Identifying species of moths (Lepidoptera) from Baihua Mountain, Beijing, China, using DNA barcodes

Abstract

DNA barcoding has become a promising means for the identification of organisms of all life-history stages. Currently, distance-based and tree-based methods are most widely used to define species boundaries and uncover cryptic species. However, there is no universal threshold of genetic distance values that can be used to distinguish taxonomic groups. Alternatively, DNA barcoding can deploy a "character-based" method, whereby species are identified through the discrete nucleotide substitutions. Our research focuses on the delimitation of moth species using DNA-barcoding methods. We analyzed 393 Lepidopteran specimens belonging to 80 morphologically recognized species with a standard cytochrome c oxidase subunit I (COI) sequencing approach, and deployed tree-based, distance-based, and diagnostic character-based methods to identify the taxa. The tree-based method divided the 393 specimens into 79 taxa (species), and the distance-based method divided them into 84 taxa (species). Although the diagnostic character-based method found only 39 so-identifiable species in the 80 species, with a reduction in sample size the accuracy rate substantially improved. For example, in the Arctiidae subset, all 12 species had diagnostics characteristics. Compared with traditional morphological method, molecular taxonomy performed well. All three methods enable the rapid delimitation of species, although they have different characteristics and different strengths. The tree-based and distance-based methods can be used for accurate species identification and biodiversity studies in large data sets, while the character-based method performs well in small data sets and can also be used as the foundation of species-specific biochips.

Keywords: Bayesian; DNA barcode; Lepidoptera; cytochrome c oxidase subunit I; diagnostic character; genetic distance; maximum likelihood; moths; neighbor joining.

Figure 1

Phylogenetic trees (N-J) of 58 Arctiidae moth specimens. Clades with different colors indicate different species. Numbers above branches indicate bootstrap values (100 not shown). The data set consists of 12 Arctiidae species from 55 specimens. All sequences cluster into monophyletic groups and these monophyletic groups and morphological identifications are consistent. These groups were also devised by ABGD software and are consistent with morphological data. Every morphological species has 2–17 diagnostic characters. All methods permit excellent assignment of species. ^aCode of sequence is the serial number of specimens, and different colors indicate different clustering relationships. ^bMonophyletic species obtained by the Tree-based method (N-J tree). ^cGroups obtained by the Distance-based method (ABGD Software). ^dNumbers of diagnostic characters obtained by the Character-based method.

Figure 2

Phylogenetic trees (N-J) of 55 Noctuidae moth specimens. Clades with different colors indicate different species. Numbers above branches indicate bootstrap values (100 not shown).As shown in the figure, the data set consists of 18 Noctuidae species from 58 specimens. All sequences cluster into a monophyletic groups and morphological identification are consistent. These groups were also devised by ABGD software and are consistent with morphological data. Excepting Niphonyx segregate which did not have any diagnostic characters, every morphological species has 2–8 diagnostic characters. All methods permit excellent to very good assignment of species.^aCode of sequence is these specimens serial number and different colors indicate different clustering relationship by N-J tree. ^bthese monophyletic species obtained by Tree-based method (N-J tree). ^cthese groups obtained by Distance-based method (ABGD Software). ^dthese diagnostic character obtained by Character-based method.

Figure 3

DNA barcoding gap analysis of COI gene sequence. (A) Full data set; (B) Arctiidae data set; (C) Noctuidae data set.

Figure 4

The automatic partition results by ABGD. (A1) Initial partition with the full data set; (B1) Initial partition with the Arctiidae data set; (C1) Initial partition with the Noctuidae data set; (A2) Recursive partition with the full data set; (B2) Recursive partition with the Arctiidae data set; (C2) Recursive partition with the Noctuidae data set; ^aP is prior intraspecific divergence (P).