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当前位置: 首页 > 文章 > 青海高原西伯利亚白刺天然居群表型多样性研究 北方园艺 2013 (9) 64-67
Position: Home > Articles > Phenotypic Diversity for Natural Populations of Nitraria sibirica Pall.in Qinghai Plateau Northern Horticulture 2013 (9) 64-67

青海高原西伯利亚白刺天然居群表型多样性研究

作  者：
张亚琦;王宁;赵越;孙奎;邹林有;李孝繁
单  位：
青海省农林科学院
关键词：
西伯利亚白刺;天然居群;表型多样性
摘  要：
以青海高原5个西伯利亚白刺天然居群为研究对象,对其8个表型性状进行了方差分析、相关分析、主成分分析与聚类分析,探讨了居群间与居群内表型多样性的变异特点,以揭示其天然居群的表型变异程度及变异规律.结果表明:各性状在居群间存在显著差异,居群间的变异是青海高原西伯利亚白刺表型总变异的主要部分.相关性分析表明,各环境因子中,年均降水量对性状的影响最大,其次为日照时数和年均温度.主成分分析表明,果形系数、核形系数、果序重、鲜果百粒重等性状是主要影响因素.聚类分析显示,西伯利亚白刺的5个天然居群可以初步分为2类,第1类为宗加居群、夏日哈居群、诺木洪居群,第2类为可鲁克湖居群与戈壁车站居群.
译  名：
Phenotypic Diversity for Natural Populations of Nitraria sibirica Pall.in Qinghai Plateau
单  位：
Academy of Agricultural and Forestry Sciences,Qinghai University,Xining,Qinghai 810016
关键词：
Nitraria sibirica Pall.%natural population%phenotypic diversity
摘  要：
Taking phenotypic variation of Nitraria sibirica Pall.in 5 natural populations form Qinghai Plateau as objective,the variance analysis,correlation analysis,principal component analysis and cluster analysis of 8 phenotypic traits in 5 natural populations of Nitraria sibirica Pall.were studied.The relationship between phenotypic variation of the natural population and different distribution areas were discussed.The results showed that there was significant difference in morphologic chatacteristics between the populations,the variation between the populations was mainly due to the phenotypic variation.The correlation analysis between the phenotypic traits of Nitraria sibirica Pall.and geography factor showed that the mean annual precipitation was the most important factor that affected the morphologic characteristics,followed by the annual sunlight and the mean annual temperature.Principal component analysis showed that the main effect factors of phenotypic traits were coefficient of fruit shape,coefficient of seed shape,drupe number per fruiting-cyme and fresh fruit 100-fruit weight.Cluster analysis showed that five natural populations of Nitraria sibirica Pall.could be divided into 2 groups by SPSS cluster analysis.

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