Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Tipping points of marine phytoplankton to multiple environmental stressors

Nature Climate Change volume 12, pages 1045–1051 (2022)Cite this article

Subjects

Abstract

Globally, anthropogenic climate change is threatening marine species. However, whether and how global marine phytoplankton, which represent the base of marine food webs, will exceed their tipping points under multiple climate factors remain unclear. Here, by establishing machine learning models, we identified the tipping points of global marine phytoplankton production and resistance under eight environmental stressors. Phytoplankton production and resistance are affected by multiple factors and the temperature and partial pressure of carbon dioxide dominate the risks for reaching their tipping points. If the current emission scenario continues, 50% (40–61% at 90% confidence) and 41% (2–80% at 90% confidence) of tropical areas would reach the tipping points of ongoing phytoplankton production and resistance decline, respectively, in 2100. Compared with single- or few-factor studies, machine learning (for example, ensemble machine learning) provides a powerful and realistic solution for policy-makers facing large-scale ecological responses to global climate changes under multiple environmental stressors.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Phytoplankton threatened by multiple climate factors.
Fig. 2: Evaluating multifactor model performance and the projected responses of phytoplankton from RCP 2.6 to RCP 8.5.
Fig. 3: Effects of multiple factors on the tipping points of phytoplankton.
Fig. 4: The effects of multiple factors on phytoplankton production, resistance and richness.

Similar content being viewed by others

Data availability

The CHL data are available from the NASA Earth Observations (https://neo.gsfc.nasa.gov/view.php?datasetId=MY1DMM_CHLORA). The partial pressure of carbon dioxide at the sea surface data are available in the NOAA National Centers for Environmental Information (NCEI Accession 0160558) v.5.5. The NPP data are available at the Ocean Productivity site (http://orca.science.oregonstate.edu/1080.by.2160.monthly.xyz.vgpm.m.chl.m.sst.php). The PAR data are available in the OceanColor data website (monthly 4 km resolution, MODIS-Aqua L3m, https://oceandata.sci.gsfc.nasa.gov/). The mean nutrient concentrations, including the nitrate, silicate and phosphate concentrations, are available in the World Ocean Atlas 2018 climatological fields (https://www.ncei.noaa.gov/access/world-ocean-atlas-2018/). The salinity and sea surface temperature data are available from the IAP Ocean Gridded Product (http://www.ocean.iap.ac.cn/). The phytoplankton data are available in the PANGAEA (https://doi.pangaea.de/10.1594/PANGAEA.904397). The future climatic changes for T, salinity, \({{{\mathrm{NO}}}}_3^ -\) and NPP of RCP 2.6 and RCP 8.5 are available in the IPCC report on Climate Change 2013 (https://www.ipcc.ch/report/ar5/wg1/). The future climatic changes for \(p_{\rm{{CO}_{2}}}\) in RCP 2.6 and RCP 8.5 are available in the RCP database (v.2.0.5, https://tntcat.iiasa.ac.at/RcpDb/). The projected tendencies of \({{{\mathrm{PO}}}}_4^{3 - }\) and \({{{\mathrm{NO}}}}_3^ -\) refer to a previous study84. Compiled data are available for others to use on figshare66, licensed under CC BY 4.0. Confidence intervals for the analysis are provided in the Supplementary Data. Source data are provided with this paper.

Code availability

The custom-written basin hopping code, R software and Python codes are available on figshare66, licensed under CC BY 4.0.

References

  1. Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).

    Article  CAS  Google Scholar 

  2. Wang, Z. et al. Environmental stability impacts the differential sensitivity of marine microbiomes to increases in temperature and acidity. ISME J. 15, 19–28 (2021).

    Article  CAS  Google Scholar 

  3. Aricò, S. et al. Integrated Ocean Carbon Research: A Summary of Ocean Carbon Research, and Vision of Coordinated Ocean Carbon Research and Observations for the Next Decade (UNESCO, 2021).

  4. Mausz, M. et al. High CO2 concentration and iron availability determine the metabolic inventory in an Emiliania huxleyi-dominated phytoplankton community. Environ. Microbiol. 2, 3863–3882 (2020).

    Article  Google Scholar 

  5. Boyce, D. G., Lewis, M. R. & Worm, B. Global phytoplankton decline over the past century. Nature 466, 591–596 (2010).

    Article  CAS  Google Scholar 

  6. Logan, C. A., Dunne, J. P., Ryan, J. S., Baskett, M. L. & Donner, S. D. Quantifying global potential for coral evolutionary response to climate change. Nat. Clim. Change 11, 537–542 (2021).

    Article  Google Scholar 

  7. O'Hara, C. C., Frazier, M. & Halpern, B. S. At-risk marine biodiversity faces extensive, expanding, and intensifying human impacts. Science 372, 84–87 (2021).

    Article  CAS  Google Scholar 

  8. Ritchie, P. D. L., Clarke, J. J., Cox, P. M. & Huntingford, C. Overshooting tipping point thresholds in a changing climate. Nature 592, 517–523 (2021).

    Article  CAS  Google Scholar 

  9. Tornqvist, T. E., Jankowski, K. L., Li, Y. X., Gonzalez & J, L. Tipping points of Mississippi Delta marshes due to accelerated sea-level rise. Sci. Adv. 6, 5512 (2020).

    Article  Google Scholar 

  10. Bonan, G. B. & Doney, S. C. Climate, ecosystems, and planetary futures: the challenge to predict life in Earth System Models. Science 359, 8328 (2018).

    Article  Google Scholar 

  11. Heinze, C. The quiet crossing of ocean tipping points. Proc. Natl Acad. Sci. USA 118, e2008478118 (2021).

    Article  CAS  Google Scholar 

  12. IPCC. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  13. Zhang, X., Wan, H., Zwiers, F. W., Hegerl, G. C. & Min, S. Attributing intensification of precipitation extremes to human influence. Geophys. Res. Lett. 40, 5252–5257 (2013).

    Article  Google Scholar 

  14. Boyd, P. W., Lennartz, S. T., Glover, D. M. & Doney, S. C. Biological ramifications of climate-change-mediated oceanic multi-stressors. Nat. Clim. Change 5, 71–79 (2015).

    Article  Google Scholar 

  15. Barton, A. D., Irwin, A. J., Finkel, Z. V. & Stock, C. A. Anthropogenic climate change drives shift and shuffle in North Atlantic phytoplankton communities. Proc. Natl Acad. Sci. USA 113, 2964–2969 (2016).

    Article  CAS  Google Scholar 

  16. Berdugo, M. et al. Global ecosystem thresholds driven by aridity. Science 367, 787–790 (2020).

    Article  CAS  Google Scholar 

  17. Cai, Y., Lenton, T. M. & Lontzek, T. S. Risk of multiple interacting tipping points should encourage rapid CO2 emission reduction. Nat. Clim. Change 6, 520–525 (2016).

    Article  Google Scholar 

  18. Dutkiewicz, S., Boyd, P. W. & Riebesell, U. Exploring biogeochemical and ecological redundancy in phytoplankton communities in the global ocean. Glob. Change Biol. 27, 1196–1213 (2021).

    Article  CAS  Google Scholar 

  19. Flombaum, P., Wang, W. L., Primeau, F. W. & Martiny, A. C. Global picophytoplankton niche partitioning predicts overall positive response to ocean warming. Nat. Geosci. 13, 116–120 (2020).

    Article  CAS  Google Scholar 

  20. Zhou, Q., Gibson, C. E. & Foy, R. H. Long-term changes of nitrogen and phosphorus loadings to a large lake in north-west Ireland. Water Res. 34, 922–926 (2000).

    Article  CAS  Google Scholar 

  21. Wang, H. & Zheng, H. Model Validation, Machine Learning (eds Dubitzky, W. et al.) (Springer, 2013).

  22. Righetti, D., Vogt, M., Zimmermann, N. E., Guiry, M. D. & Gruber, N. PhytoBase: a global synthesis of open-ocean phytoplankton occurrences. Earth Syst. Sci. Data 12, 907–933 (2020).

    Article  Google Scholar 

  23. Diepenbroek, M. et al. PANGAEA-an information system for environmental sciences. Comput. Geosci. 28, 1201–1210 (2002).

    Article  Google Scholar 

  24. Ban, Z. et al. Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles. Proc. Natl Acad. Sci. USA 117, 10492–10499 (2020).

    Article  CAS  Google Scholar 

  25. Chung, Y. et al. Ensemble machine learning-based algorithm for electric vehicle user behavior prediction. Appl. Energy 254, 113732 (2019).

    Article  Google Scholar 

  26. Forzieri, G. et al. Emergent vulnerability to climate-driven disturbances in European forests. Nat. Commun. 12, 1081 (2021).

    Article  CAS  Google Scholar 

  27. Righetti, D. et al. Global pattern of phytoplankton diversity driven by temperature and environmental variability. Sci. Adv. 5, eaau6253 (2019).

    Article  Google Scholar 

  28. Van Heerwaarden, B. & Sgrò, C. M. Male fertility thermal limits predict vulnerability to climate warming. Nat. Commun. 12, 2214 (2021).

    Article  Google Scholar 

  29. Pal, J. & Eltahir, E. Future temperature in Southwest Asia projected to exceed a threshold for human adaptability. Nat. Clim. Change 6, 197–200 (2016).

    Article  Google Scholar 

  30. Khishigbayar, J. et al. Mongolian rangelands at a tipping point? Biomass and cover are stable but composition shifts and richness declines after 20 years of grazing and increasing temperatures. J. Arid. Environ. 115, 100–112 (2015).

    Article  Google Scholar 

  31. Attayde, J. L. & Hansson, L. A. Effects of nutrient recycling by zooplankton and fish on phytoplankton communities. Oecologia 121, 47–54 (1999).

    Article  Google Scholar 

  32. Muylaert, K., Sabbe, K. & Vyverman, W. Changes in phytoplankton diversity and community composition along the salinity gradient of the Schelde estuary (Belgium/the Netherlands). Estuar. Coast. Shelf Sci. 82, 335–340 (2009).

    Article  CAS  Google Scholar 

  33. Weber, T. S. & Deutsch, C. Ocean nutrient ratios governed by plankton biogeography. Nature 467, 550–554 (2010).

    Article  CAS  Google Scholar 

  34. Sun, Y., Debeljak, P. & Obernosterer, I. Microbial iron and carbon metabolism as revealed by taxonomy-specific functional diversity in the southern ocean. ISME J. 15, 2933–2946 (2021).

    Article  CAS  Google Scholar 

  35. Gao, G. et al. Global warming interacts with ocean acidification to alter PSII function and protection in the diatom Thalassiosira weissflogii. Environ. Exp. Bot. 147, 95–103 (2018).

    Article  CAS  Google Scholar 

  36. Abirami, B., Radhakrishnan, M., Kumaran, S. & Wilson, A. Impacts of global warming on marine microbial communities. Sci. Total Environ. 791, 147905 (2021).

    Article  CAS  Google Scholar 

  37. Morley, S. A., Barnes, D. K. & Dunn, M. J. Predicting which species succeed in climate-forced polar seas. Front. Mar. Sci. 5, 507 (2019).

    Article  Google Scholar 

  38. Meredith, M. et al. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) 203–320 (Cambridge Univ. Press, 2019).

  39. Benedetti, F. Major restructuring of marine plankton assemblages under global warming. Nat. Commun. 12, 5226 (2021).

    Article  CAS  Google Scholar 

  40. Rillig, M. C. et al. The role of multiple global change factors in driving soil functions and microbial biodiversity. Science 366, 886–890 (2019).

    Article  CAS  Google Scholar 

  41. Park, J.-Y., Kug, J.-S., Badera, J., Rolph, R. & Kwon, M. Amplified arctic warming by phytoplankton under greenhouse warming. Proc. Natl Acad. Sci. USA 112, 5921–5926 (2015).

    Article  CAS  Google Scholar 

  42. Hoppe, C. J. M., Wolf, K. K. E., Schuback, N., Tortell, P. D. & Rost, B. Compensation of ocean acidification effects in arctic phytoplankton assemblages. Nat. Clim. Change 8, 529–533 (2018).

    Article  CAS  Google Scholar 

  43. Thomas, M. K., Kremer, C. T., Klausmeier, C. A. & Litchman, E. A global pattern of thermal adaptation in marine phytoplankton. Science 338, 1085–1088 (2012).

    Article  CAS  Google Scholar 

  44. Malcolm, J. R., Liu, C., Neilson, R. P., Hansen, L. & Hannah, L. Global warming and extinctions of endemic species from biodiversity hotspots. Conserv. Biol. 20, 538–548 (2006).

    Article  Google Scholar 

  45. Gaylord, B. Functional impacts of ocean acidification in an ecologically critical foundation species. J. Exp. Biol. 214, 2586–2594 (2011).

    Article  CAS  Google Scholar 

  46. Cichos, F., Gustavsson, K., Mehlig, B. & Volpe, G. Machine learning for active matter. Nat. Mach. Intell. 2, 94–103 (2020).

    Article  Google Scholar 

  47. Landschützer, P., Gruber, N. & Bakker, D. C. E. An Observation-Based Global Monthly Gridded Sea Surface pCO2 Product from 1982 Onward and its Monthly Climatology (NCEI Accession 0160558) Version 6.6. (NOAA National Centers for Environmental Information, 2020); https://www.ncei.noaa.gov/data/oceans/ncei/ocads/data/0160558/

  48. Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42, 1–20 (1997).

    Article  CAS  Google Scholar 

  49. O’Malley, R. Ocean Productivity (accessed January 2022); http://sites.science.oregonstate.edu/ocean.productivity/

  50. NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua Photosynthetically Available Radiation Data; 2022 Reprocessing (NASA OB.DAAC, Greenbelt, MD, accessed 18 January 2022); https://doi.org/10.5067/AQUA/MODIS/L3M/PAR/2022

  51. Garcia H. et al. World Ocean Atlas 2018. Vol. 4: Dissolved Inorganic Nutrients (Phosphate, Nitrate and Nitrate+Nitrite, Silicate) (National Centers for Environmental Information, 2019).

  52. Cheng, L. et al. Improved estimates of changes in upper ocean salinity and the hydrological cycle. J. Clim. 33, 10357–10381 (2020).

    Article  Google Scholar 

  53. Cheng, L. & Zhu, J. Benefits of CMIP5 multimodel ensemble in reconstructing historical ocean subsurface temperature variations. J. Clim. 29, 5393–5416 (2016).

    Article  Google Scholar 

  54. Cheng, L. et al. Improved estimates of ocean heat content from 1960 to 2015. Sci. Adv. 3, e1601545 (2017).

    Article  Google Scholar 

  55. Chavez, F. P., Messié, M. & Pennington, J. T. Marine primary production in relation to climate variability and change. Annu. Rev. Mar. Sci. 3, 227–260 (2011).

    Article  Google Scholar 

  56. Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).

    Article  CAS  Google Scholar 

  57. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  58. Dietterich, T. G. Ensemble Methods in Machine Learning (Springer, 2000).

  59. Dietterich, T. G. in The Handbook of Brain Theory and Neural Networks 2nd edn (ed. Arbib, M. A.) 405–408 (MIT Press, 2002).

  60. Gardner, M. W. & Dorling, S. R. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ. 32, 2627–2636 (1998).

    Article  CAS  Google Scholar 

  61. Fabian, P. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  62. Zhou, Z. H. Ensemble Learning in Machine Learning (Springer, 2021).

  63. Naseem, I., Togneri, R. & Bennamoun, M. Linear regression for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32, 2106–2112 (2010).

    Article  Google Scholar 

  64. Altman, N. S. An introduction to kernel and nearest-neighbor nonparametric regression. Ann. Stat. 46, 175–185 (1992).

    Google Scholar 

  65. Breiman, L. Stacked regressions. Mach. Learn. 24, 49–64 (1996).

    Article  Google Scholar 

  66. Ban, Z., Hu, X. & Li, J. Tipping points of marine phytoplankton to multiple environmental stressors. figshare https://doi.org/10.6084/m9.figshare.16909126.v4 (2021).

  67. Baturynska, I. & Martinsen, K. Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms. J. Intell. Manuf. 32, 179–200 (2021).

    Article  Google Scholar 

  68. Gu, J. Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377 (2018).

    Article  Google Scholar 

  69. Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).

    Article  Google Scholar 

  70. Liu, D. C. & Nocedal, J. On the limited memory BFGS method for large scale optimization. Math. Program. 45, 503–528 (1989).

    Article  Google Scholar 

  71. Kingma, D.P. & Ba, J. Adam: a method for stochastic optimization. In Proc. of the 3rd International Conference for Learning Representations—ICLR 2015, San Diego, CA, 7–9 May (2015).

  72. Ploton, P. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11, 4540 (2020).

    Article  CAS  Google Scholar 

  73. Zhang, T., Ramakrishnan, R. & Livny, M. BIRCH. ACM SIGMOD Rec. 25, 103–114 (1996).

    Article  Google Scholar 

  74. Mahmood, F., Khokhar, M. F. & Mahmood, Z. Investigating the tipping point of crop productivity induced by changing climatic variables. Environ. Sci. Pollut. Res. 28, 2923–2933 (2021).

    Article  Google Scholar 

  75. Hamed, K. H. Trend detection in hydrologic data: the Mann–Kendall trend test under the scaling hypothesis. J. Hydrol. 349, 350–363 (2008).

    Article  Google Scholar 

  76. Baas-Becking, L. G. M. Geobiologie of Inleiding Tot de Milieukunde (W.P. Van Stockum & Zoon, 1934).

  77. Cermeño, P. & Falkowski, P. G. Controls on diatom biogeography in the ocean. Science 325, 1539–1541 (2009).

    Article  Google Scholar 

  78. Whittaker, K. A. & Rynearson, T. A. Evidence for environmental and ecological selection in a microbe with no geographic limits to gene flow. Proc. Natl Acad. Sci. USA 114, 2651–2656 (2017).

    Article  CAS  Google Scholar 

  79. Stuart-Smith, R. D. et al. Thermal biases and vulnerability to warming in the world’s marine fauna. Nature 528, 88–92 (2015).

    Article  CAS  Google Scholar 

  80. Swift, M. L. GraphPad prism, data analysis, and scientific graphing. J. Chem. Inf. Comput. Sci. 37, 411–412 (1997).

    Article  CAS  Google Scholar 

  81. Benesty, J. et al. in Noise Reduction in Speech Processing (eds Benesty, J. et al.) Ch. 5 (Springer, 2009).

  82. Jacoby, W. G. LOESS: a nonparametric, graphical tool for depicting relationships between variables. Elect. Stud. 19, 577–613 (2000).

    Article  Google Scholar 

  83. Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  84. Laufkötter, C. et al. Drivers and uncertainties of future global marine primary production in marine ecosystem models. Biogeosciences 12, 6955–6984 (2015).

    Article  Google Scholar 

Download references

Acknowledgements

We thank Y. Zhang and B. Zhou for their suggestion on the model improvement. X.H. was supported by the National Natural Science Foundation of China (grant nos. 21722703 and 42077366), the National Key Research and Development Project (grant nos. 2020YFC1807000 and 2019YFC1804603), the 111 Program (grant no. T2017002) and Fundamental Research Funds for the Central Universities. B.Z. was supported by the Chinese Scholarship Council (grant no. 202008300027).

Author information

Authors and Affiliations

  1. Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, China

    Zhan Ban & Xiangang Hu

  2. Department of Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology, Tsinghua University, Beijing, China

    Xiangang Hu & Jinghong Li

Authors
  1. Zhan Ban
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiangang Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinghong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

X.H. designed the project. Z.B. ran the models. Z.B. and X.H. contributed to the writing of the manuscript. J.L. contributed to the revision of the manuscript.

Corresponding author

Correspondence to Xiangang Hu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Iseult Lynch, Damiano Righetti, Hao Zhu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1–23, references and Table 1.

Reporting Summary

Supplementary Data

Confidence intervals for identifying threatened species and tipping points in the analysis.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ban, Z., Hu, X. & Li, J. Tipping points of marine phytoplankton to multiple environmental stressors. Nat. Clim. Chang. 12, 1045–1051 (2022). https://doi.org/10.1038/s41558-022-01489-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-022-01489-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing