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百合属5个野生种及7个栽培品种花粉形态的观察
顾 欣1, 张延龙1
西北农林科技大学 林学院
摘要:
【目的】明确百合属植物花粉形态特征及不同野生种与栽培品种间的亲缘关系。【方法】利用扫描电镜,观察百合属5个野生种和7个栽培品种的花粉形态,并根据花粉形态的定量特征,对这12个种和品种进行聚类分析。【结果】12种百合属植物的花粉形状呈超长球形或长球形,外壁纹饰均为单基柱网纹,单萌发沟,但在网脊宽度、网眼大小及内部突起等方面存在一定的差异。聚类分析表明,卷瓣组的‘川百合’(Lilium davidii)和‘山丹’(Lilium pumilum)聚为第1类群;卷瓣组的‘卷丹’(Lilium lancifolium),百合组的‘宜昌百合’(Lilium leucanthum),亚洲百合系的‘精粹’(Elite),OT杂交系的‘黄精灵’(Yelloween)、‘塞拉诺’(Serrano)、‘木门’(Concador)聚为第2类群;百合组的‘野百合’(Lilium brownii),东方百合系的‘索邦’(Sorbonne)、‘西伯利亚’(Siberia)和‘马龙’(Marion)聚为第3类群。【结论】花粉形态的定量特征可以作为百合野生种及栽培品种的分类依据,分类结果总体支持形态学的分类结果,但并不完全一致。
关键词:  百合  花粉形态  扫描电镜  聚类分析
DOI:
分类号:
基金项目:公益性行业(农业)科研专项(200903020);陕西省林业厅项目(陕林计字[2011]70号)
Pollen morphology observation of five wild and seven cultivated lily species
Abstract:
【Objective】Pollen morphology of lily species and genetic relationship between wild and cultivated species were studied.【Method】Five wild and seven cultivated lily species were examined by scanning electron microscopy (SEM),and clustering analysis was carried out based on quantitative characteristics of pollen morphology of these species.【Result】The shapes of 12 lily species were either spheroidal or super-spheroidal,and the surface ornamentation were netted simplicolumellate and single pollen grains.There were differences in mesh ridge widths,mesh sizes and protuberances.Clustering analysis showed that Lilium davidii and Lilium pumilum,which belonged to sect.Sinomartagon,clustered in the first group.The second group included Lilium lancifolium in sect.Sinomartagon,Lilium leucanthum in sect.Leucolirion,Elite from the Asiatic hybrids,Yelloween,Serrano and Concador of the Oriental×Trumpet hybrids.Lilium brownii in sect.Leucolirion,Sorbonne,Siberia and Marion involved in the Asiatic hybrids belonged to the third group.【Conclusion】Quantitative characteristics of pollen morphology could be used in the classification of lily species and cultivars,and the results were supportive to morphological classification with certain inconsistence.
Key words:  lily  pollen characters  scanning electron microscopy (SEM)  clustering analysis