植物生态学报 ›› 2017, Vol. 41 ›› Issue (1): 43-52.DOI: 10.17521/cjpe.2016.0174

所属专题: 中国灌丛生态系统碳储量的研究

• 研究论文 • 上一篇    下一篇

中国亚热带山地杜鹃灌丛生物量分配及其碳密度估算

张蔷1,2, 李家湘3, 徐文婷1, 熊高明1, 谢宗强1,*()   

  1. 1中国科学院植物研究所植被与环境变化国家重点实验室, 北京 100093
    2中国科学院大学, 北京 100049
    3中南林业科技大学林学院, 长沙 410004
  • 收稿日期:2016-05-17 接受日期:2016-09-21 出版日期:2017-01-10 发布日期:2017-01-23
  • 通讯作者: 谢宗强
  • 作者简介:* 通信作者Author for correspondence (E-mail:sunzhiqiang1956@sina.com)
  • 基金资助:
    中国科学院战略性先导科技专项 (XDA05050302)和国家科技基础性专项(Y5220B1001)

Estimation of biomass allocation and carbon density of Rhododendron simsii shrubland in the subtropical mountainous areas of China

Qiang ZHANG1,2, Jia-Xiang LI3, Wen-Ting XU1, Gao-Ming XIONG1, Zong-Qiang XIE1,*()   

  1. 1State Key Laboratory of Vegetation and Environment Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

    2University of Chi- nese Academy of Sciences, Beijing 100049, China

    3Faculty of Forestry, Central South University of Forestry and Technology, Changsha 410004, China
  • Received:2016-05-17 Accepted:2016-09-21 Online:2017-01-10 Published:2017-01-23
  • Contact: Zong-Qiang XIE
  • About author:KANG Jing-yao(1991-), E-mail: kangjingyao_nj@163.com

摘要:

灌丛生态系统作为一个巨大的潜在碳汇, 在全球碳平衡和气候调节中发挥着重要的作用。杜鹃(Rhododendron simsii)灌丛是我国亚热带山地最为常见的灌丛类型。该文采用群落调查和数学模拟方法, 研究了中亚热带山地杜鹃灌丛的生物量和碳密度。结果表明: 1)灌木各器官最佳生物量估测模型的函数类型为幂函数和线性函数, 自变量为DD2H (D为基径, H为株高), 所有模型均达到极显著水平; 生长方程对茎生物量的拟合效果优于其对叶和当年枝生物量的拟合效果。2)灌木层平均生物量为20.78 Mg·hm-2, 其中优势树种杜鹃和白檀(Symplocos paniculata)占93.63%; 灌木层各器官生物量排序为茎>根>叶>当年枝, 根冠比为0.32, 说明生物量更多地分配到地上光合器官, 体现了灌木层植物对该区域温暖湿润的环境条件的适应。3)杜鹃灌丛群落平均总生物量为26.26 Mg·hm-2, 灌木层、草本层和凋落物层生物量分别占79.14%、7.62%和13.25%, 凋落物层生物量较高表明该研究群落具有较大的养分归还量。4)灌木层和草本层的地上生物量与地下生物量和总生物量之间存在极显著相关关系, 这种关系可用于相互间的预测。5)杜鹃灌丛群落平均总生物量碳密度为11.70 Mg·hm-2, 群落平均含碳率为44.55%, 以往通过乘以转换系数0.5得到的灌丛碳密度比实际碳密度高出12.22%, 导致对灌丛植被碳储量和碳汇能力的估测产生严重偏差。

关键词: 回归模型, 根冠比, 养分归还, 地上生物量, 地下生物量, 含碳率

Abstract:

Aims As an important potential carbon sink, shrubland ecosystem plays a vital role in global carbon balance and climate regulation. Our objectives were to derive appropriate regression models for shrub biomass estimation, and to reveal the biomass allocation pattern and carbon density in Rhododendron simsii shrubland.
Methods We conducted investigations in 27 plots, and developed biomass regression models for shrub species to estimate shrub biomass. The biomass of herb and litterfall were obtained through harvesting. Plant samples were collected from each plot to measure carbon content in different organs.
Important findings The results showed that the power and linear models were the most appropriate equation forms. The D and D2H (where D was the basal diameter (cm) and H was the shrub height (m)) were good predictors for organ biomass and total biomass of shrubs. All of the biomass models reached extremely significant level, and could be used to estimate shrub biomass with high accuracy. It was more difficult to predict leaf and annual branch biomass than stem biomass, because leaf and annual branch were susceptible to herbivores and inter-plant competition. The mean biomass of the shrub layer was 20.78 Mg·hm-2, in which Rhododendron simsii and Symplocos paniculata biomass accounted for 93.63%. Influenced by both environment and species characteristics, the biomass of the shrub layer organs was in the order of stem > root > leaf > annual branch. The root:shoot ratio of the shrub layer was 0.32, which was less than other shrubs in subtropical regions. The relative higher aboveground biomass allocation reflected the adaptation of plants to the warm and humid environment for more photosynthesis. The mean total community biomass was 26.26 Mg·hm-2, in which shrub layer, herb layer and litter layer accounted for 79.14%, 7.62% and 13.25%, respectively. Litter biomass was relatively high, which suggested that this community had high nutrient return. There were significant correlations among aboveground biomass, belowground biomass and total biomass of shrub layer and herb layer. The mean biomass carbon density of the community was 11.70 Mg·hm-2 and the carbon content ratio was 44.55%. The carbon density was usually obtained using the conversion coefficient of 0.5 in previous studies, which could overestimate carbon density by 12.22%.

Key words: regression model, root/shoot ratio, nutrient return, aboveground biomass, belowground biomass, carbon content ratio