Process-based forest productivity models and their application in forest management

Abstract

Few process-based forest productivity models have become incorporated into forest management systems. The prevalent perception is that process-based models are suited only for research applications and that management questions will be solved only by using descriptive empirical models. This is despite the fact that the latter can neither deal satisfactorily with changing environmental and management conditions nor answer all questions currently asked by managers. This paper develops the proposition that the end-use specifies the design and scale of forest simulation models, and that given the range of questions now asked in forest management a range of models is required. The spatial and temporal resolution, and the input and output data required to address typical forest management questions is examined. A survey of recent literature examines in which areas, and by whom, existing forest productivity models are being applied. It is concluded that many current management questions can be adequately answered using models in which a phenomenological approach is applied to predict annual forest growth at the stand-scale. Lumped-parameter process-based models and hybrid models provide the most immediate means through which our understanding of the biological processes underlying forest growth can be included in forest management systems. However, more detailed process-based models can play an important role in validating simpler models, in the development of generalizations applicable over long time scales and for testing hypotheses about the way trees function and respond to interacting stresses. Guidelines are also given on model structures appropriate for different classes of management questions.