Articles | Volume 18, issue 6
https://doi.org/10.5194/bg-18-1941-2021
https://doi.org/10.5194/bg-18-1941-2021
Research article
 | 
19 Mar 2021
Research article |  | 19 Mar 2021

Can machine learning extract the mechanisms controlling phytoplankton growth from large-scale observations? – A proof-of-concept study

Christopher Holder and Anand Gnanadesikan

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Cited articles

Bahl, A., Gnanadesikan, A., and Pradal, M.-A.: Variations in Ocean Deoxygenation Across Earth System Models: Isolating the Role of Parameterized Lateral Mixing, Global Biogeochem. Cy., 33, 703–724, https://doi.org/10.1029/2018GB006121, 2019. 
Belochitski, A., Binev, P., DeVore, R., Fox-Rabinovitz, M., Krasnopolsky, V., and Lamby, P.: Tree approximation of the long wave radiation parameterization in the NCAR CAM global climate model, J. Comput. Appl. Math., 236, 447–460, https://doi.org/10.1016/j.cam.2011.07.013, 2011. 
Bourel, M., Crisci, C., and Martínez, A.: Consensus methods based on machine learning techniques for marine phytoplankton presence–absence prediction, Ecol. Inform., 42, 46–54, https://doi.org/10.1016/j.ecoinf.2017.09.004, 2017. 
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. 
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Short summary
A challenge for marine ecologists in studying phytoplankton is linking small-scale relationships found in a lab to broader relationships observed on large scales in the environment. We investigated whether machine learning (ML) could help connect these small- and large-scale relationships. ML was able to provide qualitative information about the small-scale processes from large-scale information. This method could help identify important relationships from observations in future research.
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