文献类型：期刊文献

英文题名：A Bayesian Model Averaging approach for modelling tree mortality in relation to site, competition and climatic factors for Chinese fir plantations

作者：Lu, Lele[1,2] Wang, Hanchen[1] Chhin, Sophan[3] Duan, Aiguo[1] Zhang, Jianguo[1] Zhang, Xiongqing[1,2]

第一作者：Lu, Lele

通信作者：Zhang, XQ[1]

机构：[1]Chinese Acad Forestry, Res Inst Forestry, State Forestry Adm, Key Lab Tree Breeding & Cultivat, Beijing 100091, Peoples R China;[2]Nanjing Forestry Univ, Collaborat Innovat Ctr Sustainable Forestry South, Nanjing 210037, Jiangsu, Peoples R China;[3]West Virginia Univ, Div Forestry & Nat Resources, 322 Percival Hall,POB 6125, Morgantown, WV 26506 USA

年份：2019

卷号：440

起止页码：169-177

外文期刊名：FOREST ECOLOGY AND MANAGEMENT

收录：;EI(收录号：20191106636539);Scopus(收录号：2-s2.0-85062841260);WOS:【SCI-EXPANDED(收录号:WOS:000464297900017)】；

基金：The study was supported by the National Natural Science Foundation of China (No. 31670634), the Young Elite Scientists Sponsorship Program by CAST (No. 2017QNRC001), and the Fundamental Research Funds for the Central Non-profit Research Institution of CAF (CAFYBB2017ZX001-2).

语种：英文

外文关键词：Tree mortality; Chinese fir; Endogenous factors; Climate factors; Bayesian Model Averaging; Logistic stepwise regression

摘要：Relationships between tree mortality and endogenous factors and climate factors have emerged as important concerns, and logistic stepwise regression is widely used for modeling the relationships. However, this method subsequently ignores both the variables not selected because of insignificance, and the model uncertainty due to the variable selection process. Bayesian Model Averaging (BMA) selects all possible models and uses the posterior probabilities of these models to perform all inferences and predictions. In this study, Bayesian Model Averaging (BMA) and logistic stepwise regression were used to analyze tree mortality in relation to competition, site index, and climatic factors in Chinese fir (Cunninghamia lanceolata (Lamb.) plantations established at five initial planting densities (A: 1667, B: 3333, C: 5000, D: 6667, and E: 10,000 trees/ha). Results showed that the posterior probability of the best model acquired by stepwise regression was less than that of the best model (highest posterior probability) acquired by BMA for pooling the data and density level D. Especially in the other planting densities, the model selected by stepwise regression was not in the BMA models. It indicates that the BMA method performed better than logistic stepwise regression, because BMA gave accurate posterior probability by taking into account the uncertainty of the model. In addition, the mortality increased with high competition and decreased with increasing temperature. The research has important implications for managing Chinese fir plantations under climate change.