The biggest natural resource management challenges include competing views of the value and uses of those resources in society. Patuxent scientists develop methods to manage resources given those competing views under a “structured decision making” (SDM) framework. Our scientists both practice and train others in key SDM skills, such as model development and monitoring design.
What is structured decision making (SDM)? Structured decision making is an approach for careful and organized analysis of natural resource management decisions. Based in decision theory and risk analysis, SDM encompasses a simple set of concepts and helpful steps, rather than a rigidly-prescribed approach for problem solving. Key SDM concepts include making decisions based on clearly articulated fundamental objectives, recognizing the role of scientific predictions in decisions, dealing explicitly with uncertainty, and responding transparently to societal values in decision making; thus, SDM integrates science and policy explicitly. Every decision consists of several primary elements – management objectives, decision options, and predictions of decision outcomes. By analyzing each component separately and thoughtfully within a comprehensive decision framework, it is possible to improve the quality of decision-making. The core SDM concepts and steps to better decision making are useful across all types of decisions: from individuals making minor or personal decisions to complex public sector decisions involving multiple decision makers, scientists and other stakeholders. In turn, an array of simple to highly quantitative analytical methods is available for structured decision analysis.
How does SDM relate to Adaptive Resource Management (ARM). For those decisions that are iterated over time, actions taken early on may result in learning that improves management later, provided that an appropriate monitoring program is in place to provide the feedback. Adaptive management, then, is a special case of structured decision making for decisions that are iterated or linked over time.
Recent applications by Patuxent scientists include:
- Maximizing bull trout conservation through workload allocation
- Multi-species management of the horseshoe crab and shorebird populations in Delaware Bay
- White-nose syndrome management
- Wolf recovery in North America
- Habitat management for multiple wetland bird objectives on National Wildlife Refuges
- Conservation and management decisions for mountain plovers throughout the annual cycle
- Northeast Regional Science Committee research funding
- Assessing multiple-scale monitoring needs for waterbird management
- Endangered species of the Edwards Aquifer, Texas
- Glen Canyon Dam Long-term Experimental & Management Plan (LTEMP)
Below are other science projects associated with this project.
Use of Structured Decision Making to Optimize Salt Marsh Management Decisions at Northeastern National Wildlife Refuges
Assessing Amphibian Disease Risk in the Northeast
Amphibian Research and Monitoring Initiative (ARMI): Understanding Amphibian Populations in the Northeastern United States
Estimation of Density and Abundance of Biological Populations on National Parks and Wildlife Refuges Through Distance Sampling
Managing the Extinction Risk of the Shenandoah Salamander
Integrating Habitat and Harvest Management for Northern Pintails
Adaptive Management for Threatened and Endangered Species
Structured Decision Making: Methods, Applications, and Capacity-Building
Below are publications associated with this project.
Identifying objectives and alternative actions to frame a decision problem.
Structured decision making
Recent advances in applying decision science to managing national forests
An introduction to adaptive management for threatened and endangered species
- Overview
The biggest natural resource management challenges include competing views of the value and uses of those resources in society. Patuxent scientists develop methods to manage resources given those competing views under a “structured decision making” (SDM) framework. Our scientists both practice and train others in key SDM skills, such as model development and monitoring design.
What is structured decision making (SDM)? Structured decision making is an approach for careful and organized analysis of natural resource management decisions. Based in decision theory and risk analysis, SDM encompasses a simple set of concepts and helpful steps, rather than a rigidly-prescribed approach for problem solving. Key SDM concepts include making decisions based on clearly articulated fundamental objectives, recognizing the role of scientific predictions in decisions, dealing explicitly with uncertainty, and responding transparently to societal values in decision making; thus, SDM integrates science and policy explicitly. Every decision consists of several primary elements – management objectives, decision options, and predictions of decision outcomes. By analyzing each component separately and thoughtfully within a comprehensive decision framework, it is possible to improve the quality of decision-making. The core SDM concepts and steps to better decision making are useful across all types of decisions: from individuals making minor or personal decisions to complex public sector decisions involving multiple decision makers, scientists and other stakeholders. In turn, an array of simple to highly quantitative analytical methods is available for structured decision analysis.
How does SDM relate to Adaptive Resource Management (ARM). For those decisions that are iterated over time, actions taken early on may result in learning that improves management later, provided that an appropriate monitoring program is in place to provide the feedback. Adaptive management, then, is a special case of structured decision making for decisions that are iterated or linked over time.
Recent applications by Patuxent scientists include:
- Maximizing bull trout conservation through workload allocation
- Multi-species management of the horseshoe crab and shorebird populations in Delaware Bay
- White-nose syndrome management
- Wolf recovery in North America
- Habitat management for multiple wetland bird objectives on National Wildlife Refuges
- Conservation and management decisions for mountain plovers throughout the annual cycle
- Northeast Regional Science Committee research funding
- Assessing multiple-scale monitoring needs for waterbird management
- Endangered species of the Edwards Aquifer, Texas
- Glen Canyon Dam Long-term Experimental & Management Plan (LTEMP)
(Public domain.) - Science
Below are other science projects associated with this project.
Use of Structured Decision Making to Optimize Salt Marsh Management Decisions at Northeastern National Wildlife Refuges
US Fish and Wildlife Service completed a regional assessment of salt marsh integrity (SMI) on 15 National Wildlife Refuges/Refuge Complexes in the northeastern US. Developed within a structured decision making (SDM) framework, the SMI assessment provides essential baseline data on salt marsh condition relative to regional management objectives. These data now provide the basis for applying the SDM...Assessing Amphibian Disease Risk in the Northeast
Disease in amphibian populations can have a range of effects, from devastating declines following introduction of a novel pathogen to recurring breakout events on a landscape. Elucidating mechanisms underlying the effects of diseases on amphibian populations is crucial to help managers make appropriate decisions to achieve management goals for amphibians.Amphibian Research and Monitoring Initiative (ARMI): Understanding Amphibian Populations in the Northeastern United States
Currently, 90 amphibian species are recognized in the Northeast, including 59 species in the Order Caudata (salamanders) and 31 species in the Order Anura (frogs and toads). Almost half of the amphibians in the Northeast are salamanders within the family Plethodontidae. Amphibians are found in all physiographic regions of the Northeast, from sea level to the heights of the Appalachian, Adirondack...Estimation of Density and Abundance of Biological Populations on National Parks and Wildlife Refuges Through Distance Sampling
The Challenge: Assessing the status and trends of populations of biological organisms is an important management goal and a recurrent theme in USGS research. Often, the most basic question of “how many are there?” remains elusive, thus making management decisions more difficult. This study continues a long-term commitment of technical support for the use of distance sampling for wildlife...Managing the Extinction Risk of the Shenandoah Salamander
The Shenandoah salamander is an endangered salamander that is at risk of extinction due to its small, high-elevation range, competition with the co-occurring red-backed salamander, and the predicted future climate in the Appalachian mountain range. We are working with multiple partners to understand the current status of the species, predict future extinction risk, and engage stakeholders in a...Integrating Habitat and Harvest Management for Northern Pintails
The Challenge: Several blue-ribbon panels have challenged the waterfowl management world to recognize the linkages between the two primary management frameworks: harvest management under the Migratory Bird Treaty Act, and habitat management under the North American Waterfowl Management Plan. Because these two frameworks seek to manage the same populations, there needs to be better coordination, in...Adaptive Management for Threatened and Endangered Species
The Challenge: Threatened and endangered species have to be managed in the face of uncertainty, but traditionally, there has been reluctance to think about adaptive management of listed species. Management agencies with responsibility for threatened and endangered species need tools to help manage in the face of uncertainty, with the hope of reducing that uncertainty.Structured Decision Making: Methods, Applications, and Capacity-Building
The Challenge: The field of decision analysis is a rich and mature discipline that provides robust methods for helping decision makers understand the nature of their decisions, involve stakeholders and scientists in appropriate steps of the process, and develop transparent records for the public. The use of these structured approaches is emerging in natural resource management, and there is strong... - Publications
Below are publications associated with this project.
Identifying objectives and alternative actions to frame a decision problem.
In this chapter, we discuss the role of objectives and alternative actions in framing a natural resource management decision problem, with particular attention to thresholds. We outline a number of considerations in developing objectives and measurable attributes, including when utility thresholds may be needed to express the decision-makers’ values.We also discuss the development of a set of alteStructured decision making
Wildlife management is a decision-focused discipline. It needs to integrate traditional wildlife science and social science to identify actions that are most likely to achieve the array of desires society has surrounding wildlife populations. Decision science, a vast field with roots in economics, operations research, and psychology, offers a rich set of tools to help wildlife managers frame, decoRecent advances in applying decision science to managing national forests
Management of federal public forests to meet sustainability goals and multiple use regulations is an immense challenge. To succeed, we suggest use of formal decision science procedures and tools in the context of structured decision making (SDM). SDM entails four stages: problem structuring (framing the problem and defining objectives and evaluation criteria), problem analysis (defining alternativAn introduction to adaptive management for threatened and endangered species
Management of threatened and endangered species would seem to be a perfect context for adaptive management. Many of the decisions are recurrent and plagued by uncertainty, exactly the conditions that warrant an adaptive approach. But although the potential of adaptive management in these settings has been extolled, there are limited applications in practice. The impediments to practical implementa