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      Ecosystem biomonitoring with eDNA: metabarcoding across the tree of life in a tropical marine environment

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          Abstract

          Effective marine management requires comprehensive data on the status of marine biodiversity. However, efficient methods that can document biodiversity in our oceans are currently lacking. Environmental DNA (eDNA) sourced from seawater offers a new avenue for investigating the biota in marine ecosystems. Here, we investigated the potential of eDNA to inform on the breadth of biodiversity present in a tropical marine environment. Directly sequencing eDNA from seawater using a shotgun approach resulted in only 0.34% of 22.3 million reads assigning to eukaryotes, highlighting the inefficiency of this method for assessing eukaryotic diversity. In contrast, using ‘tree of life’ (ToL) metabarcoding and 20-fold fewer sequencing reads, we could detect 287 families across the major divisions of eukaryotes. Our data also show that the best performing ‘universal’ PCR assay recovered only 44% of the eukaryotes identified across all assays, highlighting the need for multiple metabarcoding assays to catalogue biodiversity. Lastly, focusing on the fish genus Lethrinus, we recovered intra- and inter-specific haplotypes from seawater samples, illustrating that eDNA can be used to explore diversity beyond taxon identifications. Given the sensitivity and low cost of eDNA metabarcoding we advocate this approach be rapidly integrated into biomonitoring programs.

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          Fishing down marine food webs

          The mean trophic level of the species groups reported in Food and Agricultural Organization global fisheries statistics declined from 1950 to 1994. This reflects a gradual transition in landings from long-lived, high trophic level, piscivorous bottom fish toward short-lived, low trophic level invertebrates and planktivorous pelagic fish. This effect, also found to be occurring in inland fisheries, is most pronounced in the Northern Hemisphere. Fishing down food webs (that is, at lower trophic levels) leads at first to increasing catches, then to a phase transition associated with stagnating or declining catches. These results indicate that present exploitation patterns are unsustainable.
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            Environmental DNA for wildlife biology and biodiversity monitoring.

            Extraction and identification of DNA from an environmental sample has proven noteworthy recently in detecting and monitoring not only common species, but also those that are endangered, invasive, or elusive. Particular attributes of so-called environmental DNA (eDNA) analysis render it a potent tool for elucidating mechanistic insights in ecological and evolutionary processes. Foremost among these is an improved ability to explore ecosystem-level processes, the generation of quantitative indices for analyses of species, community diversity, and dynamics, and novel opportunities through the use of time-serial samples and unprecedented sensitivity for detecting rare or difficult-to-sample taxa. Although technical challenges remain, here we examine the current frontiers of eDNA, outline key aspects requiring improvement, and suggest future developments and innovations for research. Copyright © 2014 Elsevier Ltd. All rights reserved.
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              Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding.

              Global biodiversity in freshwater and the oceans is declining at high rates. Reliable tools for assessing and monitoring aquatic biodiversity, especially for rare and secretive species, are important for efficient and timely management. Recent advances in DNA sequencing have provided a new tool for species detection from DNA present in the environment. In this study, we tested whether an environmental DNA (eDNA) metabarcoding approach, using water samples, can be used for addressing significant questions in ecology and conservation. Two key aquatic vertebrate groups were targeted: amphibians and bony fish. The reliability of this method was cautiously validated in silico, in vitro and in situ. When compared with traditional surveys or historical data, eDNA metabarcoding showed a much better detection probability overall. For amphibians, the detection probability with eDNA metabarcoding was 0.97 (CI = 0.90-0.99) vs. 0.58 (CI = 0.50-0.63) for traditional surveys. For fish, in 89% of the studied sites, the number of taxa detected using the eDNA metabarcoding approach was higher or identical to the number detected using traditional methods. We argue that the proposed DNA-based approach has the potential to become the next-generation tool for ecological studies and standardized biodiversity monitoring in a wide range of aquatic ecosystems.
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                Author and article information

                Contributors
                michael.stat@curtin.edu.au
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                25 September 2017
                25 September 2017
                2017
                : 7
                : 12240
                Affiliations
                [1 ]ISNI 0000 0004 0375 4078, GRID grid.1032.0, Trace and Environmental DNA (TrEnD) Laboratory, Department of Environment and Agriculture, Curtin University, ; Perth, WA 6102 Australia
                [2 ]ISNI 0000 0004 0389 4302, GRID grid.1038.a, Centre for Marine Ecosystems Research, School of Science, Edith Cowan University, ; Perth, WA 6027 Australia
                [3 ]Western Australian Fisheries and Marine Research Laboratories, Department of Primary Industries and Regional Development, Fisheries Division, Government of Western Australia, PO Box 20, North Beach, WA 6920 Australia
                Author information
                http://orcid.org/0000-0002-1663-3422
                http://orcid.org/0000-0002-3401-0704
                http://orcid.org/0000-0002-5696-7574
                http://orcid.org/0000-0002-9069-4581
                Article
                12501
                10.1038/s41598-017-12501-5
                5612959
                28947818
                c682f41c-5919-4fcd-97e0-bd609ae09748
                © The Author(s) 2017

                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
                : 21 June 2017
                : 11 September 2017
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