Sustainable management of national forests and grasslands within the National Forest System (NFS) often requires managers to make tough decisions under considerable uncertainty, complexity, and potential conflict. Resource decisionmakers must weigh a variety of risks, stressors, and challenges to sustainable management, including climate change, wildland fire, invasive species, insects, pests, diseases, demographic shifts, economic conditions, and changing societal values. The craft of natural resource decisionmaking will demand more adaptive qualities in the future and a more flexible toolkit. In light of these current and emerging challenges and the need for science delivery to keep pace with advancements in the disciplines of decision science and risk management, now is a good time to refocus on supporting and enhancing natural resource management decisionmaking. The science of decisionmaking can provide managers with useful concepts and tools to address risks, stressors, and challenges and to achieve desirable outcomes. Improving natural resource decisionmaking requires a firm understanding of how decisions are currently made and where opportunities for enhancement might exist. To that end, we surveyed U.S. Department of Agriculture (USDA), Forest Service line officers, planners, and specialists to gain an understanding of how decision-support tools and processes are used, under what circumstances they are used, and what improvements may be needed. Although responses varied, line officers principally expressed a desire for a structured decisionmaking framework that is risk informed, transparent, and adaptable; can facilitate evaluation of consequences and tradeoffs; and can be readily explained to stakeholders. More than 90 percent of respondents who had used some form of a decision-support approach stated they were satisfied with the outcome, and their primary reasons for adopting decision-support approaches were to improve the decision process and to mitigate potential social conflict. Their responses suggested that lack of information is the principal impediment to broader adoption of structured decisionmaking and decision-support approaches. We have therefore focused on reviewing the core principles of decision science and how they can be brought to bear on issues of sustainable forest and grassland management. This report outlines the rationale and pathway for incorporating decision science into the natural resource decisionmaking process. The approach we outline in this report—structured decisionmaking—is a flexible, well-established framework that helps decisionmakers better define the problem context, identify and evaluate management options, and make informed choices under complex and uncertain conditions. In particular, the initial stage of framing the problem is critical for effective problem-solving and decisionmaking. We highlight examples in which principles of decision science were incorporated into forest and grassland management. We also provide an extensive list of appropriate decision-support tools and approaches as they relate to various stages of decisionmaking processes, as well as guidance for identifying those tools and approaches. Adopting the structured decision framework can streamline decision processes, help ensure the right problem is being solved, lead to high-quality decisions that are defensible and durable under scrutiny, and ultimately lead to improved sustainable forest and grassland management. We highlight key knowledge gaps and identify training needs that could facilitate broader adoption of decision science principles within the NFS. We suggest structured decisionmaking can be more effectively used in forest and grassland management if analysts and planners are equipped to provide guidance to leadership and management on decision support; scientists clearly explain the use of predictive models in decision contexts and participate in decision processes; and decisionmakers and managers foster a collaborative, transparent, and defensible basis for decisions, using structured decision frameworks.