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Prediction of Annual Diameter Growth and Survival for Individual Trees from Periodic Measurements

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It is difficult to fit annual tree survival and diameter growth models to data that were measured, not every year, but at some interval. This study aimed to determine suitable methods to obtain parameter estimates for such a system from periodic measurements. Given a system consisting of a tree survival model and a tree diameter growth model, this article presents an iterative method for estimating system parameters. The method involves sequentially updating of the parameters of both models within the growth period. Data from 111 plots from the Southwide Seed Source Study of loblolly pine (Pinus taeda L.) were then used to evaluate this iterative approach against the averaging method that assumes a constant tree survival probability and diameter growth rate during the remeasurement interval. Results indicated that the iterative method out-performed the averaging method in predicting future individual tree survival, diameter growth, and stand basal area. The iterative method was superior because it accounted for the variable rate of diameter growth and tree survival probability as functions of ever-changing stand and tree attributes. For. Sci. 46(1):127-131.

Keywords: Pinus taeda; logistic regression; maximum likelihood estimators

Document Type: Journal Article

Publication date: February 1, 2000

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