FlowCam enumeration of live microplankton
The FlowCam (Yokogawa Fluid Imaging Technologies Inc.) is an automated imaging instrument which combines flow cytometry, microscopy and digital imagery to enumerate, identify and estimate biovolume of live plankton in situ and can also be used to process preserved plankton samples in the laboratory (Sieracki et al.1998). Equipped with different flow cell objectives (x2, x4, x10 and x20) FlowCam can capture images of plankton with a wide range of cell dimensions from >4 μm to <1 mm and can rapidly obtain information on morphometrics and concentration of individual particles.
Fine mesh vertical net hauls have been collected regularly at Station L4 since 2012. The finer mesh (63 μm) net provides a better representation of the abundant and smaller metazoans as well as larger microplankton and compliments the established time series of phytoplankton and zooplankton which began in 1992 and 1988 respectively. Live net samples collected weekly are analysed within a few hours of collection using a FlowCam VS-IV fitted with a 300 μm flow cell and a x4 objective. Analyses are run using auto-image mode and approximately 10,000 images are captured per analysis. Stored images are currently manually identified and sorted into taxonomic categories. However, we are currently developing deep learning techniques to automate the various steps involved in species classification which can otherwise be very time consuming.
FlowCam has also been used to analyse discrete water samples collected at 4-6 depths throughout the water column to determine microplankton abundance (Cornwell et al. 2020).
For more details or enquiries about FlowCam, please contact Elaine Fileman email@example.com
Cornwell L.E., E.S. Fileman, J.T. Bruun, A.G. Hirst, G.A. Tarran, H.S. Findlay, C Lewis, T.J. Smyth, A.J. McEvoy, A. Atkinson (2020). Resilience of the copepod Oithona similis to climatic variability: egg production, mortality, and vertical habitat partitioning. Frontiers in Marine Science https://doi.org/10.3389/fmars.2020.00029
Kerr, T., J. R. Clark, E. S. Fileman, C. E. Widdicombe and N. Pugeault (2020). Collaborative Deep Learning Models to Handle Class Imbalance in FlowCam Plankton Imagery. IEEE Access, vol. 8, pp. 170013-170032 doi: 10.1109/ACCESS.2020.3022242.
Sieracki, C. K., M. E. Sieracki and C. S. Yentsch (1998). An imaging-in-flow system for automated analysis of marine microplankton. Marine Ecology Progress Series 168:285-296