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      Elasmobranch diversity across a remote coral reef atoll revealed through environmental DNA metabarcoding

      , , , , ,
      Zoological Journal of the Linnean Society
      Oxford University Press (OUP)

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          Abstract

          As elasmobranchs are becoming increasingly threatened, efficient methods for monitoring the distribution and diversity of elasmobranch populations are required. Environmental DNA (eDNA) metabarcoding is a progressively applied technique that enables mass identification of entire communities and is an effective method for the detection of rare and elusive species. We performed an eDNA metabarcoding survey for fish communities around a coral reef atoll in the Chagos Archipelago (Central Indian Ocean) and assessed the diversity and distribution of elasmobranch species detected within these communities. Our eDNA survey detected 353 amplicon sequence variants (ASVs) attributed to fishes, 12 of which were elasmobranchs. There were no differences in fish communities based on the presence and absence of ASVs between sample depth (surface and 40 m) or sampling habitat, but communities based on read abundance were significantly different between habitats. The dominant elasmobranch species were grey reef (Carcharhinus amblyrhynchos) and silvertip (C. albimarginatus) sharks, and elasmobranch communities were significantly different between sampling depth and habitat. Overall, we find that eDNA metabarcoding can be used to reveal the diversity of elasmobranchs within broader taxonomic assays, but further research and development of targeted metabarcoding primers may be required before it can be integrated into a toolkit for monitoring these species.

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          Most cited references78

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          Basic local alignment search tool.

          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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            DADA2: High resolution sample inference from Illumina amplicon data

            We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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              phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

              Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Zoological Journal of the Linnean Society
                Oxford University Press (OUP)
                0024-4082
                1096-3642
                October 01 2022
                October 07 2022
                April 14 2022
                October 01 2022
                October 07 2022
                April 14 2022
                : 196
                : 2
                : 593-607
                Article
                10.1093/zoolinnean/zlac014
                e6e36f62-8b00-4b8f-b7ae-73572d044e39
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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