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Modeling Population Dynamics and Woody Biomass in Alaska Coastal Forest

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Alaska coastal forest, 6.2 million ha in size, has been managed in the past mainly through clearcutting. Declining harvest and dwindling commercial forest resources over the past 2 decades have led to increased interest in management of young-growth stands and utilization of woody biomass for bioenergy. However, existing models to support these new management systems are very limited in number and value. This study presents a density-dependent, size-specific, and species-specific matrix growth model for forest in coastal Alaska. This model enables short- and long-term predictions of stand basal area, volume, and biomass in a simple and accurate way and facilitates understanding of the ecological and economic effects of forest management alternatives. Components of forest growth were estimated from tree- and stand-level attributes from repeated measurements of 544 Forest Inventory and Analysis permanent sample plots located throughout coastal Alaska with a wide range of stand conditions. The model was tested on 293 postsample validation plots and found to have significantly higher accuracy than the Forest Vegetation Simulator, the only growth model available for the region. Analysis of residuals revealed no spatial autocorrelation, which indicates that this model is able to adequately account for the effects of physiographic factors and other differences between southeast and southcentral Alaska.
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Keywords: forest management; matrix model; population structure; spatial autocorrelation

Document Type: Research Article

Publication date: 2014-04-01

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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)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
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