Phytoplankton Modeling with an Imaging FlowCytobot: More Than Just HABs

Darren Henrichs1 and Lisa Campbell1,2, (1)Texas A&M University, Oceanography, College Station, TX, United States, (2)Texas A&M University, Biology, College Station, TX, United States
Abstract:
An 8-year time series of hourly phytoplankton community abundance has been collected using an Imaging FlowCytobot (IFCB) deployed at Port Aransas, Texas. While primarily used for early warning of harmful algal blooms (HABs), the IFCB captures images of all phytoplankton cells (10-100 μm) and permits the study of community structure and changes over time. By combining abundance estimates from the IFCB with a spatially explicit individual-based model, potential regions of origin for several species have been identified. Environmental data from a variety of sources (buoys, models, ship transects) in the northwestern Gulf of Mexico have been examined to identify which physical factors are most important for bloom formation in phytoplankton along the coast of Texas. The present study focuses on a dinoflagellate species, Prorocentrum texanum, which appears at Port Aransas, TX at approximately the same time period (Feb – Mar) every year and the co-occurring community. Individual-based modeling results indicate blooms of P. texanum originate near the coast of Louisiana and are advected toward Port Aransas by downcoast currents. Cross correlation analyses produced significant negative correlations between P. texanum abundance and coastal currents (1 month preceding), water temperature (2 months preceding), salinity (2 months preceding) and a positive correlation with Prorocentrum minimum abundance (1 month preceding). The exact timing of P. texanum bloom appearance varies from year to year and the high temporal resolution (hourly) of cell counts from the IFCB has permitted a more detailed study of the environmental factors involved in bloom formation. Future work will incorporate the high temporal resolution cell counts and environmental factors to develop predictive models for bloom formation.