Completed Projects

ECOLOGICAL OBSERVATORIES

Ecological Forecasting Initative Research Coordination Network

The 21st century is characterized by major environmental changes that alter the ecosystems society depends on for supplying clean water, storing carbon, and supporting plants and animals. Predicting and forecasting the future of these systems can help resource managers anticipate and respond to these changes. Ecological forecasts also advance scientific understanding by developing and testing models and testing alternative views of how ecological systems operate. To harness the power of forecasting, a community of practice is needed. This community will establish standards for developing and communicating forecasts. It will share best practices for generating forecasts with known uncertainties. The group will create tools and educational materials to enable ecologists to begin forecasting. The Ecological Forecasting Initiative Research Coordination Network award will build this community of practice around forecasting data from the continental-scale National Ecological Observatory Network (NEON). By focusing on data from NEON, the community will be challenged to develop and evaluate forecasts for a diversity of environments that exist across the United States. The award will train hundreds of early career researchers and graduate students in ecological forecasting.

Funding:  National Science Foundation (DEB-1926388)

Center personnel: Quinn Thomas, Freya Olsson, Austin Delany, Leah Johnson, Cayelan Carey

EDUCATION

Macrosystems EDDIE: An undergraduate training program in macrosystems science and ecological forecasting

Ecologists are increasingly analyzing big environmental datasets to make forecasts about the future health of ecosystems. However, the data analysis and modeling skills needed to successfully develop ecological forecasts are rarely taught in undergraduate classrooms. To overcome this challenge, this project will expand an existing, successful training program (Macrosystems EDDIE: Environmental Data-Driven Inquiry & Exploration) to teach students fundamental ecological concepts as they create forecasts for lakes and forests across the United States. Through Macrosystems EDDIE, students and instructors will learn how to use models, assess forecast accuracy with observational data, and communicate forecasts to managers and decision-makers. These skills will be embedded in stand-alone teaching modules that will be widely applicable to multiple disciplines and student experience levels. Macrosystems EDDIE provides an innovative new approach for teaching macrosystems ecology and has the potential to advance undergraduate science education across the U.S. By strengthening both students' quantitative skillsets and understanding of macrosystems ecology, this project will help develop a diverse, globally-competitive scientific workforce and enhanced infrastructure for macrosystems research and education.

Funding:  National Science Foundation (DEB-1926050)

Center personnel: Cayelan Carey, Mary Lofton, Quinn Thomas

WATER QUALITY

Cyberinfrastructure Enabling End-to-End Workflows for Aquatic Ecosystem Forecasting

Aquatic ecosystems in the United States and around the globe are experiencing increasing variability due to human activities. Provisioning drinking water in the face of rapid change in environmental conditions motivates the need to develop forecasts of future water quality. Near-term water quality forecasts can guide management actions over day to week time scales to mitigate potential disruptions in drinking water and other essential freshwater ecosystem services. To maximize the utility of water quality forecasts for managers and decision-makers, the forecasts must be accessible in near-real time, reliable, and continuously updated with environmental sensor data. However, developing iterative, near-term ecological forecasts requires complex cyber-infrastructure that is widely distributed, from sensors and computers collecting information at freshwater lakes and reservoirs to cloud computing services where forecast models are executed. Consequently, significant software challenges still remain for environmental scientists to easily and effectively deploy forecasting workflows. This project will address this need by designing, implementing, and deploying open-source software (FLARE: Forecasting Lake And Reservoir Ecosystems) that will enable the creation of flexible, scalable, robust, and near-real time iterative ecological forecasts. This software will be tested and widely disseminated to water utilities, drinking water managers, and many other decision-makers. FLARE will greatly advance the capability of the ecological research community to perform near-real time aquatic forecasts.

Key project outcomes

This project designed, implemented, and deployed novel cyberinfrastructure (CI) that integrates hardware and open-source software to perform flexible, scalable, and robust real-time ecological forecasts of water quality in lakes and reservoirs. To date, this project developed and successfully implemented the resulting forecasting system, named FLARE (Forecasting Lake And Reservoir Ecosystems), in 13 lakes and reservoirs. These waterbodies span shallow drinking water reservoirs to deep glacially-formed lakes and include all of the lakes in NEON, the National Ecological Observatory Network.

Key intellectual merit outcomes have been contributions to the disciplines of ecology, forecasting, and computer systems, resulting in 48 peer-reviewed journal articles. These studies have quantified fundamental controls of ecosystem predictability, determined the relative forecastability of different water quality variables, and identified the dominant sources of uncertainty in ecosystem forecasts. The computer systems and cyberinfrastructure research questions in this project focused on the development of virtualization applied to computing both in the cloud (large-scale Internet data centers) and at the edge (low-capacity, low-power devices near environmental sensors) to reduce the complexity associated with the deployment of end-to-end forecasting workflows, while presenting an accessible interface for users and developers in the ecology domain.

In total, 10 undergraduate students, 15 graduate students, 6 postdoctoral researchers, 5 research technicians, and 4 faculty received training at the interface of computer science and ecology.

78 datasets obtained from our instrumented lakes and reservoirs have been made available to the wider research community through the Environmental Data Initiative (EDI)

All of the software produced in the project has been released open-source.

159 workshops and presentations were delivered that disseminated our teaching modules and forecasting research in technical conferences and meetings.

Funding: National Science Foundation (DBI-1933016)

Center personnel:  Cayelan Carey, Quinn Thomas, Mary Lofton, Freya Olsson, Madeline Schreiber, Adrienne Breef-Pilz, Austin Delany