文献类型：期刊文献

英文题名：Integrating regional climate change into allometric equations for estimating tree aboveground biomass of Masson pine in China

作者：Fu, Liyong[1] Lei, Xiangdong[1] Hu, Zongda[2] Zeng, Weisheng[3] Tang, Shouzheng[1] Marshall, Peter[4] Cao, Lin[5] Song, Xinyu[1,6] Yu, Li[1] Liang, Jingjing[7]

第一作者：符利勇

通信作者：Lei, XD[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Sichuan Agr Univ, Coll Resources, Chengdu 611130, Sichuan, Peoples R China;[3]State Forestry Adm, Acad Forest Inventory & Planning, Beijing 100714, Peoples R China;[4]Univ British Columbia, Dept Forest Resources Management, Vancouver, BC V6T 1Z4, Canada;[5]Nanjing Forestry Univ, Coinnovat Ctr Sustainable Forestry Southern China, 159 Longpan Rd, Nanjing 210037, Jiangsu, Peoples R China;[6]Xinyang Normal Univ, Coll Comp & Informat Tech, Xinyang 464000, Henan, Peoples R China;[7]West Virginia Univ, Div Forestry & Nat Resources, Morgantown, WV 26506 USA

年份：2017

卷号：74

期号：2

外文期刊名：ANNALS OF FOREST SCIENCE

收录：;Scopus(收录号：2-s2.0-85019433179);WOS:【SCI-EXPANDED(收录号:WOS:000405798400012)】；

基金：The article was funded by the Forestry Public Welfare Scientific Research Project (No. 201504303) and the Chinese National Natural Science Foundation (Nos. 31470641, 31300534, and 31570628). We appreciate the valuable comments from the chief editor Dr. Erwin Dreyer, Dr. Jean-Michel Leban, the handling editor, and two anonymous reviewers who improved the manuscript.

语种：英文

外文关键词：Masson pine; Subtropical China; Climate change; Climate-sensitive aboveground biomass model; Nonlinearmixed-effects model; Dummy variable approach

摘要：Key message A climate-sensitive aboveground biomass (AGB) equation, in combination with nonlinear mixedeffectsmodeling and dummy variable approach, was developed to examine how climate changemay affect the allometric relationships between tree diameter and biomass. We showed that such changes in allometry need to be taken into account for estimating tree AGB in Masson pine. Context As a native species and being widely distributed in subtropical China, Masson pine (Pinus massoniana Lamb.) forests play a pivotal role in maintaining forest ecosystem functions and mitigation of carbon concentration increase at the atmosphere. Traditional biomass allometric equations do not account for a potential effect of climate on the diameterbiomass relationships. The amplitude of such an effect remains poorly documented. Aims We presented a novel method for detecting the longterm (2041-2080) effects of climate change on the diameterbiomass relationships and the potential consequences for long-term changes of biomass accumulation for Masson pine. Methods Our approach was based on a climate-sensitive AGB model developed using a combined nonlinear mixedeffects model and dummy variable approach. Various climaterelated variables were evaluated for their contributions to model improvement. Heteroscedasticity was accounted for by three residual variance functions: exponential function, power function, and constant plus function. Results The results showed that diameter at breast height, together with the long-term average of growing season temperature, total growing season precipitation, mean temperature of wettest quarter, and precipitation of wettest quarter, had significant effects on values of AGB. Excessive rain during the growing season and high mean temperature in the wettest quarter reduced the AGB, while a warm growing season and abundant precipitation in the wettest quarter increased the AGB. Conclusion Climate change significantly affected the allometric scale of biomass equation. The new climate-sensitive allometric model developed in this study may improve biomass predictions compared with the traditional model without climate effects. Our findings suggested that the AGB of Masson pine trees with the same diameter at breast height under three climate scenarios including representative concentration pathway (RCP) 2.6, RCP 4.5, and RCP 8.5 in the future period 2041- 2080 would increase by 24.8 +/- 32.7% (mean +/- standard deviation), 27.0 +/- 33.4%, and 27.7 +/- 33.8% compared with the constant climate (19502000), respectively. As a consequence, we may expect a significant regional variability and uncertainty in biomass estimates under climate change.