Development of a linear mixed-effects individual-tree basal area increment model for masson pine in Hunan Province, South-central China

• Published in: Journal of sustainable forestry Vol. 39; no. 5; pp. 526 - 541
• Main Authors: Wang, Wenwen, Bai, Yanfeng, Jiang, Chunqian, Yang, Haijun, Meng, Jinghui
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 03.07.2020; Taylor & Francis Ltd
• Subjects: a linear mixed-effects model; Autocorrelation; Bayesian analysis; Bayesian theory; China; Criteria; forest inventory; Forestry; heteroscedasticity; heteroskedasticity; Individual-tree basal area increment model; masson pine; National forests; Parameter estimation; Pine; Pinus massoniana; Standard error; Structural hierarchy; Sustainability; sustainable forestry; Trees; variance; China; Hunan China
• ISSN: 1054-9811; 1540-756X; 1540-756X
• DOI: 10.1080/10549811.2019.1688172

An individual-tree basal area increment model was developed for masson pine based on 26276 observations of 13,138 trees in 987 sample plots from the 7th (2004), 8th (2009), and 9th (2014) Chinese National Forest Inventory in Hunan Province, South-central China. The model was built using a linear mixed-effects approach with sample plots included as random effects since the data have a hierarchical stochastic structure and biased estimates of the standard error of parameter estimates could be a consequence of applying ordinary least square (OLS) for regression. In addition, within-plot heteroscedasticity and autocorrelation were also considered. The final mixed-effects model was determined according to the Akaike information criterion (AIC), Bayesian information criterion (BIC), log-likelihood (Loglik), and the likelihoodratio test (LRT). The results revealed that initial diameter (DBH), the sum of the basal area (m 2 /ha) in trees with DBHs larger than the DBH of the subject tree (BAL), number of trees per hectare (NT), and elevation (EL) had a significant impact on individual-tree basal area increment. The mixed-effects model performed much better than the basic model produced using OLS. Additionally, the variance structure of the model errors was successfully modeled using the power function. However, the autocorrelation structures were not defined because there was no autocorrelation amongst the data. It is believed that the final model will contribute to the scientific management of the masson pine.