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The biogeochemical balance of oceanic nickel cycling

Nature Geoscience volume 15, pages 906–912 (2022)Cite this article

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Abstract

Nickel is a biologically essential element for marine life, with the potential to influence diverse processes, including methanogenesis, nitrogen uptake and coral health, in both modern and past oceans. However, an incomplete view of oceanic Ni cycling has stymied understanding of how Ni may impact marine life in these modern and ancient oceans. Here we combine data-constrained global biogeochemical circulation modelling with culture experiments and find that Ni in oligotrophic gyres is both chemically and biologically labile and only minimally incorporated into diatom frustules. We then develop a framework for understanding oceanic Ni distributions, and in particular the two dominant features of the global marine Ni distribution: the deep concentration maximum and the residual pool of approximately 2 nM Ni in subtropical gyres. We suggest that slow depletion of Ni relative to macronutrients in upwelling regions can explain the residual Ni pool, and reversible scavenging or slower regeneration of Ni compared with macronutrients contributes to the distinct Ni vertical distribution. The strength of these controls may have varied in the past ocean, impacting Ni bioavailability and setting a fine balance between Ni feast and famine for phytoplankton, with implications for both ocean chemistry and climate state.

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Fig. 1: The global marine distribution of Ni, P and Si.
Fig. 2: Laboratory and field experiments test key features of Ni marine biogeochemistry.
Fig. 3: Model-predicted Ni distribution in the global oceans compared with observations for a model in which Ni is depleted more slowly than P from upwelling waters and remineralized more deeply than P.
Fig. 4: Key processes controlling global Ni cycling and their implications for past and future ocean Ni limitation.

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Data availability

Data from the GEOTRACES 2017 IDP are available at https://www.bodc.ac.uk/geotraces/data/idp2017/. Data from the Tara Pacific expedition are available at https://doi.org/10.1594/PANGAEA.875582. Data from the US GEOTRACES GP15 transect are available at https://github.com/MTEL-USC/nickel-model.

Code availability

Model code for this work is available at https://github.com/MTEL-USC/nickel-model and can be run within the AWESOME OCIM modelling environment available at https://github.com/profseth/awesomeOCIM.

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Acknowledgements

Thanks to the many scientists who contributed data to the International GEOTRACES 2017 International Data Product, including the captains and crew of research vessels, the technicians who collected samples at sea and the analysts. Funding was provided by the Simons Foundation (award # 426570SP to S.G.J.), the Australian Research Council (award # DP210101650 to M.H.) and the National Science Foundation (award #s 1736896, 1737136, 1737167, 1851222 and 1746932 to S.G.J., T.M.C., J.N.F., D.A.H. and N.T.L., respectively).

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Authors and Affiliations

  1. Department of Earth Sciences, University of Southern California, Los Angeles, CA, USA

    Seth G. John, Rachel L. Kelly, Xiaopeng Bian, M. Isabel Smith, Hengdi Liang, Benoît Pasquier, Emily A. Seelen & Shun-Chung Yang

  2. Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA

    Feixue Fu & David A. Hutchins

  3. Department of Oceanography, Texas A&M University, College Station, TX, USA

    Nathan T. Lanning & Jessica N. Fitzsimmons

  4. School of Mathematics and Statistics, University of New South Wales, Sydney, New South Wales, Australia

    Mark Holzer

  5. Department of Chemistry and Biochemistry, Northern Arizona University, Flagstaff, AZ, USA

    Laura Wasylenki

  6. College of Marine Science, University of South Florida, St Petersburg, FL, USA

    Tim M. Conway

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Contributions

Phytoplankton culturing and analysis of culture samples was done by R.L.K., X.B., S.-C.Y., E.A.S., F.F., M.I.S. and D.A.H. Analyses of natural materials and seawater samples were completed by S.-C.Y., X.B., N.T.L., J.N.F. and T.M.C. Chemical experiments and analysis were performed by S.G.J. Modelling was undertaken by S.G.J. with the assistance of H.L., B.P., M.H. and L.W. The manuscript was written by S.G.J. with advice and input from all co-authors.

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Correspondence to Seth G. John.

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Nature Geoscience thanks Benjamin Twining and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Rebecca Neely, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Base model output.

Patterns in vertical and horizontal distribution of simulated Ni for a model including only biological uptake and remineralization of Ni in soft-tissue. a) Comparison between observations (colored circles) and optimized model output (background color) are shown for the surface ocean, with white lines delineating the boundaries of the oligotrophic gyre at 0.2 μM PO42-, and black lines showing the location of depth transect data. b) Comparison between observations and optimized model output are shown for depth transects in the Atlantic and Pacific Ocean, which include GEOTRACES transects GA02 and GP15, respectively. The Ni mean depth reported above this panel refers to the average depth of model-predicted Ni in the global ocean, which can be compared to mean depths of 2174 m and 2533 m for P and Si, respectively, based on World Ocean Atlas 2009 data. c) The global fit between model and observed Ni, with the colorscale reflecting the relative data density as a percentage compared to maximum data density. d) Horizontal patterns in global depth integrated scavenging flux of Ni (which has no value for this model because no scavenging process was included), presented as a percentage of the maximum scavenging intensity. e) vertical patterns in horizontally integrated Ni scavenging flux (which has no value for this model because no scavenging process was included), presented as a percentage of the maximum scavenging intensity. Additional information about optimized model parameters and model performance metrics are presented in Table ED1.

Extended Data Fig. 2 Model output with Ni in frustules.

Patterns in vertical and horizontal distribution of simulated Ni for a model including biological uptake and remineralization of Ni in soft-tissue, and the biological uptake and remineralization of Ni due to incorporation in diatom silicate frustules. Panels are the same as for Extended Data Fig. 1.

Extended Data Fig. 3 Model output with reversible scavenging onto POC.

Patterns in vertical and horizontal distribution of simulated Ni can be evaluated for models with various parameterizations of reversible scavenging, here showing a model with reversible scavenging onto POC as determined in Weber et al.12. Panels are the same as for Extended Data Fig. 1.

Extended Data Fig. 4 Model output with reversible scavenging like Th.

Patterns in vertical and horizontal distribution of simulated Ni can be evaluated for models with various parameterizations of reversible scavenging, here showing a model with reversible scavenging taking the same patterns as Th scavenging from Hulten et al.54. Panels are the same as for Extended Data Fig. 1.

Extended Data Fig. 5 Model output with reversible scavenging like Pa.

Patterns in vertical and horizontal distribution of simulated Ni can be evaluated for models with various parameterizations of reversible scavenging, here showing a model with reversible scavenging taking the same patterns as Pa scavenging from Hulten et al.54. Panels are the same as for Extended Data Fig. 1.

Extended Data Fig. 6 Model output with reversible scavenging onto Mn oxides.

Patterns in vertical and horizontal distribution of simulated Ni can be evaluated for models with various parameterizations of reversible scavenging, here showing a model with reversible scavenging onto particulate Mn oxides, based on a Mn model from van Hulten et al.55. Panels are the same as for Extended Data Fig. 1.

Extended Data Fig. 7 Model output with reversible scavenging onto POC, variable b.

Patterns in vertical and horizontal distribution of simulated Ni can be evaluated for models with various parameterizations of reversible scavenging, here showing a model with reversible scavenging onto POC based on Weber et al.12, with the vertical distributions of scavenging sites on POC determined from an optimized power-law equation. Panels are the same as for Extended Data Fig. 1.

Extended Data Fig. 8 Model output with deeper organic Ni remineralization.

Patterns in vertical and horizontal distribution of simulated Ni, here showing a model where Ni is allowed to remineralize according to a ‘Martin curve’ power law, except that the b exponent is optimizable for Ni instead of being tied to the remineralization of P. Panels are the same as for Extended Data Fig. 1.

Extended Data Fig. 9 Diatoms collected from the North Pacific.

Light microscopy micrographs showing persistence of intact and undamaged biogenic silica shells after removal of cellular organic material using HNO3. Shown are HNO3 cleaned silica frustules of a) the centric diatom Triceratium (100X magnification), b) the pennate diatom Pseudo-nitzschia (100X magnification), and c) the centric diatom Coscinodiscus (400X magnification). Even delicate shells of d) the silicoflagellate Dictyocha (100X magnification) came through the HNO3 digestion procedure intact, as did similarly fragile silica shells of radiolarians (not shown). All cells shown were collected on a 53 µm filter.

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John, S.G., Kelly, R.L., Bian, X. et al. The biogeochemical balance of oceanic nickel cycling. Nat. Geosci. 15, 906–912 (2022). https://doi.org/10.1038/s41561-022-01045-7

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