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Annual time-series analysis of aqueous eDNA reveals ecologically relevant dynamics of lake ecosystem biodiversity

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      Abstract

      The use of environmental DNA (eDNA) in biodiversity assessments offers a step-change in sensitivity, throughput and simultaneous measures of ecosystem diversity and function. There remains, however, a need to examine eDNA persistence in the wild through simultaneous temporal measures of eDNA and biota. Here, we use metabarcoding of two markers of different lengths, derived from an annual time series of aqueous lake eDNA to examine temporal shifts in ecosystem biodiversity and in an ecologically important group of macroinvertebrates (Diptera: Chironomidae). The analyses allow different levels of detection and validation of taxon richness and community composition (β-diversity) through time, with shorter eDNA fragments dominating the eDNA community. Comparisons between eDNA, community DNA, taxonomy and UK species abundance data further show significant relationships between diversity estimates derived across the disparate methodologies. Our results reveal the temporal dynamics of eDNA and validate the utility of eDNA metabarcoding for tracking seasonal diversity at the ecosystem scale.

      Abstract

      DNA from macrobial taxa can be extracted from environmental samples, including water, and be used to assess biodiversity in the region. Here, Bista and colleagues show that temporal shifts in the biodiversity of a lake invertebrate community can be detected by analysis of environmental DNA (eDNA).

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      QIIME allows analysis of high-throughput community sequencing data.

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        MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0.

        We announce the release of the fourth version of MEGA software, which expands on the existing facilities for editing DNA sequence data from autosequencers, mining Web-databases, performing automatic and manual sequence alignment, analyzing sequence alignments to estimate evolutionary distances, inferring phylogenetic trees, and testing evolutionary hypotheses. Version 4 includes a unique facility to generate captions, written in figure legend format, in order to provide natural language descriptions of the models and methods used in the analyses. This facility aims to promote a better understanding of the underlying assumptions used in analyses, and of the results generated. Another new feature is the Maximum Composite Likelihood (MCL) method for estimating evolutionary distances between all pairs of sequences simultaneously, with and without incorporating rate variation among sites and substitution pattern heterogeneities among lineages. This MCL method also can be used to estimate transition/transversion bias and nucleotide substitution pattern without knowledge of the phylogenetic tree. This new version is a native 32-bit Windows application with multi-threading and multi-user supports, and it is also available to run in a Linux desktop environment (via the Wine compatibility layer) and on Intel-based Macintosh computers under the Parallels program. The current version of MEGA is available free of charge at (http://www.megasoftware.net).
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          Search and clustering orders of magnitude faster than BLAST.

           Robert Edgar (2010)
          Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification. UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets. Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch.
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            Author and article information

            Affiliations
            [1 ]Molecular Ecology and Fisheries Genetics Laboratory, School of Biological Sciences, Bangor University , Bangor, Gwynedd LL57 2UW, UK
            [2 ]Environment Agency, Horizon House , Deanery Road, Bristol BS1 5AH, UK
            [3 ]Department of Integrative Biology & Biodiversity Institute of Ontario, University of Guelph , Guelph, Ontario, Canada N1G 2W1
            [4 ]GABI, INRA, AgroParisTech, Université Paris-Saclay , 78350 Jouy-en-Josas, France
            Author notes
            Journal
            Nat Commun
            Nat Commun
            Nature Communications
            Nature Publishing Group
            2041-1723
            18 January 2017
            2017
            : 8
            28098255 5253663 ncomms14087 10.1038/ncomms14087
            Copyright © 2017, The Author(s)

            This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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