文献类型：期刊文献

英文题名：Effects of stand factors on tree growth of Chinese fir in the subtropics of China depends on climate conditions from predictions of a deep learning algorithm: A long-term spacing trial

作者：Wang, Zhen[1,2] Zhang, Xiongqing[1,2] Zhang, Jianguo[1] Chhin, Sophan[3]

第一作者：Wang, Zhen

通信作者：Zhang, XQ[1]

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

年份：2022

卷号：520

外文期刊名：FOREST ECOLOGY AND MANAGEMENT

收录：;EI(收录号：20222512252086);Scopus(收录号：2-s2.0-85132237615);WOS:【SCI-EXPANDED(收录号:WOS:000832827500005)】；

基金：The study was funded by the National Key Research and Develop-ment Program of China (2021YFD2201301) and the National Natural Science Foundation of China (31971645) . The authors would like to thank Dr. Aiguo Duan for the field work. We also thank Dr. Greg Dahle for providing comments on a prior version of this manuscript.

语种：英文

外文关键词：Tree growth; Stand factors; Climate factors; Sensitive analysis; Deep learning; Chinese fir

摘要：Stand and climate related variables are the main driving forces controlling individual tree growth. Two machine learning algorithms called deep learning and random forest were used to explore how annual diameter growth varied with stand and climatic variables. Data was obtained from a long-term spacing trail of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) plantations in four provinces of southern China. Results from model comparisons showed the deep learning model with 8 hidden layers and 90 neurons in each hidden layer achieved the best performance, and the RF model ranked 4th among 9 selected models. In addition, sensitivity analysis showed that individual tree growth increased with an increase in Gini coefficient, while growth decreased with an increase in stand age (A) and the basal area of larger trees (BAL). The relationships between diameter growth and summer mean maximum temperature (SMMT), as well as winter mean minimum temperature (WMMT) and annual precipitation (AP) were not constant, which depended on the range of values of each climate factor. BAL had the greatest influence on diameter growth among all the variables. From an interaction analysis, we found that climate factors exacerbated the negative effects of competition on growth. Climate change promoted the growth of younger trees but restrained the growth of older trees. With climate variables considered, tree growth under high and middle stand structural heterogeneity were similar, and observably higher than that with low stand structural heterogeneity. Positive influences of climate tended to promote tree growth under lower competition and older individuals were more vulnerable to WMMT changes. Our findings enhance our understanding of the mechanisms driving individual Chinese fir growth in southern China in the face of future climate uncertainty.