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.

KEY PROJECT OUTCOMES

Pending

KEY PAPERS

Dietze, M., R.Q. Thomas, J. Peters, C. Boettiger, A. Shiklomanov, and J. Ashander. 2023. A community convention for ecological forecasting: output files and metadata v1.0. Ecosphere 14: e4686 https://doi.org/10.1002/ecs2.4686

Dietze, M., E.P. White, A. Abeyta, C. Boettiger, N. Bueno Watts, C.C. Carey, R. Chaplin-Kramer, R.E. Emanuel, S.K. Morgan Ernest, R. Figueiredo, M.D, Gerst, L.R. Johnson, M.A. Kenney, J.S. McLachlan, I.C. Paschalidis, J.A. Peters, C.R. Rollinson, J. Simonis, K. Sullivan-Way, R. Q. Thomas, G.M. Wardle, A. Willson, J. Zwart. 2024. Near-term Ecological Forecasting for Climate Change Action. Nature Climate Change: 14: 1236–1244 https://doi.org/10.1038/s41558-024-02182-0

Thomas, R.Q. and C. Boettiger. Cyberinfrastructure to Support Ecological Forecasting Challenges. ESS Open Archive.http://doi.org/10.22541/essoar.175917344.44115142/v1

Thomas, R.Q., C. Boettiger, C.C. Carey, M.C. Dietze, L.R. Johnson, M.A. Kenney, J.S. Mclachlan, J.A. Peters, E.R. Sokol, J.F. Weltzin, A. Willson, W.M. Woelmer, and Challenge Contributors. 2023. The NEON Ecological Forecasting Challenge. Frontiers in Ecology and Environment 21: 112-113 https://doi.org/10.1002/fee.2616

Olsson F, C. Boettiger, C.C. Carey, M. Lofton and R.Q. Thomas. 2024. Can you predict the future? A tutorial for the National Ecological Observatory Network Ecological Forecasting Challenge. Journal of Open Source Education7: 259. https://doi.org/10.21105/jose.00259

Olsson, F., C.C. Carey, C. Boettiger, G. Harrison, R. Ladwig, M.F. Lapeyrolerie, A.S.L. Lewis, M.E. Lofton, F. Motealegre-Mora, J.S. Rebaey, C.J. Robbins. X. Yang, and R.Q. Thomas. 2025. What can we learn from 100,000 freshwater forecasts? A synthesis from the NEON Ecological Forecasting Challenge. Ecological Applications 35:e70004 https://doi.org/10.1002/eap.70004

Willson, A.M., H. Gallo, J.A. Peters, A. Abeyta, N. Bueno Watts, C.C. Carey, T.N. Moore, G. Smies, R.Q. Thomas, W.M. Woelmer, and J.S. McLachlan. 2023. Assessing opportunities and inequities in undergraduate ecological forecasting education. Ecology and Evolution 13: e10001. https://doi.org/10.1002/ece3.10001

Wheeler, K., M. Dietze, D. LeBauer, J. Peters, A.D. Richardson, R.Q. Thomas, K. Zhu, U. Bhat, S. Munch, R.F Buzbee, M. Chen, B. Goldstein, J.S. Guo, D. Hao, C. Jones, M. Kelly-Fair, H. Liu, C. Malmborg, N. Neupane. D. Pal, A. Ross, V. Shirey, Y. Song, M. Steen, E.A. Vance, W.M. Woelmer, J. Wynne and L. Zachmann. 2024. Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge. Agricultural and Forest Meteorology 345: 09810 https://doi.org/10.1016/j.agrformet.2023.109810

FUNDING

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.

KEY PROJECT OUTCOMES

This project developed and expanded an ongoing training program (Macrosystems EDDIE: Environmental Data-Driven Inquiry & Exploration) to teach undergraduate students the foundations of macrosystems science and ecological forecasting. The project supported the creation of ready-to-use, stand-alone teaching modules based on critical components of ecological forecasting – e.g., the iterative forecasting cycle, model-data fusion, uncertainty analysis, decision support and forecast visualization – that are widely applicable to a suite of disciplines and undergraduate student experience levels. All of the teaching modules are based on NEON (National Ecological Observatory Network) and GLEON (Global Lake Ecological Observatory Network) sites and datasets.

Macrosystems EDDIE represents the first formalized undergraduate curriculum in macrosystems ecology and ecological forecasting. Through the program, instructors and students across the U.S. and around the world have learned how to build an ecological forecasting model, apply the model to a NEON site to generate an ecological forecast, and update the forecast with new data as observations become available. By comparing forecasts among NEON ecoclimatic domains, students have learned both macrosystems ecology concepts as well as modeling and quantitative literacy skills. All teaching modules have been rigorously assessed, revised, and disseminated broadly for maximum impact through partnerships with the Science Education Research Center (SERC) at Carleton College and NEON.

Key intellectual merit outcomes have been contributions to the disciplines of education, ecology, ecological forecasting, and environmental data science, supporting 29 peer-reviewed journal articles. These studies have found that completion of a Macrosystems EDDIE module significantly increases students’ comprehension of ecological forecasting and application of macrosystems approaches, e.g., interpreting environmental variability over interacting temporal and spatial scales. Moreover, students were more likely to correctly define macrosystems concepts, interpret complex data visualizations, and apply macrosystems approaches in new contexts following module use. Further, completing one Macrosystems EDDIE teaching module significantly increased students’ self-reported proficiency, confidence, and likely future use of simulation models and ecological forecasting, as well as their perceived knowledge of ecosystem simulation models and ecological forecasts.

In addition, assessment data found that Macrosystems EDDIE ecological forecasting modules “train the trainers,” by introducing instructors to data science and ecological forecasting concepts and skills. The majority of faculty who taught a module in their classsrooms reported that they were more likely to use high-frequency/long-term datasets, such as NEON data, after teaching a module (Lofton et al. 2025). Additionally, faculty on average reported that the modules are very effective in teaching macrosystems ecology, data science, and ecosystem modeling, and very to extremely effective in teaching ecological forecasting. Finally, most instructors indicated they would teach the modules again, indicating that Macrosystems EDDIE modules will continue to help train instructors and students in macrosystems ecology, data science, and ecological forecasting in future years.

Overall, the project has had several broader impact outcomes. First, ~2500 students completed a Macrosystems EDDIE module within the project’s assessment program. The total number of students who completed a Macrosystems EDDIE teaching module is likely much higher when including courses that are not participating in the assessment, as there have been >47,000 unique views (as of the project end date) of the Macrosystems EDDIE webpages. Second, four postdoctoral researchers and one graduate student received training at the interface of education and ecology through module development and assessment. Third, all of the teaching materials developed in the project have been released open-source through the Environmental Data Initiative (EDI). Finally, >65 workshops and presentations were delivered in conferences, webinars, and other meetings that disseminated Macrosystems EDDIE teaching modules and forecasting research to interested instructors.

KEY PAPERS

Lofton, M.E., T.N. Moore, W.M. Woelmer, R.Q. Thomas, and C.C. Carey. 2025. A modular curriculum to teach undergraduates ecological forecasting improves student and instructor confidence in their data science skills. Bioscience 75: 127-138 https://doi.org/10.1093/biosci/biae089. Open Access version at ESS Open Archive

Moore, T.N., R.Q. Thomas, W.M. Woelmer, C.C Carey. 2022. Integrating ecological forecasting into undergraduate ecology curricula with an R Shiny application-based teaching module. Forecasting 4:604-633. https://doi.org/10.3390/forecast4030033

Woelmer, W.M., T.N. Moore, M.E. Lofton, R.Q. Thomas, and C.C. Carey. 2023. Embedding communication concepts in forecasting training increases students’ understanding of ecological uncertainty Ecosphere 14: e4628 https://doi.org/10.1002/ecs2.4628

FUNDING

National Science Foundation (DEB-1926050)

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.

KEY PAPERS

Figueiredo, R.J., C.C. Carey, and R.Q. Thomas. 2024. Translational Edge and Cloud Computing to Advance Lake Water Quality Forecasting. Computing in Science & Engineering. 26: 68-72. https://doi.org/10.1109/MCSE.2024.3430148

Olsson, F, T.N. Moore, C.C. Carey, A. Breef-Pilz, and R.Q. Thomas. 2024. A multi-model ensemble of baseline and process-based models improves the predictive skill of near-term lake forecasts. Water Resources Research 60: e2023WR035901 https://doi.org/10.1029/2023WR035901

Paíz, R., R.Q. Thomas, C. C. Carey, E. de Eyto, A. Delany, R. Poole, P. Nixon, M. Dillane, I.D. Jones, D.C. Pierson, V. McCarthy, S. Linnane, E. Jennings. Near-term lake water temperature forecasts can be used to anticipate the ecological dynamics of freshwater species. Ecosphere 16: e70335 http://dx.doi.org/10.1002/ecs2.70335

Thomas, R.Q, R.P. McClure, T.N. Moore, W.M. Woelmer, C. Boettiger, R.J. Figueiredo, R.T. Hensley, C.C. Carey. Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the U.S. Frontiers in Ecology and Environment 21: 220–226 https://doi.org/10.1002/fee.2623

Wander, H.L., R.Q Thomas, T.N. Moore, M.E. Lofton, A. Breef-Pilz, C.C. Carey. 2024. Data assimilation experiments inform monitoring needs for near-term ecological forecasts in a eutrophic reservoir. Ecosphere 15: e4752. https://doi.org/10.1002/ecs2.4752

Woelmer, W.M., R.Q. Thomas, F. Olsson, B.G. Steele, K.C. Weathers, and C.C. Carey. 2024. Process-Based Forecasts of Lake Water Temperature and Dissolved Oxygen Outperform Null Models, with Variability Over Time and Depth. Ecological Informatics 83: 102825. https://doi.org/10.1016/j.ecoinf.2024.102825

FUNDING

National Science Foundation (DBI-1933016)