Revealing Biotic and Abiotic Controls of Harmful Algal Blooms in a Shallow Subtropical Lake through Statistical Machine Learning.

Harmful algal blooms are a growing human and environmental health hazard globally. Eco-physiological diversity of the cyanobacteria genera that make up these blooms creates challenges for water managers tasked with controlling the intensity and frequency of blooms, particularly of harmful taxa (e.g., toxin producers, N fixers). Compounding these challenges is the ongoing debate over the efficacy of nutrient management strategies (phosphorus-only versus nitrogen and phosphorus), which increases decision-making uncertainty. To improve our understanding of how different cyanobacteria respond to nutrient levels and other biophysical factors, we analyzed a unique 17 year data set comprising monthly observations of cyanobacteria genera and zooplankton abundances, water quality, and flow in a bloom-impacted, subtropical, flow-through lake in Florida (United States). Using the Random Forests machine learning algorithm, an ensemble modeling approach, we characterized and quantified relationships among environmental conditions and five dominant cyanobacteria genera. Results highlighted nonlinear relationships and critical thresholds between cyanobacteria genera and environmental covariates, the potential for hydrology and temperature to limit the efficacy of cyanobacteria bloom management actions, and the importance of a dual nutrient management strategy for reducing bloom risk in the long term.