Introduction to Ecological Forecasting
Ecological forecasting is a tool that can be used for understanding and predicting changes in populations, communities, and ecosystems. Ecological forecasting is an emerging approach which provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. Ecological forecasters develop and update forecasts using the iterative forecasting cycle, in which they make a hypothesis of how an ecological system works; embed their hypothesis in a model; and use the model to make a forecast of future conditions. When observations become available, they can assess the accuracy of their forecast, which indicates if their hypothesis is supported or needs to be updated before the next forecast is generated.
In this module, students will apply the iterative forecasting cycle to develop an ecological forecast for a National Ecological Observation Network (NEON) site. Students will use NEON data to build an ecological model that predicts primary productivity. Using their calibrated model, they will learn about the different components of a forecast with uncertainty and compare productivity forecasts among NEON sites.
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 will work with an R Shiny interface to visualize data, build a model, generate a forecast with uncertainty, and then compare the forecast with observations. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures.
- Describe an ecological forecast and the iterative forecasting cycle
- Explore and visualize NEON data
- Construct a simple ecological model to generate forecasts of ecosystem primary productivity with uncertainty
- Adjust model parameters and inputs to study how they affect forecast performance relative to observations
- Compare productivity forecasts among NEON sites in different ecoclimatic regions
Context for Use
This entire module can be completed in one 3 hour lab period or three 60-minute lecture periods for introductory undergraduate students in Ecology, Environmental Science, Ecological Modeling, and Quantitative Ecology classes. We found that teaching this module in one longer lab section with short breaks was more conducive for introductory students than multiple 1-hour lecture periods.
Description and Teaching Materials
Quick overview of the activities in this module
See the instructor manual, provided below, for a step-by-step guide for carrying out this module. A student handout describing Activities A, B, and C, and instructor PowerPoint are also provided.
- Activity A: Students visualize data from a selected NEON site, which is used to build a simple ecological model
- Activity B: Students use their model to generate a forecast with uncertainty and assess the forecast
- Activity C: Students then update their forecast model and generate a new forecasting, completing and recommencing the forecast cycle
Why macrosystems ecology and ecological forecasting?
Macrosystems ecology is the study of ecological dynamics at multiple interacting spatial and temporal scales (e.g., Heffernan et al. 2014). For example, global climate change can interact with local land-use activities to control how an ecosystem changes over the next decades. Macrosystems ecology recently emerged as a new sub-discipline of ecology to study ecosystems and ecological communities around the globe that are changing at an unprecedented rate because of human activities (IPCC 2013). The responses of ecosystems and communities are complex, non-linear, and driven by feedbacks across local, regional, and global scales (Heffernan et al. 2014). These characteristics necessitate novel approaches for making predictions about how systems may change to improve both our understanding of ecological phenomena as well as inform resource management.
Forecasting is a tool that can be used for understanding and predicting macrosystems dynamics. To anticipate and prepare for increased variability in populations, communities, and ecosystems, there is a pressing need to know the future state of ecological systems across space and time (Dietze et al., 2018). Ecological forecasting is an emerging approach which provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. Ecological forecasts are a powerful test of the scientific method because ecologists make a hypothesis of how an ecological system works; embed their hypothesis in a model; use the model to make a forecast of future conditions; and then when observations become available, assess the accuracy of their forecast, which indicates if their hypothesis is supported or needs to be updated. Forecasts that are effectively communicated to the public and managers will be most useful for aiding decision-making. Consequently, macrosystems ecologists are increasingly using ecological forecasts to predict how ecosystems are changing over space and time.
In this module, students will apply the iterative forecasting cycle to develop an ecological forecast for a NEON site. This module will introduce students to the basic components of an ecological forecast; how a simple forecasting model is constructed; how changes to model inputs affect forecast uncertainty; and how productivity forecasts vary across ecoclimatic regions.
Workflow for this module:
- Instructor chooses method for accessing the R Shiny app (Regardless of which option you pick, all module activities are the same!):
- 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
- 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
- In any internet browser, go to: https://macrosystemseddie.shinyapps.io/module5/
- 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.
- Instructor gives a brief PowerPoint presentation that introduces ecological forecasting, the iterative forecasting cycle, and a basic ecosystem productivity model (~30mins).
- 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.
- 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.
- 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.
- 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.
- R shiny app: https://macrosystemseddie.shinyapps.io/module5/
- To run the R Shiny app locally on your own computer, please see the instructions on the GitHub page: https://github.com/MacrosystemsEDDIE/module5
- Student_Handout_Module5.docx (Microsoft Word 2007 (.docx) 1.8MB Dec15 23) - Handout with questions for students to complete
- Instructor_Manual_Module5.docx (Microsoft Word 2007 (.docx) 1.1MB Dec15 23) - Instructor manual and troubleshooting for the module
- Instructor_PowerPoint_Module5.pptx (PowerPoint 2007 (.pptx) 8.2MB Dec15 23) - PowerPoint presentation to introduce core concepts & module activities
- Getting_Started_with_Shiny_Module5.pptx (PowerPoint 2007 (.pptx) 5.2MB Dec15 23) - Additional PowerPoint slides that provide a basic orientation to using an R Shiny app
Teaching Notes and Tips
Important Note to Instructors:
The R Shiny app used in this module is continually being updated, so these module instructions will periodically change to account for changes in the code. If you have any questions or have other feedback about this module, please contact the module developers (see "We'd love your feedback" below).
We highly recommend that instructors familiarize themselves with the Shiny app prior to the lesson. This will enable you to be more prepared to answer questions related to certain areas of the app's functionalities.
- Activity A: Students plot the output of their built model with observations
- Activity B: Students use their model to generate hypotheses regarding productivity responses at their NEON site, create an ecological forecast, and assess the forecast.
- Activity C: Students update the model and forecast again. Then, they compare ecological forecasts generated for sites in different eco-regions.
References and Resources
Optional pre-class readings and videos:
Recommended for this module: Explore examples of ecological forecasts
- USA-NPN Pheno Forecast - The USA National Phenological Network (NPN) Pheno Forecast delivers short-term (6 day) threshold-based forecasts of phenological events in plants and pest insects.
- Smart & Connected Water Systems - A project which is developing a smart water system that integrates novel high-frequency sensors, cyberinfrastructure, and ecosystem forecasting techniques to improve the management of drinking water supply lakes and reservoirs.
- EcoCast - EcoCast is a fisheries sustainability tool that helps fishers and managers evaluate how to allocate fishing effort to optimize the sustainable harvest of target fish while minimizing bycatch of protected or threatened animals.
- Atlantic Sturgeon Risk of Encounter - This forecast is developed for mature Atlantic Sturgeon using historic telemetry observations matched to date, bathymetry, and sea surface temperature and ocean color from NASA's MODIS AQUA satellite.
- Grassland Production Forecast - Grass-Cast uses almost 40 years of historical data on weather and vegetation growth - combined with seasonal precipitation forecasts - to predict if rangelands are likely to produce above-normal, near-normal, or below-normal amounts of vegetation.
- Portal Project - Rodent Abundances - Forecasting a time series of rodent abundances from The Portal project. A long-term ecological study in desert ecology located near Portal, AZ, USA.
(Optional pre-class discussion questions associated with these examples of ecological forecasts can be found in the student handout, which is downloaded from the Shiny app).
General background reading on ecological forecasting:
- Dietze, M., & Lynch, H. (2019). Forecasting a bright future for ecology. Frontiers in Ecology and the Environment, 17(1), 3. https://doi.org/10.1002/fee.1994
- Dietze, M. C., Fox, A., Beck-Johnson, L. M., Betancourt, J. L., Hooten, M. B., Jarnevich, C. S., Keitt, T. H., Kenney, M. A., Laney, C. M., Larsen, L. G., Loescher, H. W., Lunch, C. K., Pijanowski, B. C., Randerson, J. T., Read, E. K., Tredennick, A. T., Vargas, R., Weathers, K. C., & White, E. P. (2018). Iterative near-term ecological forecasting: Needs, opportunities, and challenges. Proceedings of the National Academy of Sciences, 115(7), 1424–1432. https://doi.org/10.1073/pnas.1710231115
- Jackson, L. J., Trebitz, A. S., & Cottingham, K. L. (2000). An Introduction to the Practice of Ecological Modeling. BioScience, 50(8), 694. https://doi.org/10.1641/0006-3568(2000)050[0694:aittpo]2.0.co;2
- NEON's Ecological Forecast: The Science of Predicting Ecosystems
- Fundamentals of Ecological Forecasting Series
Module authorship contributions: CCC and RQT conceived the idea of this module and acquired funding for this project. TNM, CCC, and RQT developed the learning objectives. TNM, MEL, CCC, and RQT developed the code for the module, module lesson activities, student handout, and instructor PowerPoint. TNM developed text for the website with contributions from MEL, CCC and RQT. CCC and TNM led module testing and collection of student assessment data. TNM, CCC, MEL, and RQT worked with instructors to iteratively improve the module.