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草业学报 ›› 2012, Vol. 21 ›› Issue (1): 83-92.

• 研究论文 • 上一篇    下一篇

内蒙古短花针茅群落数量分类及环境解释

张庆1,牛建明1,2*,BUYANTUYEV Alexander2,丁勇3,康萨如拉1,王凤兰1,张艳楠1,杨艳1,韩砚君1   

  1. 1.内蒙古大学生命科学学院,内蒙古 呼和浩特 010021;
    2.中美生态、能源及可持续性科学内蒙古研究中心,内蒙古 呼和浩特 010021;
    3.中国农业科学院草原研究所,内蒙古 呼和浩特 010010
  • 出版日期:2012-02-20 发布日期:2012-02-20
  • 通讯作者: E-mail: jmniu2005@163.com
  • 作者简介:张庆(1981-),男,山东泰安人,讲师。
  • 基金资助:
    国家科技支撑计划课题(2011BAC07B01),国家自然科学基金(30760158,31060320)及高等学校博士学科点专项科研基金(20070126004)资助。

Ecological analysis and classification of Stipa breviflora communities in the Inner Mongolia region: the role of environmental factors

WANG Feng-lan1, ZHANG Yan-nan1, YANG Yan1, HAN Yan-jun1   

  1. 1.School of Life Sciences, Inner Mongolia University, Hohhot 010021, China;
    2.Sino-US Center for Conservation, Energy and Sustainability Science in Inner Mongolia, Hohhot 010021, China;
    3..Grassland Research Institute, Chinese Academy of Agricultural Sciences, Hohhot 010010, China
  • Online:2012-02-20 Published:2012-02-20

摘要: 群落分布格局与环境(环境因素、空间因素、生物因素)之间的关系一直以来便是生态学研究的热点。为了探讨环境对内蒙古短花针茅群落结构格局的影响,运用双向指示种分析法(two-way indicator species analysis, TWINSPAN)对内蒙古自治区202个短花针茅群落进行数量分类,并结合环境因子、空间因子(各3个)运用除趋势典范对应分析排序方法(detrended canonical correspondence analysis, DCCA)分析了环境因子、空间因子、环境因子与空间因子交互作用及其他因素对内蒙古短花针茅群落结构格局的影响。结果表明, 1)TWINSPAN将内蒙古202个短花针茅群落在第4级水平上分为16个群丛;2)DCCA前2排序轴集中了大部分信息(75.3%),第1,2排序轴分别突出反映了群落结构格局在热量、水分梯度上的变化,结合TWINSPAN划分的群丛类型构建了内蒙古短花针茅草原生态系列图式;3)因子分离分析表明,环境因子对群落结构格局的解释能力为 70.7%,其中26.5%为单纯环境因子引起,空间因子解释能力为55.6%,其中11.4%是独立于环境因子的,44.2%是环境因子和空间因子交互作用导致的,未能解释的部分达17.9%,结合其他研究有力地证明了“环境因素对植被的可解释程度是植被本身的复杂性决定的,植被越复杂,环境的可解释程度则越低”。

Abstract: Environmental characteristics, spatial heterogeneity, and biological interactions are key factors that affect ecological communities. We studied how environmental factors and spatial patterns influence the structure of Stipa breviflora communities in the Inner Mongolia region. First, we used the two-way indicator species analysis (TWINSPAN) algorithm to summarize variation in species composition and group 202 study plots. Second, we investigated the effects of three environmental and three spatial factors, together with their interaction, on the structure of S. breviflora communities by using detrended canonical correspondence analysis (DCCA). 1) The target plant communities were classified into 16 groups at the fourth level of division by TWINSPAN; 2) The first two DCCA axes, which corresponded to gradients of temperature and precipitation, respectively, explained most of the variation in community structure (75.3%). Based on these results different S. breviflora communities were arranged into an ecological series; 3) When analysed together, 70.7% of the total variance in S. breviflora community structure was explained by all environmental factors and 55.6% by all spatial factors. When these factors were examined separately, only 29.5% and 11.4% of the total variance were explained respectively, while 44.2% was simultaneously explained by the two groups of factors, and 17.9% was explained by other undetermined factors. Our study contributes to other researchers’ findings and strongly supports the conclusion that the degree to which environmental factors control the structure and spatial distribution of plant communities is determined by complexity of the vegetation pattern. The explanatory power of plant community structure by environmental factors decreases with the increase in complexity of vegetation cover.

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