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Adapting Distance-Independent Forest Growth Models to Represent Spatial Variability: Effects of Sampling Design on Model Coefficients

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If sampling variability of competition variables can be accommodated, a distance independent growth model can (1) simulate patchy stands inventoried by a grid of sampling locations; (2) prescribe treatments for each location; and (3) update inventories using plot sizes differing from those used in model calibration. Ignoring sampling properties of competition biases estimates of stand development and management responses. Our solution to reduce this bias has three steps: First, we model the sampling properties of the competition estimates depending on plot sizes and on spatial patterns. Then we use the modeled error variances when estimating parameters of the structural relation of growth to competition. Finally, we join these two products in a Structural Based Prediction (SBP) procedure that dynamically modifies coefficients during simulation. SBP increases sensitivity to spatial variation and permits valid analyses of location-specific prescriptions for varying inventory designs. In a model of diameter change for individual Pseudostuga menziesii var. glauca (Beissn.) Franco, coefficients of the competition variables estimated under the structural model were three times as large as when estimated under an ordinary least-squares criterion. This increase reduced sensitivity to crown ratio. Two variants of the Prognosis Model for Stand Development were constructed to compare behavioral changes: (1) using SBP, and (2) using the OLS-based coefficient estimates of the same diameter-growth submodel. Projections with the two variants differed in the internal structures of both uniform and irregular stands and using several sampling methods. Stand averages of volume growth were similar for uniform stands--either from uniform regeneration or from uniformity created by thinning patchy stands. However, the thinning response in patchy stands differed between the two variants. Analysis of the variance model showed little sensitivity within the range of half to twice the Poisson-based estimates of competition variance. For. Sci. 44(2):224-238.

Keywords: Measurement error models; competition indices; individual tree model; patchy stands; structural based prediction

Document Type: Journal Article

Affiliations: Research Forester, USDA Forest Service, Intermountain Research Station, Forestry Sciences Laboratory, 1221 S. Main Street, Moscow, ID 83843. Phone: (208) 883-2345;, Fax: (208) 883-2318

Publication date: 1998-05-01

More about this publication?
  • 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.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 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
    Other SAF Publications
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