Identifying seasonal shifts in freshwater phytoplankton assemblages through optimizing a flow imaging microscopy technology for the Great Lakes.

Gillian Null, Ohio University, Honors Tutorial College, Athens, OH, United States, Reagan Errera, National Oceanic and Atmospheric Administration, Great Lakes Environmental Research Laboratory, Ann Arbor, United States and David L. Fanslow, National Oceanic and Atmospheric Administration, Great Lakes Environmental Research Laboratory, Ann Arbor, MI, United States
The Laurentian Great Lakes suffer from annual cyanobacterial harmful algal blooms (cHABs), which impact human and ecological health and the economic well-being of the region. Understanding phytoplankton succession is paramount to monitoring and predicting blooms. Responding to shifts within the phytoplankton assemblage requires the ability to rapidly quantify phytoplankton in the system. High-efficiency flow imaging microscopy technology captures images of microscopic particles suspended in liquid and can be used to quantify particle abundance rapidly. Due to the unique size and shape of Microcystis aeruginosa colonies and their associated carbon matrix in the Great Lakes, improved flow imaging techniques are needed to reliably determine cyanobacteria abundance. Our goal was to develop a standard operating protocol for processing field samples using a flow imaging microscope (FlowCam 8000), that would produce consistently high-quality images and minimize technical problems such as clogging. An additional objective of the project was to develop image libraries for the Great Lakes, which are currently unavailable, to assist in training decisional networks to accurately classify phytoplankton genera. To accomplish these objectives, a sequential filtering technique was developed to allow for processing of size-fractionated phytoplankton at different magnifications. Various samples were imaged, manually classified, and used to generate image libraries from Lake Erie and Lake Huron (Saginaw Bay). The FlowCam protocol combined with image libraries will be used in the future to allow for faster algorithmic classification of phytoplankton and description of plankton communities. Our new approach has led to efficient identification of phytoplankton and phytoplankton succession within the system and can be used to monitor populations of cHABs in the Great Lakes. Based on analysis of phytoplankton communities collected from Lake Erie and Lake Huron prior to the onset of the M. aeruginosa bloom (May – July 2019), we saw shifts from small, diatom-based assemblage to large colonies of diatoms and cyanobacteria in Lake Erie and a shift from small cyanobacteria colonies to very large colonies and green algae in Lake Huron. The size and species succession was correlated to nutrient recycling trends in the system.