Plankton networks driving carbon export in the oligotrophic ocean

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Abstract

The biological carbon pump is the process by which CO2 is transformed to organic carbon via photosynthesis, exported through sinking particles, and finally sequestered in the deep ocean. While the intensity of the pump correlates with plankton community composition, the underlying ecosystem structure driving the process remains largely uncharacterized. Here we use environmental and metagenomic data gathered during the Tara Oceans expedition to improve our understanding of carbon export in the oligotrophic ocean. We show that specific plankton communities, from the surface and deep chlorophyll maximum, correlate with carbon export at 150 m and highlight unexpected taxa such as Radiolaria and alveolate parasites, as well as Synechococcus and their phages, as lineages most strongly associated with carbon export in the subtropical, nutrient-depleted, oligotrophic ocean. Additionally, we show that the relative abundance of a few bacterial and viral genes can predict a significant fraction of the variability in carbon export in these regions.

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Figure 1: Global view of carbon fluxes along the Tara Oceans circumnavigation route and associated eukaryotic lineages.
Figure 2: Ecological networks reveal key lineages associated with carbon export at 150 m at global scale.
Figure 3: Integrated plankton community network built from eukaryotic, prokaryotic and viral subnetworks related to carbon export at 150 m.
Figure 4: Key bacterial functional categories associated with carbon export at 150 m at global scale.

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Change history

  • 12 February 2016

    The author affiliations and corresponding authors were corrected. The Supplementary Material listing the Tara Oceans consortium members was also updated.

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Acknowledgements

We thank the commitment of the following people and sponsors: CNRS (in particular Groupement de Recherche GDR3280), European Molecular Biology Laboratory (EMBL), Genoscope/CEA, VIB, Stazione Zoologica Anton Dohrn, UNIMIB, Fund for Scientific Research – Flanders, Rega Institute, KU Leuven, The French Ministry of Research, the French Government ‘Investissements d’Avenir’ programmes OCEANOMICS (ANR-11-BTBR-0008), FRANCE GENOMIQUE (ANR-10-INBS-09-08), MEMO LIFE (ANR-10-LABX-54), PSL* Research University (ANR-11-IDEX-0001-02), ANR (projects POSEIDON/ANR-09-BLAN-0348, PHYTBACK/ANR-2010-1709-01, PROMETHEUS/ANR-09-PCS-GENM-217, TARA-GIRUS/ANR-09-PCS-GENM-218, SAMOSA, ANR-13-ADAP-0010), European Union FP7 (MicroB3/No.287589, ERC Advanced Grant Award to C.B. (Diatomite: 294823), Gordon and Betty Moore Foundation grant (#3790 and #2631) and the UA Technology and Research Initiative Fund and the Water, Environmental, and Energy Solutions Initiative to M.B.S., the Italian Flagship Program RITMARE to D.I., the Spanish Ministry of Science and Innovation grant CGL2011-26848/BOS MicroOcean PANGENOMICS to S.G.A., TANIT (CONES 2010-0036) from the Agència de Gestió d´Ajusts Universitaris i Reserca to S.G.A., JSPS KAKENHI grant number 26430184 to H.O., and FWO, BIO5, Biosphere 2 to M.B.S. We also thank the support and commitment of Agnès b. and Etienne Bourgois, the Veolia Environment Foundation, Region Bretagne, Lorient Agglomeration, World Courier, Illumina, the EDF Foundation, FRB, the Prince Albert II de Monaco Foundation, the Tara schooner and its captains and crew. We thank MERCATOR-CORIOLIS and ACRI-ST for providing daily satellite data during the expedition. We are also grateful to the French Ministry of Foreign Affairs for supporting the expedition and to the countries who graciously granted sampling permissions. Tara Oceans would not exist without continuous support from 23 institutes (http://oceans.taraexpeditions.org). The authors further declare that all data reported herein are fully and freely available from the date of publication, with no restrictions, and that all of the samples, analyses, publications, and ownership of data are free from legal entanglement or restriction of any sort by the various nations whose waters the Tara Oceans expedition sampled in. This article is contribution number 34 of Tara Oceans.

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L.G., S.C., Lu.B. and D.E. designed the study and wrote the paper. C.D., M.P., J.P. and Sa.S. collected Tara Oceans samples. S.K.-L. managed the logistics of the Tara Oceans project. L.G. and M.P. analysed oceanographic data. S.C. and Lu.B. analysed taxonomic data. S.C., Lu.B., D.E. and S.R. performed the genomic and statistical analyses. A.L., Y.D., L.G., S.C., Lu.B. and D.E. produced and analysed the networks. E.K., C.B. and G.G. supervised the study. M.S., J.R., E.K., C.B. and G.G. provided constructive comments, revised and edited the manuscript. Tara Oceans coordinators provided constructive criticism throughout the study. All authors discussed the results and commented on the manuscript.

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Correspondence to Lionel Guidi or Samuel Chaffron or Lucie Bittner or Damien Eveillard or Jeroen Raes or Eric Karsenti or Chris Bowler or Gabriel Gorsky.

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The authors declare no competing financial interests.

Additional information

Data described herein is available at European Nucleotide Archive under the project identifiers PRJEB402, PRJEB6610 and PRJEB7988, PANGAEA48,49,50, and a companion website (http://www.raeslab.org/companion/ocean-carbon-export.html). The data release policy regarding future public release of Tara Oceans data is described in ref. 51.

A list of authors and affiliations appears in the Supplementary Information.

Extended data figures and tables

Extended Data Figure 1 Overview of analytical methods used in the manuscript.

a, Depiction of a standard pairwise analysis that considers a sequence relative abundance matrix for s samples (s × OTUs (operational taxonomic units)) and its corresponding environmental matrix (s × p (parameters)). sPLS results emphasize OTU(s) that are the most correlated to environmental parameters. b, Depiction of a graph-based approach. Using only a relative abundance matrix (s × OTUs), WGCNA builds a graph where nodes are OTUs and edges represent significant co-occurrence. Co-occurrence scores between nodes are weights allocated to corresponding edges. These weights are magnified by a power-law function until the graph becomes scale-free. The graph is then decomposed within subnetworks (groups of OTUs) that are analysed separately. One subnetwork (group of OTUs) is considered of interest when its topology is related to the trait of interest; in the current case carbon export. For each subnetwork (for instance the subnetwork related to carbon export), each OTU is spread within a feature space that plots each OTU based on its membership to the subnetwork (x axis) and its correlation to the environmental trait of interest (that is, carbon export). A good regression of all OTUs emphasizes the putative relation of the subnetwork topology and the carbon export trait (that is, the more a given OTU defines the subnetwork topology, the more it is correlated to carbon export). c, Depiction of the machine learning (PLS) approach that was applied following subnetwork identification and selection. Greater VIP scores (that is, larger circles) emphasized most important OTUs. VIP refers to variable importance in projection and reflects the relative predictive power of a given OTU. OTUs with a VIP score greater than 1 are considered as important in the predictive model and their selection does not alter the overall predictive power.

Extended Data Figure 2 Lineage ecological subnetworks associated to environmental parameters and their structures correlating to carbon export.

ac, Global ecological networks were built using the WGCNA methodology (see Methods) and correlated to classical oceanographic parameters as well as carbon export (estimated at 150 m from particle size distribution and abundance). Each domain-specific global network is decomposed into smaller coherent subnetworks (depicted by distinct colours on the y axis) and their eigenvector is correlated to all environmental parameters. Similar to a correlation at the network scale, this approach directly links subnetworks to environmental parameters (that is, the more the taxa contribute to the subnetwork structure, the more their abundance is correlated to the parameter). a, A single eukaryotic subnetwork (n = 58, N = 1,870) is strongly associated to carbon export (r = 0.81, P = 5 × 10−15). b, A single prokaryotic subnetwork (n = 109, N = 1,527) is moderately associated to carbon export (r = 0.32, P = 9 × 10−3). c, A single viral subnetwork (n = 277, N = 5,476) is strongly associated to carbon export (r = 0.93, P = 2 × 10−15). df, The WGCNA approach directly links subnetworks to environmental parameters, that is, the more the features contribute to the subnetwork structure (topology), the more their abundance are correlated to the parameter. This measure allows to identify subnetworks for which the overall structure, summarized as the eigenvector of the subnetwork, is related to the carbon export. d, The eukaryotic subnetwork structure correlates to carbon export (r = 0.87, P = 5 × 10−16). e, The prokaryotic subnetwork structure correlates to carbon export (r = 0.47, P = 5 × 10−6). f, The viral population subnetwork structure correlates to carbon export (r = 0.88, P = 6 × 10−93). gi, Lineage subnetworks predict carbon export. PLS regression was used to predict carbon export using lineage abundances in selected subnetworks. LOOCV was performed and VIP scores computed for each lineage. g, The eukaryotic subnetwork predicts carbon export with a R2 of 0.69. h, The prokaryotic subnetwork predicts carbon export with a R2 of 0.60. i, The viral population subnetwork predicts carbon export with a R2 of 0.89. jl, Synechococcus (rather than Prochlorococcus) absolute cell counts correlate well to carbon export. j, Prochlorococcus cell counts estimated by flow cytometry do not correlate to carbon export (mean carbon flux at 150 m, r = −0.13, P = 0.27). k, Synechococcus cell counts estimated by flow cytometry correlate significantly to carbon export (r = 0.64, P = 4.0 × 10−10). l, Synechococcus / Prochlorococcus cell counts ratio correlates significantly to carbon export (r = 0.54, P = 4.0 × 10−7).

Extended Data Figure 3 Prokaryotic function subnetworks associated to environmental parameters and their structure correlate to carbon export.

ac, Global ecological networks were built for the prokaryotic functions using the WGCNA methodology (see Methods) and correlated to classical oceanographic parameters as well as carbon export. a, Two bacterial functional subnetworks (n = 441 and n = 220, N = 37,832) are associated to carbon export (r = 0.54, P = 1 × 10−7 and r = 0.42, P = 1 × 10−4). b, The WGCNA approach directly links subnetworks to environmental parameters, that is, the more the features contribute to the subnetwork structure (topology), the more their abundance are correlated to the parameter. This measure allows to identify subnetworks for which the overall structure, summarized as the eigenvector of the subnetwork, is related to the carbon export. The bacterial function subnetwork structures correlate to carbon export (FNET1 r = 0.68, P = 3 × 10−61, and FNET2 r = 0.47, P = 6 × 10−13). c, Two functional subnetworks (light and dark green, FNET1 (n = 220) and FNET2 (n = 441), respectively) are significantly associated with carbon export (FNET1: r = 0.42, P = 4 × 10−9 and FNET2: r = 0.54, P = 7 × 10−6). The highest VIP score functions from top to bottom correspond to red dots from right to left. d, PLS regression was used to predict carbon export using abundances of functions (OGs) in selected subnetworks. LOOCV was performed and VIP scores computed for each function. Light green subnetwork (FNET1) functions predict carbon export with a R2 of 0.41. Dark green subnetwork (FNET2) functions predict carbon export with a R2 of 0.48. e, Cumulative abundance of genus-level taxonomic annotations of genes encoding functions from FNET1 and FNET2 subnetworks and bacterial function subnetworks predict carbon export. Genes contributing to the relative abundance of FNET1 and FNET2 subnetwork functions were taxonomically annotated by homology searches against a non-redundant gene reference database using a last common ancestor (LCA) approach (see Methods).

Extended Data Figure 4 Viral protein cluster networks reveal potential marker genes for carbon export prediction at global scale.

a, A viral protein cluster (PC) network was built using abundances of PCs predicted from viral population contigs associated to carbon export (Fig. 2c) using the WGCNA methodology (see Methods) and correlated to classical oceanographic parameters. Two viral PC subnetworks (n = 1,879 and n = 2,147, N = 4,678, light and dark orange, VNET1 and VNET2, left and right panel respectively) are strongly associated to carbon export (VNET1: r = 0.75, P = 3 × 10−7 and VNET2: r = 0.91, P = 3 × 10−14). b, The viral PC subnetwork structures correlate to carbon export (VNET1 r = 0.91, P < 1 × 10−200, and VNET2 r = 0.96, P < 1 × 10−200). c, Size of dots is proportional to the VIP score computed for the PLS regression. d, Viral PC subnetworks predict carbon export. PLS regression was used to predict carbon export using abundances of viral protein clusters (PCs) in selected subnetworks. LOOCV was performed and VIP scores computed for each PC. Light orange subnetwork (VNET1, left panel) PCs predict carbon export with a R2 of 0.55. Dark orange subnetwork (VNET2, right panel) PCs predict carbon export with a R2 of 0.89.

Extended Data Figure 5 WGCNA and PLS regression analyses for the full eukaryotic data set.

a, A single eukaryotic subnetwork (n = 58), is strongly associated to carbon export (r = 0.79, P = 3 × 10−14). b, The eukaryotic subnetwork structure correlates to carbon export (r = 0.94, P = 4 × 10−27). c, The eukaryotic subnetwork predicts carbon export with a R2 of 0.76. d, Lineages with the highest VIP score (dot size is proportional to the VIP score in the scatter plot) in the PLS are depicted as red dots corresponding to two rhizaria (Collodaria), one copepod (Euchaeta), and three dinophyceae (Noctiluca scintillans, Gonyaulax polygramma and Gonyaulax sp. (clade 4)).

Supplementary information

Supplementary Information

This file contains Supplementary Tables 1-13. Supplementary Tables 11 and 12 were replaced on 7 March 2016 to include the sample IDs and ENA run accession numbers. (XLSX 318 kb)

Supplementary Information

This file contains a full list of coordinators in the Tara Oceans Consortium. This file was updated on 12 February 2016 to correct some errors in the affiliations of members of the consortium. (PDF 145 kb)

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Guidi, L., Chaffron, S., Bittner, L. et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature 532, 465–470 (2016). https://doi.org/10.1038/nature16942

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