植物生态学报 ›› 2007, Vol. 31 ›› Issue (1): 11-22.DOI: 10.17521/cjpe.2007.0003

• 论文 • 上一篇    下一篇

西双版纳热带季节雨林的生物量及其分配特征

吕晓涛1,2(), 唐建维1,*(), 何有才3, 段文贵3, 宋军平3, 许海龙3, 朱胜忠3   

  1. 1 中国科学院西双版纳热带植物园,云南勐腊 666303
    2 中国科学院研究生院,北京 100049
    3 西双版纳国家级自然保护区管理局勐腊管理所,云南勐腊 666300
  • 收稿日期:2005-09-19 接受日期:2006-01-26 出版日期:2007-09-19 发布日期:2007-01-30
  • 通讯作者: 唐建维
  • 作者简介:* tangjw@xtbg.org.cn
  • 基金资助:
    中国科学院野外台站基金项目;“西部之光”资助项目和中国科学院知识创新工程重大项目(KZCX1-SW-01)

BIOMASS AND ITS ALLOCATION IN TROPICAL SEASONAL RAIN FOREST IN XISHUANGBANNA, SOUTHWEST CHINA

LÁ Xiao-Tao1,2(), TANG Jian-Wei1,*(), HE You-Cai3, DUAN Wen-Gui3, SONG Jun-Ping3, XU Hai-Long3, ZHU Sheng-Zhong3   

  1. 1Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Yunnan 666303, China,
    2Graduate University of Chinese Academy of Sciences, Beijing 100049, China, and
    3Mengla Institute of Conservation, Xishuangbanna Bureau of National Nature Reserve, Mengla, Yunnan 666300, China
  • Received:2005-09-19 Accepted:2006-01-26 Online:2007-09-19 Published:2007-01-30
  • Contact: TANG Jian-Wei
  • About author:First author contact:E-mail of the first author: lxtao@xtbg.org.cn.

摘要:

根据3块1 hm2 样地的调查资料,利用123株样木数据建立以胸径(D)为单变量的生物量预测方程。采用样木回归分析法(乔木层、木质藤本)和样方收获法(灌木层、草本层),获取西双版纳热带季节雨林的生物量,并分析了其组成和分配特征。结果表明,西双版纳热带季节雨林的总生物量为(423.908±109.702) Mg·hm-2(平均值±标准差,n=3),其中活体植物生物量占95.28%,粗死木质残体占4.07%,地上凋落物占 0.64%。在其层次分配方面:乔木层优势明显,占98.09%±0.60%;其次为木质藤本,占0.83%±0.31%;灌木层和草本层生物量均小于木质藤本的生物量;附生植物最低,仅为0.06%±0.03%。总生物量的器官分配以茎所占比例最高,达68.33%;根、枝、叶的比例分别为18.91%、11.07%和1.65%。乔木层生物量的径级分配主要集中于中等径级和最大径级。大树(D>70 cm)具有较高的生物量,占整个乔木层的43.67%±12.67%。树种分配方面,生物量排序前10位的树种占乔木层总生物量的63.43%±4.09%,生物量集中分配于少量优势树种。西双版纳热带季节雨林乔木层叶面积指数为6.39±0.85。西双版纳热带季节雨林乔木层的地上生物量位于世界热带湿润森林的中下范围。

关键词: 生物量, 生物量分配, 回归模型, 热带季节雨林, 西双版纳

Abstract:

Aims Changes in the biomass of tropical forests play an important role in the global carbon cycle, but the biomass of these forests has been poorly quantified. A strategy for regional biomass estimation should supplement previous surveys with new data. Accurate data are necessary for reducing the uncertainty in the carbon budget of tropical regions.

Methods Biomass and its allocation were estimated for the tropical seasonal rain forest in Xishuangbanna, southwest China. Regression models relating tree biomass to DBH (diameter at breast height, 1.3 m) were developed, and a power-law allometric relationship W=aDb was used to estimate the tree biomass, where W is the biomass of a tree (kg of leaves, branches, stems or roots), a and b are constants and D is the DBH (cm). Other biomass components were sampled in different quadrats in three 1 hm2 permanent research plots: shrubs (ten 25 m2 quadrats), herbs (ten 4 m2 quadrats), dead wood (the whole plot), large fallen branches (twenty-five 25 m2 quadrats) and litterfall (twenty-five 1 m2 quadrats). This method was used for estimating above- and below-ground biomass of live and dead plants (trees, seedlings, shrubs, herbs, woody lianas, epiphytes, coarse woody debris and litterfall).

Important findings Total biomass for the three plots was 370.163, 550.119 and 351.442 Mg·hm-2, with an average of (423.908±109.702) Mg·hm-2 (95% confidence interval). Living biomass made up 95.28% of the total biomass, with coarse woody debris and litterfall comprising the rest. Most living biomass (98.09%±0.60%) (Mean±SD, n=3) of the seasonal rain forest was concentrated in the tree layer. In the allocation of total biomass, stems accounted 68.33% and roots, branches and leaves made up 18.91%, 11.07% and 1.65%, respectively. The biomass allocation among different DBH classes was concentrated in the middle and largest classes, with large trees (D>70 cm) accounting for 43.67% ± 12.67%. The most important ten species, in terms of biomass, made up 63.43% of the tree layer. Leaf area index (LAI) of the tree layer for the three plots was 5.73, 7.35 and 6.08, with an average of 6.39. Estimated aboveground biomass in our study sites fell within the range of published values for tropical moist forests and was lower than that of Malaysian and Cameroon rain forests, but higher than some neotropical rain forests. In terms of total biomass, Xishuangbanna tropical seasonal rain forest is also higher than moist forest in Brazil.

Key words: biomass, biomass allocation, regression models, tropical seasonal rain forest, Xishuangbanna