Summary: | Dissertation Our oceans are a key part of the Earth system, an underappreciated bastion of our carbon cycle, and the home of incredible biodiversity, yet marine ecosystems are extremely challenging to model with a range of feedbacks that are not understood. Particularly poorly understood are linkages between physics and biology at the submesoscale (horizontally O(0.1-10) km, vertically O(.1) km, temporally O(1) day) that could help explain broad scale properties in ocean biology. Persistent fronts of western boundary currents like the Gulf Stream are hotspots for submesoscale dynamics that may influence phytoplankton productivity and diversity with ramifications across marine ecosystems. In this dissertation I: 1) review the use of drones and ocean color remote sensing for observing biology at the submesoscale and below, and articulate a vision for addressing key observational needs with drones, 2) develop more robust methods for retrieving ocean color from drones, 3) integrate observations from new and existing sampling technologies to investigate phytoplankton enhancement and shifts in phytoplankton community composition on the Gulf Stream front, and 4) with a frontal eddy as a mesocosm investigate community composition in the eddy vs shelf and Gulf Stream water and then more broadly understand their impact on the marine ecosystem of the Gulf Stream and mid-Atlantic Bight. This work revealed seasonality contrasting that of the typical North Atlantic blooms, but more similar to Sargasso Sea water at this latitude with peak biomass in winter. We found shifting nutrient limitations and observed chlorophyll-a enhancement on the front in a large minority of samples with an indication of winter being more likely for enhancement. We speculate about linkages between this enhancement and ageostrophic circulation on the front but did not directly demonstrate this. Community composition shifts were present in all transects across the Gulf Stream front, though no consistent pattern emerged, with gradual shifts, step ...
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