Tool Development to Characterize Flow Regime in Oyster Aquaculture Systems

Principal Investigator:

Matthew Gray

Start/End Year:

2019 - 2020


University of Maryland Center for Environmental Science

Co-Principal Investigator:

Brendan Campbell, University of Maryland Center for Environmental Science, Horn Point Laboratory


Strategic focus area:

Sustainable fisheries and aquaculture


The aquaculture industry in Maryland is far from optimized partly due to the lack of knowledge relating oyster growth and morphology to the physical environment (flow and jostling). For sessile suspension feeders, such as oysters, water flow is critical to optimize under culture conditions as it regulates food delivery and waste export. Additionally, jostling may inhibit feeding activity and influence shell shape, the latter of which may influence product marketability. It is also unknown how different off-bottom cage types affect the flow regime. This lack of understanding is not unique to Chesapeake Bay waters but is a concern for oyster farmers globally. The tools required to make these assessments are also not readily available to researchers and stakeholders due to technical limitations and high cost. To fill this knowledge gap, our main objectives are to: 1) develop and validate simple and inexpensive tools (specifically clod cards, ammonia peepers, and accelerometers) that have the potential to monitor fine-scale, interior physical conditions of off-bottom cages 2) draw relationships between oyster growth and quality with a changing physical environment 3) test tools among varying gear types available at the Horn Point Laboratory Demonstration Farm (HPLDF) to examine differences in gear performance 4) provide tools and education to local stakeholders on the importance of considering flow when planning farm operations. Additionally, these tools may be used to better understand how biofouling communities persist and influence the movement of water through gear. Collectively, this applied research project aims to develop tools and collect information to provide deeper understanding of gear performance, which is fundamental for farm optimization and profitability.

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