Rules of Life
Applying rules of life to forecast emergent behavior of phytoplankton and advance water quality management
Drinking water safety is threatened globally by increasing phytoplankton blooms in lakes and reservoirs, which pose major threats to water quality via harmful toxins, scums, and changes in taste and odor. To improve drinking water management in the face of global change, this project proposes to develop the first automated, real-time lake phytoplankton forecasting system that quantifies uncertainty in water quality predictions. If managers had forecasts of phytoplankton blooms, they could preemptively act to mitigate water quality impairment, such as by adapting water treatment, thereby decreasing costs and improving drinking water safety. The project team plans to integrate cutting-edge lake ecosystem and statistical modeling with new computing capacity to deliver 1 to 35 day-ahead forecasts of phytoplankton blooms to water managers daily for several U.S. lakes. Researchers intend to work with water managers on the forecasting system to generate valuable knowledge about how best to effectively communicate forecasts for improved water resource decision-making. The project team also plans to develop teaching modules on forecasting and freshwater ecosystems for high school students and community college students in water management/wastewater certificate programs, thereby improving both water quality and water worker training in central Appalachia. The teaching modules will be made available to colleges and universities across the U.S. as part of an existing educational program that has reached over 100,000 students to date.
Phytoplankton blooms in lakes are a type of emergent behavior that can have ecosystem-scale, societally important consequences by degrading water quality, yet are challenging to predict. A fundamental Rule of Life governs this behavior: ecosystem-scale emergence is a function of environmental dynamics operating on individual organisms (e.g., temperature and light effects on phytoplankton growth rates), mediated by population and community processes (e.g., multi-species interactions that promote increased phytoplankton biomass). This project will apply a Rules of Life approach to solve a major societal problem by implementing emergent phytoplankton behavior into predictive models to generate real-time lake water quality forecasts with cloud and edge computing tools. This research is uniquely enabled by a transdisciplinary team with expertise that spans the biological sciences, social and decision sciences, physical sciences, computer and data sciences, and statistics, as well as long-term partnerships with managers, educators, and community members. Advances from this convergent, use-inspired research approach will include: 1) improved understanding of how a Rule of Life can be used to predict emergent, ecosystem-scale phenomena; 2) new cyberinfrastructure for transferring data from environmental sensors to the cloud; 3) generation of novel, computationally-tractable statistical methods for real-time forecasting with individual-based models; 4) greater understanding of how water management and ecosystem dynamics interact to control phytoplankton; 5) creation of new tools that effectively communicate forecast uncertainty; and 6) capacity-building by providing innovative training for researchers, managers, and students that broadens STEM participation across central Appalachia. Through novel, cross-disciplinary integration, this project aims to develop a forecasting system that will become a model for drinking water systems in communities globally.
Funding: National Science Foundation (EF-2318861)
Center Personnel: Cayelan Carey, Quinn Thomas, Mary Lofton, Madeline Schreiber, Adrienne Breef-Pilz, Austin Delany, Bobby Grammacy, Ryan Calder
External Colloborators: Renato Figueiredo (University of Florida)
Graduate students: Mahabub Chowdhury (Public Health), Katie Hoffman (Biological Sciences), Parul Patil (Statistics)
Partners: Western Virginia Water Authority, Mountain Empire Community College, Computional Model and Data Analytics program at Virginia Tech
