Applying decision analysis to diverse domains: An introduction to the special issue
No abstract available.
INFORMS Journal on Applied Analytics↗
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Results for “Decision Analysis”
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Decision analysis of restoration actions for faunal conservation and other stakeholder values: Dauphin Island, Alabama
Dauphin Island is a barrier island located in the northern Gulf of Mexico and serves as the only barrier island providing protection to much of the State of Alabama’s coastal natural resources. The ecosystem spans over 3,500 acres of barrier island habitat including, beach, dune, overwash fans, intertidal wetlands, maritime forest and freshwater ponds. In addition, Dauphin Island provides protection to approximately one-third of the Mississippi Sound estuarine habitats in its lee including oyster reefs, mainland marshes and seagrasses. The habitat supports a variety of species including at least 347 species of birds, some of which are Federally or State listed species that either pass through or reside on the island. The island enhances the region’s recreational and commercial fishery habitat through maintenance and protection of water quality in the sound and adjacent nearshore habitats. Dauphin Island also serves as the location for cultural resources, the United States Air Force’s (USAF) early warning radar station, the State’s marine education facilities, infrastructure for the oil and gas industry, and a vibrant tourism economy. Consequently, anthropogenic actions (e.g., structural changes) and externally driven natural factors (e.g., storms and sea level rise) that impact Dauphin Island could affect both the conservation and economic value of the island.
Alabama↗
A Workshop Investigating the Potential for the Application of Decision Analysis Principles and Processes to Geoenvironmental Situations: Selenium in West Virginia
Explore the source record for details and available documents.
Open-File Report↗
Decision analysis tools: use of the Legal-Institutional Analysis Model: Chapter 9
No abstract available.
Book chapter↗
Rapid prototyping for decision structuring: An efficient approach to conservation decision analysis
No abstract available.
Book chapter↗
An introduction to decision science for conservation
Biodiversity conservation decisions are difficult, especially when they involve differing values, complex multidimensional objectives, scarce resources, urgency, and considerable uncertainty. Decision science embodies a theory about how to make difficult decisions and an extensive array of frameworks and tools that make that theory practical. We sought to improve conceptual clarity and practical application of decision science to help decision makers apply decision science to conservation problems. We addressed barriers to the uptake of decision science, including a lack of training and awareness of decision science; confusion over common terminology and which tools and frameworks to apply; and the mistaken impression that applying decision science must be time consuming, expensive, and complex. To aid in navigating the extensive and disparate decision science literature, we clarify meaning of common terms: decision science , decision theory , decision analysis , structured decision-making , and decision-support tools . Applying decision science does not have to be complex or time consuming; rather, it begins with knowing how to think through the components of a decision utilizing decision analysis (i.e., define the problem, elicit objectives, develop alternatives, estimate consequences, and perform trade-offs). This is best achieved by applying a rapid-prototyping approach. At each step, decision-support tools can provide additional insight and clarity, whereas decision-support frameworks (e.g., priority threat management and systematic conservation planning) can aid navigation of multiple steps of a decision analysis for particular contexts. We summarize key decision-support frameworks and tools and describe to which step of a decision analysis, and to which contexts, each is most useful to apply. Our introduction to decision science will aid in contextualizing current approaches and new developments, and help decision makers begin to apply decision science to conservation problems.
Conservation Biology↗
An analysis of the linked decisions in the confiscation of illegally traded turtles
Over the last few decades, freshwater turtles have become more common in the illegal wildlife trade because of growing global demand. Illegally traded turtles may be intercepted by several different agencies with separate jurisdictions. When turtles are confiscated, uncertainties may make releasing them back to the wild difficult. We used tools from decision analysis to achieve the following three objectives: (1) map elements of the decision process and their relationships in the illegal turtle trade using conceptual models, (2) outline the linked decisions for turtle confiscation and repatriation using decision trees, and (3) evaluate the decision trees for two example scenarios, one with moderate uncertainty and one with high uncertainty. We used the wood turtle ( Glyptemys insculpta ) as a case study, which is a species of conservation concern in part due to illegal wildlife trafficking. We conducted 23 semi-structured interviews of decision makers in law enforcement, biologists, land managers, and zoo staff. Interviews revealed that decisions regarding the disposition of confiscated turtles are complicated by uncertainty in disease status and origin. Decision makers that handle confiscated turtles also recognize that their decisions are often made in sequence and dependent on the outcome of antecedent decisions. In evaluating our decision trees, we found that the optimal decisions for example scenarios were similar and insensitive to uncertainty. Future applications of the decision trees by decision makers would involve a decision analyst to parameterize and interpret the choices and consequences involved in working through these decision trees. Collectively, our work shows how the use of decision trees can help structure and evaluate risky decisions for repatriating confiscated wildlife.
Conservation Science and Practice↗
Navigating the science-policy interface
As a wildlife population ecologist who wants to conduct useful science, I find the Endangered Species Act (ESA), like other federal wildlife statutes, an intriguing read. The topic is in my wheelhouse—fish, wildlife, and plants, with a focus at the population and species levels. There is an emphasis on science, in fact, the “best scientific and commercial data available.” And there are intriguing questions: what threats does a species face? What habitat would be critical for its survival? Could any federal actions put the species or critical habitat in greater peril? I am not alone in this attraction. Hundreds of scientists continue to consider the types of scientific analysis suggested by the ESA. The enthusiasm is palpable. If the first cursory read of the ESA is intriguing to me as a scientist, the second close read is tantalizing—I realize that something very attractive is just out of reach. I know how to estimate the probability of extinction, but I do not know what “in danger of extinction” means. I know how to evaluate the incremental change in status that might arise from some level of proposed take, but I do not know what “is not likely to jeopardize the continued existence” of a species means. The standards expressed in the statute are not stated in purely scientific terms. Thus, ESA decisions cannot be based solely on science, and require additional policy interpretation. Clarity about these policy interpretations—even awareness that they are needed—can be hard to find, leaving a gap between what I can provide as a scientist and what an ESA decision maker needs. This awareness of the interaction between science and policy is also occurring in the larger field of conservation science, where there has been an increasing recognition of a research-implementation gap, the need for actionable science, and the promise of translational ecology. All of these terms emphasize that science alone does not result in action; instead, action arises out of decisions that are informed both by science and by values. At the interface of science and policy, a scientist can deliver relevant knowledge, and a decision maker can explain the policy context in which that science is needed. As a scientist wanting to conduct useful science, I crave this two-way conversation. But how can this conversation be structured in a meaningful and appropriate way? In this chapter, I explore how decision analysis can be used to navigate the science-policy interface for ESA decisions. Decision analysis is a large, well-established field that studies how decisions are made and how they could be made, with explicit attention given to clarifying and separating the values-based and science-based elements of a decision; identifying the impediments that make a decision difficult; and providing tools to overcome those impediments. There have been concerted efforts to apply formal decision analysis to ESA decisions, but the practice is not yet widespread across both the U.S. Fish and Wildlife Service and the National Marine Fisheries Service (the Services). The chapter begins with an introduction to decision analysis and how it seeks to bridge the science-policy interface. In subsequent sections, I explore how a decision analyst might frame listing and reclassification decisions, recovery planning, section 7 consultation, budget allocations, and a few other ESA decisions, with an emphasis on two questions: for each type of decision, what policy clarifications does the decision maker need to make; and knowing the policy context, what type of scientific assessment is needed. In the final discussion, I identify common themes among the types of decisions, and offer thoughts on how decision analysis could be more widely used to integrate science into ESA decisions.
Book chapter↗
Using structured decision making to incorporate ecological and social values into harvest decisions: Case studies of white-tailed deer and walleye
Harvest decisions for fish and wildlife populations often include conflicting ecological, economic, and social values. Using decision analysis, such as structured decision making and adaptive management, as a framework to aid decision makers in multi-objective decision making for setting harvest regulations can lead to a more transparent and resilient decision. The process includes opportunities for inclusion of stakeholders’ concerns, either through multi-party workshops or the use of social science techniques to elicit objectives (i.e., values) and predict consequences of management actions. The authors present two case studies of using decision analysis to determine stakeholders’ objectives, identify alternative harvest strategies, predict the consequences of these alternatives on all objectives, and analyze tradeoffs among objectives. A case study of white-tailed deer ( Odocoileus virginianus ) in New York State provides an example of combining predictive population modeling and implementation of survey instruments statewide to determine optimal region-specific harvest regulations. Harvest management of walleye ( Sander vitreus ) provides an example of the inclusion of commercial and recreational angler groups in a series of workshops to make decisions about harvest quotas for one of the world’s largest freshwater fisheries.
Book chapter↗
A matter of tradeoffs: reintroduction as a multiple objective decision
Decision making in guidance of reintroduction efforts is made challenging by the substantial scientific uncertainty typically involved. However, a less recognized challenge is that the management objectives are often numerous and complex. Decision makers managing reintroduction efforts are often concerned with more than just how to maximize the probability of reintroduction success from a population perspective. Decision makers are also weighing other concerns such as budget limitations, public support and/or opposition, impacts on the ecosystem, and the need to consider not just a single reintroduction effort, but conservation of the entire species. Multiple objective decision analysis is a powerful tool for formal analysis of such complex decisions. We demonstrate the use of multiple objective decision analysis in the case of the Florida non-migratory whooping crane reintroduction effort. In this case, the State of Florida was considering whether to resume releases of captive-reared crane chicks into the non-migratory whooping crane population in that state. Management objectives under consideration included maximizing the probability of successful population establishment, minimizing costs, maximizing public relations benefits, maximizing the number of birds available for alternative reintroduction efforts, and maximizing learning about the demographic patterns of reintroduced whooping cranes. The State of Florida engaged in a collaborative process with their management partners, first, to evaluate and characterize important uncertainties about system behavior, and next, to formally evaluate the tradeoffs between objectives using the Simple Multi-Attribute Rating Technique (SMART). The recommendation resulting from this process, to continue releases of cranes at a moderate intensity, was adopted by the State of Florida in late 2008. Although continued releases did not receive support from the International Whooping Crane Recovery Team, this approach does provide a template for the formal, transparent consideration of multiple, potentially competing, objectives in reintroduction decision making.
Journal of Wildlife Management↗
Strategic conservation of an imperiled freshwater mussel, the Dwarf Wedgemussel, in North Carolina
To be effective, managers of imperiled species must face the unavoidable tradeoff between conservation benefits and constrained budgets and must not be paralyzed by scientific uncertainty. Decision analysis can help meet these challenges when used to develop cost-effective strategies to recover or improve the status of species. The U.S. Fish and Wildlife Service, along with state partners, developed a structured decision analysis to guide conservation of Dwarf Wedgemussel (Alasmidonta heterodon) in North Carolina. The Dwarf Wedgemussel is federally listed as endangered, and North Carolina is the southern-most extent of its range, where small and vulnerable populations occur in the Tar and Neuse River basins. The main threat in the Neuse River basin is habitat loss due to anthropogenic land use changes. In contrast, the Tar River basin primarily has been affected by recent drought and stream habitat loss due to beaver impoundments, although habitat has been somewhat buffered from development. A collaborative team used multiple-objective decision analysis to compare the ability of conservation strategies to maximize species persistence while accounting for uncertainty in management effectiveness and variation in in the importance of different management objectives. The decision analysis helped managers evaluate tradeoffs regarding Dwarf Wedgemussel distribution within the Neuse River and Tar River basins. The most cost-effective and robust strategies traded off some opportunity for persistence in the Neuse River for protection of populations in the Tar River basin. The decision analysis is being used to guide efforts to conserve Dwarf Wedgemussel in North Carolina, although challenges continue due to constrained budgets, workload management, and limited regulatory tools.
North Carolina↗
Balancing habitat delivery for breeding marsh birds and nonbreeding waterfowl: An integrated waterbird management and monitoring approach at Clarence Cannon National Wildlife Refuge, Missouri
The Clarence Cannon National Wildlife Refuge (CCNWR) in the Mississippi River flood plain of eastern Missouri provides high quality emergent marsh and moist-soil habitat benefitting both nesting marsh birds and migrating waterfowl. Staff of CCNWR manipulate water levels and vegetation in the 17 units of the CCNWR to provide conditions favorable to these two important guilds. Although both guilds include focal species at multiple planning levels and complement objectives to provide a diversity of wetland community types and water regimes, additional decision support is needed for choosing how much emergent marsh and moist-soil habitat should be provided through annual management actions. To develop decision guidance for balanced delivery of high-energy waterfowl habitat and breeding marsh bird habitat, two measureable management objectives were identified: nonbreeding Anas Linnaeus (dabbling duck) use-days and Rallus elegans (king rail) occupancy of managed units. Three different composite management actions were identified to achieve these objectives. Each composite management action is a unique combination of growing season water regime and soil disturbance. The three composite management actions are intense moist-soil management (moist-soil), intermediate moist-soil (intermediate), and perennial management, which idles soils disturbance (perennial). The two management objectives and three management options were used in a multi-criteria decision analysis to indicate resource allocations and inform annual decision making. Outcomes of the composite management actions were predicted in two ways and multi-criteria decision analysis was used with each set of predictions. First, outcomes were predicted using expert-elicitation techniques and a panel of subject matter experts. Second, empirical data from the Integrated Waterbird Management and Monitoring Initiative collected between 2010 and 2013 were used; where data were lacking, expert judgment was used. Also, a Bayesian decision model was developed that can be updated with monitoring data in an adaptive management framework. Optimal resource allocations were identified in the form of portfolios of composite management actions for the 17 units in the framework. A constrained optimization (linear programming) was used to maximize an objective function that was based on the sum of dabbling duck and king rail utility. The constraints, which included management costs and a minimum energetic carrying capacity (total moist-soil acres), were applied to balance habitat delivery for dabbling ducks and king rails. Also, the framework was constrained in some cases to apply certain management actions of interest to certain management units; these constraints allowed for a variety of hypothetical Habitat Management Plans, including one based on output from a hydrogeomorphic study of the refuge. The decision analysis thus created numerous refuge-wide scenarios, each representing a unique mix of options (one for each of 17 units) and associated benefits (i.e., outcomes with respect to two management objectives). Prepared in collaboration with the U.S. Fish and Wildlife Service, the decision framework presented here is designed as a decision-aiding tool for CCNWR managers who ultimately make difficult decisions each year with multiple objectives, multiple management units, and the complexity of natural systems. The framework also provides a way to document hypotheses about how the managed system functions. Furthermore, the framework identifies specific monitoring needs and illustrates precisely how monitoring data will be used for decision-aiding and adaptive management.
Missouri↗
Shale Gas Development and Brook Trout: Scaling Best Management Practices to Anticipate Cumulative Effects
Shale gas development may involve trade-offs between energy development and benefits provided by natural ecosystems. However, current best management practices (BMPs) focus on mitigating localized ecological degradation. We review evidence for cumulative effects of natural gas development on brook trout (Salvelinus fontinalis) and conclude that BMPs should account for potential watershed-scale effects in addition to localized influences. The challenge is to develop BMPs in the face of uncertainty in the predicted response of brook trout to landscape-scale disturbance caused by gas extraction. We propose a decision-analysis approach to formulating BMPs in the specific case of relatively undisturbed watersheds where there is consensus to maintain brook trout populations during gas development. The decision analysis was informed by existing empirical models that describe brook trout occupancy responses to landscape disturbance and set bounds on the uncertainty in the predicted responses to shale gas development. The decision analysis showed that a high efficiency of gas development (e.g., 1 well pad per square mile and 7 acres per pad) was critical to achieving a win-win solution characterized by maintaining brook trout and maximizing extraction of available gas. This finding was invariant to uncertainty in predicted response of brook trout to watershed-level disturbance. However, as the efficiency of gas development decreased, the optimal BMP depended on the predicted response, and there was considerable potential value in discriminating among predictive models through adaptive management or research. The proposed decision-analysis framework provides an opportunity to anticipate the cumulative effects of shale gas development, account for uncertainty, and inform management decisions at the appropriate spatial scales.
Environmental Practice↗
Allocating funds under the National Fish Habitat Action Plan
Each year, the Director of the U.S. Fish and Wildlife Service (Service), with advice from a Fisheries Management Team, allocates funding to support the National Fish Habitat Action Plan. The Service distributes the funds to Fish Habitat Partnerships (FHPs), who, in turn, undertake projects that “protect, restore, or enhance fish and aquatic habitats or otherwise directly support habitat-related priorities of Fish Habitat Partnerships.” Initially, this allocation was made based on a simple formula: larger FHPs received twice the allocation of smaller FHPs. But as the number of partnerships grew, and as funding grew at a slower rate, inequities developed among the FHPs. In 2012, the Service convened a structured decision making process to develop a more equitable, transparent, and strategic formula for annual funding allocation. The initial decision analysis, which focused on strategic aspects of the allocation, is described in this chapter. Deliberate consideration of decision analysis concepts brought about two advances: a focus on the fundamental long-term objective of maximizing the sustainability of aquatic species populations; and recognition that the benefits of the relatively small investment by the Service occur through leveraging contributions from management partners and increasing the efficiency of on-the-ground projects. Four allocation strategies were evaluated, using formal expert judgment methods, against an array of ecological and administrative objectives. The resulting consequence table was presented to Service managers to illustrate the considerations that underlie an allocation strategy. The insights of this initial decision analysis led to further internal discussions within the Service, and development of a fully articulated allocation method. In December 2013, the Director of the Service approved this new, competitive, performance-based method for allocating funds to FHPs, and it has been used since then to guide decision making. This case study illustrates the power of problem framing, the importance of articulating fundamental objectives, and the value of making transparent the hidden predictions at the heart of any decision.
Book chapter↗
A three-pipe problem: Dealing with complexity to halt amphibian declines
Natural resource managers are increasingly faced with threats to managed ecosystems that are largely outside of their control. Examples include land development, climate change, invasive species, and emerging infectious diseases. All of these are characterized by large uncertainties in timing, magnitude, and effects on species. In many cases, the conservation of species will only be possible through concerted action on the limited elements of the system that managers can control. However, before an action is taken, a manager must decide how to act, which is, if done well, not easy. In addition to dealing with uncertainty, managers must balance multiple potentially competing objectives, often in cases when the management actions available to them are limited. Guidance in making these types of challenging decisions can be found in the practice known as decision analysis. We demonstrate how using a decision-analytic approach to frame decisions can help identify and address impediments to improved conservation decision making. We demonstrate the application of decision analysis to two high-elevation amphibian species. An inadequate focus on the decision-making process, and an assumption that scientific information is adequate to solve conservation problems, must be overcome to advance the conservation of amphibians and other highly threatened taxa.
Biological Conservation↗
A decision-analytical framework for developing harvest regulations
The development of harvest regulations for fish or wildlife is a complex decision that needs to weigh multiple objectives, consider a set of alternative regulatory options, integrate scientific understanding about the population dynamics of the harvested species as well as the human response to regulations, account for uncertainty, and provide an avenue for feedback from monitoring programs. The author describes how the field of decision analysis provides a framework for structuring such decisions and tools for navigating the components. At the center of any harvest management endeavor is a set of objectives that may include providing harvest opportunity, conserving the harvested population long into the future, and satisfying hunters, anglers, or trappers; tools from multi-criteria decision analysis are useful in finding the right balance among competing objectives. The population dynamics of harvested populations are often stochastic; tools from risk analysis and dynamic optimization can be used to find state-dependent policies that manage variation. Finally, harvest regulations are often set in the face of uncertainty; value-of-information methods can be used to evaluate the importance of that uncertainty, and adaptive management methods can be used to reduce it.
Book chapter↗