Author: Abhinav Sharma
Advisor: Kevin Crowthers
Category: First Year – Mass Academy
Abstract: Phytoplankton lie at the base of marine food webs and are major regulators of climate and biogeochemical cycling, accounting for over half of primary production and the absorption of 30% of carbon emissions. With global warming modifying ocean conditions, understanding the drivers and impacts of changing phytoplankton dynamics is crucial. However, one-factor experiments have limited applicability due to the heterogeneity in oceanic conditions and biological responses and preferences among different phytoplankton groups. Conversely, multi-factor experiments produce confounding results. Therefore, a computational approach was taken wherein a series of models was developed. All data were derived from the NOAA’s comprehensive World Ocean Database (WOD). Total oceanic chlorophyll concentration was used as an indicator for primary production. To assess accuracy in forecasting capabilities and potential impacts on primary production, for multiple environmental parameters, a time series was developed using sinusoidal regression, as was a linear regression model of the directional relationship held with chlorophyll. Model fitness was variable, as R-Squared values for the first and second set of models ranged from 0.077 to 0.847, and 0.07 to 0.54, respectively. Subsequently, driving parameters behind chlorophyll levels were identified using principal component analysis. Results indicated pH, followed by salinity and pressure, as the most influential parameters. Overall results indicate that the proposed computational apparatus is viable for analyzing phytoplankton dynamics, but that iteration in the form of model modification and greater data implementation is necessary. This apparatus could serve as a significant tool for policymaking related to aquatic ecosystem management.
UN SDGs:
SDG 6 – Clean Water and Sanitation
SDG 13 – Climate Action
SDG 14 – Life Below Water
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