Using Machine Learning to uncover Ecological Mechanisms controlling abundance of Phytoplankton Size Classes from Large-scale Observations

Using Machine Learning to uncover Ecological Mechanisms controlling abundance of Phytoplankton Size Classes from Large-scale Observations | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 March 2025 V1 Latest version Share on Using Machine Learning to uncover Ecological Mechanisms controlling abundance of Phytoplankton Size Classes from Large-scale Observations Authors : Sandupal Dutta 0000-0002-9614-9437 [email protected] and Anand Gnanadesikan 0000-0001-5784-1116 Authors Info & Affiliations https://doi.org/10.22541/au.174283798.85639647/v1 Published Global Biogeochemical Cycles Version of record Peer review timeline 266 views 204 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Phytoplankton size classes (PSCs) determine many fundamental biogeochemical processes including nutrient uptake, energy transfer through marine food webs, ocean carbon export, and gas exchange with 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 machine learning (ML) and explainable AI (XAI) techniques. The target variables were PSCs obtained using satellite products, i.e. SeaWiFS/ MODIS/ Copernicus products. The environmental predictors were nutrients, light, mixed layer depth, salinity, temperature, and upwelling. The ML algorithm used was the Random Forest Regressor (RFR) and XAI techniques were used to discern the relationship between predictors and PSCs abundance. About 85\% to 95\% of the variability of the size classes in the observational datasets was accounted for by environmental variables known to influence phytoplankton biomass. Although different size classes responded similarly to the environmental drivers, their scale of response varied. The dominant predictors were found to be shortwave radiation, dissolved iron and, temperature. Out of the twelve satellite products across PSCs, ten showed a contrast between the sub-tropical gyres and remaining parts of the World. The different satellite products show sensitivity to iron, shortwave radiation and sea surface temperature across the same range of values, but with different magnitudes. The Copernicus products show less sensitivity to iron with picoplankton being the only product positively related to sea surface temperature. Supplementary Material File (1026978_0_merged_1741986142.pdf) Download 7.36 MB Information & Authors Information Version history V1 Version 1 24 March 2025 Peer review timeline Published Global Biogeochemical Cycles Version of Record 28 Mar 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords iron limitation light limitation phytoplankton random forests satellite oceanography temperature limitation Authors Affiliations Sandupal Dutta 0000-0002-9614-9437 [email protected] Johns Hopkins University View all articles by this author Anand Gnanadesikan 0000-0001-5784-1116 Johns Hopkins University View all articles by this author Funding Information U.S. Department of Energy SC0025209 Anand Gnanadesikan Metrics & Citations Metrics Article Usage 266 views 204 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Sandupal Dutta, Anand Gnanadesikan. Using Machine Learning to uncover Ecological Mechanisms controlling abundance of Phytoplankton Size Classes from Large-scale Observations. Authorea . 24 March 2025. DOI: https://doi.org/10.22541/au.174283798.85639647/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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