ed. T. M. Shenk and A. B. Franklin, 147–170. Washington, D.C.: Island Press.Google Scholar
  26. Lande, R. 1987. Extinction thresholds in demographic-models of territorial populations. American Naturalist 130:624–635.CrossRefGoogle Scholar
  27. Lee, K. N. 1993. Compass and gyroscope: Integrating science and politics for the environment. Washington, D.C.: Island Press.Google Scholar
  28. Lubow, B. C. 1995. SDP: Generalized software for solving stochastic dynamic optimization problems. Wildlife Society Bulletin 23:738–742.Google Scholar
  29. Lyons, J. E., M. C. Runge, H. P. Laskowski, and W. L. Kendall. 2008. Monitoring in the context of structured decision-making and adaptive management. Journal of Wildlife Management 72:1683–1692.CrossRefGoogle Scholar
  30. Martin, J., M. C. Runge, J. D. Nichols, B. C. Lubow, and W. L. Kendall. 2009a. Structured decision making as a conceptual framework to identify thresholds for conservation and management. Ecological Applications 19:1079–1090.CrossRefGoogle Scholar
  31. Martin, J., C. L. McIntyre, J. E. Hines, J. D. Nichols, J. A. Schmutz, and M. C. MacCluskie. 2009b. Dynamic multistate site occupancy models to evaluate hypotheses relevant to conservation of Golden Eagles in Denali National Park, Alaska. Biological Conservation 142:2726–2731.CrossRefGoogle Scholar
  32. Martin, J., P. L. Fackler, J. D. Nichols, M. C. Runge, C. McIntyre, B. L. Lubow, M. G. McCluskie, and J. A. Schmutz. 2011. An adaptive management framework for optimal control of recreational activities in Denali National Park. Conservation Biology 25:316–323.Google Scholar
  33. Maxwell, D., and S. Jennings. 2005. Power of monitoring programmes to detect decline and recovery of rare and vulnerable fish. Journal of Applied Ecology 42:25–37.CrossRefGoogle Scholar
  34. McCarthy, M. A., and H. P. Possingham. 2007. Active adaptive management for conservation. Conservation Biology 21:956–963.CrossRefGoogle Scholar
  35. McGowan, C., D. R. Smith, J. A. Sweka, J. Martin, J. D. Nichols, R. Wong, J. E. Lyons, L. J. Niles, K. Kalasz, J. Brust, M. Klopfer, and B. Spear. 2011. Multi-species modeling for adaptive management of horseshoe crabs and red knots in the Delaware Bay. Natural Resource Modeling 24:117–156.Google Scholar
  36. Miranda, M. J., and P. L. Fackler. 2002. Applied computational economics and finance. Cambridge: MIT Press.Google Scholar
  37. Nichols, J. D. 2001. Using models in the conduct of science and management of natural resources. In Modeling in natural resource management: development, interpretation and application, ed T. M. Shenk and A. B. Franklin, 11–34. Washington, D.C.: Island Press.Google Scholar
  38. Nichols, J. D., and B. K. Williams. 2006. Monitoring for conservation. Trends in Ecology & Evolution 21:668–673.CrossRefGoogle Scholar
  39. Pascual, M., and F. Guichard. 2005. Criticality and disturbance in spatial ecological systems. Trends in Ecology & Evolution 20:88–95.CrossRefGoogle Scholar
  40. Runge, M. C., F. A. Johnson, M. G. Anderson, M. D. Koneff, E. T. Reed, and S. E. Mott. 2006. The need for coherence between waterfowl harvest and habitat management. Wildlife Society Bulletin 34:1231–1237.CrossRefGoogle Scholar
  41. Seber, G. A. F. 1982. The estimation of animal abundance and related parameters. New York: MacMillian Press.Google Scholar
  42. Thompson, R. L. 2002. Sampling. New York: Wiley.Google Scholar
  43. Walters, C. J., and R. Hilborn. 1978. Ecological optimization and adaptive management. Annual Review of Ecology and Systematics 9:157–188.CrossRefGoogle Scholar
  44. Walters, C. J. 1986. Adaptive management of renewable resources. New York: Macmillan.Google Scholar
  45. Williams, B. K. 1982. Optimal stochastic control in natural resource management – framework and examples. Ecological Modelling 16:275–297.CrossRefGoogle Scholar
  46. Williams, B. K. 1989. Review of dynamic optimization methods in renewable natural resource management. Natural Resource Modeling 3:137–216.Google Scholar
  47. Williams, B. K. 1996. Adaptive optimization of renewable natural resources: Solution algorithms and a computer program. Ecological Modelling 93:101–111.CrossRefGoogle Scholar
  48. Williams, B. K. 1997. Approaches to the management of waterfowl under uncertainty. Wildlife Society Bulletin 25:714–720.Google Scholar
  49. Williams, B.K., and J.D. Nichols. In press. Optimization in natural resources conservation. In Thresholds for conservation, ed. G. Gunterspergen and P. Geissler: Wiley, New York.Google Scholar
  50. Williams, B. K., J. D. Nichols, and M. J. Conroy. 2002. Analysis and management of animal populations: Modeling, estimation, and decision making. San Diego: Academic Press.Google Scholar
  51. Williams, B. K., R. C. Szaro, and C. D. Shapiro. 2007. Adaptive management: The U.S. Department of the Interior technical guide. Washington, D.C.: U.S. Department of the Interior.Google Scholar
  52. Williams, B. K. 2009. Markov decision processes in natural resources management: Observability and uncertainty. Ecological Modelling 220:830–840.CrossRefGoogle Scholar
  53. Yoccoz, N. G., J. D. Nichols, and T. Boulinier. 2001. Monitoring of biological diversity in space and time. Trends in Ecology & Evolution 16:446–453.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2014

Authors and Affiliations

  • James D. Nichols
    • 1
  • Mitchell J. Eaton
    • 2
  • Julien Martin
    • 3
  1. 1.U.S. Geological SurveyPatuxent Wildlife Research CenterLaurelUSA
  2. 2.Southeast Climate Science CenterU.S. Geological SurveyRaleighUSA
  3. 3.Florida Fish and Wildlife Conservation CommissionFish and Wildlife Research InstituteSt PetersburgUSA

Personalised recommendations