Abstract
Chironomids are one of the most biodiverse and abundant members of freshwater ecosystems. They are a food source for many organisms, including fish and water mites. The accurate identification of chironomids is essential for many applications in ecological research, including determining which chironomid species are present in the diets of diverse predators. Larval and adult chironomids from diverse habitats, including lakes, rivers, inland gardens, coastal vegetation, and nearshore habitats of the Great Lakes, were collected from 2012 to 2019. After morphological identification of chironomids, DNA was extracted and cytochrome oxidase I (COI) barcodes were PCR amplified and sequenced. Here we describe an analysis of biodiverse adult and larval chironomids in the Great Lakes region of North America based on new collections to improve chironomid identification by curating a chironomid DNA barcode database, thereby expanding the diversity and taxonomic specificity of DNA reference libraries for the Chironomidae family. In addition to reporting many novel chironomid DNA barcodes, we demonstrate here the use of this chironomid COI barcode database to improve the identification of DNA barcodes of prey in the liquefied diets of water mites. The species identifications of the COI barcodes of chironomids ingested by Lebertia davidcooki and L. quinquemaculosa are more diverse for L. davidcooki and include Parachironomus abortivus, Cryptochironomus ponderosus. Parachironomus tenuicaudatus, Glyptotendipes senilis, Dicrotendipes modestus, Chironomus riparius, Chironomus entis/plumosus, Chironomus maturus, Chironomus crassicaudatus, Endochironomus subtendens, Cricotopus sylvestris, Cricotopus festivellus, Orthocladius obumbratus, Tanypus punctipennis, Rheotanytarsus exiguus gr., and Paratanytarsus nr. bituberculatus.
Funder
National Institutes of Health
Sharon L. Ram Aquatic Sciences Fund
Environmental Protection Agency
Subject
Nature and Landscape Conservation,Agricultural and Biological Sciences (miscellaneous),Ecological Modeling,Ecology
Cited by
7 articles.
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