Ecological research increasingly requires understanding drivers and responses that operate at multiple, interconnected spatial and temporal scales. To understand complex ecological feedbacks, a macrosystems approach has emerged that uses high-frequency data and simulation modeling to predict ecosystem responses to changes in drivers at multiple scales. Despite the increasing use of these approaches, however, undergraduate ecology curricula rarely include concepts in macrosystems ecology or simulation modeling and other advanced computational skills. Through Macrosystems EDDIE (Environmental Data-Driven Inquiry & Exploration), we are developing a suite of hands-on, data-driven modules that instructors can use to introduce fundamental macrosystems topics to undergraduate students at a range of experience levels. Modules combine high-frequency environmental data from GLEON (Global Lake Ecological Observatory Network) and NEON (National Ecological Observatory Network) with whole-lake ecosystem simulation models to guide students through inquiry-based activities on macrosystems topics through the lens of limnology. We taught these modules in ten undergraduate ecology courses at nine colleges and universities and used student pre- and post-module questionnaires to test whether the use of ecosystem simulation models through guided modules can effectively teach students macrosystems thinking.
Results/Conclusions
Our experience with Macrosystems EDDIE indicates that using whole-ecosystem simulation models embedded within inquiry-based learning modules is a powerful means to foster macrosystems thinking in undergraduate ecology students. When we tested the Macrosystems EDDIE “Climate Change Effects on Lake Temperatures” and “Cross-Scale Emergence” modules in classrooms from diverse institutions in Fall 2017 and Spring 2018, we found that module use significantly increased students’ self-reported knowledge of ecosystem simulation modeling (>50% increase) and macrosystems ecology (>30% increase) relative to their pre-module knowledge. In addition, using a simulation model to develop and test individual hypotheses, and visualize and interpret model output, increased students’ perceptions of the value of high-frequency environmental data and the ability to run multiple simulations to study and predict potential effects on climate change on ecosystem processes. Importantly, module use reduced the performance gap among students, with the largest post-module gains seen in students who initially reported the lowest level of knowledge and perceived value. These results build on our previous research that R-based modules can improve computational literacy and quantitative skills for undergraduate ecology students, and suggest that integrating simulation modeling activities into undergraduate ecology curricula will enhance students’ abilities to interpret complex and non-linear dynamics in ecosystems.