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Compendium of 530 metagenome-assembled bacterial and archaeal genomes from the polar Arctic Ocean

Nature Microbiology volume 6, pages 1561–1574 (2021)Cite this article

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Abstract

The role of the Arctic Ocean ecosystem in climate regulation may depend on the responses of marine microorganisms to environmental change. We applied genome-resolved metagenomics to 41 Arctic seawater samples, collected at various depths in different seasons during the Tara Oceans Polar Circle expedition, to evaluate the ecology, metabolic potential and activity of resident bacteria and archaea. We assembled 530 metagenome-assembled genomes (MAGs) to form the Arctic MAGs catalogue comprising 526 species. A total of 441 MAGs belonged to species that have not previously been reported and 299 genomes showed an exclusively polar distribution. Most Arctic MAGs have large genomes and the potential for fast generation times, both of which may enable adaptation to a copiotrophic lifestyle in nutrient-rich waters. We identified 38 habitat generalists and 111 specialists in the Arctic Ocean. We also found a general prevalence of 14 mixotrophs, while chemolithoautotrophs were mostly present in the mesopelagic layer during spring and autumn. We revealed 62 MAGs classified as key Arctic species, found only in the Arctic Ocean, showing the highest gene expression values and predicted to have habitat-specific traits. The Artic MAGs catalogue will inform our understanding of polar microorganisms that drive global biogeochemical cycles.

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Fig. 1: Metagenomic genome reconstruction of the Tara Oceans Polar Circle expedition.
Fig. 2: Taxonomical annotation and novelty of Arctic Ocean MAGs.
Fig. 3: Potential autotrophy in RuBisCO-coding MAGs.
Fig. 4: Chemolithoautotrophic Arctic Ocean MAGs.
Fig. 5: Composition and biogeography of the 530 Arctic microbial MAGs.
Fig. 6: Expression patterns and metabolic potential of sentinel polar MAGs in the Arctic Ocean.

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Data availability

Accession numbers for the data used and generated in this study can be found in Supplementary Table 12, which includes the Arctic MAGs Catalogue and their functional annotation (European Bioinformatics Institute BioStudies ID: S-BSST451) and the co-assembly of metagenomic samples used to generate the metagenomic bins (European Nucleotide Archive PRJEB41575). Accession numbers for the metagenomic and metatranscriptomic samples used in the fragment recruitment analyses can be found in Supplementary Table 13. Publicly available datasets used in this study include the following: CheckM v.1.0.11 (https://github.com/Ecogenomics/CheckM/releases/tag/v1.1.0), GTDB release 89 (https://data.gtdb.ecogenomic.org/releases/release89/), SILVA 132 (https://www.arb-silva.de/documentation/release-132/), KEGG release 89.1 (https://www.genome.jp/kegg/docs/relnote.html) and Pfam database release 31.0 (http://ftp.ebi.ac.uk/pub/databases/Pfam/releases/Pfam31.0/). Source data are provided with this paper.

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Acknowledgements

Tara Oceans (which includes both the Tara Oceans and Tara Oceans Polar Circle expeditions) would not exist without the leadership of the Tara Ocean Foundation and the continuous support of 23 institutes (http://oceans.taraexpeditions.org). We thank SHOOK Studio for assistance with designing the figures. This work acknowledges the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S). We thank the commitment of the following sponsors and research funding agencies: the Spanish Ministry of Economy and Competitiveness (project MAGGY, grant no. CTM2017-87736-R and Polar EcoGen PID2020-116489RB-I00), Horizon 2020-Research and Innovation Framework Programme (Atlantic ECOsystems assessment, forecasting & sustainability, grant no. H2020-BG-2019-2), Centre National de la Recherche Scientifique (in particular Groupement de Recherche GDR3280 and the Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans-GOSEE), European Molecular Biology Laboratory, Genoscope/Commissariat à l’Énergie Atomique et aux Énergies Alternatives, the French Ministry of Research and the French Government’s ‘Investissements d’Avenir’ programmes OCEANOMICS (project no. ANR-11-BTBR-0008), FRANCE GENOMIQUE (project no. ANR-10-INBS-09-08), MEMO LIFE (project no. ANR-10-LABX-54), Paris Sciences et Lettres University (project no. ANR-11-IDEX-0001-02), Eidgenössische Technische Hochschule Zürich and Helmut Horten Foundation, the Swiss National Foundation (project no. 205321_184955), MEXT/JSPS/KAKENHI (project nos. 16H06429, 16K21723, 16H06437 and 18H02279). We also thank the support and commitment of agnès b. and E. Bourgois, the Prince Albert II de Monaco Foundation, the Veolia Foundation, Region Bretagne, Lorient Agglomération, Serge Ferrari, World Courier and King Abdullah University of Science and Technology. The global sampling effort was enabled by countless scientists and crews who sampled aboard the Tara from 2009 to 2013. We thank Mercator/Coriolis and ACRI-ST for providing daily satellite data during the expedition. We also thank the countries who graciously granted sampling permissions. All data reported herein are fully and freely available from the date of publication, with no restrictions; all of the analyses, publications and ownership of data are free from legal entanglement or restriction by the various nations whose waters the Tara Oceans expeditions sampled in. This article is contribution number 122 of Tara Oceans.

Author information

Authors and Affiliations

  1. Department of Marine Biology and Oceanography, Institut de Ciències del Mar, Barcelona, Spain

    Marta Royo-Llonch, Pablo Sánchez, Clara Ruiz-González, Marta Sebastián & Silvia G. Acinas

  2. Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, Eidgenössische Technische Hochschule Zürich, Zürich, Switzerland

    Guillem Salazar, Lucas Paoli & Shinichi Sunagawa

  3. Systems Biology Program, Centro Nacional de Biotecnología, Madrid, Spain

    Carlos Pedrós-Alió

  4. Genoscope, Institut de Biologie François-Jacob, Commissariat à l’Energie Atomique, Université Paris-Saclay, Evry, France

    Karine Labadie & Olivier Jaillon

  5. Institut de biologie de l’Ecole normale supérieure, Ecole normale supérieure, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Paris Sciences & Lettres Université Paris, Paris, France

    Federico M. Ibarbalz, Lucie Zinger, Eric Karsenti & Chris Bowler

  6. Universidad de Buenos Aires - CONICET, Centro de Investigaciones del Mar y la Atmósfera (CIMA), Buenos Aires, Argentina

    Federico M. Ibarbalz

  7. Laboratoire des Sciences du Numérique de Nantes, Centre National de la Recherche Scientifique, Université de Nantes, Nantes, France

    Benjamin Churcheward, Samuel Chaffron & Damien Eveillard

  8. Research Federation (FR2022) Tara Oceans GO-SEE, Paris, France

    Colomban de Vargas, Gabriel Gorsky, Nigel Grimsley, Lionel Guidi, Pascal Hingamp, Olivier Jaillon, Fabrice Not, Christian Sardet, Sabrina Speich, Lars Setmmann, Samuel Chaffron, Damien Eveillard, Eric Karsenti, Patrick Wincker & Chris Bowler

  9. Directors’ Research European Molecular Biology Laboratory, Heidelberg, Germany

    Stefanie Kandels & Eric Karsenti

  10. Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, Evry, France

    Patrick Wincker

  11. School of Marine Sciences, University of Maine, Orono, ME, USA

    Emmanuel Boss & Lee Karp-Boss

  12. Takuvik International Research Laboratory (IRL 3376), Université Laval-Centre National de la Recherche Scientifique, Université Laval, Quebec City, Quebec, Canada

    Marcel Babin

  13. Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany

    Peer Bork & Stefanie Kandels

  14. Max Delbrück Center for Molecular Medicine, Berlin, Germany

    Peer Bork

  15. Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany

    Peer Bork

  16. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK

    Guy Cochrane

  17. Sorbonne Université, Centre National de la Recherche Scientifique, Station Biologique de Roscoff, AD2M ECOMAP, Roscoff, France

    Colomban de Vargas

  18. Sorbonne Université, Centre National de la Recherche Scientifique, Laboratoire d’Océanographie de Villefanche, Villefranche-sur-mer, France

    Gabriel Gorsky, Lionel Guidi, Christian Sardet & Lars Setmmann

  19. Centre National de la Recherche Scientifique Unité Mixte de Recherche 7232, Biologie Intégrative des Organismes Marins, Banyuls-sur-Mer, France

    Nigel Grimsley

  20. Sorbonne Universités Paris 06, Observatoire Océanologique de Banyuls Université Pierre et Marie Curie, Banyuls-sur-Mer, France

    Nigel Grimsley

  21. Aix-Marseille Université, Université de Toulon, Centre National de la Recherche Scientifique, Institut de Recherche pour le Développement, Mediterranean Institute of Oceanography UM 110, Marseille, France

    Pascal Hingamp

  22. Stazione Zoologica Anton Dohrn, Naples, Italy

    Daniele Iudicone

  23. Sorbonne Université, Centre National de la Recherche Scientifique - UMR7144 - Ecology of Marine Plankton Group, Station Biologique de Roscoff, Place Georges Teissier, Roscoff, France

    Fabrice Not

  24. Institute for Chemical Research, Kyoto University, Kyoto, Japan

    Hiroyuki Ogata

  25. PANGAEA, Data Publisher for Earth and Environmental Science, University of Bremen, Bremen, Germany

    Stéphane Pesant

  26. MARUM, Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany

    Stéphane Pesant

  27. Bigelow Laboratory for Ocean Sciences, East Boothbay, ME, USA

    Nicole Poulton

  28. Department of Microbiology and Immunology, Rega Institute, Katholieke Universiteit Leuven, Leuven, Belgium

    Jeroen Raes

  29. Center for the Biology of Disease, Vlaams Instituut voor Biotechnologie Katholieke Universiteit Leuven, Leuven, Belgium

    Jeroen Raes

  30. Department of Applied Biological Sciences, Vrije Universiteit Brussel, Brussels, Belgium

    Jeroen Raes

  31. Department of Geosciences, Laboratoire de Météorologie Dynamique, Ecole Normale Supérieure, Paris, France

    Sabrina Speich

  32. Ocean Physics Laboratory, University of Western Brittany, Brest, France

    Sabrina Speich

  33. Departments of Microbiology and Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH, USA

    Matthew B. Sullivan

Authors
  1. Marta Royo-Llonch
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  2. Pablo Sánchez
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  3. Clara Ruiz-González
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  4. Guillem Salazar
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  5. Carlos Pedrós-Alió
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  6. Marta Sebastián
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  7. Karine Labadie
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  8. Lucas Paoli
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  9. Federico M. Ibarbalz
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  10. Lucie Zinger
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  11. Benjamin Churcheward
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  12. Samuel Chaffron
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  13. Damien Eveillard
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  14. Eric Karsenti
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  15. Shinichi Sunagawa
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  16. Patrick Wincker
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  17. Lee Karp-Boss
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  18. Chris Bowler
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  19. Silvia G. Acinas
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Consortia

Tara Oceans Coordinators

  • Silvia G. Acinas
  • , Marcel Babin
  • , Peer Bork
  • , Emmanuel Boss
  • , Chris Bowler
  • , Guy Cochrane
  • , Colomban de Vargas
  • , Gabriel Gorsky
  • , Nigel Grimsley
  • , Lionel Guidi
  • , Pascal Hingamp
  • , Daniele Iudicone
  • , Olivier Jaillon
  • , Stefanie Kandels
  • , Lee Karp-Boss
  • , Eric Karsenti
  • , Fabrice Not
  • , Hiroyuki Ogata
  • , Stéphane Pesant
  • , Nicole Poulton
  • , Jeroen Raes
  • , Christian Sardet
  • , Sabrina Speich
  • , Lars Setmmann
  • , Matthew B. Sullivan
  • , Shinichi Sunagawa
  •  & Patrick Wincker

Contributions

M.R.-L. developed the methodology, analysed the data, designed the data visualizations and wrote the manuscript. P.S. developed the methodology and analysed the data. C.R.-G., G.S., L.P., F.I., B.C. and M.S. analysed the data. L.Z. and S.C. analysed the data and contributed to the interpretation of the findings. C.P.-A., L.K.-B. and C.B. contributed to the interpretation of the findings and provided critical reading of the manuscript. K.L. and P.W. coordinated all sequencing efforts. D.E. and S.S. provided critical reading of the manuscript. E.K. is the director of the Tara Oceans expedition. C.B., L.K.-B. and M.B. directed the Tara Oceans Polar Circle expedition. The Tara Oceans coordinators conceptualized the research, organized the sampling efforts and revised the manuscript. S.G.A. created the study design, developed the methodology, analysed the data and helped to write the manuscript.

Corresponding author

Correspondence to Silvia G. Acinas.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Microbiology thanks Eric Collins, David Pearce and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 NMDS of the 16S miTAG community composition of the 41 Tara Oceans Polar Circle metagenomes.

Colors delimit the 9 groups of samples used for co-assembly in order to build Arctic bins. Shape indicates the ocean layer from which each metagenomic sample was collected.

Source data

Extended Data Fig. 2 Taxonomic classification of the 27 partial ribosomal genes encoded in the 530 MQ and HQ Arctic MAGs.

a, Number of Arctic MAGs assigned to each phylum in the Bacteria domain. b, Number of Arctic MAGs assigned to each phylum in the Archaea domain.

Source data

Extended Data Fig. 3 Rank-abundance curve of Arctic MAGs in Arctic metagenomes.

MAGs are sorted in X axis by their accumulated RPKGs in the 37 Arctic metagenomes (including the sub-Arctic North Atlantic) used in this study. MAGs are colored by phyla and the those recruiting at least 200 RPKGs are labelled with extended taxonomic annotation. Taxonomic annotation reaches the furthest level of classification for each MAG and the number in parenthesis is the MAG’s identification code.

Source data

Extended Data Fig. 4 Transcript abundance (RPKM; reads per gene kilobase per million of sequenced reads) of the marker gene coxL (Carbon monoxide dehydrogenase large chain) K03520 from the aerobic carbon-monoxide dehydrogenase.

a, Polar maps with the accumulated metatranscriptomic RPKMs of 9 Arctic MAGs expressing the CO fixing coxL Form I, color-coded by CAFF region. The size of the dot is proportional to the accumulated metatranscriptomic RPKMs. In the dot plot below, RPKMs of 9 Arctic MAGs expressing CO fixing coxL Form I colored based on taxonomic annotation at the phylum level. Accumulated RPKMs per sample is depicted with a dashed black line. b, Polar maps with the accumulated metatranscriptomic RPKMs of 105 Arctic MAGs expressing coxL Form II, color-coded by CAFF region. The size of the dot is proportional to the accumulated metatranscriptomic RPKGs. Absent maps mean that no recruitment was found for that specific metabolism/domain/layer. In the dot plot below, RPKMs of 9 Arctic MAGs expressing coxL Form II colored based on taxonomic annotation at the phylum level. Accumulated RPKMs per sample is depicted with a dashed black line.

Source data

Extended Data Fig. 5 Pan-Arctic profiles of Tara Arctic Ocean MAGs catalogue.

a, Quantification and taxonomic classification of MAGs based on their pan-Arctic profiles. Stacked barplots representative of the number of pan-Arctic MAGs (left) and non pan-Arctic MAGs (right), colored by phylum. MAGs have been classified based on the number of Arctic regions they are present, represented in the X axis. Absolute number of MAGs per X axis category can be seen in the top barplots. More details for pan-Arctic categorization can be found in the Methods sections. b, Quantification and taxonomic classification of MAGs with a limited distribution, found only in one Arctic Region. Stacked barplots representative of the number of non pan-Arctic MAGs present only in one Arctic region, colored by phylum. The different Arctic regions and their season of sampling are found in axis X. Absolute number of MAGs per X axis category can be seen in the top barplots.

Source data

Extended Data Fig. 6 Differences in estimated minimum growth times and optimal growth temperatures across biogeographic categories, mean 16S rRNA gene copy number across latitude and differences in estimated complete genome sizes and optimal growth temperatures between different niche breadth categories.

a, Distribution of Minimum Generation Times estimated for each individual MAG, grouped by their biogeographical categorization (n = 153 MAGs classified as Arctic only, 123 MAGs classified as Arctic & NAO, 23 MAGs classified as bipolar, 4 MAGs classified as NAO only and 227 MAGs classified as Other latitudes). Data are shown as box plots (Tukey style): the lower and upper hinges correspond to the first and third quartiles (the 25th and 75th percentiles), the horizontal line indicates the median and the whiskers indicate the lowest and highest points within 1.5× the interquartile ranges of the lower (first) or upper (third) quartile, respectively. Data beyond the end of the whiiskers are outlying points and are plotted individually. Statistical support was calculated using the two-sided Dunnett-Tukey-Kramer Pairwise Multiple Comparison Test Adjusted for Unequal Variances and Unequal Sample Sizes (DTK) and CI 95. DTK test shows significant differences between the ‘Arctic only’ and the ‘Other latitudes’ MAGs. b, Distribution of Optimal Growth Temperatures estimated for each individual MAG, grouped by their biogeographical categorization (n = 153 MAGs classified as Arctic only, 123 MAGs classified as Arctic & NAO, 23 MAGs classified as bipolar, 4 MAGs classified as NAO only and 227 MAGs classified as Other latitudes). Boxplots describe the data as in a. DTK was performed as in a and shows significant differences (p-value < 0.05) between the ‘Arctic only’ and the ‘Other latitudes’ MAGs. c, Dots correspond to Tara Oceans samples from surface and subsurface chlorophyll maxima and are place across latitude depending on their estimated number of ribosomal copies (derived from miTAGs, see Methods). d, Distribution of estimated complete assembly size of MAGs based on their niche breadth category. Statistical support was calculated using the two-sided Dunnett-Tukey-Kramer Pairwise Multiple Comparison Test Adjusted for Unequal Variances and Unequal Sample Sizes (DTK) and CI 95. DTK test shows significant differences in estimated complete assembly size (p-value < 0.05) between MAGs classified as habitat specialists and those uncategorized (n = 38 MAGs classified as generalists, 111 MAGs classified as specialists, 381 uncategorised MAGs). Data are shown as box plots (Tukey style): the lower and upper hinges correspond to the first and third quartiles (the 25th and 75th percentiles), the horizontal line indicates the median and the whiskers indicate the lowest and highest points within 1.5× the interquartile ranges of the lower (first) or upper (third) quartile, respectively. Data beyond the end of the whiskers are outlying points and are plotted individually. e) Distribution of optimal growth temperatures of MAGs based on their niche breadth category (n = 38 MAGs classified as generalists, 111 MAGs classified as specialists, 381 uncategorised MAGs). DTK was performed as in a and shows (p-value < 0.05) between the generalists and the uncategorised MAGs.

Source data

Extended Data Fig. 7 Disentangling generalists and specialists within the 530 Arctic MAGs.

a, Distribution of Arctic MAGs based on their mean read recruitments in Arctic metagenomic samples (RPKG, X axis) and their Levin’s Index (i.e, niche breadth, Y axis). The color gradient depicts the occurrence (that is, % of samples where a given MAG is present) in the Arctic metagenomic dataset and shape indicates their niche breadth category (generalists, specialists and uncategorised). b, Number of habitat generalists (orange), specialists (blue) and uncategorised MAGs (grey) in each biogeographic category shown in bar plots (n = 530 MAGs examined over 32 Arctic metagenomes). The adjacent boxplots show the distribution of assembly sizes within each subcategory (upscaled to 100% of genome completeness) and statistically significant differences have been highlighted with an asterisk (DTK test, p-value < 0.05). Box plots are presented horizontally and in Tukey style: the lower (left) and upper (right) hinges correspond to the first and third quartiles (the 25th and 75th percentiles), the vertical line indicates the median and the whiskers indicate the lowest and highest points within 1.5× the interquartile ranges of the lower (first) or upper (third) quartile, respectively. Data beyond the end of the whiskers are outlying points and are plotted individually. Statistical support was calculated using the two-sided Dunnett-Tukey-Kramer Pairwise Multiple Comparison Test Adjusted for Unequal Variances and Unequal Sample Sizes (DTK) and CI 95. Adjacent stacked barplots indicate their taxonomic composition at the phylum level. Asterisks in the taxonomic annotation legend indicate phyla from domain Archaea, lack of asterisk indicates domain Bacteria. c, Abundances of generalists (n = 38 MAGs; orange), specialists (n = 111 MAGs; blue) and uncategorised (n = 381 MAGs; grey) MAGs in Arctic metagenomes (n = 32 samples, 18 SRF, 7 SCM, 7 MES; filled boxplots) and metatranscriptomes (n = 29 samples, 18 SRF, 7 SCM, 4 MES; empty boxplots) across the three ocean layers. Boxplots describe the data as in b. There are no significant differences between the groups.

Source data

Extended Data Fig. 8 Seawater temperature in samples where maximum metagenomic recruitment occurred per MAG, per niche breadth category.

a, Histogram of the number of MAGs in the station where their maximum metagenomic RPKG occurred, colored by niche breadth category and horizontally separated by layer. Bottom heatmap above X axis represents the temperature of each sample. b, Distribution of temperatures in those samples where maximum metagenomic RPKG per MAG occurred, by niche category and layer (n = 38 MAGs classified as generalists and 111 MAGs classified as specialists tested against 32 metagenomes, including 18 SRF, 7 SCM and 7 MES). Data are shown as box plots (Tukey style): the lower and upper hinges correspond to the first and third quartiles (the 25th and 75th percentiles), the horizontal line indicates the median and the whiskers indicate the lowest and highest points within 1.5× the interquartile ranges of the lower (first) or upper (third) quartile, respectively. Data beyond the end of the whiiskers are outlying points and are plotted individually.

Source data

Extended Data Fig. 9 Genes found in specialists but not in generalists.

Quantification of genes annotated against KEGG database that were found in specialist MAGs but not in generalist, colored by pathway.

Source data

Extended Data Fig. 10 Map with the reference stations with metagenomic and metatranscriptomic samples used in the study.

Samples are colored based on the expedition. Supplementary Table 4 contains more details about environmental metadata of these stations.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–9 and text on taxonomical classification of Arctic MAGs.

Reporting Summary

Peer Review Information

Supplementary Tables

Source data

Source Data Fig. 1

Source data for the generation of all plots in Fig. 1.

Source Data Fig. 2

Source data for the generation of all plots in Fig. 2.

Source Data Fig. 3

Source data for the generation of all plots in Fig. 3.

Source Data Fig. 3

Sequence alignment in FASTA format (RoyoLlonch_NatMicrobiol_2021_SOURCEDATA_Figure_3A_Rubisco_alignment.fasta) and phylogenetic tree in Newick format (RoyoLlonch_NatMicrobiol_2021_SOURCEDATA_Figure_3A_Rubisco_ phylogenetic_tree.nwk) for the generation of Fig. 3a.

Source Data Fig. 4

Source data for the generation of all plots in Fig. 4.

Source Data Fig. 5

Source data and statistical data for the generation of all plots in Fig. 5.

Source Data Fig. 6

Source data for the generation of all plots in Fig. 6.

Source Data Extended Data Fig. 1

Statistical source data for the generation of Extended Data Fig. 1.

Source Data Extended Data Fig. 2

Source data for the generation of all plots in Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Source data for the generation of Extended Data Fig. 3.

Source Data Extended Data Fig. 4

Source data for the generation of all plots in Extended Data Fig. 4.

Source Data Extended Data Fig. 5

Source data for the generation of all plots in Extended Data Fig. 5.

Source Data Extended Data Fig. 6

Source data and statistical data for the generation of all plots in Extended Data Fig. 6.

Source Data Extended Data Fig. 7

Source data and statistical data for the generation of all plots in Extended Data Fig. 7.

Source Data Extended Data Fig. 8

Source data for the generation of all plots in Extended Data Fig. 8.

Source Data Extended Data Fig. 9

Source data for the generation of Extended Data Fig. 9.

Source Data Extended Data Fig. 10

Source data for the generation of Extended Data Fig. 10.

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Royo-Llonch, M., Sánchez, P., Ruiz-González, C. et al. Compendium of 530 metagenome-assembled bacterial and archaeal genomes from the polar Arctic Ocean. Nat Microbiol 6, 1561–1574 (2021). https://doi.org/10.1038/s41564-021-00979-9

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