Macrosystems EDDIE

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Project Description

Ecologists are increasingly using models, based on large datasets of observations obtained through environmental sensor networks, to study ecosystems and forecast future change. Developing forecasts requires skills in data analysis, ecological modeling, quantitative reasoning, and computing. However, modeling and computational skills are rarely taught in undergraduate classrooms, representing a major gap in training students to tackle complex environmental challenges.

Macrosystems EDDIE modules will help students across the U.S. to learn the foundations of macrosystems ecology through modeling and forecasting. Through our modules, both students and their instructors will learn how to quickly and efficiently run ecological models and generate forecasts for multiple NEON and GLEON sites. Thus, students will simultaneously learn the core concepts of macrosystems science and develop the quantitative skill sets needed to conduct the next generation of environmental research. Macrosystems EDDIE modules, which are centered on the frontier of macrosystems ecology, will enable undergraduate students to contribute to high-level ecological research. Engaging undergraduate students in hands-on modeling and forecasting activities with real-world applications translates into a workforce with increased data science, systems thinking, and quantitative skills.

Our interdisciplinary team is developing flexible classroom modules that introduce undergraduate students to the core concepts of macrosystems ecology and ecological forecasting. Each module utilizes long-term, high-frequency, and sensor-based datasets from diverse sources, including the Global Lakes Ecological Observatory Network, the United States Geological Survey, the Long Term Ecological Research Network, and the National Ecological Observatory Network.

Each module can be adapted for use in introductory, intermediate, and advanced courses in ecology and related fields, in order to enhance students' understanding of macrosystems ecology and ecological forecasting, their computational skills, and their ability to conduct inquiry-based studies.

Project Goals

Our objective is to develop stand-alone, modular classroom activities for undergraduate students that use publicly-available, long-term, and high-frequency datasets to explore the core concepts of macrosystems ecology and ecological forecasting while developing quantitative literacy.

The Macrosystems EDDIE modules are specifically designed to help students achieve the following pedagogical goals:

  1. Improve students' ability to understand and predict how local, regional, and continental processes interact to mediate responses to human activities
  2. Gain computational skills through engagement in ecological forecasting, simulation modeling, computer programming, distributed computing, and the analysis of large datasets
  3. Develop hypotheses, conduct inquiry-based studies to test them, and evaluate if their hypotheses are supported or rejected by data

Where Do Our Data Sets Come From?

Macrosystems EDDIE modules utilize long-term, high-frequency, and sensor-based datasets from diverse, publicly-accessible sources. Click the links below to learn more about our data providers.


During 2017-2023, we will be using pre- and post-module student questionnaires and soliciting instructor feedback to assess whether our Macrosystems EDDIE modules are achieving their pedagogical goals. These assessments will allow us to determine whether the modules are helping increase students' understanding of macrosystems ecology and ecological forecasting skills, and will allow us to revise modules as needed to maximize their utility to instructors and students. Previous assessments of EDDIE modules found that students who completed EDDIE modules had significantly improved data manipulation skills, an increased understanding of how to use large datasets, and a greater appreciation for the value of high-resolution and long-term data. Thus, in addition to developing critical quantitative and modeling skills, working with high-frequency sensor datasets cements the real-world application of basic ecological concepts.

Project Support

Macrosystems EDDIE is supported by funding from NSF EF-1702506, DEB-1926050, and DBI-1933016.

We welcome your feedback!

If you have any comments, questions, or suggestions to provide to the Macrosystems EDDIE team, please fill out our brief Feedback Form! We also invite your feedback at

References and Resources

  • Dietze, M. C., Fox, A., Beck-Johnson, L. M., Betancourt, J. L., Hooten, M. B., Jarnevich, C. S., Keitt, T. H., Kenney, M. A., Laney, C. M., Larsen, L. G., Loescher, H. W., Lunch, C. K., Pijanowski, B. C., Randerson, J. T., Read, E. K., Tredennick, A. T., Vargas, R., Weathers, K. C., & White, E. P. (2018). Iterative near-term ecological forecasting: Needs, opportunities, and challenges. Proceedings of the National Academy of Sciences, 115(7), 1424–1432.
  • Heffernan, J.B., et al. 2014. "Macrosystems ecology: understanding ecological patterns and processes at continental scales." Frontiers in Ecology and the Environment 12.1: 5-14. Available:
  • IPCC. 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Page (T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P. M. Midgley, Eds.). Cambridge University Press, New York, NY. Available:
  • Weathers, K. C., P. M. Groffman, E. Van Dolah, E. Bernhardt, N. B. Grimm, K. McMahon, J. Schimel, M. Paolisso, R. Maranger, S. Baer, K. Brauman, and E. Hinckley. 2016. Frontiers in Ecosystem Ecology from a Community Perspective: The Future is Boundless and Bright. Ecosystems 19:753–770. Available:

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