Data from: "Testing the potential of DNA barcoding in vertebrate radiations: the case of the littoral cichlids (Pisces, Perciformes, Cichlidae) from Lake Tanganyika" in Genomic Resources Notes accepted XXXX 2016 to XXXX 2016

We obtained 398 COI barcodes of 96 morphospecies of Lake Tanganyika (LT) cichlids from the littoral zone. The potential of DNA barcoding in these fishes was tested using both species identification and species delineation methods. The best match (BM) and best close match (BCM) methods were used to evaluate the overall identification success. For this, three libraries were analysed in which the specimens were categorised into Operational Taxonomic Units (OTU) in three alternative ways: A) morphologically distinct, including undescribed, species, B) valid species and C) complexes of morphologically similar or closely related species. For libraries A, B and C, 73, 73 and 96% (BM) and 72, 70 and 94% (BCM) of the specimens were correctly identified. Additionally, the potential of two species delineation methods was tested. The General Mixed Yule Coalescent (GMYC) analysis suggested 70 hypothetical species, while the Automatic Barcode Gap Discovery (ABGD) method revealed 115 putative species. Although the ABGD method had a tendency to over-split, it outperformed the GMYC analysis in retrieving the species. In most cases where ABGD suggested over-splitting, this was due to intraspecific geographical variation. The failure of the GMYC method to retrieve many species could be attributed to discrepancies between mitochondrial gene trees and the evolutionary histories of LT cichlid species. Littoral LT cichlids have complex evolutionary histories that include instances of hybridisation, introgression and rapid speciation. Nevertheless, although the utility of DNA barcoding in identification is restricted to the level of complexes, it has potential for species discovery in cichlid radiations.我们从坦噶尼喀湖（Lake Tanganyika, LT）沿岸带的慈鲷科鱼类中，获取了96个形态种（morphospecies）共计398条COI条形码（COI barcode）。本研究采用物种鉴定与物种划分方法，评估了DNA条形码在该类群鱼类中的应用潜力。我们使用最佳匹配法（best match, BM）与最佳近缘匹配法（best close match, BCM）来评估整体鉴定成功率，为此构建了三类样本库，均以三种不同方式将样本划分为操作分类单元（Operational Taxonomic Units, OTU）：A）包含未描述物种在内的形态学独立物种；B）有效命名物种；C）形态相似或亲缘关系密切的物种复合群。针对A、B、C三类样本库，BM法的正确鉴定率分别为73%、73%与96%，BCM法则分别为72%、70%与94%。此外，本研究还测试了两种物种划分方法的应用潜力。广义混合约尔-溯祖模型（General Mixed Yule Coalescent, GMYC）分析结果显示存在70个假定物种，而自动条形码间隙发现法（Automatic Barcode Gap Discovery, ABGD）则揭示了115个推测物种。尽管ABGD法存在过度拆分的倾向，但其在物种检索效果上优于GMYC分析。在ABGD法出现过度拆分的多数案例中，该现象均由种内地理变异所致。GMYC法未能检索到多数物种的原因，可归因于线粒体基因树与坦噶尼喀湖慈鲷物种演化历史之间的不一致性。坦噶尼喀湖沿岸带慈鲷拥有复杂的演化历史，其中包括杂交、基因渐渗与快速物种形成事件。尽管如此，尽管DNA条形码在物种鉴定方面的应用局限于物种复合群层面，但其在慈鲷辐射演化类群的物种发现中仍具备应用潜力。