Time-series metabarcoding analysis of zooplankton diversity of the NW Atlantic continental shelf. (18th February 2019)

• Record Type: Journal Article
• Authors: Bucklin, Ann; Yeh, Heidi D; Questel, Jennifer M; Richardson, David E; Reese, Bo; Copley, Nancy J; Wiebe, Peter H
• Editors: Fields, David
• Is Part Of: ICES journal of marine science. Volume 76:Number 4(2019)
• Page Start: 1162
• Page End: 1176
• Publication Date: 2019-02-18
• Subjects: DNA metabarcoding -- ocean monitoring -- zooplankton biodiversity; Ocean -- Periodicals; Fisheries -- Periodicals; Fishes -- Periodicals; Marine biology -- Bibliography -- Periodicals; 551.4605
• DOI: 10.1093/icesjms/fsz021
• Languages: English
• ISSNs: 1054-3139

Abstract

Abstract: Biodiversity of zooplankton is central to the functioning of ocean ecosystems, yet morphological taxonomic analysis requires teams of experts and detailed examination of many samples. Metabarcoding (DNA sequencing of short amplified regions of one or a few genes from environmental samples) is a powerful tool for analysis of the composition and diversity of natural communities. The 18S rRNA V9 hypervariable region was sequenced for 26 zooplankton samples collected from the Gulf of Maine, Georges Bank, and Mid-Atlantic Bight during ecosystem monitoring surveys by the U.S. Northeast Fisheries Science Center during 2002–2012. A total of 7 648 033 sequences and 22 072 operational taxonomic units (OTUs) were identified and classified into 28 taxonomic groups of plankton. Comparative analysis of molecular (V9 sequence numbers) and morphological (abundance counts) focused on seven taxonomic groups and revealed similar patterns of variation among years and regions. Sequence numbers and abundance counts showed positive correlation for all groups, with significant correlations ( p < 0.05) for Calanoida, Gastropoda, and Chaetognatha. Shannon diversity index values calculated using sequence numbers and abundance counts showed highly significant correlation ( r = 0.625; p < 0.001) across all regions during 2002–2012. This study demonstrates the potential of metabarcoding for time-series analysis of zooplankton biodiversity, ocean ecosystem assessment, and fisheries management.