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      Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap

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          Abstract

          A decade after environmental scientists integrated high‐throughput sequencing technologies in their toolbox, the genomics‐based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end‐users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or “in development”, hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics‐based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (a) Taxonomy‐based analyses focused on identification of known bioindicators or described taxa; (b) De novo bioindicator analyses; (c) Structural community metrics including inferred ecological networks; and (d) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programmes that leverage recent analytical advancements, while pointing out current limitations and future research needs.

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          The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

          SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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            Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences

            Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community’s functional capabilities. Here we describe PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this ‘predictive metagenomic’ approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.
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              Cryptic species as a window on diversity and conservation.

              The taxonomic challenge posed by cryptic species (two or more distinct species classified as a single species) has been recognized for nearly 300 years, but the advent of relatively inexpensive and rapid DNA sequencing has given biologists a new tool for detecting and differentiating morphologically similar species. Here, we synthesize the literature on cryptic and sibling species and discuss trends in their discovery. However, a lack of systematic studies leaves many questions open, such as whether cryptic species are more common in particular habitats, latitudes or taxonomic groups. The discovery of cryptic species is likely to be non-random with regard to taxon and biome and, hence, could have profound implications for evolutionary theory, biogeography and conservation planning.
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                Author and article information

                Contributors
                tristan.cordier@gmail.com
                Journal
                Mol Ecol
                Mol Ecol
                10.1111/(ISSN)1365-294X
                MEC
                Molecular Ecology
                John Wiley and Sons Inc. (Hoboken )
                0962-1083
                1365-294X
                18 June 2020
                July 2021
                : 30
                : 13 , Environmental DNA for Biodiversity and Ecosystem Monitoring ( doiID: 10.1111/mec.v30.13 )
                : 2937-2958
                Affiliations
                [ 1 ] Department of Genetics and Evolution Science III University of Geneva Geneva Switzerland
                [ 2 ] AZTI Marine Research Basque Research and Technology Alliance (BRTA) Spain
                [ 3 ] Red Sea Research Center (RSRC) Biological and Environmental Sciences and Engineering (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia
                [ 4 ] Agroécologie INRAE University of Bourgogne University Bourgogne Franche‐Comté Dijon France
                [ 5 ] UMR CARRTEL, INRA USMB Thonon-les-Bains France
                [ 6 ] Department of Biological Sciences Macquarie University Sydney NSW Australia
                [ 7 ] School of Natural Sciences Bangor University Gwynedd UK
                [ 8 ] Department of Ecology Technische Universität Kaiserslautern Kaiserslautern Germany
                [ 9 ] Benthic Resources and Processes Group Institute of Marine Research Tromsø Norway
                [ 10 ] Aquatic Ecosystem Research Faculty of Biology University of Duisburg‐Essen Essen Germany
                [ 11 ] Centre for Water and Environmental Research (ZWU) University of Duisburg‐Essen Essen Germany
                [ 12 ] Coastal & Freshwater Group Cawthron Institute Nelson New Zealand
                [ 13 ] Institute of Marine Science University of Auckland Warkworth New Zealand
                [ 14 ] ID‐Gene Ecodiagnostics Geneva Switzerland
                [ 15 ] Institute of Oceanology Polish Academy of Sciences Sopot Poland
                [ 16 ] Basque Foundation for Science IKERBASQUE Bilbao Spain
                Author notes
                [*] [* ] Correspondence

                Tristan Cordier, Department of Genetics and Evolution, University of Geneva, Science III, Geneva, Switzerland.

                Email: tristan.cordier@ 123456gmail.com

                Author information
                https://orcid.org/0000-0001-7398-4790
                https://orcid.org/0000-0002-8592-3079
                https://orcid.org/0000-0001-9792-8451
                https://orcid.org/0000-0002-5809-3372
                https://orcid.org/0000-0002-3323-4167
                https://orcid.org/0000-0002-5465-913X
                https://orcid.org/0000-0001-5180-5659
                https://orcid.org/0000-0002-7138-6364
                Article
                MEC15472
                10.1111/mec.15472
                8358956
                32416615
                231ae3d8-1aa5-4837-a644-a7b3337e5bf4
                © 2020 The Authors. Molecular Ecology published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 25 April 2020
                : 31 October 2019
                : 06 May 2020
                Page count
                Figures: 2, Tables: 1, Pages: 22, Words: 19884
                Funding
                Funded by: Swiss National Science Foundation , doi 10.13039/501100001711;
                Award ID: 31003A_179125
                Funded by: European Cross‐Border Cooperation Programme
                Funded by: ‘Ramón y Cajal' contract
                Award ID: RYC‐2012‐11404
                Funded by: Spanish Ministry of Economy and Competitiveness
                Funded by: Saudi Aramco‐KAUST Center for Marine Environmental Observations
                Funded by: French Agence Nationale de la Recherche
                Award ID: ANR‐17‐CE32‐011
                Funded by: ERA‐NET C‐IPM BioAWARE
                Funded by: Office Français de la Biodiversité (OFB)
                Funded by: UK Natural Environment Research Council
                Award ID: NE/N003756/1
                Award ID: NE/N006216/1
                Funded by: German Science Foundation (DFG)
                Award ID: STO414/15‐1
                Funded by: New Zealand Ministry for Business, Innovation and Employment
                Award ID: CAWX1904
                Award ID: C05X1707
                Funded by: DNAqua‐Net COST Action
                Award ID: CA15219
                Funded by: European Union
                Funded by: IKERBASQUE (Basque Foundation for Science)
                Funded by: Basque Government (project microgAMBI)
                Categories
                Special Issue
                NOVEL APPROACHES TO MONITOR ECOSYSTEMS
                Invited Review and Syntheses
                Custom metadata
                2.0
                July 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.5 mode:remove_FC converted:12.08.2021

                Ecology
                biodiversity,biomonitoring,ecosystem management,environmental dna,implementation strategy,metabarcoding

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