Conclusions
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.