Forest simulation models

Sustainable forest management represents a paradigm for forestry. Traditional forestry objectives aimed at sustainable yield are being replaced with those of sustainable ecosystem management. Such a shift in forest management requires an effective transfer of results from forest researchers to managers. In order to predict the potential impacts of changes in environment (such as climate, land-use, fire disturbance and forest harvesting) on the sustainability of forest ecosystems, forest resource managers will require forest simulation models. Forest simulation models have a long history in forestry and have proven to be useful tools for forest management. There have been two basic approaches to modeling forest growth and dynamics: empirical and mechanistic forest simulation models. This chapter reviews and compares four major types of forest simulation models: (1) growth and yield models (empirical approach), (2) succession models (empirical-mechanistic hybrid approach), (3) process models (mechanistic approach), and (4) hybrid models. Their applications are described in four case studies. The advantages and disadvantages of the different modeling approaches are discussed. Case studies deal with predicting future forest conditions under different management options, simulating the potential effects of climate change, and effects of fire disturbance on structure and function of forest ecosystems in Canada. There is still a gal) between foresters and ecologists in developing and using forest simulation models. There will not be a single, super model that will satisfy all of the diverse demands and purposes simultaneously. Diversified modeling approaches integrated into a decision-support system, an important tool for evaluating the sustainability of forest ecosystem in a changing environment, are emphasized.