Zahiri, R. et al., 2014. A transcontinental challenge--a test of DNA barcode performance for 1,541 species of Canadian Noctuoidea (Lepidoptera). PloS one, 9(3), pp. e92797

This study provides a first, comprehensive, diagnostic use of DNA barcodes for the Canadian fauna of noctuoids or "owlet" moths (Lepidoptera: Noctuoidea) based on vouchered records for 1,541 species (99.1% species coverage), and more than 30,000 sequences. When viewed from a Canada-wide perspective, DNA barcodes unambiguously discriminate 90% of the noctuoid species recognized through prior taxonomic study, and resolution reaches 95.6% when considered at a provincial scale. Barcode sharing is concentrated in certain lineages with 54% of the cases involving 1.8% of the genera. Deep intraspecific divergence exists in 7.7% of the species, but further studies are required to clarify whether these cases reflect an overlooked species complex or phylogeographic variation in a single species. Non-native species possess higher Nearest-Neighbour (NN) distances than native taxa, whereas generalist feeders have lower NN distances than those with more specialized feeding habits. We found high concordance between taxonomic names and sequence clusters delineated by the Barcode Index Number (BIN) system with 1,082 species (70%) assigned to a unique BIN. The cases of discordance involve both BIN mergers and BIN splits with 38 species falling into both categories, most likely reflecting bidirectional introgression. One fifth of the species are involved in a BIN merger reflecting the presence of 158 species sharing their barcode sequence with at least one other taxon, and 189 species with low, but diagnostic COI divergence. A very few cases (13) involved species whose members fell into both categories. Most of the remaining 140 species show a split into two or three BINs per species, while Virbia ferruginosa was divided into 16. The overall results confirm that DNA barcodes are effective for the identification of Canadian noctuoids. This study also affirms that BINs are a strong proxy for species, providing a pathway for a rapid, accurate estimation of animal diversity.