MODULE DEVELOPMENT AND TESTING
Module teaching materials were developed by T.N. Moore, M.E. Lofton, C.C. Carey, and R.Q. Thomas to provide instructors of undergraduate ecology courses with a ready-to-use, adaptable module that could be implemented in a 3-4 hour time period to introduce undergraduates to ecological forecasting.
As the fifth module within the suite of Macrosystems EDDIE (www.macrosystemseddie.org) teaching materials, this module was developed to teach students macrosystems ecology and ecolgoical forecasting. The overarching goal of this module is for students to learn fundamental concepts about ecological forecasting and build a forecast for a NEON site. Students work with an R Shiny application to visualize data, build a model, generate a forecast with uncertainty, and then compare the forecast with observations. As a secondary goal, Macrosystems EDDIE modules introduce students to advanced computational tools as a way to manage, analyze, visualize, and interpret high-frequency and long-term ecological data sets; conduct ecosystem modeling; and generate ecological forecasts with quantified uncertainty.
The specific student learning goals for this module are that by the end of the module, students will be able to:
- Describe an ecological forecast and the iterative forecasting cycle.
- Explore and visualize NEON data (Activity A).
- Construct a simple ecological model to generate forecasts of ecosystem primary productivity with uncertainty (Activity B).
- Adjust model parameters and inputs to study how they affect forecast performance relative to observations (Activity B).
- Compare productivity forecasts among NEON sites in different ecoclimatic regions (Activity C).
The module was assessed by volunteer faculty testers during the 2021-2022 academic year. Faculty testers provided feedback that was used to update and optimize teaching materials. Moore, Carey, and Thomas also used student pre- and post-module assessment questions to gauge effectiveness of teaching materials for achieving module learning goals. Pedagogical specialists with the Science Education Resource Center at Carleton College assisted with assessment development and implementation.
MODULE WORKFLOW
Workflow for this module:
1. Instructor chooses method for accessing the R Shiny app (Regardless of which option you pick, all module activities are the same!):
a. In any internet browser, go to: https://macrosystemseddie.shinyapps.io/module5/
- This option works well if there are not too many simultaneous users (<50)
- The app generally does not take a long time to load but requires consistent internet access
- It is important to remind students that they need to save their work as they go, because this webpage will time-out after 15 idle minutes. It is frustrating for students to lose their progress, so a good rule of thumb is to get them to save their progress after completing each objective
b. The most stable option for large classes is downloading the app and running locally, see instructions at: https://github.com/MacrosystemsEDDIE/module5
- Once the app is downloaded and installed (which requires an internet connection), the app can be run offline locally on students' computers
- This step requires R and RStudio to be downloaded on a student's computer, which may be challenging if a student does not have much R experience (but this could be done prior to instruction by an instructor on a shared computer lab)
- If you are teaching the module to a large class and/or have unstable internet, this is the best option
2. Give students their handout ahead of time to read over prior to class or ask students to download the handout from the module Shiny app page when they arrive to class. There is an optional pre-class reading assignment and questions that students may complete prior to arriving to class. During class, the module is set up for students to complete discussion questions in the handout (Word document) as they navigate through the R Shiny app activities. As they navigate through the app, students will be prompted to answer questions in their handout, as well as download plots that they generate within the app and copy-paste them into their handout. The handout can be submitted to the instructor for potential grading.
3. Instructor gives a brief PowerPoint presentation that introduces ecological forecasting, the iterative forecasting cycle, and a basic ecosystem productivity model (~30mins).
4. After the presentation, the students divide into pairs. Each pair selects their own NEON site and visualizes their site's data, which is used to build and calibrate an ecosystem productivity model (Activity A). The two students within a pair each build their own model with unique inputs and parameters to compare the performance of two different models for the same ecosystem. For virtual instruction, we recommend putting two pairs together (n=4 students) into separate Zoom breakout rooms during this activity so the two pairs can compare results.
5. The instructor then introduces Activities B and C, potentially revisiting some of the slides from the introductory presentation as a reminder to students about the next steps. For virtual instruction, this would entail having the students come back to the main Zoom room for a short check-in.
6. The students work in their pairs to forecast primary productivity at their chosen site using each model, and investigate how the forecast uncertainty changes with different model inputs and parameters (Activity B). At the end of Activity B, students assess their forecasts. They may also compare their forecasts with their partner's. For virtual instruction, we recommend putting the two pairs back into the same Zoom breakout rooms. Optionally, instructors may bring the class back together at the end of Activity B to discuss performance of students' initial forecasts before beginning Activity C.
7. Student pairs then update their forecast models and generate a second forecast, thus completing and recommencing the iterative forecast cycle (Activity C). The students work together in a group to present the results from their site and different models to the rest of the class. The class may discuss why the forecasts are similar or different among the different sites and models.
For more information, we refer users to the website and R Shiny application listed below.
WEBSITE & PUBLICATIONS
Moore, T.N., Lofton, M.E., Carey, C.C., and Thomas, R.Q. 2024. Macrosystems EDDIE Module 5: Introduction to Ecological Forecasting. http://module5.macrosystemseddie.org.
Moore, T.N., Lofton, M.E., Carey, C.C., and Thomas, R.Q. 2024. Macrosystems EDDIE Module 5: Introduction to Ecological Forecasting version 2 (R Shiny application) (v2.0). Zenodo. https://doi.org/10.5281/zenodo.10733117
