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Evaluation of Four Methods to Estimate Parameters of an Annual Tree Survival and Diameter Growth Model

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An approach to simultaneously estimate parameters of an annual tree growth model was developed, in which the sum of log-likelihood functions for tree survival and diameter growth was maximized. Four methods for acquiring interim values of stand density were evaluated: (1) Updating Attributes, in which individual tree values were summarized at the end of each year within the growth period to predict interim stand-level attributes, (2) Predicting Attributes, in which stand attributes were predicted annually using a stand-level model, (3) Linear Interpolation, in which stand attributes were predicted by linear interpolation, and finally (4) Initial Values, in which stand attributes at the beginning of the growth period were used as predictors throughout the growing period, and the rate of change for tree survival and diameter was assumed to be constant for this period. Data from the Southwide Seed Source Study of loblolly pine (Pinus taeda L.) showed that, overall, the Updating Attributes and Predicting Attributes produced better evaluation statistics in predicting tree survival and diameter growth than did the other two methods. A simulation study confirmed that these two methods produced the least biased estimates of parameters. Compared with the Updating Attributes method, the Predicting Attributes method produced similar evaluation statistics in predicting tree survival and diameter growth and similar bias in estimating model parameters. The Predicting Attributes method therefore offers a reasonable alternative to the Updating Attributes method, because of the ease of programming in available software languages.
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Keywords: Pinus taeda; annual prediction; individual tree model; maximum likelihood estimation; simultaneous estimation

Document Type: Research Article

Publication date: 2008-10-01

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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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