Using Ecological Forecasts to Guide Decision Making

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Provenance: Tadhg Moore, Virginia Polytechnic Institute and State Univ
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• Describe what ecological forecasts are and how they are used (Activity A)
• Identify the components of a structured decision (Activity A, B, C)
• Examine how an ecological forecast may affect decision-making (Activity B)
• Understand how forecast user needs affect forecasting decision support (Activity B, C)
• Discuss factors which influence uncertainty in forecast output and how forecast uncertainty relates to decision-making (Activity A, B, C)
• Create visualizations tailored to specific forecast users (Activity C)

This entire module can be completed in one 2-3 hour lab period or two 60-minute lecture periods for introductory undergraduate students. Activities A and B could be completed with upper-level students in two 60-minute lecture periods, with Activity C as a separate add-on activity.

This module is recommended for introductory undergraduate students in Applied Ecology, Environmental Science, Environmental Studies, and Environmental Social Science. Module Activities A and C can be tailored to focus on specific types of ecological forecasts for classes whose curriculum may be tailored to a certain type of ecosystem (e.g., terrestrial forecasts).

It is helpful for the instructor to have a working knowledge of the basic components of ecological forecasts and structured decision-making to help troubleshoot and respond to student questions. We provide a brief introduction to these topics as part of the Teaching Materials, below.

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.

1. Introduction to Ecological Forecasting: Pre-readings and PowerPoint in class
2. Activity A: Explore an existing ecological forecast
3. Activity B: Make decisions informed by a real water quality forecast
4. Activity C: Explore how different visualizations might impact decision-making

Why macrosystems ecology?

Macrosystems ecology is the study of ecological dynamics at multiple interacting spatial and temporal scales (e.g., Heffernan et al. 2014). 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.

Ecological forecasting

Forecasting is a tool that can be used for understanding changes in macrosystems ecology. 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 if it needs to be updated. Consequently, macrosystems ecologists are increasingly using ecological forecasts to predict how ecosystems are changing over space and time.

The theme of this module is understanding how forecasts are connected to decision-making of forecast users, or the managers, policy-makers, and other members of society who use forecasts to inform decision-making.Ecological forecasts have vast potential for aiding decision-making for range of different forecast users, yet may be challenging to understand because they inherently are associated with uncertainty in alternate future outcomes which have not yet occurred. This module will teach students the basic components of an ecological forecast; how to connect forecast visualizations to forecast user needs for aiding decision-making; and to create their own visualizations of probabilistic forecasts of ecological variables for a specific forecast user.

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. The R Shiny app is set up for students to complete discussion questions as they navigate through the module activities. Thus, students could answer questions 1-3 prior to the start of instruction and can save their progress, which will allow them to return at a different time. The questions can be saved and downloaded as a Microsoft Word file at the end of the module, which could be submitted to their instructor for potential grading.
2. Give a brief (~20 minutes) PowerPoint presentation that introduces ecological forecasts and uncertainty, how forecasts can be used by forecast users to guide decision-making, and describes different ways of visualizing forecast uncertainty. Slide notes are embedded within the PowerPoint document and included below.
3. After the presentation, the students transition to the Shiny App, where they can work individually or in pairs. For virtual instruction, we recommend putting two sets of partners (pairs) together (n=4 students total) into separate Zoom breakout rooms during this activity.
4. Student first complete Activity A. In this activity, students answer questions about ecological forecasts which they choose from a curated list of current forecasting systems and then compare their responses with a partner.
5. Once students complete Activity A, you can check in with students and have some group discussion regarding their visualization analysis and to answer any lingering questions. Group discussion questions for each activity are included below under the respective sections for each activity below. Then introduce Activity B and C with a few PowerPoint slides reminding students of the scope of the activities. For virtual instruction, this would entail having the students come back to the main Zoom room for a short check-in.
6. The students then return to their partner and pairs to complete Activity B, where they will role-play as drinking water managers and make decisions about optimizing multiple objectives using two different forecast visualizations (Activity B). Students first must use structured decision-making techniques to deconstruct their management objectives. They then create hypotheses about how to manage the drinking water reservoir as the forecasts are updated with observations and uncertainty changes over time, followed by discussion of how the different forecast visualizations influenced their ability to make decisions about managing the reservoir.
7. Once students complete Activity B, you can choose to check in with students and have group discussion using the guiding questions below.
8. The students then work individually on Activity C, in which they will choose a forecast user of a drinking water quality forecast and customize a visualization for their forecast user. Students identify a decision which their forecast user needs to make (e.g., whether or not to go swimming in a lake based on a chlorophyll-a threshold) and answer questions which will guide their decisions in creating a customized forecast visualization. The students make a hypothesis about how different types of forecast visualizations will aid in their forecast user's decision-making. Students then compare their visualizations with their partner (Activity C).

Teaching Materials:

• R shiny app: https://macrosystemseddie.shinyapps.io/module8/
  • To run the R Shiny app locally on your own computer, please see the instructions on the GitHub page: https://github.com/MacrosystemsEDDIE/module8
• Pre_Class_Student_Handout_Module8.docx (Microsoft Word 2007 (.docx) 576kB Dec15 23) - Handout for students to complete prior to the module
• Instructor_Manual_Module8.docx (Microsoft Word 2007 (.docx) 71kB Dec15 23) - Instructor manual and troubleshooting for the module.
• Instructor_PowerPoint_Module8.pptx (PowerPoint 2007 (.pptx) 17.1MB Dec15 23) - PowerPoint presentation to introduce core concepts & module activities
  • Getting_Started_With_Shiny_Module8.pptx (PowerPoint 2007 (.pptx) 1.7MB Dec15 23) - Additional PowerPoint slides that provide a basic orientation to using an R Shiny app

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

• Students choose from a list of existing ecological forecasts and answer questions to identify basic components of a forecast, forecast applications and forecast users, and examine how forecasts are visualized.
• Students use a real water quality forecasting system to make decisions about managing a drinking water reservoir for recreation and health. Students identify the components of a structured decision and explore how forecast uncertainty changes over time.
• Students learn different ways to visualize uncertainty in forecast output and discuss the value of different types of visualizations for different forecast user decision-making purposes. Finally, students create their own version of an ecological forecasting visualization tailored for a specific forecast user.

Optional pre-class readings and videos

• 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
• Spiegelhalter, D., Pearson, M., & Short, I. (2011). Visualizing uncertainty about the future. Science, 333 (6048), 1393–1400. https://doi.org/10.1126/science.1191181
• Videos:
  • NEON's Ecological Forecast: The Science of Predicting Ecosystems
  • Fundamentals of Ecological Forecasting Series: Intro to Environmental Decision Making & PROACT

Tools and data used in this module

• Thomas, R.Q., R.J. Figueiredo, V. Daneshman, B.J. Bookout, L.K. Puckett, and C.C. Carey. A Near-Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time. 2020. Water Resources Research 56(11):1-20. https://doi.org/10.1029/2019WR026138
• Carey, C.C., W.M. Woelmer, M.E. Lofton, R.J. Figueiredo, B.J. Bookout, R.S. Corrigan, V. Daneshmand, A.G. Hounshell, D.W. Howard, A.S.L. Lewis, R.P. McClure, H.L. Wander, N.K. Ward, and R.Q. Thomas. Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting. 2022. Inland Waters 12(1):107-120.https://doi.org/10.1080/20442041.2020.1816421