13
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A decadal perspective on north water microbial eukaryotes as Arctic Ocean sentinels

      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

          The North Water region, between Greenland and Ellesmere Island, with high populations of marine birds and mammals, is an Arctic icon. Due to climate related changes, seasonal patterns in water column primary production are changing but the implications for the planktonic microbial eukaryote communities that support the ecosystem are unknown. Here we report microbial community phenology in samples collected over 12 years (2005–2018) from July to October and analysed using high throughput 18S rRNA V4 amplicon sequencing. Community composition was tied to seasonality with summer communities more variable than distinct October communities. In summer, sentinel pan-Arctic species, including a diatom in the Chaetoceros socialis-gelidus complex and the picochlorophyte Micromonas polaris dominated phytoplankton and were summer specialists. In autumn, uncultured undescribed open water dinoflagellates were favored, and their ubiquity suggests they are sentinels of arctic autumn conditions. Despite the input of nutrients into surface waters, autumn chlorophyll concentrations remained low, refuting projected scenarios that longer ice-free seasons are synonymous with high autumn production and a diatom dominated bloom. Overall, the summer sentinel microbial taxa are persisting, and a subset oceanic dinoflagellate should be monitored for possible ecosystem shifts as later autumn ice formation becomes prevalent elsewhere.

          Related collections

          Most cited references60

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

          RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

          Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              MUSCLE: multiple sequence alignment with high accuracy and high throughput.

              We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
                Bookmark

                Author and article information

                Contributors
                nastasia.freyria.1@ulaval.ca
                Connie.Lovejoy@bio.ulaval.ca
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                16 April 2021
                16 April 2021
                2021
                : 11
                : 8413
                Affiliations
                [1 ]GRID grid.23856.3a, ISNI 0000 0004 1936 8390, Institut de Biologie Intégrative et des Systèmes, , Université Laval, ; Quebec, QC Canada
                [2 ]GRID grid.23856.3a, ISNI 0000 0004 1936 8390, Département de Biologie, Institut de Biologie Intégrative et des Systèmes, , Université Laval, ; Quebec, QC G1R1V6 Canada
                [3 ]GRID grid.462036.5, Institut de Biologie de L’École Normale Supérieure (IBENS), ; 75005 Paris, France
                Article
                87906
                10.1038/s41598-021-87906-4
                8052464
                33863972
                f26742a7-25e1-41f1-b27a-591128f13926
                © The Author(s) 2021

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 October 2020
                : 6 April 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000161, Networks of Centres of Excellence of Canada;
                Award ID: ArcticNet
                Award ID: ArcticNet
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100010785, Canada First Research Excellence Fund;
                Award ID: Sentinelle Nord
                Award ID: Sentinelle Nord
                Award ID: Sentinelle Nord
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003150, Fonds Québécois de la Recherche sur la Nature et les Technologies;
                Award ID: Québec Océan
                Award ID: Québec Océan
                Award ID: Québec Océan
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000038, Natural Sciences and Engineering Research Council of Canada;
                Award ID: Discovery, Northern Supplement, Ship Time
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                microbial communities,environmental microbiology,marine biology,biodiversity,biogeography,microbial ecology,ecology,microbiology,ocean sciences

                Comments

                Comment on this article