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Using Segmented Regression to Estimate Stages and Phases of Stand Development

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Size-density trajectories on the logarithmic scale are generally thought to consist of two major stages. The first major stage is often referred to as the density-independent mortality stage in which the probability of mortality is independent of stand density; in the second stage, often referred to as the density-dependent mortality or self-thinning stage, the probability of mortality is related to stand density. Within the self-thinning stage, segments of a size-density trajectory consisting of a nonlinear approach to a linear portion, a linear portion, and a divergence from the linear portion are generally assumed. Here, we define the maximum size-density relationship (MSDR) dynamic thinning line as the linear portion. A loblolly pine (Pinus taeda L.) planting density study was used to demonstrate the process of using segmented regression models for estimating stages and phases of stand development. After results from the segmented regression model analyses were obtained, full versus reduced model tests indicated that a portion of self-thinning can be represented as linear. Estimates of the logarithm of quadratic mean diameter (lnD q) and logarithm of trees per hectare (lnN) where the linear component begins and ends were obtained from the segmented regression analyses and used as response variables predicted as a function of planting density. Predicted values of the lnD q and lnN allow for the MSDR dynamic thinning line boundary level and slope to be estimated for any planting density. Estimates showed that MSDR dynamic thinning line boundaries did not all attain the same level but varied by planting density.
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Keywords: Pinus taeda; loblolly pine; plantations; self-thinning; size-density trajectories

Document Type: Research Article

Publication date: 2008-04-01

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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.
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    June 1, 2016 to Feb. 28, 2017

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