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Restructuring of plankton genomic biogeography in the surface ocean under climate change

Nature Climate Change volume 12, pages 393–401 (2022)Cite this article

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Abstract

The impact of climate change on diversity, functioning and biogeography of marine plankton remains a major unresolved issue. Here environmental niches are evidenced for plankton communities at the genomic scale for six size fractions from viruses to meso-zooplankton. The spatial extrapolation of these niches portrays ocean partitionings south of 60° N into climato-genomic provinces characterized by signature genomes. By 2090, under the RCP8.5 future climate scenario, provinces are reorganized over half of the ocean area considered, and almost all provinces are displaced poleward. Particularly, tropical provinces expand at the expense of temperate ones. Sea surface temperature is identified as the main driver of changes (50%), followed by phosphate (11%) and salinity (10%). Compositional shifts among key planktonic groups suggest impacts on the nitrogen and carbon cycles. Provinces are linked to estimates of carbon export fluxes which are projected to decrease, on average, by 4% in response to biogeographical restructuring.

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Fig. 1: Eukaryotic signature genomes of provinces of eukaryote enriched size classes.
Fig. 2: Global biogeographies of size-structured plankton provinces projected on WOA13 dataset.
Fig. 3: Modelled present and future global biogeographies of the three and seven provinces for the small metazoan enriched size fraction (20–180 μm) and the protist enriched size fraction (0.8–5 μm) and associated compositional shifts in marine hexanauplia and diazotrophs.
Fig. 4: Global impact of climate change on plankton community assemblages and carbon export.
Fig. 5: Drivers of plankton biogeographical restructuring in response to climate change.

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Data Availability

All data used are available at https://github.com/institut-de-genomique/NCLIM-20102618B. All coordinates of ocean partitionings from this study are available at https://figshare.com/articles/dataset/Biogeographies_genomic_provinces/1907162079.

Code Availability

All codes used are available at https://github.com/institut-de-genomique/NCLIM-20102618B.

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Acknowledgements

P.F. was supported by a CFR doctoral fellowship and the NEOGEN impulsion grant from the Direction de la Recherche Fondamentale of the CEA. This study received funding from the European Union’s Horizon 2020 Blue Growth research and innovation programme under grant agreement number 862923 (project AtlantECO), ATIGE Genopole postdoctoral fellowship (T.O.D.), HYDROGEN/ANR-14-CE23–0001 (T.O.D.) and ANR-11-IDEX-0004–17-EURE-0006. M.G. acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 820989 (project COMFORT). This study benefited from access to high-performance computing resources through GENCI- [TGCC/CINES/IDRIS] and the ESPRI computing and data centre (https://mesocentre.ipsl.fr), which is supported by CNRS, Sorbonne Université, Ecole Polytechnique and CNES and through national and international grants. We thank the commitment of the Research Federation for the Study of Global Ocean Systems Ecology and Evolution (FR2022/TaraGOSEE) and of Stazione Zoologica Anton Dohrn. We thank T. Roy for preparation of the climatic data, S. Henson for providing carbon export data, LAGE (Laboratoire d’Analyses Génomiques des Eucaryotes, CEA) members for stimulating discussions on this project, M. Mariadassou, S.D. Ayata and B.H. Mele for discussions on statistics and climate envelope models, C. Scarpelli and members of the scientific computation team from Genoscope for support on computations, L. Bopp for initial discussions on this project and on climate models and N. Le Bescot (TernogDesign) for help with the figures. We thank all members of the Tara Oceans consortium for maintaining a creative environment and for their constructive criticism. Tara Oceans would not exist without the Tara Ocean Foundation and the continuous support of 23 institutes (https://oceans.taraexpeditions.org/).

This article is contribution number 128 of Tara Oceans.

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Authors and Affiliations

  1. Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Université d’Evry, Université Paris-Saclay, Evry, France

    Paul Frémont, Jade Leconte, Tom O. Delmont, Patrick Wincker & Olivier Jaillon

  2. Research Federation for the Study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans, Paris, France

    Paul Frémont, Jade Leconte, Tom O. Delmont, Patrick Wincker & Olivier Jaillon

  3. LSCE-IPSL, CEA, CNRS, Université Paris-Saclay, Gif-sur-Yvette, France

    Marion Gehlen & Mathieu Vrac

  4. Stazione Zoologica Anton Dhorn, Naples, Italy

    Daniele Iudicone

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Contributions

P.F., M.G. and O.J. conceived the study. P.F. computed the results and compiled and analysed the data. M.G., O.J. and J.L. conducted a preliminary study. M.V. wrote the bias correction algorithm. P.F. wrote the initial draft of the paper. T.O.D., P.F., M.G., O.J., D.I., J.L., M.V. and P.W. discussed the results and contributed to writing the paper.

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Correspondence to Paul Frémont, Marion Gehlen or Olivier Jaillon.

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The authors declare no competing interests.

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Nature Climate Change thanks Levente Bodrossy, Robert Ptacnik and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Study pipeline.

Metagenomic data from the 2009–2013 Tara Oceans expedition and in situ measurements of physicochemical variables (World Ocean Atlas 2013, WOA13)33 are combined to define environmental niches at the plankton community level across 6 size fractions. Bias corrected outputs from a mean model of 6 Earth System Models (Supplementary Table 1) and WOA13 data are then used to project global plankton provinces for present day and end of the century conditions under a high warming scenario (RCP8.5)36. Variables are Sea Surface Temperature (SST), Salinity (Sal), Dissolved silica (Si), Nitrate (NO3), Phosphate (PO4), Iron (Fe) and a seasonality index of nitrate (SI NO3).

Extended Data Fig. 2 Prokaryotic signature genomes of provinces of the prokaryote (0.22–3 μm) and protist (0.8–5 μm) enriched size classes.

Indexes of presence enrichment52 for 1888 genomes of prokaryotic plankton32 in corresponding provinces are clustered and represented in a colour scale. Signature genomes (see Methods) are found for almost all provinces, their number and taxonomies are summarized (detailed list in Supplementary Table 6). A genome is considered to be signature of a province if the presence enrichment index is superior to 0.5 with this province and inferior to 0.1 for all other provinces of the given size class.

Extended Data Fig. 3 Distribution of deltas between future temperature at each sampling site (surface) minus either the mean or maximum temperature within their contemporary genomic province.

For most of the sites and across size fractions the future temperature projected by the bias adjusted ESM ensemble model is higher than both the maximum and mean contemporary temperature of their genomic province.

Extended Data Fig. 4 Global geographical patterns for provinces of four plankton size fractions in present day and at the end of the century.

(a, c, e, g) Present day and (b, d, f, h) end of century biogeographies of size classes 180–2000, 5–20, 0.22–3 and 0–0.2 μm respectively. At each grid point of the maps the dominant province is represented using a darkness of colour proportional to its presence probability. Dots represent areas of uncertainty (where the delta of probability between the dominant and another province is inferior to 0.5). Expansion of tropical provinces and shrinkage of temperate provinces are consistently projected in all size fractions. We generated these map using R-package maps51.

Extended Data Fig. 5 Bray-Curtis dissimilarity index and assemblage change maps comparing present day with end of the century projections of dominant provinces in principal fisheries53 (4 last deciles) and Exclusive Economic Zones54.

Bray-Curtis dissimilarity index and assemblage changes in (a, c) Principal fisheries and (b, d) Exclusive Economic Zones. Assemblage changes in (e) Principal fisheries and (f) Exclusive Economic Zones in areas projected to encounter an important change (Bray-Curtis dissimilarity index superior to 1/6). We generated these map using R-package maps51.

Extended Data Fig. 6 Projected compositional shifts in marine hexanauplia in areas of dominant province change.

(a) 180–2000 μm and (b) 5–20 μm. Top: Locations of dominant province change using colours corresponding to the type of province transition. Bottom: Circular plots summarizing significant compositional shifts in marine hexanauplia classified by size (‘not classified’ when no preferential size class is found). Each type of transition is represented by an arrow coloured according to the map and in grey if they represent less than 2% of the transitions. Barplots represent mean relative abundances of each group of organism. Arrows point towards the end of the century projected province and their widths are proportional to the area of change. Significant compositional changes in a type of organism are represented by triangles of the associated transition colour. We generated these map using R-package maps51.

Extended Data Fig. 7 Projected compositional shifts in bacterial diazotrophs in areas of dominant community change.

(a) 180–2000 μm (b) 20–180 μm (c) 5–20 μm and (d) 0.22–3 μm. Top: Locations of dominant province change using colours corresponding to the type of province transition. Bottom: Circular plots summarizing significant compositional shifts in marine diazotrophs. Each type of transition is represented by an arrow coloured according to the map and in grey if they represent less than 2% of the transitions. Barplots represent mean relative abundances of each group of organism. Arrows points towards the end of the century projected province and their widths are proportional to the area of change. Significant compositional changes in a type of organism are represented by triangles of the associated transition. We generated these map using R-package maps51.

Extended Data Fig. 8 Projected compositional changes in phototrophs in areas of dominant community change.

(a) 180–2000 μm (b) 20–180 μm (c) 5–20 μm (d) 0.8–5 μm and (e) 0.22–3 μm. Top: Locations of dominant province change using colours corresponding to the type of province transition. Bottom: Circular plots summarizing significant compositional shifts in phototrophs classified by size (‘not classified’ when no preferential size class is found). Each type of transition is represented by an arrow coloured according to the map and in grey if they represent less than 2% of the transitions. Barplots represent mean relative abundances of each group of organism. Arrows point towards the end of the century projected province and their widths are proportional to the area of change. Significant compositional changes in a type of organism are represented by triangles of the associated transition colour. We generated these map using R-package maps51.

Extended Data Fig. 9 Maps of carbon export flux changes in link with organisms’ projected changes.

Significant composition changes based on genomes relative abundances are represented for phototrophs, marine nitrogen fixers (Diazotrophic cyanobacteria) and copepods. For each map, transitions from several characteristic size classes are represented (a) Top: Diatoms 0.22–20 μm. Bottom: Diatoms 20–2000 μm (b) Top: Cyanobacteria 0.22–20 μm. Bottom: Cyanobacteria 20–2000 μm (c) Top: Other Algae 0.22–20 μm. Bottom: Other Algae 20–2000 μm (d) Diazotrophs 0.8–20 μm (e) Copepods 20–2000 μm. We generated these map using R-package maps51.

Extended Data Fig. 10 Association rules between changes in carbon flux and changes in organism relative abundances.

Association rules in (a) temperate/subpolar (latitude > 40° (<−40°)), (b) subtropical North (20° to 40° latitude), (c) subtropical South (−20° to −40° latitude) and (d) equatorial regions (-20° to 20° latitude). Each line represents an association rule between a change in carbon export found by the Apriori algorithm45 (first column: mean change in carbon export and second column: sign of the change and lift of the rule (equation 5). The other columns represent the changes in community composition (red: decrease of the given group, green: increase) associated with this change in carbon export.

Supplementary information

Supplementary Information

Supplementary Discussion 1–6, Figs. 1–18 and Tables 1–3.

Reporting Summary.

Supplementary Table 4

Genomic provinces and associated WOA13 environmental parameters.

Supplementary Table 5

Results of genomic provinces niches models cross-validation.

Supplementary Table 6

Signature genomes of genomic provinces.

Supplementary Table 7

Centroid shifts of genomic provinces.

Supplementary Table 8

Covered areas of genomic provinces.

Supplementary Table 9

Total carbon export fluxes.

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Frémont, P., Gehlen, M., Vrac, M. et al. Restructuring of plankton genomic biogeography in the surface ocean under climate change. Nat. Clim. Chang. 12, 393–401 (2022). https://doi.org/10.1038/s41558-022-01314-8

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