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Marine zooplankton are rapid-responders and useful indicators of environmental variability and climate change impacts on pelagic ecosystems on time scales ranging from seasons to years to decades. The systematic complexity and taxonomic diversity of the zooplankton assemblage has presented significant challenges for routine morphological (microscopic) identification of species in samples collected during ecosystem monitoring and fisheries management surveys. Metabarcoding using the mitochondrial Cytochrome Oxidase I (COI) gene region has shown promise for detecting and identifying species of some – but not all – taxonomic groups in samples of marine zooplankton. This study examined species diversity of zooplankton on the Northwest Atlantic Continental Shelf using 27 samples collected in 2002-2012 from the Gulf of Maine, Georges Bank, and Mid-Atlantic Bight during Ecosystem Monitoring (EcoMon) Surveys by the NOAA NMFS Northeast Fisheries Science Center. COI metabarcodes were identified using the MetaZooGene Barcode Atlas and Database ( https://metazoogene.org/MZGdb ) specific to the North Atlantic Ocean. A total of 181 species across 23 taxonomic groups were detected, including a number of sibling and cryptic species that were not discriminated by morphological taxonomic analysis of EcoMon samples. In all, 67 species of 15 taxonomic groups had ≥ 50 COI sequences; 23 species had >1,000 COI sequences. Comparative analysis of molecular and morphological data showed significant correlations between COI sequence numbers and microscopic counts for 5 of 6 taxonomic groups and for 5 of 7 species with >1,000 COI sequences for which both types of data were available. Multivariate statistical analysis showed clustering of samples within each region based on both COI sequence numbers and EcoMon counts, although differences among the three regions were not statistically significant. The results demonstrate the power and potential of COI metabarcoding for identification of species of metazoan zooplankton in the context of ecosystem monitoring.
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MetaZooGene Intercalibration Experiment ( MZG ‐ ICE ): Metabarcoding Marine Zooplankton Diversity of the Global Ocean
ABSTRACT DNA metabarcoding of zooplankton biodiversity is used increasingly for monitoring global ocean ecosystems, requiring comparable data from different research laboratories and ocean regions. The MetaZooGene Intercalibration Experiment (MZG‐ICE) was designed to examine1 and analyse patterns of variation of DNA sequence data resulting from multi‐gene metabarcoding of 10 zooplankton samples carried out by 10 research groups affiliated with the Scientific Committee for Ocean Research (SCOR). Aliquots of DNA extracted from the 10 zooplankton samples were distributed to MZG‐ICE groups for metabarcoding of four gene regions: V1‐V2, V4 and V9 of nuclear 18S rRNA and mitochondrial COI. Molecular protocols and procedures were recommended; substitutions were allowed as necessary. Resulting data were uploaded to a common repository for centralised statistics and bioinformatics. Based on proportional sequence numbers for abundant phyla, overall patterns of variation were consistent across many—but not all—MZG‐ICE groups. V9 showed highest similarity, followed (in order) by V4, V1‐V2, and COI. Outlier data were hypothesised to result from the use of different PCR protocols and sequencing platforms, and possible contamination. MZG‐ICE results indicated that DNA metabarcoding data from different laboratories and research groups can provide reliable, accurate and valid descriptions of biodiversity of zooplankton throughout the ocean. Recommendations included: pre‐screening QA/QC of raw data, detailed records for laboratory protocols, reagents, and instrumentation, and centralised bioinformatics and multivariate statistics. In the absence of universal agreement on standardised protocols or best practices, intercalibration is the best way forward toward validation of DNA metabarcoding of zooplankton diversity for global ocean monitoring.
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- Award ID(s):
- 1840868
- PAR ID:
- 10656440
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Molecular Ecology Resources
- Volume:
- 26
- Issue:
- 1
- ISSN:
- 1755-098X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Abstract Characterization of species diversity of zooplankton is key to understanding, assessing, and predicting the function and future of pelagic ecosystems throughout the global ocean. The marine zooplankton assemblage, including only metazoans, is highly diverse and taxonomically complex, with an estimated ~28,000 species of 41 major taxonomic groups. This review provides a comprehensive summary of DNA sequences for the barcode region of mitochondrial cytochrome oxidase I (COI) for identified specimens. The foundation of this summary is the MetaZooGene Barcode Atlas and Database (MZGdb), a new open-access data and metadata portal that is linked to NCBI GenBank and BOLD data repositories. The MZGdb provides enhanced quality control and tools for assembling COI reference sequence databases that are specific to selected taxonomic groups and/or ocean regions, with associated metadata (e.g., collection georeferencing, verification of species identification, molecular protocols), and tools for statistical analysis, mapping, and visualization. To date, over 150,000 COI sequences for ~ 5600 described species of marine metazoan plankton (including holo- and meroplankton) are available via the MZGdb portal. This review uses the MZGdb as a resource for summaries of COI barcode data and metadata for important taxonomic groups of marine zooplankton and selected regions, including the North Atlantic, Arctic, North Pacific, and Southern Oceans. The MZGdb is designed to provide a foundation for analysis of species diversity of marine zooplankton based on DNA barcoding and metabarcoding for assessment of marine ecosystems and rapid detection of the impacts of climate change.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1111/1755-0998.70090
