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Ubiquitous abundance distribution of non-dominant plankton across the global ocean

Nature Ecology & Evolutionvolume 2pages12431249 (2018) | Download Citation


Marine plankton populate 70% of Earth’s surface, providing the energy that fuels ocean food webs and contributing to global biogeochemical cycles. Plankton communities are extremely diverse and geographically variable, and are overwhelmingly composed of low-abundance species. The role of this rare biosphere and its ecological underpinnings are however still unclear. Here, we analyse the extensive dataset generated by the Tara Oceans expedition for marine microbial eukaryotes (protists) and use an adaptive algorithm to explore how metabarcoding-based abundance distributions vary across plankton communities in the global ocean. We show that the decay in abundance of non-dominant operational taxonomic units, which comprise over 99% of local richness, is commonly governed by a power-law. Despite the high spatial turnover in species composition, the power-law exponent varies by less than 10% across locations and shows no biogeographical signature, but is weakly modulated by cell size. Such striking regularity suggests that the assembly of plankton communities in the dynamic and highly variable ocean environment is governed by large-scale ubiquitous processes. Understanding their origin and impact on plankton ecology will be important for evaluating the resilience of marine biodiversity in a changing ocean.

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The authors are very grateful to F. d’Ovidio, V. Anjou and S. Audic, who participated in the early stages of this work, O. Missa, G. Sommeria-Klein and E. van Nimwegen for discussions on neutral models and model fitting, and the Tara Oceans consortium. The support of the informatics platform of IBENS has been essential for the computational part of this study. This work has received support under the programmes ‘Investissements d’Avenir’, launched by the French Government and implemented by ANR with the references ANR-10-LABX-54 MEMOLIFE, ANR-10-IDEX-0001-02 PSL* Research University and OCEANOMICS, as well as from the EU project MicroB3. C.B. additionally acknowledges funding from the ERC Advanced Award ‘Diatomite’, the Louis D Foundation of the Institut de France and the Radcliffe Institute of Advanced Study at Harvard University for a scholars fellowship during the 2016/17 academic year. This research was supported in part by National Science Foundation grant NSF PHY-1125915, NIH grant R25GM067110 and Gordon and Betty Moore Foundation grant 2919.01. This article is contribution number 76 of Tara Oceans.

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Author notes

  1. These authors contributed equally: Lucie Zinger, Shruti Malviya.


  1. Institut de Biologie de l’Ecole Normale Supérieure, Département de Biologie, Ecole Normale Supérieure, CNRS, INSERM, PSL Research University, Paris, France

    • Enrico Ser-Giacomi
    • , Lucie Zinger
    • , Shruti Malviya
    • , Eric Karsenti
    • , Chris Bowler
    •  & Silvia De Monte
  2. Simons Center, National Centre for Biological Sciences, Bangalore, India

    • Shruti Malviya
  3. Station Biologique de Roscoff, UMR 7144, CNRS, Roscoff, France

    • Colomban De Vargas
  4. Directors’ Research, EMBL, Heidelberg, Germany

    • Eric Karsenti
  5. Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Plön, Germany

    • Silvia De Monte


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S.D.M. conceived and directed the study. E.S.-G. obtained the analytical results, designed the adaptive algorithm and produced the fits. S.M. carried out the preliminary analysis on the protist abundance distributions. L.Z. performed statistical analysis on the fitted parameters. S.D.M., E.S.-G., L.Z. and C.B. interpreted the results and wrote the manuscript. C.D.V. and E.K. provided access to the Tara Oceans dataset and commented on early versions of the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Enrico Ser-Giacomi or Silvia De Monte.

Supplementary information

  1. Supplementary Information

    Supplementary Methods and Results, Supplementary Figures 1–4, Supplementary Tables 1–3, Supplementary References

  2. Reporting Summary

  3. Supplementary Data

    This supplementary information provides all SADs, RADs and associated fits considered in this study

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