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.
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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.
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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).
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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.
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Confidence intervals for identifying threatened species and tipping points in the analysis.
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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
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DOI: https://doi.org/10.1038/s41558-022-01489-0
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