植物生态学报 ›› 2016, Vol. 40 ›› Issue (4): 364-373.DOI: 10.17521/cjpe.2015.0235

• 研究论文 • 上一篇    下一篇

天山森林生态系统碳储量格局及其影响因素

许文强1,*(), 杨辽1, 陈曦1, 高亚琪2, 王蕾2   

  1. 1中国科学院新疆生态与地理研究所荒漠与绿洲生态国家重点实验室, 乌鲁木齐 830011
    2新疆林业科学院现代林业研究所, 乌鲁木齐 830063
  • 收稿日期:2015-06-21 接受日期:2015-11-27 出版日期:2016-04-29 发布日期:2016-04-30
  • 通讯作者: 许文强
  • 基金资助:
    中国科学院战略性先导科技专项 (XDA05050202)和国家自然科学基金(41271323)

Carbon storage, spatial distribution and the influence factors in Tianshan forests

Wen-Qiang XU1,*(), Liao YANG1, Xi CHEN1, Ya-Qi GAO2, Lei WANG2   

  1. 1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi 830011, China
    and
    2Research Institute of Modern Forestry, Xinjiang Academy of Forestry, Ürümqi 830063, China
  • Received:2015-06-21 Accepted:2015-11-27 Online:2016-04-29 Published:2016-04-30
  • Contact: Wen-Qiang XU

摘要:

科学地估算亚洲中部天山雪岭杉(Picea schrenkiana)生态系统碳密度与碳储量是评价新疆森林碳汇潜力、评估森林在减缓大气CO2浓度上升、应对气候变化等方面功能的关键, 对干旱区森林生态系统的保育和可持续发展具有重要意义。该文基于在天山雪岭杉林区布设的70个野外样地调查数据, 结合新疆森林资源连续清查数据, 全面估算了天山雪岭杉生态系统的碳密度和碳储量, 分析了其分布格局与影响因素。结果表明: 天山雪岭杉不同龄组叶、枝、干和根的含碳率变化不显著, 其乔木层平均含碳率为49%, 而林下植被(凋落物、草本等)平均含碳率仅为42%。雪岭杉森林生态系统单位面积生物量为187.98 Mg·hm-2, 其中乔木层生物量占生态系统总生物量的98.93%。乔木层各组分生物量大小为: 干>根>枝>叶, 而各龄组生物量排序为: 成熟林>中龄林>近熟林>过熟林>幼龄林。雪岭杉生态系统碳密度为544.57 Mg·hm-2, 碳储量为290.84 Tg C, 其中植被碳密度为92.57 Mg·hm-2, 植被碳储量为53.14 Tg C, 土壤碳密度为452.00 Mg·hm-2, 土壤碳储量为237.70 Tg C。天山雪岭杉生态系统碳密度分异与不同林区林带垂直宽度变化具有很高的相关性, 其生态系统碳密度西高东低的分布格局和它所处的环境因子西优东劣的变异是相一致的, 即不同的环境因素组合是造成天山雪岭杉生态系统碳密度差异的主要原因。

关键词: 生物量, 含碳率, 碳密度, 环境因子, 调查样地, 雪岭杉

Abstract:

Aims
Accurate estimation of carbon density and storage is among the key challenges in evaluating ecosystem carbon sink potentials for reducing atmospheric CO2 concentration. It is also important for developing future conservation strategies and sustainable practices. Our objectives were to estimate the ecosystem carbon density and storage of Picea schrenkiana forests in Tianshan region of Xinjiang, and to analyze the spatial distribution and influencing factors.
Methods
Based on field measurements, the forest resource inventories, and laboratory analyses, we studied the carbon storage, its spatial distribution, and the potential influencing factors in Picea schrenkiana forest of Tianshan. Field surveys of 70 sites, with 800 m2 (28.3 m × 28.3 m) for plot size, was conducted in 2011 for quantifying arbor biomass (leaf, branch, trunk and root), grass and litterfall biomass, soil bulk density, and other laboratory analyses of vegetation carbon content, soil organic carbon content, etc.
Important findings
The carbon content of the leaf, branch, trunk and root of Picea schrenkiana is varied from 46.56% to 52.22%. The vegetation carbon content of arbor and the herbatious/litterfall layer was 49% and 42%, respectively. The forest biomass of Picea schrenkiana was 187.98 Mg·hm-2, with 98.93% found in the arbor layer. The biomass in all layers was in the order of trunk (109.81 Mg·hm-2) > root (39.79 Mg·hm-2) > branch (23.62 Mg·hm-2) > leaf (12.76 Mg·hm-2). From the age-group point of view, the highest and the lowest biomass was found at the mature forest (228.74 Mg·hm-2) and young forest (146.77 Mg·hm-2), respectively. The carbon density and storage were 544.57 Mg·hm-2 and 290.84 Tg C, with vegetation portion of 92.57 Mg·hm-2 and 53.14 Tg C, and soil portion of 452.00 Mg·hm-2 and 237.70 Tg C, respectively. The spatial distribution of carbon density and storage appeared higher in the western areas than those in the eastern regions. In the western Tianshan Mountains (e.g., Ili district), carbon density was the highest, whereas the central Tianshan Mountains (e.g., Manas County, Fukang City, Qitai County) also had high carbon density. In the eastern Tianshan Mountains (e.g., Hami City), it was low. This distribution seemed consistent with the changes in environmental conditions. The primary causes of carbon density difference might be a combined effects of multiple environmental factors such as terrain, precipitation, temperature, and soil.

Key words: biomass, carbon content, carbon density, environmental factors, sampling plots, Picea schrenkiana