Successful implementation of ecosystem management requires strategic forest management planning, including the ability to forecast future forest composition. With advances in ecological modeling, many forms of succession models are available. In this review, we provide a broad synthesis of the methods used to model forest succession and discuss their suitability for strategic forest management planning. Qualitative models underpin theoretical understanding of forest succession but require expression in more formal quantitative forms to be applicable to strategic planning. Quantitative modeling methods can be differentiated as empirical or mechanistic. Empirical models rely on observational data of successional change. Mechanistic models rely on knowledge of underlying ecological processes to simulate succession. Hybrid mechanistic models represent a compromise in ecological modeling between empirical robustness and theoretical understanding. With their increased flexibility for scenario planning and enhanced representation of ecosystem processes, hybrid models are well suited to addressing the multiple environmental and social factors increasingly being considered under ecosystem management. However, empirical models still remain a suitable and practical alternative because hybrid models require increased resources to initialize, operate, and interpret; emphasize understanding rather than prediction; and assume that the modeled processes they represent are adequately understood.
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