文献类型：期刊文献

英文题名：Projecting Stand Survival and Basal Area Based on a Self-Thinning Model for Chinese Fir Plantations

作者：Zhang, Xiongqing[1,2] Cao, Quang, V[3] Wang, Hanchen[4] Duan, Aiguo[4] Zhang, Jianguo[1]

第一作者：Zhang, Xiongqing;张雄清

通信作者：Zhang, XQ[1];Zhang, XQ[2]

机构：[1]Chinese Acad Forestry, Res Inst Forestry, State Forestry Adm, State Key Lab Tree Genet & Breeding,Key Lab Tree, Beijing, Peoples R China;[2]Nanjing Forestry Univ, Collaborat Innovat Ctr Sustainable Forestry South, Nanjing, Peoples R China;[3]Louisiana State Univ, Ctr Agr, Sch Renewable Nat Resources, Baton Rouge, LA 70803 USA;[4]Chinese Acad Forestry, Res Inst Forestry, Beijing, Peoples R China

年份：2020

卷号：66

期号：3

起止页码：361-370

外文期刊名：FOREST SCIENCE

收录：;EI(收录号：20203609128621);Scopus(收录号：2-s2.0-85090031589);WOS:【SCI-EXPANDED(收录号:WOS:000538869100010)】；

基金：The study was supported by the Young Elite Scientists Sponsorship Program of State Administration of Forestry and Grassland (2019132605), National Natural Science Foundation of China (No. 31670634, 31971645), and the Young Elite Scientists Sponsorship Program by CAST (No. 2017QNRC001). Partial support for data analysis was received from the National Institute of Food and Agriculture, USDA, McIntire-Stennis project LAB94379.

语种：英文

外文关键词：Chinese fir; self-thinning rule; stand survival; stand basal area

摘要：The self-thinning rule has played a critical role in controlling stand stocking and modeling stand development in forest stands. Chinese fir (Cunninghamia lanceolata) is a native and fast-growing tree species used for timber production and is widely grown in southern China. Effective management of this important tree species requires accurate and reasonable predictions of stand growth and survival. Remeasured data from 48 plots distributed in Fujian, Jiangxi, Guangxi, and Sichuan provinces were used to develop models to predict stand survival and basal area based on the self-thinning trajectories. These trajectories were constructed using a self-thinning slope of -1.605, as suggested by Reineke (1933) (Method 1), and the slopes estimated either from two groups of sites (Method 2) or from climate variables (Method 3). Results indicated that the stand growth and survival models using Method 3 performed best, followed by Method 2 and Method 1. In addition, stand growth and survival curves predicted from Method 3 were more similar in shape to those from the observed values, as compared with Method 1. Overall, the models based on the self-thinning lines using climate-sensitive slopes provided reasonable predictions of the stand development dynamics. Therefore, these results facilitate modeling of the relation between stand growth/survival and self-thinning under climate change. Study Implications: The self-thinning rule describes the mortality related to competition among trees within even-aged stands, and it is a basic topic of research and discussion for more than 80 years. Chinese fir (Cunninghamia lanceolata), a native and fast-growing tree species used for commercial plantation establishment, is widely grown in southern China. For Chinese fir plantations, however, it has been reported that the self-thinning slope varied with site and climate. Remeasured data from 48 plots distributed in Fujian, Jiangxi, Guangxi, and Sichuan provinces were used to develop models to predict stand survival and basal area based on self-thinning trajectories. These trajectories were constructed using the slope of -1.605, as suggested by Reineke (1933) (Method 1), with the slopes estimated either from two groups of sites (Method 2) or from climate variables (Method 3). Results indicated that stand survival and basal area models using Method 3 performed best, followed by Method 2 and Method 1. In addition, stand growth and survival curves predicted from Method 3 were more similar in shape to those from the observed values than those from Method 1. Because the models developed in this study are based on the self-thinning rule, they are compatible with the dynamics of stand development and therefore should be able to provide reasonable predictions outside the range of the data.