Date of Award
2025
Degree Type
Dissertation
Degree Name
Doctor of Philosophy in Oceanography
Specialization
Biological Oceanography
Department
Oceanography
First Advisor
Keisuke Inomura
Abstract
Phytoplankton are microscopic, primary producers in aquatic ecosystems worldwide. They are key links in global biogeochemical cycles, converting inorganic compounds into bioavailable nutrients which fuel fisheries. Through photosynthesis, phytoplankton play an essential role in the carbon cycle and marine biological pump, drawing carbon from the atmosphere, which is eventually sequestered in the oceans, regulating our Earth’s climate. Due to their influential role in large scale processes, there have been many efforts to characterize and quantify phytoplankton impacts to ecosystem health and global nutrient cycles. Often, phytoplankton physiology has been highly simplified in global or ecosystem models, limiting their representation to a few equations that focus on nutrient uptake and growth. These models focus on nutrient uptake and elemental quotas rather than macromolecular allocation. In this dissertation work, we use and improve a predictive model of phytoplankton elemental and macromolecular allocation with the ultimate objective of increasing cellular resolution and connection between the environment and phytoplankton cells across model scales. First, we evaluated the skill of the model with parameter values calibrated for a single species (Synechococcus linearis) to predict the elemental and macromolecular composition of diverse phytoplankton taxa. This model reveals significant skill (Model vs. data linear slope=0.69, R2=0.89, when excluding low growth rates), supporting the hypothesis that the underpinning mechanisms hold much in common across taxa as well as supporting its application as a model of acclimation at the community scale. However, model skill is improved (Model vs. data linear fit slope=1.05, R2=0.98) when we modify allocation parameter values for each individual taxon, effectively defining allocation strategies as traits which aligned with broad taxonomic groups, providing insights into likely adaptations.
After identifying allocation strategies and traits among taxonomic groups, we added the temperature dependency to the phytoplankton model to improve the link between the phytoplankton cellular response and a changing environment. We found, when temperature increases under nitrogen (N) and phosphorus (P) co-limitation, the model shows less investment in phosphorus-rich RNA molecules relative to nitrogen-rich proteins, leading to a more severe decrease in cellular P:C than N:C causing increased cellular N:P values. Under P limitation, the model shows a similar pattern, but when excess P is available under N limitation, we predict lowered N:P due to the effect of luxury uptake of P. In this study, we compared model predictions to idealized laboratory data, and thus continued our exploration of the model’s accuracy by conducting a mesocosm experiment to compare to model predictions.
Our mesocosm experiment investigated the impact of warming water on phytoplankton physiology and further validated the temperature dependent phytoplankton cell model. We found that higher temperatures double the maximum cellular density (cells L-1) of phytoplankton, suggesting high temperature stimulates cell division over maximizing carbon storage. Also, the cells in warmer waters dedicated fewer resources to proteins and RNA production, leading to higher fractions of carbon allocated to storage. The model predictions and mesocosm results closely aligned in elemental stoichiometry and predictions of N-based macromolecular allocation. After we validated the model with two different types of experiment, we incorporated the cellular model into two larger scale models: a simple upwelling model and a global ecosystem model.
In the first large scale model, we coupled a one-dimensional physical model of a coastal boundary with this computationally efficient model of phytoplankton and ran three simulations to represent a neutral, El Niño, and La Niño year. We investigated these large scale oscillations because the difference in biological patterns has been observed but the underlying cellular mechanisms have been largely unexplored. We found that varying temperatures and nutrient limitations between simulations caused differences in phytoplankton elemental stoichiometry, macromolecular allocation, and growth rates. The model predicted the highest maximum growth rate occurred during an El Niño year, while the La Niña event provided suitable conditions so that phytoplankton cells may maintain a maximum growth rate far from the coastal boundary. In addition to high maximum growth rates, higher temperatures also led to increased C storage, diminishing phytoplankton nutritional quality closer to the coastal boundary.
Lastly, we incorporated the temperature dependent phytoplankton model into a global ecosystem model to evaluate the effect of temperature dependency on phytoplankton and the resultant biogeochemistry in the global ocean. We compared global patterns using temperature dependent and independent phytoplankton models to demonstrate the value of adding temperature dependencies to phytoplankton representations. Then, we used the temperature dependent cell model to predict how global patterns of elemental stoichiometry and macromolecular allocation may change in a warming ocean. As a result, we found temperature dependency is a key factor that connects phytoplankton to their environment, altering elemental ratios up to 30 percent, changing phytoplankton growth rates and size class distribution as well as nutrient allocation in the polar and subpolar regions. In our simulations of increased global sea surface temperatures, the model predicts increased phytoplankton growth rates and large carbon storage anomalies in the high latitudes, suggesting potential changes to carbon export in future oceans.
This dissertation work highlights the importance of including more detailed information in phytoplankton models as this resolution gives insight into the underlying mechanisms of ecosystem functioning or biogeochemical distributions across the ocean. The temperature dependent cell model can be used in varying scales to assess the changing nutritional quality of phytoplankton and how that may affect ecosystem health or the biological pump. This work suggests that large scale models may be missing key factors that affect global estimations of carbon drawdown, sequestration, or remineralization. In future work, this model may be incorporated into climate projection models to assess the role of phytoplankton in future oceans or into ecosystem models to forecast impact to economic industries such as fisheries with changing environmental conditions.
Recommended Citation
Armin, Gabrielle M., "QUANTIFYING LINKS BETWEEN PHYTOPLANKTON PHYSIOLOGY AND THE ENVIRONMENT" (2025). Open Access Dissertations. Paper 4437.
https://digitalcommons.uri.edu/oa_diss/4437