Use of the FlowCAM for semi-automated recognition and enumeration of red tide cells ( Karenia brevis) in natural plankton samples

Abstract

Early detection is the most effective way to mitigate the effects of harmful algal blooms (HAB). Cell counts based on examination of microplankton samples using settling chambers and visual inspection with an inverted microscope are tedious and time consuming, and counting precision is generally poor at low cell densities. The FlowCAM is a continuous imaging flow cytometer designed to characterize particles in the microplankton size range (20–200 μm diameter). In this study we examined the ability of the FlowCAM to improve routine monitoring protocols for HAB species by automatically recording information on size and fluorescence per cell. This will eliminate the need to examine cells outside the ranges of these measurements for our target species, Karenia brevis. We also tested the ability of image comparison software to match images of cells in mixed assemblages to images of the target species. For simple mixtures of cultured dinoflagellates, the ability of the image matching software to discriminate target cells varied greatly depending on how similar the two species were in size and shape. When target cells were added to natural plankton samples, the image recognition software correctly identified 80–90% of the target cells, but misidentified 20–50% of non-target cells in the size range of the target species. We conclude that the FlowCAM is less tedious and time-consuming than microscopy, allowing for examination of more cells for greater counting precision. The cell recognition software helps reduce the numbers of cells that must be screened, but images must still be examined by a trained operator to identify the HAB species of interest.