Periodic Reporting for period 1 - PHY-GO (Evaluating linkages between phytoplankton community structure and carbon export using BGC-Argo floats)
Reporting period: 2023-06-01 to 2025-05-31
Not applicable
The main objectives of PHY-GO were to use concomitant measurements from cytometry, microscopy, and bio-optical sensors (e.g. Chl a fluorescence and optical backscatter, hereafter bbp) to develop an empirical model of phytoplankton community structure that could be applied to data collected by BGC-Argo floats—thereby providing insights into the partitioning of marine carbon at the global scale. The project consisted of four main objectives: 1) Constructing a matchup dataset of bio-optical, physical, and ecological data collected by the project sponsor and collaborators at Laboratoire d’Océanographie de Villefranche; 2) use machine learning to develop a model predicting phytoplankton community composition in response to bio-optical measurements alone; 3) evaluate relationships between phytoplankton community composition and variability in bio-optical measurements; 4) apply the model to better understand relationships between phytoplankton community structure and biogeochemical cycling in the global ocean.
All these objectives were largely completed during the fellowship period. Completion of the first objective produced a unique dataset consisting of more than 300 complete samples capturing phytoplankton dynamics across diverse locations, seasons, and depths. The PHY-GO dataset is one of the largest of its kind and provides a foundation for long-term advances in phytoplankton ecology and will support transformational research long beyond the end of the fellowship period.
Completion of the second objective yielded a novel neural network model providing predictions of heterotrophic bacteria, picophytoplankton (e.g. Prochlorococcus, Synechococcus, and picophytoeukaryotes) and nanophytoplankton. Although accurate predictions of microphytoplankton remain challenging due to their limited representation in the PHY-GO dataset, the model gives exceedingly high results in validation tests (e.g. R2 > 0.75 for all other groups), suggesting it is sufficiently robust to provide ecologically informative predictions. Interestingly, while relationships between gross community composition and bio-optical measurements were sufficiently strong to support predictions of community composition based on Chl a and bbp, phytoplankton diversity and taxonomy did not appear to contribute to variability in bio-optical measurements. Therefore, objective 3 was not completed beyond obtaining this null result.
Indeed, because the model relies only on pressure, temperature, salinity, Chl a, and bbp as inputs, it can be applied to an enormous amount of existing BGC-Argo data, thereby providing a strong foundation for the completion of objective 4. While work on this objective remains in its early stages, comparisons of 1) model predictions of phytoplankton community structure and 2) depth-resolved estimates of net primary production based on existing BGC-Argo data indicate an outsized role of nanophytoplankton in carbon cycling at the global scale, with small-sized diatoms emerging as key contributors to carbon cycling in high-latitude regions. Incorporation of physical measurements from BGC-Argo floats, biogeochemical models, and remote sensing platforms will provide further insight into the environmental drivers of these relationships.
All these objectives were largely completed during the fellowship period. Completion of the first objective produced a unique dataset consisting of more than 300 complete samples capturing phytoplankton dynamics across diverse locations, seasons, and depths. The PHY-GO dataset is one of the largest of its kind and provides a foundation for long-term advances in phytoplankton ecology and will support transformational research long beyond the end of the fellowship period.
Completion of the second objective yielded a novel neural network model providing predictions of heterotrophic bacteria, picophytoplankton (e.g. Prochlorococcus, Synechococcus, and picophytoeukaryotes) and nanophytoplankton. Although accurate predictions of microphytoplankton remain challenging due to their limited representation in the PHY-GO dataset, the model gives exceedingly high results in validation tests (e.g. R2 > 0.75 for all other groups), suggesting it is sufficiently robust to provide ecologically informative predictions. Interestingly, while relationships between gross community composition and bio-optical measurements were sufficiently strong to support predictions of community composition based on Chl a and bbp, phytoplankton diversity and taxonomy did not appear to contribute to variability in bio-optical measurements. Therefore, objective 3 was not completed beyond obtaining this null result.
Indeed, because the model relies only on pressure, temperature, salinity, Chl a, and bbp as inputs, it can be applied to an enormous amount of existing BGC-Argo data, thereby providing a strong foundation for the completion of objective 4. While work on this objective remains in its early stages, comparisons of 1) model predictions of phytoplankton community structure and 2) depth-resolved estimates of net primary production based on existing BGC-Argo data indicate an outsized role of nanophytoplankton in carbon cycling at the global scale, with small-sized diatoms emerging as key contributors to carbon cycling in high-latitude regions. Incorporation of physical measurements from BGC-Argo floats, biogeochemical models, and remote sensing platforms will provide further insight into the environmental drivers of these relationships.
Although the fellowship period has ended, PHY-GO remains an active and evolving project with results that hold strong potential for future uptake and impact. One of the central tasks for ensuring further uptake of PHY-GO results is the open access publication of the PHY-GO dataset on a central repository for use by other researchers. The bulk of the work on the dataset has already been assembled and validated, and robust workflows have been developed to ingest new data as it becomes available. That said, there are still additional cytometry and microscopy samples that need to be analyzed and incorporated into the dataset prior to publication. This work will be completed within the next year in collaboration with researchers at the host institution.
In addition to the publication of the dataset, further uptake of PHY-GO results is also contingent upon the publication of the neural network model developed in objective 2. This will likely be completed in conjunction with the manuscript providing results for objective 4, which will be developed over the next six months. Additional training data and refinement of the model may be required to strengthen its generalizability, highlighting a need for further research and demonstration activities.
In addition to the publication of the dataset, further uptake of PHY-GO results is also contingent upon the publication of the neural network model developed in objective 2. This will likely be completed in conjunction with the manuscript providing results for objective 4, which will be developed over the next six months. Additional training data and refinement of the model may be required to strengthen its generalizability, highlighting a need for further research and demonstration activities.