23
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Partitioning of diet between species and life history stages of sympatric and cryptic snappers (Lutjanidae) based on DNA metabarcoding

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Lutjanus erythropterus and L. malabaricus are sympatric, sister taxa that are important to fisheries throughout the Indo-Pacific. Their juveniles are morphologically indistinguishable (i.e. cryptic). A DNA metabarcoding dietary study was undertaken to assess the diet composition and partitioning between the juvenile and adult life history stages of these two lutjanids. Major prey taxa were comprised of teleosts and crustaceans for all groups except adult L. erythropterus, which instead consumed soft bodied invertebrates (e.g. tunicates, comb jellies and medusae) as well as teleosts, with crustaceans being notably absent. Diet composition was significantly different among life history stages and species, which may be associated with niche habitat partitioning or differences in mouth morphology within adult life stages. This study provides the first evidence of diet partitioning between cryptic juveniles of overlapping lutjanid species, thus providing new insights into the ecological interactions, habitat associations, and the specialised adaptations required for the coexistence of closely related species. This study has improved our understanding of the differential contributions of the juvenile and adult diets of these sympatric species within food webs. The diet partitioning reported in this study was only revealed by the taxonomic resolution provided by the DNA metabarcoding approach and highlights the potential utility of this method to refine the dietary components of reef fishes more generally.

          Related collections

          Most cited references45

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?

          Abstract Advances in DNA sequencing technology have revolutionized the field of molecular analysis of trophic interactions, and it is now possible to recover counts of food DNA sequences from a wide range of dietary samples. But what do these counts mean? To obtain an accurate estimate of a consumer's diet should we work strictly with data sets summarizing frequency of occurrence of different food taxa, or is it possible to use relative number of sequences? Both approaches are applied to obtain semi‐quantitative diet summaries, but occurrence data are often promoted as a more conservative and reliable option due to taxa‐specific biases in recovery of sequences. We explore representative dietary metabarcoding data sets and point out that diet summaries based on occurrence data often overestimate the importance of food consumed in small quantities (potentially including low‐level contaminants) and are sensitive to the count threshold used to define an occurrence. Our simulations indicate that using relative read abundance (RRA) information often provides a more accurate view of population‐level diet even with moderate recovery biases incorporated; however, RRA summaries are sensitive to recovery biases impacting common diet taxa. Both approaches are more accurate when the mean number of food taxa in samples is small. The ideas presented here highlight the need to consider all sources of bias and to justify the methods used to interpret count data in dietary metabarcoding studies. We encourage researchers to continue addressing methodological challenges and acknowledge unanswered questions to help spur future investigations in this rapidly developing area of research.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            GenBank

            GenBank® (www.ncbi.nlm.nih.gov/genbank/) is a comprehensive database that contains publicly available nucleotide sequences for 370 000 formally described species. These sequences are obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects, including whole genome shotgun (WGS) and environmental sampling projects. Most submissions are made using the web-based BankIt or the NCBI Submission Portal. GenBank staff assign accession numbers upon data receipt. Daily data exchange with the European Nucleotide Archive (ENA) and the DNA Data Bank of Japan (DDBJ) ensures worldwide coverage. GenBank is accessible through the NCBI Nucleotide database, which links to related information such as taxonomy, genomes, protein sequences and structures, and biomedical journal literature in PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. Recent updates include changes to policies regarding sequence identifiers, an improved 16S submission wizard, targeted loci studies, the ability to submit methylation and BioNano mapping files, and a database of anti-microbial resistance genes.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversity.

              Documenting the diversity of marine life is challenging because many species are cryptic, small, and rare, and belong to poorly known groups. New sequencing technologies, especially when combined with standardized sampling, promise to make comprehensive biodiversity assessments and monitoring feasible on a large scale. We used this approach to characterize patterns of diversity on oyster reefs across a range of geographic scales comprising a temperate location [Virginia (VA)] and a subtropical location [Florida (FL)]. Eukaryotic organisms that colonized multilayered settlement surfaces (autonomous reef monitoring structures) over a 6-mo period were identified by cytochrome c oxidase subunit I barcoding (>2-mm mobile organisms) and metabarcoding (sessile and smaller mobile organisms). In a total area of ∼ 15.64 m(2) and volume of ∼ 0.09 m(3), 2,179 operational taxonomic units (OTUs) were recorded from 983,056 sequences. However, only 10.9% could be matched to reference barcodes in public databases, with only 8.2% matching barcodes with both genus and species names. Taxonomic coverage was broad, particularly for animals (22 phyla recorded), but 35.6% of OTUs detected via metabarcoding could not be confidently assigned to a taxonomic group. The smallest size fraction (500 to 106 μm) was the most diverse (more than two-thirds of OTUs). There was little taxonomic overlap between VA and FL, and samples separated by ∼ 2 m were significantly more similar than samples separated by ∼ 100 m. Ground-truthing with independent assessments of taxonomic composition indicated that both presence-absence information and relative abundance information are captured by metabarcoding data, suggesting considerable potential for ecological studies and environmental monitoring.
                Bookmark

                Author and article information

                Contributors
                miwatakahashi582@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                9 March 2020
                9 March 2020
                2020
                : 10
                : 4319
                Affiliations
                [1 ]ISNI 0000 0004 0375 4078, GRID grid.1032.0, School of Molecular and Life Sciences, , Curtin University, Kent Street, Bentley, ; Perth, WA 6102 Australia
                [2 ]ISNI 0000 0004 0470 8815, GRID grid.438303.f, Australian Museum Research Institute, , Australian Museum, ; 1 William Street, Sydney, NSW 2010 Australia
                [3 ]ISNI 0000 0004 1936 7910, GRID grid.1012.2, School of Biological Sciences, , University of Western Australia, ; 35 Stirling Highway, Perth, WA 6009 Australia
                [4 ]ISNI 0000 0004 0445 3226, GRID grid.484196.6, Western Australian Fisheries and Marine Research Laboratories, Department of Primary Industries and Regional Development, , Government of Western Australia, ; P.O. Box 20, North Beach, WA 6920 Australia
                [5 ]Environmental Protection Authority, 215 Lambton Quay, Wellington, 6011 New Zealand
                Article
                60779
                10.1038/s41598-020-60779-9
                7062689
                32152406
                f738b1d2-13e2-48a8-a9ff-9132e89a5664
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 November 2019
                : 17 February 2020
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                community ecology,ecological networks,molecular ecology,ecology
                Uncategorized
                community ecology, ecological networks, molecular ecology, ecology

                Comments

                Comment on this article