Morphological identification and COI barcodes of adult flies help determine species identities of chironomid larvae (Diptera, Chironomidae)

Abstract

AbstractEstablishing reliable methods for the identification of benthic chironomid communities is important due to their significant contribution to biomass, ecology and the aquatic food web. Immature larval specimens are more difficult to identify to species level by traditional morphological methods than their fully developed adult counterparts, and few keys are available to identify the larval species. In order to develop molecular criteria to identify species of chironomid larvae, larval and adult chironomids from Western Lake Erie were subjected to both molecular and morphological taxonomic analysis. Mitochondrial cytochrome c oxidase I (COI) barcode sequences of 33 adults that were identified to species level by morphological methods were grouped with COI sequences of 189 larvae in a neighbor-joining taxon-ID tree. Most of these larvae could be identified only to genus level by morphological taxonomy (only 22 of the 189 sequenced larvae could be identified to species level). The taxon-ID tree of larval sequences had 45 operational taxonomic units (OTUs, defined as clusters with >97% identity or individual sequences differing from nearest neighbors by >3%; supported by analysis of all larval pairwise differences), of which seven could be identified to species or ‘species group’ level by larval morphology. Reference sequences from the GenBank and BOLD databases assigned six larval OTUs with presumptive species level identifications and confirmed one previously assigned species level identification. Sequences from morphologically identified adults in the present study grouped with and further classified the identity of 13 larval OTUs. The use of morphological identification and subsequent DNA barcoding of adult chironomids proved to be beneficial in revealing possible species level identifications of larval specimens. Sequence data from this study also contribute to currently inadequate public databases relevant to the Great Lakes region, while the neighbor-joining analysis reported here describes the application and confirmation of a useful tool that can accelerate identification and bioassesment of chironomid communities.