文献类型：期刊文献

英文题名：Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

作者：Wang, Wenwen[1] Bai, Yanfeng[2] Jiang, Chunqian[2] 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

年份：2020

卷号：2020

期号：161

起止页码：1-14

外文期刊名：JOVE-JOURNAL OF VISUALIZED EXPERIMENTS

收录：;Scopus(收录号：2-s2.0-85087833188);WOS:【SCI-EXPANDED(收录号:WOS:000585806100010)】；

基金：This research was funded by the Fundamental Research Funds for the Central Universities, grant number 2019GJZL04. We thank Professor Weisheng Zeng at the Academy of Forest Inventory and Planning, National Forestry and Grassland Administration, China for providing access to data.

语种：英文

摘要：Here, we developed an individual-tree model of 5-year basal area increments based on a dataset including 21898 Picea asperata trees from 779 sample plots located in Xinjiang Province, northwest China. To prevent high correlations among observations from the same sampling unit, we developed the model using a linear mixed-effects approach with random plot effect to account for stochastic variability. Various tree- and stand-level variables, such as indices for tree size, competition, and site condition, were included as fixed effects to explain the residual variability. In addition, heteroscedasticity and autocorrelation were described by introducing variance functions and autocorrelation structures. The optimal linear mixed-effects model was determined by several fit statistics: Akaike's information criterion, Bayesian information criterion, logarithm likelihood, and a likelihood ratio test. The results indicated that significant variables of individual-tree basal area increment were the inverse transformation of diameter at breast height, the basal area of trees larger than the subject tree, the number of trees per hectare, and elevation. Furthermore, errors in variance structure were most successfully modeled by the exponential function, and the autocorrelation was significantly corrected by first-order autoregressive structure (AR(1)). The performance of the linear mixed-effects model was significantly improved relative to the model using ordinary least squares regression.