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Global-scale shifts in marine ecological stoichiometry over the past 50 years

Nature Geoscience (2025)Cite this article

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Abstract

The elemental stoichiometry of carbon (C), nitrogen (N) and phosphorus (P) regulates marine biogeochemical cycles and underpins the Redfield ratio paradigm. However, its global variability and response to environmental change remain poorly constrained. Here we compile a global dataset of 56,031 plankton (particulate) and 388,515 seawater (dissolved) samples from 1971 to 2020, spanning surface to 1,000 m depth, to assess spatial and temporal dynamics in marine C:N:P ratios. We show that planktonic C:P and N:P, and oceanic C:N and C:P ratios, consistently exceed Redfield ratio throughout the study period, indicating widespread deviation from canonical stoichiometry. Planktonic C:N and N:P ratios rose markedly in the late twentieth century, followed by a decline, suggesting a progressive alleviation of P limitation, probably driven by increased anthropogenic P inputs. Depth-resolved patterns show decreasing oceanic C:N and C:P, and increasing N:P ratios with depth, attributable to differential remineralization and microbial nutrient cycling. Our findings highlight dynamic, non-static stoichiometric patterns over decadal scales, offering critical observational constraints for refining the representation of elemental cycling in biogeochemical models and improving projections of marine ecosystem responses to global change.

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Fig. 1: Global marine ecological stoichiometry patterns.
Fig. 2: Distributions of C, N and P concentrations in planktonic and oceanic environments.
Fig. 3: Depth-related changes in oceanic concentrations and stoichiometric ratios of C, N and P.
Fig. 4: Temporal variation in marine ecological stoichiometric ratios over the past five decades.
Fig. 5: A conceptual diagram illustrating the spatiotemporal variations of marine ecological stoichiometries of C, N and P.

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Data availability

The datasets generated and analysed during the current study are available via figshare at https://doi.org/10.6084/m9.figshare.27282792 (ref. 60).

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Acknowledgements

J.L. is funded by the National Natural Science Foundation of China (grant no. 42207107) and the Horizon Europe Framework Programme (grant no. 101205485). K.I. is supported by a grant from the Simons Foundation (grant no. LS-ECIAMEE-00001549). Ji.C. is granted by the National Natural Science Foundation of China (grant nos. 32471685 and 42361144886) and Shaanxi Province Natural Science Foundation for Distinguished Young Scholar (grant no. 2024JC-JCQN-32).

Author information

Authors and Affiliations

  1. State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Ac ademy of Sciences, Xi’an, China

    Ji Liu, Jiacong Zhou, Yixuan Zhang, Siyi Sun & Ji Chen

  2. Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, Wuhan, China

    Ji Liu, Hai Wang & Juan Mou

  3. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain

    Josep Penuelas

  4. CREAF, Bellaterra, Spain

    Josep Penuelas

  5. Laboratorio de Biodiversidad y Funcionamiento Ecosistémico, Instituto de Recursos Naturales y Agrobiología de Sevilla, Consejo Superior de Investigaciones Científicas, Seville, Spain

    Manuel Delgado-Baquerizo & Guiyao Zhou

  6. Department of Earth System Science, University of California, Irvine, Irvine, CA, USA

    Adam C. Martiny

  7. Marine and Environmental Biology, University of Southern California, Los Angeles, CA, USA

    David A. Hutchins

  8. Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, USA

    Keisuke Inomura

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

    Michael W. Lomas

  10. Department of Earth and Planetary Sciences, Yale University, New Haven, CT, USA

    Mojtaba Fakhraee & Noah Planavsky

  11. Department of Plant Sciences, University of Cambridge, Cambridge, UK

    Adam Pellegrini

  12. Department of Ecology, Faculty of Science, Charles University, Prague, Czechia

    Tyler J. Kohler

  13. Department of Geosciences, and at the High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA

    Curtis A. Deutsch

  14. Harbor Branch Oceanographic Institute, Florida Atlantic University, Ft. Pierce, FL, USA

    Brian Lapointe

  15. Ministry of Education Key Laboratory for Transboundary Ecosecurity of Southwest China, School of Ecology and Environmental Science, Yunnan University, Kunming, China

    Yong Zhang

  16. Key Laboratory of Agro-ecological Processes in Subtropical Regions, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China

    Yanyan Li & Wei Zhang

  17. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Yong Li & Junji Cao

  18. Institute of Global Environmental Change, Department of Earth and Environmental Science, School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an, China

    Ji Chen

  19. Guanzhong Plain Ecological Environment Change and Comprehensive Treatment National Observation and Research Station, Xi’an, China

    Ji Chen

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Contributions

J.L. conceived the study. J.L., H.W. and J.M. designed the methodology, conducted the investigation and generated the visualizations. J.L. drafted the paper. Ji.C. and J.P. supervised the project. J.L., H.W., J.M., J.P., M.D.-B., A.C.M., G.Z., D.A.H., K.I., M.W.L., M.F., A.P., T.J.K., C.A.D., N.P., B.L., Yo.Z., Ya.L., J.Z., Yi.Z., S.S., Yo.L., W.Z., Ju.C. and Ji.C. reviewed and edited the paper.

Corresponding author

Correspondence to Ji Chen.

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Nature Geoscience thanks Dag Hessen, Helena Klip and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: James Super, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Fluxes of carbon (C), nitrogen (N), and phosphorus (P) in atmospheric inputs to the surface versus surface returns to the atmosphere from 1971 to 2000 (a) and 2001 to 2020 (b).

The data on the global C cycle are from Reay et al.61, Graber et al.62, and Friedlingstein et al.19. The data on the global N are from Galloway et al.63, Zhang et al.64, and Ackerman et al.18. The data on the global P cycle are from Wang et al.65.

Extended Data Fig. 2 Trends in the planktonic concentrations (a) and stoichiometries (b) of carbon (C), nitrogen (N) and phosphorus (P) at different seawater depths.

Δ represents the amount of change in the planktonic C, N, and P concentrations at different seawater depths relative to that of the surface water. Predictor variables (C, N, P) plus lowercase 0 represent the mean value of the predictor variable at the seawater surface. Predictor variables (C:N, C:P, N:P) plus lowercase 0 represent the median value of the predictor variable at the seawater surface. The solid lines and shading area represent the mean for ΔC, ΔN, and ΔP or median for ΔC:N, ΔC:P, and ΔN:P and 95% confidence intervals of the predictor variables, respectively. Predictor variables divided by depth represent the change coefficient of the predictor variables with increasing seawater depth in the epipelagic and mesopelagic zones. ***, p < 0.001.

Extended Data Fig. 3 Temporal trends in planktonic (a, n = 28135) and oceanic (b, n = 177487) ecological stoichiometry of the Pacific Ocean.

The boxplot represents the distribution of data for all years from 1970 to 2020. For the boxplot, the straight line in the center represents the median, or 2nd quartile (Q2), the top edge of the box represents the 3rd quartile (Q3), and the bottom edge of the box represents the 1st quartile (Q1). The black and blue dashed lines represent the Redfield ratio and the ecological stoichiometric ratio established in this study, respectively. The error bars represent the mean and standard deviation of the stoichiometric ratios for each year. The significance of the non-zero coefficients in the segmented model fitting is evaluated through a two-tailed t-test. If p > 0.05, it indicates that there is no obvious trend of change in the stoichiometric ratio over time. The shaded area of the fitted line represents the 95% confidence interval of the predicted values. The shaded area at the segmentation point represents the uncertainty of the time breakpoints, which is derived from the standard deviation of the breakpoint estimates across iterations based on resampling. *, p < 0.05; **, p < 0.01; ***, p < 0.001.

Extended Data Fig. 4 Temporal trends in planktonic (a, n = 23788) and oceanic (b, n = 105541) ecological stoichiometry of the Atlantic Ocean.

The boxplot represents the distribution of data for all years from 1970 to 2020. For the boxplot, the straight line in the center represents the median, or 2nd quartile (Q2), the top edge of the box represents the 3rd quartile (Q3), and the bottom edge of the box represents the 1st quartile (Q1). The black and blue dashed lines represent the Redfield ratio and the ecological stoichiometric ratio established in this study, respectively. The error bars represent the mean and standard deviation of the stoichiometric ratios for each year. The significance of the non-zero coefficients in the segmented model fitting is evaluated through a two-tailed t-test. If p > 0.05, it indicates that there is no obvious trend of change in the stoichiometric ratio over time. The shaded area of the fitted line represents the 95% confidence interval of the predicted values. The shaded area at the segmentation point represents the uncertainty of the time breakpoints, which is derived from the standard deviation of the breakpoint estimates across iterations based on resampling. *, p < 0.05; **, p < 0.01; ***, p < 0.001.

Extended Data Fig. 5 Temporal trends in planktonic (a, n = 3612) and oceanic (b, n = 45558) ecological stoichiometry of the Indian Ocean.

The boxplot represents the distribution of data for all years from 1970 to 2020. For the boxplot, the straight line in the center represents the median, or 2nd quartile (Q2), the top edge of the box represents the 3rd quartile (Q3), and the bottom edge of the box represents the 1st quartile (Q1). The black and blue dashed lines represent the Redfield ratio and the ecological stoichiometric ratio established in this study, respectively. The error bars represent the mean and standard deviation of the stoichiometric ratios for each year. The significance of the non-zero coefficients in the segmented model fitting is evaluated through a two-tailed t-test. If p > 0.05, it indicates that there is no obvious trend of change in the stoichiometric ratio over time. The shaded area of the fitted line represents the 95% confidence interval of the predicted values. The shaded area at the segmentation point represents the uncertainty of the time breakpoints, which is derived from the standard deviation of the breakpoint estimates across iterations based on resampling. *, p < 0.05; **, p < 0.01; ***, p < 0.001.

Extended Data Fig. 6 Temporal trends in planktonic (a, n = 296) and oceanic (b, n = 59929) ecological stoichiometry of the Arctic Ocean.

The boxplot represents the distribution of data for all years from 1970 to 2020. For the boxplot, the straight line in the center represents the median, or 2nd quartile (Q2), the top edge of the box represents the 3rd quartile (Q3), and the bottom edge of the box represents the 1st quartile (Q1). The black and blue dashed lines represent the Redfield ratio and the ecological stoichiometric ratio established in this study, respectively. The error bars represent the mean and standard deviation of the stoichiometric ratios for each year. The significance of the non-zero coefficients in the segmented model fitting is evaluated through a two-tailed t-test. If p > 0.05, it indicates that there is no obvious trend of change in the stoichiometric ratio over time. The shaded area of the fitted line represents the 95% confidence interval of the predicted values. The shaded area at the segmentation point represents the uncertainty of the time breakpoints, which is derived from the standard deviation of the breakpoint estimates across iterations based on resampling. *, p < 0.05; **, p < 0.01; ***, p < 0.001.

Extended Data Table 1 A summary of the global planktonic and oceanic ecological stoichiometric ratio of different sea areas across different depth ranges
Full size table

Supplementary information

Supplementary Information

Supplementary methods, Figs. 1–14 and Tables 1–3.

Source data

Source Data 1

Statistical source data (marine ecological stoichiometry data).

Source Data 2

Statistical source data (global carbon, nitrogen and phosphorus flux data).

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Liu, J., Wang, H., Mou, J. et al. Global-scale shifts in marine ecological stoichiometry over the past 50 years. Nat. Geosci. (2025). https://doi.org/10.1038/s41561-025-01735-y

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