FROM METAGENOMICS AND MICROSCOPY ANALYSIS TO RAPID ON-SITE MONITORING: FRESHWATER MICROCYSTIS HARMFUL ALGAL BLOOMS IN NEW YORK
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Freshwater Harmful Algal Blooms (HABs), dominated by cyanobacteria like Microcystis, pose significant ecological and health risks due to their production of cyanotoxins such as microcystins. Despite monitoring advancements, key gaps remain in understanding their microbial drivers and developing rapid, accessible tools for toxicity assessment. This dissertation addresses these challenges by integrating microbiome analysis, machine learning, and innovative screening technologies, focusing on the Finger Lakes region of New York State.Distinct microbiome compositions in Cayuga Lake were identified, with Microcystis as the primary toxigenic cyanobacterium in high-toxin blooms. A strong correlation between Microcystis abundance and microcystin levels highlighted its role in bloom toxicity. qPCR assays targeting the mcyA gene demonstrated high accuracy for detecting high-toxin blooms with both benchtop and handheld devices. HABScreenScope, a novel participatory science tool, integrated a handheld microscope with an online machine learning platform, achieving high accuracy in predicting chlorophyll A and microcystin levels while identifying toxigenic taxa like Microcystis. Its affordability and reliability make it a valuable resource for participatory science HAB monitoring and risk management. Furthermore, the study linked Microcystis colony morphology traits, such as roundness and solidity, to genetic diversity and toxin production, providing insights into the ecological and genetic drivers of bloom dynamics. By combining microbiome and metagenomic analysis, machine learning, and rapid screening tools, this dissertation advances HAB monitoring and management, offering practical solutions for risk assessment and ecological protection. Future research should refine predictive models, improve scalability, and extend applications to diverse ecological contexts.