Modelling is an important tool for understanding the complexity of forest ecosystems and the variety of interactions of ecosystem components, processes and values. This book describes the hybrid approach to modelling forest ecosystems and their possible response to natural and management-induced disturbance. The book describes the FORECAST family of ecosystem management models at three different spatial scales (tree, stand and landscape), and compares them with alternative models at these three spatial scales.

The book will help forest managers to understand what to expect from ecosystem-based forest models; serve as a tool for use in teaching about sustainability, scenario analysis and value trade-offs in natural resources management; and assist policy makers, managers and researches working in assessment of sustainable forest management and ecosystem management. Several real-life examples of using the FORECAST family of models in forest management and other applications are presented from countries including Canada, China, Spain and the USA, to illustrate the concepts described in the text. The book also demonstrates how these models can be extended for scenario and value trade-off analysis through visualization and educational or management games.

Hamish Kimmins: Following an undergraduate degree in Forestry from the University of Wales and an M.Sc. in Forest Entomology from the University of California at Berkeley, Dr. Kimmins, received his PhD in Forest Ecology at Yale University, focusing on the relationship between ecosystem function and herbivore population dynamics. Since 1969 he has been a Professor of Forest Ecology at the University of British Columbia, Canada, where he has presented courses on forest ecology, ecosystem classification, ecological aspects of silviculture, environmental issues in forestry, ecosystem function and response to disturbance, and modelling forest ecosystems to undergraduates, graduate students and professional foresters. He has developed and until recently tutorsed a UBC web-based Distance Education course in Forest Ecology. For the past thirty three years, Dr. Kimmins has worked to develop ecologically-based forest ecosystem management models, from the spatially-explicit, individual tree stand model FORCEE, to the a-spatial stand model FORECAST, to the spatial, local landscape, complex disturbance patch model LLEMS, and the spatial watershed ecosystem management model POSSIBLE FOREST FUTURES. These models range from a high school education forest management game (FORTOON), to decision support and research tools, to scenario analysis and value trade-off assessment tools. He is currently an emeritus professor after holding a Senior Canada Research Chair in Modelling the Sustainability of Forest Ecosystems, and is Director of the Forest Ecosystem Management Modelling Group in the Department of Forest Sciences. Dr. Kimmins is a former member of UNESCO's World Commission on the Ethics of Science and Technology (COMEST) and serves on many science advisory boards.

Juan A. Blanco: Dr. Blanco studied Agricultural Engineering at the Public University of Navarra, Spain. He was awarded a Ph.D. in Forest Ecology from the Public University in 2004, for his examination of the influence of forest management on nutrient cycles in pine forests of the Pyrenees. He has published several papers and book chapters on this topic. In 2003, he also collaborated with the team from the Technical University of Oruro to study environmental issues in the Uru-Uru Lake, Bolivia. He then moved to Vancouver, where he is currently a Postdoctoral Fellow in the Department of Forest Sciences, University of British Columbia. His work is centered on the use of ecological forest models for developing and assessing long-term sustainable forest management practices, a subject on which he has published several papers in international journals. He is currently collaborating in Canada, Spain, Cuba and China in research projects to assess the long-term sustainability of forest management with ecological models.

Brad Seely: Dr. Seely was awarded a Ph.D. in terrestrial ecology from the Department of Biology at Boston University in 1996. He then worked as a Postdoctoral Fellow with Hamish Kimmins in the Forest Ecosystem Management Simulation group at UBC, developing the FORECAST and LLEMS models. Presently, he is a Research Associate in the Department of Forest Sciences at UBC where he develops and tests forest ecosystem management models at multiple spatial scales. His specific interests lie in the development and application of process-based models of stand growth and development. He has also conducted research and developed models to examine the interactions between forest management, site productivity, hydrologic processes, carbon sequestration and climate change.

Clive Welham: Dr. Welham was awarded his B.Sc. (1983) and M.Sc. (1986) degrees from the University of Manitoba, and his Ph.D. (1993) from Simon Fraser University, in Vancouver. His academic career began with an emphasis on evolutionary ecology and ecological modeling. Following a Postdoctoral Fellowship in the Botany Department at UBC, he joined the Forest Ecosystem Management Simulation Group, in the Department of Forest Sciences, to which he still belongs. His work at UBC is principally concerned with forest ecosystem model development, testing and validation, at spatial scales from the individual tree to the landscape. Specific interests include ecosystem reclamation, carbon dynamics, and climate change. Clive also teaches a graduate level course in ecosystem modeling, and occasional undergraduate courses.

Kim Scoullar: Mr. Scoullar is a professional programmer with more than 35 years of experience developing code for ecosystem models. He was the main developer of the FORCYTE series of models commissioned under the Energy from the Forest (ENFOR) project by the Government of Canada. Mr. Scoullar has developed educational software (FORTOON) and software for forest management and research (FORECAST, LLEMS, FORCEE). He is also collaborating in the development of CALP-Forester.