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Machine learning identifies a strong association between warming and reduced primary productivity in an oligotrophic ocean gyre
D’Alelio, D.; Rampone, S.; Cusano, L.M.; Morfino, V.; Russo, L.; Sanseverino, N.; Cloern, J.E.; Lomas, M.W. (2020). Machine learning identifies a strong association between warming and reduced primary productivity in an oligotrophic ocean gyre. NPG Scientific Reports 10(1): 12 pp. https://dx.doi.org/10.1038/s41598-020-59989-y
In: Scientific Reports (Nature Publishing Group). Nature Publishing Group: London. ISSN 2045-2322; e-ISSN 2045-2322
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| Auteurs | | Top |
- D’Alelio, D.
- Rampone, S.
- Cusano, L.M.
- Morfino, V.
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- Russo, L.
- Sanseverino, N.
- Cloern, J.E.
- Lomas, M.W.
|
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| Abstract |
Phytoplankton play key roles in the oceans by regulating global biogeochemical cycles and production in marine food webs. Global warming is thought to affect phytoplankton production both directly, by impacting their photosynthetic metabolism, and indirectly by modifying the physical environment in which they grow. In this respect, the Bermuda Atlantic Time-series Study (BATS) in the Sargasso Sea (North Atlantic gyre) provides a unique opportunity to explore effects of warming on phytoplankton production across the vast oligotrophic ocean regions because it is one of the few multidecadal records of measured net primary productivity (NPP). We analysed the time series of phytoplankton primary productivity at BATS site using machine learning techniques (ML) to show that increased water temperature over a 27-year period (1990–2016), and the consequent weakening of vertical mixing in the upper ocean, induced a negative feedback on phytoplankton productivity by reducing the availability of essential resources, nitrogen and light. The unbalanced availability of these resources with warming, coupled with ecological changes at the community level, is expected to intensify the oligotrophic state of open-ocean regions that are far from land-based nutrient sources. |
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