Climatic Change

, Volume 107, Issue 1, pp 185-202

First online:

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

  • Julien MartinAffiliated withFish and Wildlife Conservation Commission, Fish and Wildlife Research Institute Email author 
  • , Paul L. FacklerAffiliated withAgricultural and Resource Economics, North Carolina State University
  • , James D. NicholsAffiliated withPatuxent Wildlife Research Center, United States Geological Survey
  • , Bruce C. LubowAffiliated withNatural Resource Ecology Laboratory, Colorado State University
  • , Mitchell J. EatonAffiliated withPatuxent Wildlife Research Center, United States Geological Survey
  • , Michael C. RungeAffiliated withPatuxent Wildlife Research Center, United States Geological Survey
  • , Bradley M. StithAffiliated withJacobs Technology, contracted to U.S. Geological Survey, Southeast Ecological Science Center
  • , Catherine A. LangtimmAffiliated withU.S. Geological Survey, Southeast Ecological Science Center, Sirenia Project

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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.