Communication of model uncertainty is of paramount importance for advancing the utility of ecological research findings for decision-making. Our teaching module provides an introduction to concepts and skills needed for ecology students to increase their visualization literacy, engage in data science applications, and develop decision support tools. Introduction to ecological forecasting concepts at an early educational stage, including an improved understanding of the importance of ecological forecasting for societal benefit, is increasingly necessary for training the next generation of predictive ecologists to meet both European (Nativi et al. 2021) and U.S. government agency directives (Vought and Droegemeier, 2020; Arsenault et al. 2020; NOAA, 2022; CDC 2022). Moreover, by teaching ecological forecasting and uncertainty communication skills via a real-world decision-making scenario, this module helps to emphasize the relevance and lower the barrier of entry to ecology. Through an R Shiny interface that is easy to implement for educators in a range of classroom experience levels, this 3-hour, adaptable module fills a critical gap in undergraduate ecology curricula. By introducing students to uncertainty communication and ecological forecasting early in their careers, this module can help train the next generation of ecologists to conduct societally relevant research and tackle pressing ecological challenges.

ACKNOWLEDGEMENTS: We are grateful to the students who participated in this module and their instructors, especially Kait Farrell, Hilary Dugan, Kait Reinl, Sheila Lyons-Sobaski, Matt Aiello-Lammens, and graduate teaching assistants, who provided constructive feedback which substantially improved the quality of the module. We thank Melissa Kenney for her invaluable feedback on module activities, as well as the Carey and Schreiber Labs for their feedback throughout module development, especially Caroline Bryant, Arpita Das, and Jacob Wynne, who completed pilot versions of this module. We thank Kait Farrell and Alex Hounshell for their contributions to the Macrosystems EDDIE program, as well as Kristin O’Connell, Monica Bruckner, Ashley Carlson, Cailin Huyck Orr, Ellen Iverson, and the Science Education Resource Center at Carleton College for coordinating website development and survey administration. Lastly, we thank members of the Ecological Forecasting Initiative and the Global Lake Ecological Observatory Network for providing support and feedback on module content and fostering community around these topics. Our study was financially supported by NSF DEB-1926050, DBI-1933016, and DEB-1926388.

AUTHOR CONTRIBUTIONS: CCC developed the Macrosystems EDDIE program. CCC and RQT expanded the EDDIE program for forecasting and acquired funding for the project. WMW and CCC conceived of the study design. WMW led the development of module materials, with substantial guidance from RQT, CCC, and TNM. TNM assisted with R Shiny app development, as well as administration of module assessment. WMW, TNM, and MEL co-analyzed qualitative assessment questions. WMW led the analysis of module assessment results and manuscript writing, with significant input from CCC and MEL. All authors edited and approved of the final manuscript.

CONFLICT OF INTEREST STATEMENT:The authors declare no conflict of interest.