Kekkonen Mari, Mutanen Marko, Kaila Lauri, Nieminen Marko, Hebert Paul D N
Finnish Museum of Natural History, University of Helsinki, Zoology Unit, University of Helsinki, Helsinki, Finland; Biodiversity Institute of Ontario, University of Guelph, Guelph, Ontario, Canada.
Department of Genetics and Physiology, University of Oulu, Oulu, Finland.
PLoS One. 2015 Apr 7;10(4):e0122481. doi: 10.1371/journal.pone.0122481. eCollection 2015.
The accelerating loss of biodiversity has created a need for more effective ways to discover species. Novel algorithmic approaches for analyzing sequence data combined with rapidly expanding DNA barcode libraries provide a potential solution. While several analytical methods are available for the delineation of operational taxonomic units (OTUs), few studies have compared their performance. This study compares the performance of one morphology-based and four DNA-based (BIN, parsimony networks, ABGD, GMYC) methods on two groups of gelechioid moths. It examines 92 species of Finnish Gelechiinae and 103 species of Australian Elachistinae which were delineated by traditional taxonomy. The results reveal a striking difference in performance between the two taxa with all four DNA-based methods. OTU counts in the Elachistinae showed a wider range and a relatively low (ca. 65%) OTU match with reference species while OTU counts were more congruent and performance was higher (ca. 90%) in the Gelechiinae. Performance rose when only monophyletic species were compared, but the taxon-dependence remained. None of the DNA-based methods produced a correct match with non-monophyletic species, but singletons were handled well. A simulated test of morphospecies-grouping performed very poorly in revealing taxon diversity in these small, dull-colored moths. Despite the strong performance of analyses based on DNA barcodes, species delineated using single-locus mtDNA data are best viewed as OTUs that require validation by subsequent integrative taxonomic work.
生物多样性的加速丧失使得需要更有效的物种发现方法。用于分析序列数据的新型算法方法与迅速扩展的DNA条形码文库相结合,提供了一种潜在的解决方案。虽然有几种分析方法可用于划分操作分类单元(OTU),但很少有研究比较它们的性能。本研究比较了基于形态学的一种方法和基于DNA的四种方法(BIN、简约网络、ABGD、GMYC)在两组麦蛾科蛾类上的性能。它研究了由传统分类学划分的92种芬兰麦蛾亚科和103种澳大利亚巢蛾亚科。结果显示,对于这两个分类群,所有四种基于DNA的方法在性能上存在显著差异。巢蛾亚科的OTU数量范围更广,与参考物种的OTU匹配度相对较低(约65%),而麦蛾亚科的OTU数量更一致,性能更高(约90%)。当只比较单系物种时,性能有所提高,但分类群依赖性仍然存在。没有一种基于DNA的方法能与非单系物种正确匹配,但单例处理得很好。在揭示这些小型、颜色暗淡的蛾类的分类群多样性方面,形态物种分组的模拟测试表现很差。尽管基于DNA条形码的分析表现强劲,但使用单基因座线粒体DNA数据划分的物种最好被视为需要通过后续综合分类工作进行验证的OTU。