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DNA barcoding vs. morphological identification of...
Journal article

DNA barcoding vs. morphological identification of larval fish and embryos in Lake Huron: Advantages to a molecular approach

Abstract

The Great Lakes provide habitat to over 160 species of freshwater fish, many of which are ecologically and economically important. Concern for management and conservation of declining fish populations makes it important that accurate identification techniques are used for environmental monitoring programs. DNA barcoding may be an effective alternative to morphological identification for industrial monitoring programs of larval and embryonic fish, but comparisons of the two approaches with species from the Great Lakes are limited. It may be particularly important to examine this issue in the Great Lakes because a relatively young group of post-glacial fish species are present which may be difficult to resolve using morphology or genetics. Six hundred and fifty seven larval fish were identified from Lake Huron (Ontario, Canada), using morphology and DNA barcoding. DNA barcoding was used to identify 103 embryos that morphology could not identify. Morphological identification and DNA barcoding had a percent similarity of 76.9%, 96.6% and 96.6% at the species, genus, and family levels, respectively. Thirty-seven specimens were damaged and unidentifiable using morphology; 35 of these were successfully identified using DNA barcoding. However, 23 other specimens produced no PCR product for barcoding using 2 different primer sets. Discrepancies between morphology and DNA barcoding were driven by two major factors: inability of cytochrome oxidase I to resolve members of the genus Coregonus and limited resolution of morphological features for Catostomus. Both methods have pros and cons; however, DNA barcoding is more cost-effective and efficient for industrial monitoring programs.

Authors

Hulley EN; Taylor NDJ; Zarnke AM; Somers CM; Manzon RG; Wilson JY; Boreham DR

Journal

Journal of Great Lakes Research, Vol. 44, No. 5, pp. 1110–1116

Publisher

Elsevier

Publication Date

October 1, 2018

DOI

10.1016/j.jglr.2018.07.013

ISSN

0380-1330

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