The proliferation of DNA data is revolutionizing all fields of systematic research. DNA barcode sequences, now available for millions of specimens and several hundred thousand species, are increasingly used in algorithmic species delimitations. This is complicated by occasional incongruences between species and gene genealogies, as indicated by situations where conspecific individuals do not form a monophyletic cluster in a gene tree. In two previous reviews, non-monophyly has been reported as being common in mitochondrial DNA gene trees. We developed a novel web service “Monophylizer” to detect non-monophyly in phylogenetic trees and used it to ascertain the incidence of species non-monophyly in COI (a.k.a. cox1) barcode sequence data from 4977 species and 41,583 specimens of European Lepidoptera, the largest data set of DNA barcodes analyzed from this regard. Particular attention was paid to accurate species identification to ensure data integrity. We investigated the effects of tree-building method, sampling effort, and other methodological issues, all of which can influence estimates of non-monophyly. We found a 12% incidence of non-monophyly, a value significantly lower than that observed in previous studies. Neighbor joining (NJ) and maximum likelihood (ML) methods yielded almost equal numbers of non-monophyletic species, but 24.1% of these cases of non-monophyly were only found by one of these methods. Non-monophyletic species tend to show either low genetic distances to their nearest neighbors or exceptionally high levels of intraspecific variability. Cases of polyphyly in COI trees arising as a result of deep intraspecific divergence are negligible, as the detected cases reflected misidentifications or methodological errors. Taking into consideration variation in sampling effort, we estimate that the true incidence of non-monophyly is ∼23%, but with operational factors still being included. Within the operational factors, we separately assessed the frequency of taxonomic limitations (presence of overlooked cryptic and oversplit species) and identification uncertainties. We observed that operational factors are potentially present in more than half (58.6%) of the detected cases of non-monophyly. Furthermore, we observed that in about 20% of non-monophyletic species and entangled species, the lineages involved are either allopatric or parapatric—conditions where species delimitation is inherently subjective and particularly dependent on the species concept that has been adopted. These observations suggest that species-level non-monophyly in COI gene trees is less common than previously supposed, with many cases reflecting misidentifications, the subjectivity of species delimitation or other operational factors.

This study examines the performance of DNA barcodes (mt cytochrome c oxidase 1 gene) in the identification of 1004 species of Lepidoptera shared by two localities (Finland, Austria) that are 1600 km apart. Maximum intraspecific distances for the pooled data were less than 2% for 880 species (87.6%), while deeper divergence was detected in 124 species. Despite such variation, the overall DNA barcode library possessed diagnostic COI sequences for 98.8% of the taxa. Because a reference library based on Finnish specimens was highly effective in identifying specimens from Austria, we conclude that barcode libraries based on regional sampling can often be effective for a much larger area. Moreover, dispersal ability (poor, good) and distribution patterns (disjunct, fragmented, continuous, migratory) had little impact on levels of intraspecific geographic divergence. Furthermore, the present study revealed that, despite the intensity of past taxonomic work on European Lepidoptera, nearly 20% of the species shared by Austria and Finland require further work to clarify their status. Particularly discordant BIN (Barcode Index Number) cases should be checked to ascertain possible explanatory factors such as incorrect taxonomy, hybridization, introgression, and Wolbachia infections.

BackgroundThe geometrid moths of Europe are one of the best investigated insect groups in traditional taxonomy making them an ideal model group to test the accuracy of the Barcode Index Number (BIN) system of BOLD (Barcode of Life Datasystems), a method that supports automated, rapid species delineation and identification.Methodology/Principal FindingsThis study provides a DNA barcode library for 219 of the 249 European geometrid moth species (88%) in five selected subfamilies. The data set includes COI sequences for 2130 specimens. Most species (93%) were found to possess diagnostic barcode sequences at the European level while only three species pairs (3%) were genetically indistinguishable in areas of sympatry. As a consequence, 97% of the European species we examined were unequivocally discriminated by barcodes within their natural areas of distribution. We found a 1:1 correspondence between BINs and traditionally recognized species for 67% of these species. Another 17% of the species (15 pairs, three triads) shared BINs, while specimens from the remaining species (18%) were divided among two or more BINs. Five of these species are mixtures, both sharing and splitting BINs. For 82% of the species with two or more BINs, the genetic splits involved allopatric populations, many of which have previously been hypothesized to represent distinct species or subspecies. Conclusions/SignificanceThis study confirms the effectiveness of DNA barcoding as a tool for species identification and illustrates the potential of the BIN system to characterize formal genetic units independently of an existing classification. This suggests the system can be used to efficiently assess the biodiversity of large, poorly known assemblages of organisms. For the moths examined in this study, cases of discordance between traditionally recognized species and BINs arose from several causes including overlooked species, synonymy, and cases where DNA barcodes revealed regional variation of uncertain taxonomic significance.

Many cold adapted species occur in both montane settings and in the subarctic. Their disjunct distributions create taxonomic complexity because there is no standardized method to establish whether their allopatric populations represent single or different species. This study employs DNA barcoding to gain new perspectives on the levels and patterns of sequence divergence among populations of 122 arctic-alpine species of Lepidoptera from the Alps, Fennoscandia and North America. It reveals intraspecific variability in the barcode region ranging from 0.00–10.08%. Eleven supposedly different species pairs or groups show close genetic similarity, suggesting possible synonymy in many cases. However, a total of 33 species show evidence of cryptic diversity as evidenced by the presence of lineages with over 2% maximum barcode divergence in Europe, in North America or between the two continents. Our study also reveals cases where taxonomic names have been used inconsistently between regions and exposes misidentifications. Overall, DNA barcodes have great potential to both increase taxonomic resolution and to make decisions concerning the taxonomic status of allopatric populations more objective.

The study of global biodiversity will greatly benefit from access to comprehensive DNA barcode libraries at continental scale, but such datasets are still very rare. Here, we assemble the first high-resolution reference library for European butterflies that provides 97% taxon coverage (459 species) and 22,306 COI sequences. We estimate that we captured 62% of the total haplotype diversity and show that most species possess a few very common haplotypes and many rare ones. Specimens in the dataset have an average 95.3% probability of being correctly identified. Mitochondrial diversity displayed elevated haplotype richness in southern European refugia, establishing the generality of this key biogeographic pattern for an entire taxonomic group. Fifteen percent of the species are involved in barcode sharing, but two thirds of these cases may reflect the need for further taxonomic research. This dataset provides a unique resource for conservation and for studying evolutionary processes, cryptic species, phylogeography, and ecology.