Title:

Spatiotemporal variability in phytoplankton community composition in the Great Bay Estuary

Phytoplankton, as the base of the food chain, are an important but highly variable source of nutrients for filter feeding bivalves. While considerable work has been done to map interannual phytoplankton community composition in the Gulf of Maine, less has been done in New Hampshire’s Great Bay, an area with a growing shellfish aquaculture sector. This project utilizes a five-year weekly time series of Flowcam imagery in tandem with machine learning assisted identification via Ecotaxa to examine changes in community composition on both an annual and interannual time scale from two locations in the Great Bay estuary. We then mapped these changes in community onto environmental parameters in order to examine driving factors. Overall, this work highlights new ways to use high-throughput instrumentation and machine learning to better understand phytoplankton community dynamics.

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