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

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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.The biosphere is changing at unprecedented rates, requiring ecologists to use macrosystems science approaches to make forecasts about the future state of populations, communities, and ecosystems. Macrosystems EDDIE will provide the training needed to make ecological forecasting accessible to all ecologists, from undergraduates to senior researchers, by distilling complex topics and skills to an introductory level via interactive tools and visualizations. The teaching modules will analyze aquatic and terrestrial data from the National Ecological Observatory Network (NEON) and Global Lake Ecological Observatory Network (GLEON) to explore how the predictability of ecological dynamics varies among ecosystems and across different spatial scales. All teaching modules will be rigorously assessed, revised, and disseminated broadly for maximum impact through the Science Education Research Center (SERC). Beyond undergraduate classrooms, module teaching materials will be used by Ecological Forecasting Initiative researchers, thereby supporting collaborative, interdisciplinary science across multiple institutions. By teaching ecologists at multiple experience levels how to retrieve, analyze, and visualize NEON and GLEON data, our goal is to enable widespread use of computational research approaches while advancing macrosystems ecology and ecological forecasting.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.