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

Holder, Christopher; Gnanadesikan, Anand

doi:https://doi.org/10.5194/bg-18-1941-2021

Articles | Volume 18, issue 6

https://doi.org/10.5194/bg-18-1941-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-18-1941-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 6

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

Model code and software

Dataset and scripts for manuscript "Can machine learning extract the mechanisms controlling phytoplankton growth from large-scale observations? – A proof of concept study" Christopher Holder and Anand Gnanadesikan https://doi.org/10.5281/zenodo.3932387

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.