Recent advances in applying decision science to managing national forests
Highlights
► Structured decision making can help in management of forest and grassland ecosystems. ► We list a wide array of models, techniques, and tools available for each SDM stage. ► We discuss three case studies of SDM use in land management and project plans. ► Successful use needs input by decision-makers, analysts, scientists, and stakeholders. ► Use of SDM fosters clearness, rigor, clarity, and inclusiveness in forest management.
Introduction
Natural resource management is ultimately about human decision making. Natural resource management decisions are often influenced by varying levels of complexity, uncertainty, and conflict that may extend well beyond the life of the decision process participants. The scope and complexity of issues that need to be addressed is part of the reason that decision-making in natural resource management is difficult. In the United States, public land managers and planners consider several overarching issues regarding sustainability, including loss of native forests and grasslands, degradation of ecosystem services derived from them, effects of climate change, and managing in the face of ecosystem disturbances from fire, insects, disease, development pressures, and changing demographic and settlement patterns (USDA Forest Service, 2010). Approaches and tools that facilitate good decision-making can help managers and planners provide for sound, science-based natural resource management decisions.
Decision science is a broad field with roots in economics, but it has since drawn expertise from many fields and has been applied in many contexts. Decision science provides a sound theoretical basis, and a specific framework and method, for making sound decisions under uncertainty by using formal decision analysis techniques and methods of risk analysis and risk management. Decision analysis is “a formalization of common sense for decision problems which are too complex for informal use of common sense” (Keeney, 1982, p. 806). In this paper, “decision-maker” refers to line officers, staff officers, and others, at all administrative levels of an agency or organization, and private landowners. A “decision” and its implementation constitute an “irrevocable allocation of resources…not a mental commitment to follow a course of action but rather the actual pursuit of the course of action” (Howard, 1966).
Decision science is applied increasingly in management of natural resources (Haynes and Cleaves, 1999), including fisheries (Runge et al., 2011a), wildlife (Johnson et al., 1997), forestry (Ogden and Innes, 2009), rangeland (Bashari et al., 2009), and fire (Calkin et al., 2011). This paper presents a structured approach to use of decision science – referred to here as structured decision making (SDM) – in forest and natural resource management on federal public lands of the United States, in particular those administered by the US Forest Service. SDM includes rigorous procedures for defining and structuring problems, analyzing problems and devising alternative solutions, making a decision on which course of action to follow (“decision point”), and implementing the decision and monitoring results (Fig. 1).
Managing forests and grasslands of US Forest Service’s National Forest System (NFS) lands, in particular, provides a compelling context for the role of SDM under the complex, multiple-use mandates of the National Forest Management Act and its implementing regulations and under mandates of the National Environmental Policy Act (NEPA). NEPA is essentially a disclosure process supporting decisions, and provides a structured framework much like parts of SDM for evaluation of environmental impacts, although SDM provides a richer and more general structure for all steps in the management and decision process, including dealing explicitly with uncertainties in all steps of the decision process. However, SDM will not solve all conflicts of social perceptions and political interests.
Land management options have narrowed in the 21st century and, as issues have become more complex and as these decisions have become more difficult, many managers are turning to processes that examine and evaluate a problem in a more structured way. Managers and administrators are seeking objective, replicable, and explicit ways to assess choices and their probable outcomes by which to make the best management decisions, but keeping up with advances in the field of decision science is difficult at best. The purpose of this paper is to describe the stages of SDM and, with use of three case studies, present how it is being used in NFS resource management. Our goal is to make natural resource managers, planners, and scientists more aware of the availability of SDM approaches and tools, how they have been successfully used in NFS, and how they can be used more effectively. We describe the basis and methods of SDM, present three case studies of its use in NFS, and provide conclusions and lessons for its future use.
Section snippets
SDM as a decision support framework
SDM is a framework that supports sound decision-making. As adapted from Hammond et al. (1999), each stage of SDM – problem structuring, problem analysis, decision point, and implementation and monitoring – consists of sub-stages, some of which are linked with feedback loops to denote learning from monitoring and adaptive management (Fig. 1). Each stage further entails interaction and collaboration among decision makers, stakeholders, scientists, and analysts. Decision makers have primary
Case studies
We present three case studies to illustrate the application of SDM to NFS land and resource management and which exemplify various stages in the SDM process and use of particular SDM tools.
A change of paradigm
For some decision makers and scientists, SDM is a change in paradigm because it makes explicit a previously implicit values-focused approach, and does not assume that science alone provides answers to complex, multi-objective problems, but rather that a decision needs to integrate science with policy. One consequence of the SDM framework is that the decision context drives the science needs, not the other way around. Another consequence is that the policy and scientific analysts need to
Acknowledgments
This work was prompted by the US Forest Service Deputy Chief for Research and Development, Jim Reaves, and his team of Research Station managers, and is part of a larger effort by the Forest Service to develop syntheses of major contemporary research themes. We thank the US Forest Service Research Executive Team for their support. Thanks to Deborah Finch for providing operational support through her partnership with the University of Arizona, and to the University of Arizona’s School of Natural
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