Problem Area 2
Innovative approaches to assessment and prediction are needed to improve understanding of the realities and implications of ecosystem change.
To achieve long-term societal goals of sustaining forests, more is needed than the knowledge gained from detection and monitoring. Although they are necessary first steps, detection and monitoring alone insufficient for assessment and prediction. Synthetic frameworks that analyze, interpret, and present information in ways that relate to the pressing needs of policy makers, planners and managers are also needed. The end users of knowledge may struggle to ascribe meaning and value to science results that lack broader spatial or temporal contexts. While a tremendous volume of potentially relevant science and monitoring information is available for addressing forest problems, much of it is not being used as effectively as it might be. Moreover, certain critical information is readily acquirable, but not yet available. The Center conducts foundational research targeted on these critical phenomena 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, mathematical 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, these assessments rigorously 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 is a mainstay of the Center’s efforts and 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.
Assessments and predictions become exceedingly important and challenging when they target problems that impact multiple aspects of highly complex systems. Unfortunately, many ecosystems are inherently dynamic across spatial and temporal scales or levels of organization all-the-while they are experiencing novel changes in invasive species, land use/land cover, extremes in weather or climate, or other uncharacteristic disturbances. As demand for a range of ecosystem services grows, assessments often involve characterizing potentially controversial tradeoffs. Such trade-off can be most acute when the future is most uncertain.
Problem 2a. Foundational knowledge of the key patterns and processes that influence ecosystem change is sometimes lacking. Fine-scale research, such as fieldwork and data analyses, can improve or validate ecosystem models.
Problem 2b. 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. The quality of management decisions that involve high uncertainty can be improved through quantitative risk assessment. 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. Predicting future change in ecosystems and services can lead to earlier intervention, improved management, mitigation, or adaptation, but accurate predictive tools and forecasts are often lacking. Improved prediction can make forest management decisions more proactive than reactive.