文献类型：期刊文献

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

作者：Wang, Wenwen[1] Bai, Yanfeng[2] Jiang, Chunqian[2] Yang, Haijun[3] Meng, Jinghui[1]

第一作者：Wang, Wenwen

通信作者：Meng, JH[1]

机构：[1]Beijing Forestry Univ, Res Ctr Forest Management Engn, Natl Forestry & Grassland Adm, Beijing, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forestry, Beijing, Peoples R China;[3]Hainan Trop Ocean Univ, Sch Ocean Sci & Technol, Sanya, Peoples R China

年份：2020

卷号：39

期号：5

起止页码：526-541

外文期刊名：JOURNAL OF SUSTAINABLE FORESTRY

收录：;EI(收录号：20194607687018);Scopus(收录号：2-s2.0-85074830130);WOS:【SCI-EXPANDED(收录号:WOS:000541823500007)】；

基金：This research was funded by National Key R&D Program of China, grant number 2017YFC0505604.

语种：英文

外文关键词：Individual-tree basal area increment model; a linear mixed-effects model; masson pine; heteroscedasticity; autocorrelation

摘要：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.