Climatic Change

, Volume 107, Issue 1–2, pp 185–202 | Cite as

Structured decision making as a proactive approach to dealing with sea level rise in Florida

  • Julien Martin
  • Paul L. Fackler
  • James D. Nichols
  • Bruce C. Lubow
  • Mitchell J. Eaton
  • Michael C. Runge
  • Bradley M. Stith
  • Catherine A. Langtimm


Sea level rise (SLR) projections along the coast of Florida present an enormous challenge for management and conservation over the long term. Decision makers need to recognize and adopt strategies to adapt to the potentially detrimental effects of SLR. Structured decision making (SDM) provides a rigorous framework for the management of natural resources. The aim of SDM is to identify decisions that are optimal with respect to management objectives and knowledge of the system. Most applications of SDM have assumed that the managed systems are governed by stationary processes. However, in the context of SLR it may be necessary to acknowledge that the processes underlying managed systems may be non-stationary, such that systems will be continuously changing. Therefore, SLR brings some unique considerations to the application of decision theory for natural resource management. In particular, SLR is expected to affect each of the components of SDM. For instance, management objectives may have to be reconsidered more frequently than under more stable conditions. The set of potential actions may also have to be adapted over time as conditions change. Models have to account for the non-stationarity of the modeled system processes. Each of the important sources of uncertainty in decision processes is expected to be exacerbated by SLR. We illustrate our ideas about adaptation of natural resource management to SLR by modeling a non-stationary system using a numerical example. We provide additional examples of an SDM approach for managing species that may be affected by SLR, with a focus on the endangered Florida manatee.


Water Demand Saltwater Intrusion Structure Decision Making Structural Uncertainty Stochastic Dynamic Programming 
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.


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  1. Anderson DR (1975) Optimal exploitation strategies for an animal population in a Markovian environment: a theory and an example. Ecology 56:1281–1297CrossRefGoogle Scholar
  2. Bellman R (1957) Dynamic programming. Princeton University Press, Princeton, NJGoogle Scholar
  3. Burgman M (2005) Risks and decisions for conservation and environmental management. Cambridge University Press, Cambridge, UKCrossRefGoogle Scholar
  4. Clark CW, Mangel M (2001) Dynamic state variable models in ecology. Oxford University Press, New York, NYGoogle Scholar
  5. Clemen RT, Reilly T (2001) Making hard decisions with decision tools. Duxbury, Pacific Grove, CAGoogle Scholar
  6. Edwards HH, Pollock KH, Ackerman B, Reynolds J, Powell J (2007) Components of detection probability in manatee aerial surveys during winter. J Wildl Manage 71:2052–2060CrossRefGoogle Scholar
  7. Fish and Wildlife Research Institute (2007) Florida manatee management plan. FWCC, St Petersburg, FLGoogle Scholar
  8. Fonnesbeck CJ (2005) Solving dynamic wildlife resource optimization problems using reinforcement learning. Nat Resour Model 18:1–39CrossRefGoogle Scholar
  9. Halpern BS, Regan HM, Possingham HP, McCarthy MA (2006) Accounting for uncertainty in marine reserve design. Ecol Lett 9:2–11CrossRefGoogle Scholar
  10. Hartman D (1979) Ecology and behavior of the manatee (Trichechus manatus). In: American society of mammalogists Florida special publication, vol 5, p 153Google Scholar
  11. IPCC (2007) The physical science basis contribution of working group I the fourth assessment report of the IPCC. Cambridge University Press, CambridgeGoogle Scholar
  12. Johnson FA, Moore CT, Kendall WL, Dubowsky JA, Caithamer DF, Kelley JR, Williams BK (1997) Uncertainty and the management of mallard harvests. J Wildl Manage 61:202–216CrossRefGoogle Scholar
  13. Langtimm CA, Dorazio R, Stith BM, Doyle T (2011) A new aerial survey and hierarchical model to estimate manatee abundance. J Wildl Manage 75:399–412CrossRefGoogle Scholar
  14. Leeper DA, Flannery MS, Kelly MH (2010) Recommended minimum flows for the Homosassa River system. July 12, 2010 Peer-Review Draft for the Southwest Florida Management District. Accessed 28 November 2010
  15. Lubow BC (1995) SDP: generalized software for solving stochastic dynamic optimization problems. Wildl Soc B 23:738–742Google Scholar
  16. MacKenzie DI, Nichols JD, Royle JA, Pollock KH, Hines JE, Bailey LL (2006) Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Elsevier, San Diego, CAGoogle Scholar
  17. Martin J, Runge MC, Nichols JD, Lubow BC, Kendall WL (2009) Structured decision making as a conceptual framework to identify thresholds for conservation and management. Ecol Appl 19:1079–1090CrossRefGoogle Scholar
  18. Martin J, Kendall WL, O’Connell AF, Simons TR, Waldstein AH, Smith GW, Schulte SA, Rikard M, Converse SJ, Runge MC (2010) Optimal control of native predators. Biol Conserv 143:1751–1758CrossRefGoogle Scholar
  19. Martin J, Fackler PL, Nichols JD, Lubow BL, Runge, MC, McIntyre CL, Lubow BL, McCluskie MC, Schmutz JA (2011) An adaptive-management framework for optimal control of hiking near Golden Eagle nests in Denali National Park. Conserv Biol 25:316–323Google Scholar
  20. Maschinski J, Ross MS, Liu H, O’Brien J, von Wettberg EJ, Haskin KE (2011) Sinking ships: conservation options for endemic taxa threatened by sea-level rise. Clim Change. doi: 10.1007/s10584-011-0083-z Google Scholar
  21. Melbourne BA, Hastings A (2008) Extinction risk depends strongly on factors contributing to stochasticity. Nature 454:100–103CrossRefGoogle Scholar
  22. Milly PCD, Betancourt J, Falkenmark M, Hirsch RM, Kundzewicz ZW, Lettenmaier DP, Stouffer RJ (2008) Stationarity is dead: whither water management? Science 319:573–574CrossRefGoogle Scholar
  23. Miranda MJ, Fackler PL (2002) Applied computational economics and finance. The MIT Press, Cambridge, MAGoogle Scholar
  24. Moore AL, Hauser CE, McCarthy, MA (2008) How we value the future affects our desire to learn. Ecol Appl 18:1061–1069CrossRefGoogle Scholar
  25. Nichols JD, Williams BK (2006) Monitoring for conservation. Trends Ecol Evol 21:668–673CrossRefGoogle Scholar
  26. Nichols JD et al (2011) Climate change, uncertainty and natural resource management. J Wildl Manage 75:6–18CrossRefGoogle Scholar
  27. Noss R (2011) Between the devil and the deep blue sea: Florida’s unenviable position with respect to sea-level rise. Clim Change (this volume)Google Scholar
  28. Park J, Obeysekera J, Irizarry M, Barnes J, Trimble P, Park-Said W (2011) Storm surge projections and implications for water management in South Florida. Clim Change. doi: 10.1007/s10584-011-0079-8 Google Scholar
  29. Parkinson RW, Donoghue JF (2010) Bursting the bubble of doom and adapting to SLR. Shoreline: March issueGoogle Scholar
  30. Peterman RM, Anderson JL (1999) Decision analysis: a method for taking uncertainties into account in risk-based decision making. Hum Ecol Risk Assess 5:2431–2446CrossRefGoogle Scholar
  31. RECOVER (2005) The RECOVER team’s recommendations for interim targets for the comprehensive everglades restoration project. South Florida Water Management District and US Army Corps, West Palm Beach, FLGoogle Scholar
  32. Rouhani S, Sucsy P, Hall G, Osburn W, Wild M (2005) Analysis of Blue Spring discharge data to determine a minimum flow regime. Prepared for the St. Johns River Water Management District, Palatka, FL. 48 pp. + appendices. Accessed 28 November 2010
  33. Stith BM, Reid JP, Langtimm CA, Swain ED, Doyle TJ, Slone DH, Decker JD, Soderqvist LE (2010) Temperature inverted haloclines provide winter warm-water refugia for manatees in southwest Florida. Estuar Coasts. doi: 10.1007/s12237-010-9286-1 Google Scholar
  34. Thrower J (2006) Adaptive management and NEPA: how a nonequilibrium view of ecosystems mandates flexible regulation. Ecol Law Q 33:871–896Google Scholar
  35. Williams BK (1996) Adaptive optimization and the harvest of biological populations. Math Biosci 136:1–20CrossRefGoogle Scholar
  36. Williams BK, Johnson FA, Wilkins K (1996) Uncertainty and the adaptive management of waterfowl harvests. J Wildl Manage 60:223–232CrossRefGoogle Scholar
  37. Williams BK, Nichols JD, Conroy MJ (2002) Analysis and management of animal populations. Academic, San Diego, CAGoogle Scholar
  38. Williams BK, Szaro RC, Shapiro CD (2007) Adaptive management: the US department of the interior technical guide. US Department of the Interior, Washington, DCGoogle Scholar
  39. Yoccoz NG, Nichols JD., Boulinier T (2001) Monitoring biodiversity in space and time: concepts, methods and designs. Trends Ecol Evol 16:446–453CrossRefGoogle Scholar

Copyright information

© U.S. Government 2011

Authors and Affiliations

  • Julien Martin
    • 1
  • Paul L. Fackler
    • 3
  • James D. Nichols
    • 2
  • Bruce C. Lubow
    • 4
  • Mitchell J. Eaton
    • 2
  • Michael C. Runge
    • 2
  • Bradley M. Stith
    • 5
  • Catherine A. Langtimm
    • 6
  1. 1.Fish and Wildlife Conservation CommissionFish and Wildlife Research InstituteSt PetersburgUSA
  2. 2.Patuxent Wildlife Research CenterUnited States Geological SurveyLaurelUSA
  3. 3.Agricultural and Resource EconomicsNorth Carolina State UniversityRaleighUSA
  4. 4.Natural Resource Ecology LaboratoryColorado State UniversityFort CollinsUSA
  5. 5.Jacobs Technology, contracted to U.S. Geological SurveySoutheast Ecological Science CenterGainesvilleUSA
  6. 6.U.S. Geological Survey, Southeast Ecological Science CenterSirenia ProjectGainesvilleUSA

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