FLOW IMAGING MICROSCOPY IN AQUACULTURE: HARMFUL ALGAE MONITORING AND SHELLFISH HATCHERY APPLICATIONS

Aquaculture systems, spanning both finfish and shellfish production, depend on timely, reliable information on plankton communities and early life stages to support both operational decision-making and risk management. Monitoring harmful algal blooms (HABs) and assessing hatchery performance remain methodologically challenging, as traditional microscopy is time-intensive and often limits throughput, while single-parameter approaches do not fully capture biological variability. As a result, complementary analytical techniques are increasingly used to improve both environmental monitoring and hatchery workflows.

Flow imaging microscopes (FIM) like FlowCam enable the rapid imaging, enumeration, and measurement of particles in liquid samples while preserving a visual record of each object. In aquaculture contexts, this approach supports HAB monitoring across finfish and shellfish systems by providing quantitative and image-based insights into phytoplankton community composition, including the detection of bloom-forming or harmful taxa. These datasets enable more consistent monitoring and support faster decision-making compared to conventional methods.

At the same time, FIM is being adopted in shellfish hatchery research to streamline microscopy-based workflows and expand the scope of data collection. Applications include egg quantification, larval enumeration and morphometrics, and the characterization of phytoplankton used as feed. Imaging-based analysis increases throughput while generating standardized datasets that support reproducibility, training, and retrospective analysis. Ongoing method development efforts, including approaches to assess larval viability, aim to further extend the utility of the technique in hatchery settings.

This presentation introduces the role of FlowCam in aquaculture, outlines practical considerations for its implementation across monitoring and hatchery applications, and presents case examples illustrating its use in HAB assessment and shellfish production workflows. By positioning FIM within a broader analytical framework, we highlight its potential to enhance data richness, improve efficiency, and support integrated monitoring strategies across aquaculture systems.

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