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- Title
Nonlinear Mixed-Effects (NLME) Diameter Growth Models for Individual China-Fir (<i>Cunninghamia lanceolata</i>) Trees in Southeast China.
- Authors
Xu, Hao; Sun, Yujun; Wang, Xinjie; Fu, Yao; Dong, Yunfei; Li, Ying
- Abstract
An individual-tree diameter growth model was developed for Cunninghamia lanceolata in Fujian province, southeast China. Data were obtained from 72 plantation-grown China-fir trees in 24 single-species plots. Ordinary non-linear least squares regression was used to choose the best base model from among 5 theoretical growth equations; selection criteria were the smallest absolute mean residual and root mean square error and the largest adjusted coefficient of determination. To account for autocorrelation in the repeated-measures data, we developed one-level and nested two-level nonlinear mixed-effects (NLME) models, constructed on the selected base model; the NLME models incorporated random effects of the tree and plot. The best random-effects combinations for the NLME models were identified by Akaike's information criterion, Bayesian information criterion and −2 logarithm likelihood. Heteroscedasticity was reduced with two residual variance functions, a power function and an exponential function. The autocorrelation was addressed with three residual autocorrelation structures: a first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)] and a compound symmetry structure (CS). The one-level (tree) NLME model performed best. Independent validation data were used to test the performance of the models and to demonstrate the advantage of calibrating the NLME models.
- Subjects
SOUTHEAST China; CHINA fir; POPULATION biology; NONLINEAR analysis; LEAST squares; REGRESSION analysis
- Publication
PLoS ONE, 2014, Vol 9, Issue 8, p1
- ISSN
1932-6203
- Publication type
Article
- DOI
10.1371/journal.pone.0104012