intro-ecoforecast.qmd

# Introduction to Ecological Forecasting {#sec-eddiemod5}

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 that 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.

[**Background presentation**](https://cdn.serc.carleton.edu/files/eddie/teaching_materials/modules/instructor_powerpoint_module5.pptx)

[**Rshiny App**](https://macrosystemseddie.shinyapps.io/module5/)

## 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>

## Problem set

Complete activities A, B, and C in the R Shiny application and submit answers to the questions in the [assignment word document](https://cdn.serc.carleton.edu/files/eddie/teaching_materials/modules/student_handout_module5.docx)

## Module references

Moore, T.N., R.Q. Thomas, W.M. Woelmer, C.C Carey. 2022. Integrating ecological forecasting into undergraduate ecology curricula with an R Shiny application-based teaching module. *Forecasting* 4:604-633. <https://doi.org/10.3390/forecast4030033>

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](https://doi.org/10.22541/essoar.171269260.08508117/v1)

This module was developed by Moore, T.N., Lofton, M.E., C.C. Carey, and R.Q. Thomas. 03 July 2023. Macrosystems EDDIE: Introduction to Ecological Forecasting. Macrosystems EDDIE Module 5, Version 2. <http://module5.macrosystemseddie.org>. Module development was supported by NSF grants DEB-1926050 and DBI-1933016.