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    刘学森, 李娜, 张雪云, 肖丽, 江律, 罗乐, 于超, 张启翔. 新疆单叶蔷薇居群表型变异及多样性研究[J]. 北京林业大学学报, 2024, 46(2): 51-61. DOI: 10.12171/j.1000-1522.20220525
    引用本文: 刘学森, 李娜, 张雪云, 肖丽, 江律, 罗乐, 于超, 张启翔. 新疆单叶蔷薇居群表型变异及多样性研究[J]. 北京林业大学学报, 2024, 46(2): 51-61. DOI: 10.12171/j.1000-1522.20220525
    Liu Xuesen, Li Na, Zhang Xueyun, Xiao Li, Jiang Lü, Luo Le, Yu Chao, Zhang Qixiang. Phenotypic variation and diversity of natural Rosa persica populations in Xinjiang of northwestern China[J]. Journal of Beijing Forestry University, 2024, 46(2): 51-61. DOI: 10.12171/j.1000-1522.20220525
    Citation: Liu Xuesen, Li Na, Zhang Xueyun, Xiao Li, Jiang Lü, Luo Le, Yu Chao, Zhang Qixiang. Phenotypic variation and diversity of natural Rosa persica populations in Xinjiang of northwestern China[J]. Journal of Beijing Forestry University, 2024, 46(2): 51-61. DOI: 10.12171/j.1000-1522.20220525

    新疆单叶蔷薇居群表型变异及多样性研究

    Phenotypic variation and diversity of natural Rosa persica populations in Xinjiang of northwestern China

    • 摘要:
      目的 单叶蔷薇为蔷薇属唯一的单叶物种,是月季育种中的重要材料,在我国主要分布于新疆北部地区,但由于人为的破坏和环境的改变导致其面临濒危灭绝的风险。本研究通过分析单叶蔷薇表型多样性,探究其表型变异规律,旨在为更好地保护和利用单叶蔷薇种质资源提供理论指导。
      方法 本研究以9个单叶蔷薇天然居群中270个单株为研究材料,对其19个表型性状数据进行收集,利用巢式方差分析、皮尔逊相关性分析、主成分分析和聚类分析方法,探究其表型变异规律与多样性水平。
      结果 (1)9个单叶蔷薇天然居群表型多样性水平较高,19个表型性状的变异系数和香农多样性指数均值分别为15.90%和2.031;9个居群的表型变异系数在10.32%(P4) ~ 13.19%(P8),表现出中等程度的变异,香农多样性指数在1.274(P5) ~ 1.825(P8)之间,其中P8居群呈现出较高的多样性水平。(2)19个表型性状在居群间和居群内均存在极显著性差异(P < 0.01),居群间的平均表型分化系数为41.23%,表型变异主要来源于居群内。(3)皮尔逊相关性分析发现,单叶蔷薇花径与叶面积、花瓣面积与花斑百分比等部分性状间存在显著相关性。(4)主成分分析共提炼出5个主成分,累计贡献率为80.463%,贡献率最大的2个主成分主要解释花与叶的性状。(5)聚类分析可将单叶蔷薇9个居群分为2类,第1类为大花类,包括P6、P7、P8;第2类为小花类,包括P1、P2、P3、P4、P5、P9。
      结论 新疆单叶蔷薇具有较高的表型多样性和变异水平,且变异的来源主要集中在居群内。

       

      Abstract:
      Objective Rosa persica, the only single-leaf species of Rosa, is an important material for rose breeding. It is mainly distributed in northern Xinjiang of northwestern China, but it is at risk of extinction due to human damage and environmental changes. This study analyzed the phenotypic diversity of Rosa persica and explored the rule of its phenotypic variation, aiming to provide theoretical guidance for better protection and utilization of Rosa persica germplasm resources.
      Method In this study, 270 individual plants from 9 natural populations of Rosa persica were used as research materials, and the data of 19 phenotypic traits were collected. Nested analysis of variance, Pearson correlation analysis, principal component analysis and cluster analysis were used to explore the phenotypic variation and diversity level of the plants.
      Result (1) The phenotypic diversity of 9 natural populations was higher, and the mean coefficient of variation and Shannon diversity index of 19 phenotypic traits was 15.90% and 2.031, respectively. The phenotypic coefficient of variation of 9 populations ranged from 10.32% (P4) to 13.19% (P8), showing moderate variation. The Shannon diversity index ranged from 1.274 (P5) to 1.825 (P8), and the P8 population showed a high diversity level. (2) The 19 phenotypic traits had significant differences between populations and within populations (P < 0.01). The average phenotypic differentiation coefficient between populations was 41.23%, and the phenotypic variation was mainly from within populations. (3) Pearson correlation analysis showed that there were significant correlations between flower diameter and leaf area, petal area and flower spot percentage. (4) A total of 5 principal components were extracted from the principal component analysis, with a cumulative contribution rate of 80.463%, and the two principal components with the largest contribution rate mainly explained the characteristics of flowers and leaves. (5) The 9 populations of Rosa persica could be divided into 2 groups by cluster analysis. The first group was large flower group, including P6, P7 and P8, and the second group was small flower group, including P1, P2, P3, P4, P5 and P9.
      Conclusion The phenotypic diversity and variation level of Rosa persica in Xinjiang are high, and the sources of variation are mainly concentrated in the population.

       

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