Collaborative Research: Quantifying the genetic basis of the cyanobacterial environmental niche to improve bloom forecasting

About This Grant

Harmful algal blooms (HABs) are increasing in frequency and severity in lakes, rivers, and coastal areas around the world. Fueled by nutrient runoff and changing weather patterns, HABs threaten aquatic ecosystems, harm public health, and damage local economies including fisheries and tourism that depend on clean water. Despite the growing impact, major challenges exist to predicting when and where HABs will occur. This project aims to transform how we forecast these events by understanding the biology of one of the major microorganisms that cause them, cyanobacteria. By linking genetics and laboratory experiments to the environmental conditions that promote or inhibit cyanobacterial growth, we will determine which genes help these organisms thrive under different conditions. We will integrate this data-driven work with cutting edge mathematical models to enhance bloom forecasting. These models will help communities better manage water resources and protect public health. Ultimately, this research will not only improve our ability to forecast harmful blooms, but also enhance our broader understanding of how microorganisms respond to environmental conditions. This research addresses the central question of how cyanobacterial bloom dynamics will respond to changing environmental conditions by integrating molecular, ecological, and statistical approaches. The project will (i) map the ecological niches of key bloom-forming cyanobacteria under a realistic range of environmental conditions; (ii) identify the genetic basis of environmental fitness in the model cyanobacterium Synechococcus elongatus using a high-throughput barcoded mutant library; and (iii) develop a hybrid forecasting model that integrates mechanistic growth responses with correlative environmental models. This approach represents a significant advance over current HAB forecasting methods, which rely heavily on empirical correlations with environmental data and fail to account for microbial physiology or evolution. The research will generate a new class of forecasting tools that incorporate fitness landscapes and genotype–environment interactions, providing a mechanistic link between environmental variability and bloom dynamics. Findings will be broadly relevant to microbial ecology and evolutionary genetics, and will establish a generalizable framework for predicting ecologically important microbial behavior under environmental variability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.