Using random forests to compare the sensitivity of observed particulate
inorganic and particulate organic carbon to environmental conditions
Abstract
The balance between particulate inorganic carbon (PIC) and particulate
organic carbon (POC) holds significant importance in carbon storage
within the ocean. A recent investigation delved into the spatial
distribution of phytoplankton and the physiological mechanisms governing
their growth. Employing random forests, a machine learning technique,
this study unveiled apparent relationships between POC and 10
environmental fields. In this work, we extend the use of random forests
to compare how observed PIC and POC respond to environmental conditions.
Our findings indicate that while both exhibit similar responses to
certain environmental drivers, PIC is less sensitive to iron and more
sensitive to light. Intriguingly, both PIC and POC display reduced
sensitivity to CO2, contrary to previous studies, possibly due to the
elevated pCO2 in our dataset. This research sheds light on the
underlying processes influencing carbon sequestration and ocean
productivity.