文献类型：期刊文献

英文题名：A Comparison of Hierarchical and Non-Hierarchical Bayesian Approaches for Fitting Allometric Larch (Larix. spp.) Biomass Equations

作者：Chen, Dongsheng[1] Huang, Xingzhao[2] Sun, Xiaomei[1] Ma, Wu[3] Zhang, Shougong[1]

第一作者：陈东升

通信作者：Zhang, SG[1]

机构：[1]Chinese Acad Forestry, Key Lab Tree Breeding & Cultivat, State Forestry Adm, Res Inst Forestry, Beijing 100091, Peoples R China;[2]Anhui Agr Univ, Sch Forestry & Landscape Architecture, Hefei 230036, Peoples R China;[3]W Virginia Univ, Sch Nat Resources, Morgantown, WV 26506 USA

年份：2016

卷号：7

期号：1

起止页码：1-13

外文期刊名：FORESTS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000369493700009)】；

基金：This research was supported by the National Natural Science Foundation (No. 31430017 and No. 31300536). Numerous researchers assisted with the data collection, but we particularly thank the staff of the Changlinggang Forest Farm, the Xiaolongshan Research Institute of Forestry, the MulanWeichang National Forestry Administration Bureau, the Dagujia Forest Farm, the Mengjiagang Forest Farm and the Wuerqihan Forestry Bureau. We also thank Y.C. Lei for his constructive suggestions and helpful comments regarding the manuscript.

语种：英文

外文关键词：larch; non-hierarchical Bayesian approach; hierarchical Bayesian approach; biomass model

摘要：Accurate biomass estimations are important for assessing and monitoring forest carbon storage. Bayesian theory has been widely applied to tree biomass models. Recently, a hierarchical Bayesian approach has received increasing attention for improving biomass models. In this study, tree biomass data were obtained by sampling 310 trees from 209 permanent sample plots from larch plantations in six regions across China. Non-hierarchical and hierarchical Bayesian approaches were used to model allometric biomass equations. We found that the total, root, stem wood, stem bark, branch and foliage biomass model relationships were statistically significant (p-values < 0.001) for both the non-hierarchical and hierarchical Bayesian approaches, but the hierarchical Bayesian approach increased the goodness-of-fit statistics over the non-hierarchical Bayesian approach. The R-2 values of the hierarchical approach were higher than those of the non-hierarchical approach by 0.008, 0.018, 0.020, 0.003, 0.088 and 0.116 for the total tree, root, stem wood, stem bark, branch and foliage models, respectively. The hierarchical Bayesian approach significantly improved the accuracy of the biomass model (except for the stem bark) and can reflect regional differences by using random parameters to improve the regional scale model accuracy.