Problem Area 2
Innovative approaches to assessment and prediction are needed to improve understanding of the realities and implications of ecosystem change.
Problem 2a: FUNDAMENTAL UNDERSTANDING. Foundational knowledge of the key patterns and processes that influence ecosystem change is sometimes lacking. Process based research across scales can improve or validate ecosystem models. This can involve or integrate fieldwork and/or larger scale datasets.
Problem 2b: STRESSORS AND RESPONSES. Ecosystem values and services can be affected by uncharacteristic or novel changes in weather and climate, land use or land cover change, wildfire, and invasive species. As implications and impacts of these stressors are rarely certain, applied theory and innovative approaches to modeling can anticipate problems before or as they evolve. This problem includes the need for syntheses of bodies of existing knowledge to make knowledge more accessible.
Problem 2c QUANTITATIVE RISK ASSESSMENT. The quality of management decisions that involve high uncertainty can be improved through a variety of quantitative risk assessment approaches. As decisions often impact multiple values at once, a key need for applied assessments is to address proposed solutions in terms of their likely and conditional tradeoffs.
Problem 2d MODELING AND PREDICTION. Predicting change in ecosystems and services under a variety of projections or scenarios can lead to earlier intervention, improved management, mitigation, or adaptation, but accurate predictive tools and forecasts are often lacking. Improved prediction across a range of environmental or management scenarios can make forest management decisions more proactive than reactive.
To achieve long-term societal goals of sustaining forests, more information is needed than the knowledge gained from monitoring. Although it is a necessary first step, monitoring – including when it involves the application of indicators – is insufficient for understanding the implications of existing and emerging threats and providing information support for strategic management responses. Assessment involves developing synthetic frameworks that analyze, interpret, and present information in ways that relate and respond to the pressing needs of policy makers, planners, and managers, as articulated in departmental and agency strategic plans. The end users of knowledge may struggle to ascribe meaning and value to science that lacks context. The Center conducts foundational research when such knowledge is likely to improve the quality of future assessments and predictions. The Center also conducts synthetic assessments to help forest practitioners better interpret the significance and relevance of published science.
Assessments provide a set of approaches to digest and structure applied knowledge, though these vary greatly in their formality, rigor, and purpose. Some are largely narrative descriptions of the status and problems experienced by forests, while others attempt to rigorously quantify risks and tradeoffs to multiple values of concern. The most advanced assessments provide policy or management options for problem solution and communicate the uncertainties and assumptions from imperfect models or datasets. Guided by a clear vision or framework for knowledge acquisition and application, assessments connect foundational science, monitoring, and implementation. They also help identify and prioritize information most relevant to the decisions surrounding a particular set of issues. Within such a framework, scientific models can synthesize or organize related information in ways that make it more accessible and interpretable.
Quantitative risk assessment provides a powerful approach to addressing uncertainty in forest management. This process involves the formal consideration of values so that they are unambiguously expressed as measures, followed by a formal evaluation of the factors that, in a causal sense, put those measures at risk. This framework then allows an exploration of consequences and how they are likely to vary across scenarios or management alternatives. The quantitative aspect is founded on the statistical concepts of probability and likelihood, and includes flexible and readily updatable tools, such as Bayesian information networks. Part of the flexibility of these networks stems from their ability to integrate the effects of multiple independent drivers to address multiple outcomes as part of a comprehensive comparative risk assessment process. Likewise, modern optimization techniques, including spatial optimization models and machine learning, can incorporate aspects such as practical constraints on managers as well as competing objectives and tradeoffs when determining optimal solutions across varied management scenarios.
Assessment also involves formal approaches to predicting variability, over time and across space, of threats, ecological conditions, and interactions thereof—including into the future—to provide context for decision-making and planning. For example, predicting likely future ecological consequences of climate change involves a variety of modeling approaches that quantify observed patterns as a basis for predicting outcomes under modeled future climate scenarios.
Assessments and predictions become exceedingly important when they target problems that impact multiple aspects of highly complex systems. Many ecosystems are inherently dynamic across spatial and temporal scales or levels of organization, experiencing novel changes in invasive species, land use/land cover, wildland fire, extremes in weather and climate, or other uncharacteristic disturbances. As demand for a range of ecosystem services grows, assessments through time often involve characterizing potentially controversial and challenging tradeoffs. Such tradeoffs can be most acute when the future is most uncertain.
