文献类型：期刊文献

英文题名：Site-Specific Allometric Models for Prediction of Above- and Belowground Biomass of Subtropical Forests in Guangzhou, Southern China

作者：Zhao, Houben[1,2] Li, Zhaojia[1,2] Zhou, Guangyi[1,2] Qiu, Zhijun[1,2] Wu, Zhongmin[1,2]

第一作者：Zhao, Houben;赵厚本

通信作者：Zhou, GY[1];Zhou, GY[2]

机构：[1]Chinese Acad Forestry, Res Inst Trop Forestry, Guangzhou 510520, Guangdong, Peoples R China;[2]Beijiangyuan Natl Forest Ecosyst Res Stn, Guangzhou 510520, Guangdong, Peoples R China

年份：2019

卷号：10

期号：10

外文期刊名：FORESTS

收录：;EI(收录号：20194207561374);Scopus(收录号：2-s2.0-85073415788);WOS:【SCI-EXPANDED(收录号:WOS:000498395600039)】；

基金：This research was funded by the Basic Science Foundation of Research Institute of Tropical Forestry, Chinese Academy of Forestry, grant number CAFYBB2017ZX002-3, CAFYBB2019SZ003, and Key Scientific and Technological Projects of Guangzhou forestry and garden bureau, grant number 2016-06.

语种：英文

外文关键词：allometric equation; mixed-species regression; aboveground biomass; belowground biomass; root-shoot ratio; subtropical forest

摘要：Tree allometric models that are used to predict the biomass of individual tree are critical to forest carbon accounting and ecosystem service modeling. To enhance the accuracy of such predictions, the development of site-specific, rather than generalized, allometric models is advised whenever possible. Subtropical forests are important carbon sinks and have a huge potential for mitigating climate change. However, few biomass models compared to the diversity of forest ecosystems are currently available for the subtropical forests of China. This study developed site-specific allometric models to estimate the aboveground and the belowground biomass for south subtropical humid forest in Guangzhou, Southern China. Destructive methods were used to measure the aboveground biomass with a sample of 144 trees from 26 species, and the belowground biomass was measured with a subsample of 116 of them. Linear regression with logarithmic transformation was used to model biomass according to dendrometric parameters. The mixed-species regressions with diameter at breast height (DBH) as a single predictor were able to adequately estimate aboveground, belowground and total biomass. The coefficients of determination (R-2) were 0.955, 0.914 and 0.954, respectively, and the mean prediction errors were -1.96, -5.84 and 2.26%, respectively. Adding tree height (H) compounded with DBH as one variable ((DBHH)-H-2) did not improve model performance. Using H as a second variable in the equation can improve the model fitness in estimation of belowground biomass, but there are collinearity effects, resulting in an increased standard error of regression coefficients. Therefore, it is not recommended to add H in the allometric models. Adding wood density (WD) compounded with DBH as one variable ((DBHWD)-W-2) slightly improved model fitness for prediction of belowground biomass, but there was no positive effect on the prediction of aboveground and total biomass. Using WD as a second variable in the equation, the best-fitting allometric relationship for biomass estimation of the aboveground, belowground, and total biomass was given, indicating that WD is a crucial factor in biomass models of subtropical forest. Root-shoot ratio of subtropical forest in this study varies with species and tree size, and it is not suitable to apply it to estimate belowground biomass. These findings are of great significance for accurately measuring regional forest carbon sinks, and having reference value for forest management.