3 Understanding Uncertainty in Ecological Forecasts
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. 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. 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 and quantify forecast uncertainty. There are a number of approaches that forecasters can use to reduce uncertainty, which will be explored in this module.
3.1 Option: code based assignment
This chapter sets the foundation for Chapter 9. In prep for Chapter 9, there is code-based version of this chapter that provides example code and introduces key that will guide your work adding uncertainty to your forecast from Chapter 8.
The code-based assignment will require you to create a GitHub repository with your completed code from the NEON Forecasting Challenge workshop Chapter 8. You will build on this repository throughout the rest of section 1 of the book. You can learn more about setting up Git and GitHub in Section 7.3.
3.2 Reading
Dietze, M. C., Fox, A., Beck-Johnson, L. M., Betancourt, J. L., Hooten, M. B., Jarnevich, C. S., et al. (2018). Iterative near-term ecological forecasting: Needs, opportunities, and challenges. Proc Natl Acad Sci U S A, 115(7), 1424–1432. https://doi.org/10.1073/pnas.1710231115
3.3 Problem set
- Fork repository for the assignment.
- Complete activities A, B, and C (through question 16) in the Rmarkdown document. You will be modifying the Rmarkdown document with the answers to the questions (both text and code).
- Knit the Rmarkdown Rmd to html.
- Commit Rmarkdown and html to GitHub
3.4 Module Reference
Lofton, M.E., T.N. Moore, W.M. Woelmer, R.Q. Thomas, and C.C. Carey. A modular curriculum to teach undergraduates ecological forecasting improves student and instructor confidence in their data science skills. ESS Open Archive. https://doi.org/10.22541/essoar.171269260.08508117/v1
This module was developed by Moore, T. N., Lofton, M.E., Carey, C.C. and Thomas, R. Q. 24 July 2023. Macrosystems EDDIE: Understanding Uncertainty in Ecological Forecasts. Macrosystems EDDIE Module 6, Version 2. http://module6.macrosystemseddie.org. Module development was supported by NSF grants DEB-1926050 and DBI-1933016.