Tipping points of marine phytoplankton to multiple environmental stressors

Zhan Ban, Xiangang Hu () and Jinghong Li
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Zhan Ban: Nankai University
Xiangang Hu: Nankai University
Jinghong Li: Tsinghua University

Nature Climate Change, 2022, vol. 12, issue 11, 1045-1051

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41558-022-01489-0

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