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Estimating Individual Stand Size‐Density Trajectories and a Maximum Size‐Density Relationship Species Boundary Line Slope

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Understanding self-thinning patterns of a species helps resource managers make decisions about proper planting densities and timings of thinnings. It is generally assumed that size‐density trajectories of even-aged, monospecific, self-thinning stands consist of three phases: first, a curved approach to a linear portion that represents moderate rates of intraspecific mortality; second, a linear portion where tree density/ha (N) is at its maximum for a particular quadratic mean diameter (Dq), often termed the maximum size‐density relationship (MSDR) dynamic thinning line; and third, a divergence from the linear portion. In this article, segmented regression was used to determine what observations are within various phases of self-thinning for a loblolly pine (Pinus taeda L.) planting density trial located in central Mississippi. Those observations estimated to be within the MSDR dynamic thinning line phase of individual plots were combined to estimate a MSDR species boundary line slope (−1.640) using a linear mixed-effects model approach. Based on the N and Dq, where the MSDR dynamic thinning line phase was estimated to begin using segmented regression analyses, and the estimated MSDR species boundary line slope of −1.640, planting density-specific maximum values of Reineke's stand density index were predicted.
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Keywords: Pinus taeda L; Reineke's stand density index; loblolly pine; mixed-effects models

Document Type: Research Article

Publication date: 2010-08-01

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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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