Abstract
Forest process models are mathematical representations of biological systems that incorporate our understanding of physiological and ecological mechanisms into predictive algorithms. These models were originally designed and used for research purposes, but are being developed for use in practical forest management. Process models designed for research typically require complicated and intensive data, whereas models designed for management strive to use simpler and more readily available data and provide predictions useful for forest managers. In this article, we review some different types of process models, examine their requirements and utility in research and forest management, and discuss research priority areas that will increase their accuracy and application. We conclude that soil and nutritional limitations are the most difficult model components in predicting growth responses using process models. FOR. Sci. 47(1):2–8.