Science can be thought of as having three main components: 1) knowing - inductive, descriptive, experience-based, founded on real measures of real systems; the foundation for hypotheses and synthesis, often the "big picture", implicitly complex, generally inflexible in the case of environmental and social change; 2) understanding - analytical, experimental, generally short-term and small spatial scale, hypothesis testing, disciplinary, reductionist, simplified to promote hypothesis testing, "jigsaw-puzzle" research, potentially flexible in the face of social and environmental change but generally limited flexibility because of simplification; 3) predicting - integrative, synthesis, application of knowing and understanding in decision making and policy, explicitly complex, flexible in the face of environmental and social change if knowing and understanding are combined at the ecosystem level. The first and last of these are often considered "soft" science since they are essentially complex, and as a consequence their products are to some extent untestable in a statistical sense (e.g. lack of replication, time and space scales that are often inconsistent with rigorous testing, frequently too complex for experimental evaluation, often lacking long term data sets for model testing). The second is "hard" science (rigorous experimental design, statistically sound, publishable in the "best" journals) that facilitates the rejection of wrong ideas and erroneous interpretations about subcomponents and individual processes in ecosystems. A complete science requires all three of these components, as does forest management and policy. However, inductive and synthesis activities are frequently less well accepted in science than hypothetico-deductive research, and the majority of research funding generally goes for rigorous, "hard" science. Experience-based "knowing" and the products of synthesis are relatively easy to apply in forest management and policy formulation. The unsynthesized products of "understanding" science are generally harder to incorporate into policy and practice because their simplicity and spatial and temporal scales do not integrate well with the complexity and scales of real world problems and issues, and do not facilitate the scenario and tradeoff analyses that lie at the heart of policy and management planning. The inflexibility of experience in the face of change requires its integration with understanding, but the complexity of this combination requires synthesis in decision support tools before it can be brought to bear effectively on policy and practice Forest ecology has been recognized as the biophysical foundation for sustainable forest management (SFM) and stewardship. All three components of science are involved in this discipline, but rarely are the relative contributions of the three components compared and evaluated in a way that can identify gaps in research strategy and funding, and in synthesis and application in policy and practice. What are the key needs for inductive, deductive and integrating activities in forest ecology that will serve best the needs of policy makers and practitioners? How well is the inherent complexity of forest ecosystems in space and time addressed in research, and in the decision-support and scenario and value tradeoff analyses that are integral to SFM and developing stewardship? What are the major barriers limiting the application of scientific knowledge to management practice and how can we work to overcome these barriers? This topic is the focus of the 6th North American Forest Ecology Workshop, a meeting held every second year in North America. The 6th workshop will be hosted at UBCs Faculty of Forestry in June 2007. Keynote speakers will share their experiences from the USA, Canada, Mexico, Thailand (SE Asia) and Australia with the application of forest ecological knowledge in sustainable forest management and stewardship. They will identify the degree to which th ...
Schivatcheva, Tina. 2008. North American Forest Ecology Workshop: From Science to Stewardship - knowing, understanding, applying. Forest Investment Account (FIA) - Forest Science Program. Forest Investment Account Report
Topic: FLNRORD Research Program
Keywords: Forest, Investment, Account, (FIA), British, Columbia
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