Society invests in science to advance human interests and limit human impacts on the environment. However, despite great progress in forest science, governments and forest companies remain reluctant to invest adequately in research. We believe this reflects the perception that forest science serves itself more than forestry, and that one determinant of this perception is a misunderstanding of science. Science involves knowing, understanding and predicting. Many feel that only the reductionist, disciplinary, hypothetico-deductively-derived understanding component is hard science. Inductively-derived knowledge and experience are often regarded as soft science. Predicting future states of forests involves complex hypotheses that are not amenable to traditional hypothesis testing and, according to some, this renders prediction of complex systems soft science. Science-based forest policy frequently employs hard science: the understanding component of science based on reductionist, jigsaw puzzle research. Necessary for the development of SFM, this is not sufficient for reliable prediction of possible forest ecosystem futures, for which knowledge and understanding must be synthesized into decision support systems at appropriate temporal, spatial and complexity scales. These should be linked to visualization software to create a common language by which to communicate to a diversity of audiences the available choices and their possible consequences.
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