Date of Award
Master of Science (MS)
College of Science and Mathematics
Thesis Sponsor/Dissertation Chair/Project Chair
As harmful algal blooms (HABs) are becoming an increasing global threat to the health of people, animals, and aquatic ecosystems, finding ways to efficiently detect and manage blooms is critical. Traditional methods of identifying and enumerating phytoplankton cells involve light microscopy; however, this is a time-consuming and labor-intensive process. Meanwhile, digital imaging flow cytometry is a relatively novel and rapid method of enumerating and identifying particles within phytoplankton samples. Previous studies have documented comparable digital flow cytometry results to microscopy results; however, there are concerns relating to the underestimation of cells and misidentification of particles with their automated classification systems. Before digital imaging flow cytometry can be implemented into HAB monitoring protocols, a complete, thorough, and systematic comparison to light microscopy is needed using freshwater samples with a wide temporal and spatial range. This study investigates the accuracy and discrepancy of collected phytoplankton community data obtained by digital imaging flow cytometry and by light microscopy methods. The results demonstrate that microscopy cell densities (p < 0.001) and natural unit densities (p < 0.001) for both phytoplankton and cyanobacteria were significantly higher than the results obtained by the digital imaging flow cytometry methods. Additionally, taxa richness varied between the two methods, with the microscopy detecting significantly more phytoplankton taxa than digital imaging flow cytometry (p = 0.016). While digital imaging flow cytometry methods have potential in accurately enumerating and identifying phytoplankton, the findings of this study demonstrate that improvements to the digital imaging flow cytometry are needed before this method can be applied to routine HAB monitoring protocols.
Mazzaro, Melissa, "Assessment of Digital Imaging Flow Cytometry in its Application of Harmful Algal Blooms Monitoring" (2022). Theses, Dissertations and Culminating Projects. 870.