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• 技术与方法 • 上一篇    

黄河三角洲湿地草本植被的双变量主坐标排序

熊雄;贺强;崔保山   

  1. 北京师范大学环境学院 环境模拟与污染控制国家重点联合实验室, 北京 100
    875
  • 收稿日期:2007-12-24 修回日期:1900-01-01 出版日期:2008-09-10 发布日期:2008-09-10

Double principal coorclinate analysis of herbaceous vegetation in wetlands of the Yellow River Delta, China.

XIONG Xiong;HE Qiang;CUI Bao-shan   

  1. State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
  • Received:2007-12-24 Revised:1900-01-01 Online:2008-09-10 Published:2008-09-10

摘要: 利用双向指示种(TWINSPAN)分类技术将黄河三角洲湿地草本植被划分为7个群落类型, 然后应用双变量主坐标分析(double principal coordinate analysis, DPCoA)法对其进行排序, 结果表明: 在物种组成上,芦苇+盐地碱蓬群落为芦苇群落和盐地碱蓬+芦苇群落、盐地碱蓬群落、盐地碱蓬+补血草+碱蓬群落、补血草群落的过渡类型,而芦苇+穗状狐尾藻群落与其他群落类型差异较大;黄河三角洲湿地草本植被的分布主要与土壤因子中的土壤盐分、土壤pH等紧密相关,而与土壤全磷、全氮、有机质等养分无显著相关关系。将DPCoA和其他一些常用的植被排序方法进行了比较,相对于主分量分析(PCA)和除趋势对应分析(DCA)而言,DPCoA信息保留量更高,能够将物种组成和类别上较为接近的植物群落聚集在一起,而将差异较大的植物群落在排序图中分散开来,在揭示群落间相互关系以及植被与环境之间关系上可能更为有效。

关键词: 植物多酚, 性质, 农业, 环境, 应用

Abstract: The herbaceous vegetation in wetlands of the Yellow River Delta, China was classified into 7 community types by TWINSPAN. Double principal coordinate analysis (DPCoA) was then applied for the ordination of the communities. The results from DPCoA ordination indicated that in species composition, Ass. Phragmites australis+ Suaeda salsa was a transitional type of Ass. Phragmites australis and Ass.Suaeda salsa+Phragmites australis, Ass. Suaeda salsa, Ass. Suaeda salsa+Limonium sinense+Suaeda glauca, and Ass. Limonium sinense, while Ass. Phragmites australis+Myriophyllum spicatum showed a great difference from others. The distribution of different community types was significantly correlated with soil salinity and soil pH, but not significantly correlated with soil total phosphorus, total nitrogen, and organic matter. Compared with principal component analysis (PCA) and detrended correspondence analysis (DCA), DPCoA may keep more accumulative information from the original data. It congregated the communities in the ordination chart which had fewer differences in their species composition and species taxonomy, but dispersed the communities which had greater differences in their species composition and species taxonomy. Therefore, DPCoA may be more efficient in investigating the structural relationships between different commnnty types and their relationships to environmental variables.

Key words: Plant polyphenols, Property, Agriculture, Environment, Application