Using Machine Learning to Uncover Ecological Mechanisms Controlling Abundance of Phytoplankton Size Classes From Large‐Scale Observations

Abstract Phytoplankton size classes (PSCs) and Phytoplankton functional types (PFTs) determine many fundamental biogeochemical processes including nutrient uptake, energy transfer through marine food webs, ocean carbon export, and gas exchange with the atmosphere. Discerning the causes of spatio‐temporal variability of PSCs is a scientific priority for understanding the ocean's role in and response to climate change. This study intends to decipher the relationships between the abundance of PSCs and environmental predictors using explainable machine learning (XAI) techniques. The target variables were PSCs obtained using three different satellite products: size‐resolved phytoplankton carbon from Kostadinov, Milutinović, et al. (2016), https://doi.org/10.5194/os‐12‐561‐2016 , chlorophyll from MODIS divided according to the algorithm of Hirata et al. (2011), https://doi.org/10.5194/bg‐8‐311‐2011 , and a third product from the Copernicus Marine Service. The environmental predictors were nutrients, light, mixed layer depth, salinity, sea surface temperature (sst), and upwelling. The ML algorithm used was the Random Forest Regressor (RFR). XAI techniques were used to discern the relationship between predictors and PSCs abundance. About 85%–95% of the variability of the size classes in the observational data sets was accounted for by environmental variables known to influence phytoplankton biomass. Although different size classes responded similarly to the environmental drivers (with the exception of Copernicus picoplankton) their scale of response varied. The dominant predictors were found to be shortwave radiation, ammonia, dissolved iron and sea surface temperature. The different satellite products show sensitivity to iron, shortwave radiation, sst and ammonia across the same range of values, but with different magnitudes. Copernicus picoplankton is the only product which is positively related to sst.