Thursday, October 03, 2019, 12:30PM - 1:30PM
SEEC room S225
Characterizing nuisance algal responses to environmental stressors using high-throughput sequencing
Bloom-forming and toxin-producing algae can decrease ecological condition, impair drinking water, and prevent recreational use of streams and rivers. Little is known about how nuisance algae in streams respond to drought, nutrient pollution, and pesticide contamination. DNA metabarcoding offers a time-and cost-effective alternative to traditional morphological analysis to increase nuisance algal detection in stream biofilms. Predictive modeling can be used to understand how environmental stressors affect taxon presence. I considered, among others, the cyanobacteria Anabaena, Lyngbya, Microcystis, and Nostoc; the green algae Cladophora, Mougeotia, and Zygnema; Euglena; and the diatom Didymospheniaas potential nuisance algae in streams. I present results comparing the sensitivity of morphology-based counts and 23S rRNA DNA metabarcoding in detecting key nuisance algae genera and their relationships to environmental stressors across 95 northeastern United States streams along an urban gradient. For each genus, I constructed predictive models using taxon relative abundance and stream hydrology, nutrients, major ions, and pesticides to determine likelihoods of nuisance algaloccurrence in impacted streams. My results suggest DNA-based methods and predictive modeling can determine conditions in which nuisance algae affect water quality for human use.
Pizza and science will be served!