NSF Award
2317681
The Arctic Ocean is undergoing rapid environmental change, with cascading effects on nearly all aspects of the polar marine ecosystem, including the abundance and community composition of marine microbes, microscopic organisms (i.e., bacteria and archaea) that play critical roles in the cycling of elements within the ice-ocean system. Numerical modeling is a critical method for testing ecological mechanisms and predicting the impacts of change. However, current approaches are typically not well resolved for bacterial diversity or activity rates, a source of uncertainty for future projections of critical marine ecosystem functions such as biological carbon drawdown. Through statistical exploration of sequence-based observations of the current Arctic microbial community, this research aims to transform our understanding of cellular environmental responses into a scale relevant for ecosystem processes and improve numerical modeling of microbial community structure and functional diversity. This project funds one post-doctoral scholar and supports undergraduate student training to build capacity in computational Arctic research. <br/><br/>Leveraging publicly archived genomic, biogeochemical, and environmental time-series data from the central Arctic Ocean, this project seeks to update a microbial oriented, one-dimensional biogeochemical model to assess the variable contributions of specific members of the polar bacterial community in modeled ecological processes. Applying machine learning modeling techniques, upper ocean community structure data will be segmented into distinct bacterial ecotypes. Predicted metabolic information and co-located biogeochemical rate measurements will be used to identify critical physiological and functional differences among community types. These results will inform modifications to the bacterial state variable(s) used within the numerical modeling framework. A series of modeling experiments will then be used to compare model skill between the machine-learning integrated and base model frameworks and explore possible improvements to the fidelity of Arctic climate change predictions. Adapted source codes of the produced model will be made accessible through open-source archiving as a resource for the Arctic science community.<br/><br/>This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
