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Microbial Ecology

, Volume 78, Issue 2, pp 388–408 | Cite as

Diversity and Composition of Pelagic Prokaryotic and Protist Communities in a Thin Arctic Sea-Ice Regime

  • António Gaspar G. de SousaEmail author
  • Maria Paola Tomasino
  • Pedro Duarte
  • Mar Fernández-Méndez
  • Philipp Assmy
  • Hugo Ribeiro
  • Jaroslaw Surkont
  • Ricardo B. Leite
  • José B. Pereira-Leal
  • Luís Torgo
  • Catarina Magalhães
Environmental Microbiology

Abstract

One of the most prominent manifestations of climate change is the changing Arctic sea-ice regime with a reduction in the summer sea-ice extent and a shift from thicker, perennial multiyear ice towards thinner, first-year ice. These changes in the physical environment are likely to impact microbial communities, a key component of Arctic marine food webs and biogeochemical cycles. During the Norwegian young sea ICE expedition (N-ICE2015) north of Svalbard, seawater samples were collected at the surface (5 m), subsurface (20 or 50 m), and mesopelagic (250 m) depths on 9 March, 27 April, and 16 June 2015. In addition, several physical and biogeochemical data were recorded to contextualize the collected microbial communities. Through the massively parallel sequencing of the small subunit ribosomal RNA amplicon and metagenomic data, this work allows studying the Arctic’s microbial community structure during the late winter to early summer transition. Results showed that, at compositional level, Alpha- (30.7%) and Gammaproteobacteria (28.6%) are the most frequent taxa across the prokaryotic N-ICE2015 collection, and also the most phylogenetically diverse. Winter to early summer trends were quite evident since there was a high relative abundance of thaumarchaeotes in the under-ice water column in late winter while this group was nearly absent during early summer. Moreover, the emergence of Flavobacteria and the SAR92 clade in early summer might be associated with the degradation of a spring bloom of Phaeocystis. High relative abundance of hydrocarbonoclastic bacteria, particularly Alcanivorax (54.3%) and Marinobacter (6.3%), was also found. Richness showed different patterns along the depth gradient for prokaryotic (highest at mesopelagic depth) and protistan communities (higher at subsurface depths). The microbial N-ICE2015 collection analyzed in the present study provides comprehensive new knowledge about the pelagic microbiota below drifting Arctic sea-ice. The higher microbial diversity found in late winter/early spring communities reinforces the need to continue with further studies to properly characterize the winter microbial communities under the pack-ice.

Keywords

Arctic Ocean Microbiota SSU rRNA amplicon Diversity Structure Prokaryotes Protists Sea ice 

Abbreviations

N-ICE2015

Norwegian young sea ICE expedition 2015

SSU rRNA

small subunit ribosomal RNA

FYI

first-year ice

MYI

multiyear ice

DOM

dissolved organic matter

CTD

conductivity, temperature, and depth

PAR

photosynthetically active radiation

SOP

Standard Operating Procedure

OTU

operational taxonomic unit

ML

maximum likelihood

PR2

Protist Ribosomal Reference database

NB

Nansen Basin

TR

Transition Region

YP

Yermak Plateau

Chla

chlorophyll alpha

PSW

polar surface water

AW

Atlantic water

MAW

modified Atlantic water

DOC

dissolved organic carbon

Notes

Acknowledgments

The author AGGS would like to address a special acknowledgement to the Master in Cell and Molecular Biology (M:BCM) held during 2015-17 at Faculty of Sciences, University of Porto (FCUP). We also would like to thank Dr. Anna Silyakova (Centre for Arctic Gas Hydrate, Environment and Climate, Tromsø) for critical reviewing the section “High frequencies of hydrocarbon-degrading bacteria in the Arctic.”

Authors’ Contributions

AGGS carried out the bioinformatics analysis of SSU rRNA amplicon datasets, analyzed all the data, and wrote the manuscript. MPT and CM supervised all the work. CM and PD designed the sampling campaign. PD and MFM collected and filtrated the samples aboard the R.V. Lance. AGGS and CM extracted the DNA to sequence. LT developed R scripts used in this study. JS, RBL, and JBPL performed phylogenetic analysis. MPT and HR critically reviewed the bioinformatics pipelines, particularly, the 18S rRNA amplicon analysis, and the marine hydrocarbon-degrading bacteria part, respectively. PA and CM funded the work. All authors improved, reviewed, and approved the final manuscript.

Funding

This work was financially supported by the Centre for Ice, Climate and Ecosystems at the Norwegian Polar Institute and the Research Council of Norway (project Boom or Bust no. 244646) and Structured Program of R&D&I MarInfo-NORTE-01-0145-FEDER-000031, funded by the NORTE2020 through the European Regional Development Fund (ERDF). MF-M and PA were supported by Norwegian Ministries of Foreign Affairs and Climate and Environment through the program Arktis 2030 (project ID Arctic). This project was also funded by PROPOLAR through a grant to CM and a scholarship to AGGS (NITRONICE project) and by Portuguese Science and Technology Foundation (FCT) through a grant to CM (NITROLIMIT-PTDC 2017-PTDC/CTA-AMB/30997/2017).

Compliance with Ethical Standards

Competing Interests

The authors declare that they have no competing interests.

Supplementary material

248_2018_1314_MOESM1_ESM.docx (75 kb)
Supplementary Figure S1 - Correspondence between samples and water mass based on conservative temperature (°C) and absolute salinity (g·kg-1). The values of the physical variables were retrieved from Table 1. Only seven dots are displayed in the scatter plot since the conservative temperature and absolute salinity are the same for the samples NB_5, NB_50, TR_5, and TR_50. AW stands for Atlantic Water; PSW, Polar Surface Water; MAW, Modified Atlantic Water; PSWw, warm Polar Surface Water.
248_2018_1314_MOESM2_ESM.docx (376 kb)
Supplementary Figure S2 - Microbial ecological networks for the top 100 prokaryotic and eukaryotic OTUs. a Network of the top 100 prokaryotic and eukaryotic OTUs. b Network highlighting the SAR11-Dinophyceae associations. c Network highlighting the Alcanivorax-Marinobacter-Dinophyceae associations. d Network highlighting the Thaumarchaeota-Nitrospinae-Chlorohyta associations. e Network highlighting the Bacteroidetes-Diatomea-Phaeocystis-SAR92 associations. Positive associations are represented by green edges and negative associations by red edges. Node size is proportional to the geometric mean of the relative abundance of OTUs. The “Otu” prefix “p” means prokaryotic and the “e” means eukaryotic.
248_2018_1314_MOESM3_ESM.docx (166 kb)
Supplementary Figure S3 – Comparison of taxonomic profiles of 16S rRNA genes obtained with amplicon and metagenomic datasets. Percentage of 16S rRNA reads retrieved from the surface (5 m), subsurface (20 or 50 m) and mesopelagic (250 m) seawater at Nansen Basin (NB, 09.03.2015), Transition Region (TR, 27.04.2015) and Yermak Plateau (YP, 16.06.2015; see Supplementary Table S1). a Percentage of the most abundant taxa (≥ 20% across samples) from metagenomic 16S rRNA reads. b Absolute number of metagenomic 16S rRNA reads. c Percentage of Alcanivorax and Marinobacter genera from 16S rRNA amplicon reads. d Percentage of Alcanivorax and Marinobacter genera from metagenomic 16S rRNA reads.
248_2018_1314_MOESM4_ESM.docx (1005 kb)
Supplementary Figure S4 – Biogeographical distribution of environmental Alcanivorax 16S rRNA gene sequences. The maximum likelihood tree under the GTRGAMMA model with 1000 bootstrap replicates includes 38 nucleotide sequences spanning 357 bp positions. Two Alcanivorax-OTUs from the 16S rRNA amplicon dataset (“pOtu00002” and “pOtu00028”), six Alcanivorax metagenomic 16S rRNA reads recovered from the metagenomic 16S rRNA reads dataset (from sample NB_250, named “MG-1”-“MG-6”), and twenty nine Alcanivorax metagenomic sequences from the 16S rRNA Public Assembled Metagenomes database (identified through the accession number of the project in IMG/M). It was assumed a mesopelagic depth (200–1000 m) for sequences retrieved from “Ga0031697” and “Ga0031693” since both reported the collection depth as “deep ocean”. The 16S rRNA gene sequence of Rhodospirillum sulfurexigens JA143T (NCBI accession number AM710622.1) was included as outgroup. The scale bar represents nucleotide substitutions per site. Bootstrap values >50% are displayed.
248_2018_1314_MOESM5_ESM.docx (45 kb)
Supplementary Table S1 – Features of sampling conditions of microbial N-ICE2015 collection.
248_2018_1314_MOESM6_ESM.docx (46 kb)
Supplementary Table S2 - Description of 16S rRNA libraries from N-ICE2015 project.
248_2018_1314_MOESM7_ESM.docx (48 kb)
Supplementary Table S3 - Description of 18S rRNA libraries from N-ICE2015 project.
248_2018_1314_MOESM8_ESM.xls (1.3 mb)
Supplementary Table S4 - Distribution of prokaryotic taxa across N-ICE2015 collection at phylum, class, order, family, genus and OTU levels. Percentage of taxa at given taxonomic level, inclusive the absolute number of reads assigned to each OTU (97% sequence similarity threshold), within the samples and across the prokaryotic N-ICE2015 collection. The OTU table includes just prokaryotic taxa (see the lineages removed in the “Methods” section) classified against SILVA reference database (v. 1.2.8) after excluding the rare clusters (<5 observations across samples) and rarefying at even sampling depth (38,232 sequences).
248_2018_1314_MOESM9_ESM.xls (2.4 mb)
Supplementary Table S5 - Raw prokaryotic OTU table from the 16S rRNA libraries of N-ICE2015 collection. The OTU table includes all the taxa (inclusive those lineages that were removed from the main OTU table used in this manuscript, see the “Methods” section) classified against SILVA reference database (v. 1.2.8) without excluding the rare clusters neither rarefying (the no. of sequences goes from 59,627, at TR_5, to 212,529, at YP_5).
248_2018_1314_MOESM10_ESM.xls (1.6 mb)
Supplementary Table S6 - Distribution of eukaryotic taxa across N-ICE2015 collection at phylum, class, order, family, genus and OTU levels. Percentage of taxa at given taxonomic level, inclusive the absolute number of reads assigned to each OTU (98% sequence similarity threshold), within the samples and across the eukaryotic N-ICE2015 collection. The OTU table includes just interesting protist taxa (see the lineages removed in the “Methods” section) classified against SILVA reference database (v. 1.2.8) after excluding the rare clusters (<5 observations across samples) and rarefying at even sampling depth (43,289 sequences).
248_2018_1314_MOESM11_ESM.xls (894 kb)
Supplementary Table S7 - Eukaryotic OTU table from the 18S rRNA libraries of N-ICE2015 collection assigned against the Protist Ribosomal Reference database (PR2). Percentage and absolute number of reads assigned to each OTU (98% sequence similarity threshold), within the samples and across the eukaryotic N-ICE2015 collection. The OTU table includes protist taxa (see the lineages removed in the “Methods” section) classified against PR2 (v. 4.5) after excluding the rare clusters (<5 observations across samples) and rarefying at even sampling depth (43,647 sequences).
248_2018_1314_MOESM12_ESM.xls (156 kb)
Supplementary Table S8 – Prokaryotic taxonomic table from the metagenomic 16S rRNA reads dataset.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • António Gaspar G. de Sousa
    • 1
    • 2
    Email author
  • Maria Paola Tomasino
    • 1
  • Pedro Duarte
    • 3
  • Mar Fernández-Méndez
    • 3
  • Philipp Assmy
    • 3
  • Hugo Ribeiro
    • 1
  • Jaroslaw Surkont
    • 4
  • Ricardo B. Leite
    • 4
  • José B. Pereira-Leal
    • 4
  • Luís Torgo
    • 5
    • 6
  • Catarina Magalhães
    • 1
    • 2
  1. 1.CIIMAR/CIMAR – Interdisciplinary Centre of Marine and Environmental ResearchUniversity of PortoPortoPortugal
  2. 2.Department of Biology, Faculty of SciencesUniversity of PortoPortoPortugal
  3. 3.Norwegian Polar InstituteFram CentreTromsøNorway
  4. 4.Instituto Gulbenkian de CiênciaOeirasPortugal
  5. 5.LIAAD - Laboratory of Artificial Intelligence and Decision SupportINESC TecPortoPortugal
  6. 6.Faculty of Computer ScienceDalhousie UniversityHalifaxUSA

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