Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts (Instructor Materials)

This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts, a ~3-hour educational module for undergraduates. Ecological forecasting is an emerging approach that provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. Forecast uncertainty is derived from multiple sources, including model parameters and driver data, among others. Knowing the uncertainty associated with a forecast enables forecast users to evaluate the forecast and make more informed decisions. This module will guide students through an exploration of the sources of uncertainty within an ecological forecast, how uncertainty can be quantified, and steps that can be taken to reduce the uncertainty in a forecast that students develop for a lake ecosystem, using data from the National Ecological Observatory Network (NEON). Students will visualize data, build a model, generate a forecast with uncertainty, and then compare the contributions of various sources of forecast uncertainty to total forecast uncertainty. The flexible, three-part (A-B-C) structure of this module makes it adaptable to a range of student levels and course structures. There are two versions of the module: an R Shiny application which does not require students to code, and an RMarkdown version which requires students to read and alter R code to complete module activities. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module6/. GitHub repositories are available for both the R Shiny (https://github.com/MacrosystemsEDDIE/module6) and RMarkdown versions (https://github.com/MacrosystemsEDDIE/module6_R) of the module, and both code repositories have been published with DOIs to Zenodo (R Shiny version at https://zenodo.org/doi/10.5281/zenodo.10380759 and RMarkdown version at https://zenodo.org/doi/10.5281/zenodo.10380339). Readers are referred to the module landing page for additional information (https://serc.carleton.edu/eddie/teaching_materials/modules/module6.html).