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当前位置: 首页 > 文章 > 以雾灵山夜蛾科为材料评估进化模型对DNA条码分类的影响 昆虫学报 2012,55 (10) 1193-1204
Position: Home > Articles > Effects of different evolution models on DNA barcoding evaluated with the Noctuidae from Wuling Mountain, Hebei, northern China Acta Entomologica Sinica 2012,55 (10) 1193-1204

以雾灵山夜蛾科为材料评估进化模型对DNA条码分类的影响

作  者：
迟美妍;韩辉林;高强;杨聪慧;金倩;李俊;陈付强;武春生;张爱兵
单  位：
东北林业大学林学院;首都师范大学生命科学学院;中国科学院动物研究所动物进化与系统学重点实验室
关键词：
鳞翅目;夜蛾科;COI基因;进化模型;DNA条形码;系统发育树;物种鉴定
摘  要：
为了探究进化模型对DNA条形码分类的影响,本研究以雾灵山夜蛾科44个种的标本为材料,获得COI基因序列。使用邻接法(neighbor-joining)、最大简约法(maximum parsimony)、最大似然法(maximum likelihood)以及贝叶斯法(Bayesian inference)构建系统发育树,并且对邻接法的12种模型、最大似然法的7种模型、贝叶斯法的2种模型进行模型成功率的评估。结果表明,邻接法的12种模型成功率相差不大,较稳定;最大似然法及贝叶斯法的不同模型成功率存在明显差异,不稳定;最大简约法不基于模型,成功率比较稳定。邻接法及最大似然法共有6种相同的模型,这6种模型在不同的方法中成功率存在差异。此外,分子数据中存在单个物种仅有一条序列的情况,显著降低了模型成功率,表明在DNA条形码研究中,每个物种需要有多个样本。
译  名：
Effects of different evolution models on DNA barcoding evaluated with the Noctuidae from Wuling Mountain, Hebei, northern China
作  者：
CHI Mei-Yan1, HAN Hui-Lin2, GAO Qiang1, YANG Cong-Hui1, JIN Qian1, LI Jun1, CHEN Fu-Qiang3, WU Chun-Sheng3, ZHANG Ai-Bing1,*(1. College of Life Sciences, Capital Normal University, Beijing 100048, China; 2. Forestry College, Northeast Forestry University, Harbin 150040, China; 3. Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China)
关键词：
Lepidoptera; Noctuidae; COI gene; evolution model; DNA barcoding; phylogenetic trees; species identification
摘  要：
The effects of evolution models on success rates of species identification via DNA barcoding were evaluated based on specimens of 44 Noctuidae species collected from Wuling Mountain, Hebei, northern China. Different phylogenetic tree reconstruction methods, including the neighbor-joining, the maximum parsimony, the maximum likelihood and the Bayesian inference, were used to reconstruct phylogenetic trees based on the COI gene sequences of these species, and success rates of different models (12 models for the neighbor-joining, 7 models for the maximum likelihood method, and 2 models for the Bayesian inference) were evaluated systematically. The results showed that there were no significant differences in success rates of species identification among 12 different models for the neighbor-joining method, but significant differences in success rates were found among different evolution models for both maximum likelihood and Bayesian methods. As a comparison, the maximum parsimony method which is not based on evolution models obtained stable success rates of species identification. The 6 evolutionary models shared by the neighbor-joining and maximum likelihood methods obtained different success rates between these two methods. In addition, we found that singletons in the database largely reduced success rates of species identification, suggesting that deep intraspecific sampling be required in the future studies of DNA barcoding.

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