摘要
选择44个在杜鹃分类中的重要性状用SPSS10.0进行分析,其中二元性状18个、多元性状13个、数量性状13个。Q聚类采用平均欧氏距离平方系数,R聚类采用相关系数距离,聚类方法采用类间平均链锁法。Q聚类结果将46种杜鹃花属植物分为特征明显的2大类群,即有鳞类杜鹃和无鳞的常绿类杜鹃,在2大类群下的聚类结果与传统的形态分类基本吻合,但美容杜鹃和汶川星毛杜鹃的亚组归属值得商榷。R聚类结果表明性状被分为明显不同的组,鳞片出现部位、花器构造、外部器官尺寸等性状群内有较强的相关关系。主成分分析中,前3个主成分累积贡献率达60.39%,表明杜鹃分类中存在重要性状,主要是有无鳞片、有无腺体和毛被以及叶、花、果的大小等;对分类贡献不大的性状主要有雄蕊数、萼片是否具毛、萼裂片长短、花冠颜色、花丝基部毛、花冠毛等。主成分分析结果与Q聚类结果基本一致,揭示在杜鹃分类时应选择重要性状,避免过多次要性状的干扰。
The mathematic classification of 46 species in Rhododendron was studied with 44 morphologic characters, including 18 dualistic characters, 13 multi-characters and 13 quantitative characters, by using SPSS（10.0. The squared Euclidean distance coefficient was used in case clustering and the Pearson correlation was used in variable clustering by within-groups linkage. The 46 species of Rhododendron were divided into two caboodles by case clustering： one was lepidote and the other was evergreen. The different groups were formed under two caboodles and the groups were similar to the traditional classification except R. calophytum and R. asterochnoum. The results of variable clustering showed that the various characters were decided in different groups. Some characters had strong correlativity within a group, such as the position of squama, flower formation, the size of external organs. In principal component analyses （PCA）, the accumulative contribution of the first three principal components was up to 60.9 %, which showed there were some representative characters which could be used in classification of Rhododendron, such as squama, gland, hair and the size of leaves, flowers and fruits. However, the number of stamen, hair on sepal, size of sepal, color of crown, hair on lower silk and hair on crown etc were not so reliable for the classification. The results of PCA were consistent with that in case clustering, which suggested that we should pay more attention to choose characters in classifying.
出处
《林业科学》
EI
CAS
CSCD
北大核心
2009年第8期67-75,共9页
Scientia Silvae Sinicae
关键词
杜鹃花属
数量分类
主成分分析
聚类分析
Rhododendron
mathematic classification
principal component analyses （PCA）
cluster analysis
