Introduction to Ecological Forecasting

This module was developed by Moore, T.N., C.C. Carey, and R.Q. Thomas. 23 January 2021. Macrosystems EDDIE: Introduction to Ecological Forecasting. Macrosystems EDDIE Module 5, Version 1. http://module5.macrosystemseddie.org. Module development was supported by NSF grants DEB-1926050 and DBI-1933016.

Summary

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.

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Learning Goals

By the end of this module, students will be able to:

  • 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 3-4 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

We are currently developing the instructor manual, student handout, and instructor PowerPoint. These will be posted on this site when they are completed (expected December 2020).

  • 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
  • Activity C: Students then apply a forecast to a different NEON site and compare the different forecasts

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:

  1. Give students their handout ahead of time to read over prior to class, or distribute handouts when they arrive to class.For virtual instruction, we recommend uploading the handout to a learning management system (e.g., Blackboard, Canvas, Moodle) for students to fill in questions as they proceed through the module activities.
  2. Instructor gives a brief PowerPoint presentation that introduces ecological forecasting, the iterative forecasting cycle, forecast uncertainty, and a basic ecosystem productivity model.
  3. 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.
  4. The instructor then introduces Activity B using a few PowerPoint slides. For virtual instruction, this would entail having the students come back to the main Zoom room for a short check-in.
  5. The students work in their pairs to create hypotheses about how they expect their site's productivity to change in the future, forecast the productivity using each model, and investigate how the forecast uncertainty changes with different model inputs and parameters (Activity B). Students first compare their forecasts with their partner's and then revisit their initial hypotheses to see if they are supported or need to be updated. For virtual instruction, we recommend putting the two pairs back into the same Zoom breakout rooms.
  6. Student pairs then apply their ecological model to a second NEON site (the same site that the other team in their breakout room is working on) and generate ecological forecasts for this second site using their initial productivity model (Activity C). The students work together in a group to present the results from their two sites and two different models and discuss why the forecasts are similar or different among the different sites and models.

Teaching Materials:

Currently in development, will be coming soon!

Teaching Notes and Tips

Important Note to Instructors:

The R Shiny app was built using R version 4.0.3. All dependent R packages used in this module are continually being updated, so these module instructions will periodically change to account for changes in the code.

If you have any questions or any problems with this module, please reach out to us at MacrosystemsEDDIE@gmail.com.

Assessment

  • 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, update the model and forecast again.
  • Activity C: Students run forecasts at a different NEON site and compare ecological forecasts.

References and Resources

Optional pre-class readings and videos:

Articles:

  • 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

Videos: