A Review of Forest Succession Models and Their Suitability for Forest Management Planning
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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Document Type: Research Article
Publication date: 2009-02-01
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Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
Forest Science is published bimonthly in February, April, June, August, October, and December.
2016 Impact Factor: 1.782 (Rank 17/64 in forestry)
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June 1, 2016 to Feb. 28, 2017
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Journal of Forestry
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