生物多样性 ›› 2016, Vol. 24 ›› Issue (4): 407-420.DOI: 10.17520/biods.2015353

所属专题: 中国西南干旱河谷的植物多样性

• 研究报告 • 上一篇    下一篇

金沙江干旱河谷植物群落的数量分类及其结构分异的环境解释

刘晔1, 许玥2, 石松林3, 彭培好4, 沈泽昊2,*()   

  1. 1 北京大学深圳研究生院城市规划与设计学院, 深圳 518055。
    2 北京大学城市与环境学院生态学系, 地表过程分析与模拟教育部重点实验室, 北京 100871。
    3 中国科学院生态环境研究中心城市与区域生态国家重点实验室, 北京 100085 。
    4 成都理工大学旅游与城乡规划学院, 成都 610058
  • 收稿日期:2015-12-14 接受日期:2016-03-12 出版日期:2016-04-20 发布日期:2016-05-11
  • 通讯作者: 沈泽昊
  • 基金资助:
    国家自然科学基金(41371190)和交通运输部西部计划项目(2008 318 799 17)

Quantitative classification and environmental interpretations for the structural differentiation of the plant communities in the dry valley of Jinshajiang River

Ye Liu1, Yue Xu2, Songlin Shi3, Peihao Peng4, Zehao Shen2,*()   

  1. 1 School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055.
    2 Department of Ecology, College of Urban and Environmental Sciences, the Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871.
    3 State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085.
    4 College of Tourism and Urban-rural Planning, Chengdu University of Technology, Chengdu 610058
  • Received:2015-12-14 Accepted:2016-03-12 Online:2016-04-20 Published:2016-05-11
  • Contact: Shen Zehao

摘要:

植物群落的空间分异格局是异质生境条件下物种性状、种间相互作用等生态学过程共同作用的结果, 对其分析有助于深入理解群落构建进程。本文基于金沙江流域干旱河谷116个样点562个样方的植物群落调查数据, 采用自适应仿射传播聚类的方法进行群落数量分类, 运用莫兰特征向量地图, 和方差分解的方法对影响群落结构的空间和环境因子进行分析。结果表明: (1)自适应仿射传播聚类将金沙江干旱河谷的植物群落分为30组, 可归为7个植被型, 23个群系, 以稀树草原(30.0%)、暖性落叶阔叶灌丛(55.7%)为最主要的植被类型。(2)年均温和干燥指数是限制金沙江干旱河谷植物群落分布的主要环境因子。稀树草原、肉质灌丛、常绿阔叶灌丛是典型的干热河谷植被类型; 暖性落叶阔叶灌丛、常绿硬叶林是干暖河谷植被的优势类型; 暖性针叶林、落叶阔叶林则主要在干温河谷环境占优势。(3)纯环境因子可以解释群落物种组成变化的5.5%, 纯空间因子可以解释的物种组成变化为22.5%, 有空间结构的环境因子部分为6.6%, 未解释的部分为65.4%。在诸多环境因子中, 年均温及干燥指数的不同显示了不同群落生境的重要差异, 并显著影响到群落的分布格局。大尺度的空间因子则主要通过地理隔离对群落结构的差异产生影响。

关键词: 群落结构, 数量分类, 空间分异, 环境因子, 自适应仿射传播聚类, 莫兰特征向量地图, 方差分解

Abstract:

The structural differentiation of plant communities are associated with species traits, and interspecific interactions in heterogeneous environment. The comprehensive analysis of spatial variation in species assemblages may help infer processes shaping ecological communities. Based on field investigation of 116 sites and 562 sampling points in the dry valley of Jinshajiang River, combined with vegetation classification by adaptive-affinity propagation, we used Moran’s Eigenvector Maps and variation partitioning to quantify the effects of spatial and environmental factors on the community structure. The results showed that: (1) the plant communities were divided into 30 groups by Adaptive-AP, and classified into 7 vegetation types, 23 formations. Savanna (30.0%) and warm deciduous broadleaved thicket (55.7%) were the main vegetation types. (2) Mean annual temperature (MAT) and aridity index (k) are two dominant climate factors limiting the distribution of plant community types in the dry valley of Jinshajiang River. Savanna, succulent thicket and evergreen broadleaved thicket are dominant vegetation types in typical dry-hot valley. Warm deciduous broadleaved thicket and evergreen sclerophyllous forest are dominant in dry-warm valley. Warm needle-leaved forest and deciduous broadleaved forest are more adaptive to lower temperature. (3) The pure environmental fraction can explain 5.5% of the species composition variation, the pure broad-scale spatial fraction can explain 22.5% of the species composition variation, 6.6% can be explained by the fraction corresponding to broad-scale structured environment and the unexplainable part was 65.4%. Among all the factors, MAT and k indicated the critical difference among the community habitats, which has prominent impact on the change of community composition. The broad-scale spatial factors played an important role in shaping the community structure by geographic isolation.

Key words: community structure, vegetation classification, structural differentiation, environmental factor, adaptive-affinity propagation, Moran’s eigenvector maps, variation partitioning