Macrosystems EDDIE Module 6: 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 that 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 several approaches that forecasters can use to reduce uncertainty, which will be explored in this module.

This module will guide students through an exploration of the sources of uncertainty within an ecological forecast, how uncertainty can be quantified, and steps that can be taken to reduce the uncertainty in a forecast students develop for a lake ecosystem.

This repository contains the RMarkdown version of Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts. Instructional materials associated with teaching the module can be found here. The module is also available as an R Shiny application for students and instructors who wish to complete module activities without coding.

Assignment

All work for this assignment is in the assignment directory. Code is contained in the module6.Rmd notebook, and final rendered output files (module6.html) will be generated in the assignment directory as well. The general rubric you will be graded on is found in the rubric.md file.

Other files

In addition, this repository includes the following files and folders:

• README.md a general overview of the repository in markdown format.
• lesson.md a description of the assignment.
• rubric.md the rubric for how the module will be assessed.
• .gitignore an optional file, used to ignore common file types we don't want to accidentally commit to GitHub. Most projects should use this.
• *.Rproj an R-Project file created by RStudio for its own configuration of the repo files. Some people prefer to .gitignore this file.
• /module_admin a folder containing data and code required to run the module. This folder is needed to complete module activities but students and instructors do not need to modify anything in this folder to run the module.

How to run the module

Students will need R and RStudio downloaded on their computers to run this module. Students may access and run the code associated with this module in two ways:

Option 1: Download a zip file of the code (easiest option)

1. Click the green "Code" button near the top of the repository page.
2. In the dropdown menu, select "Download ZIP".
3. Unzip the downloaded file; it will contain all the files you need to run the module. Use the instructions above to help you get started.

Option 2: Create an R Project (advanced option)

1. If you don't already have one, make a GitHub account.
2. Near the top of the repository page, click the "Fork" button to create your own copy of the module code repository in your GitHub account.
3. Open RStudio on your computer.
4. In the top right corner, click the "Project: (None)" button.
5. In the dropdown menu, click "New Project".
6. Select "Version Control".
7. Select "Git".
8. Go back to your internet browser. To retrieve the URL for this code repository, click the green "Code" button near the top of the repository page and copy the HTTPS link in the dropdown menu.
9. Go back to RStudio. Paste the link into the "Repository URL" box. Type a name for your project into the "Project directory name" box. Select where you would like the project to be located on your computer in the "Create project as a subdirectory of" box.
10. Click "Create project".
11. RStudio should create a project which allows you to access and manipulate files locally.

Committing and pushing changes back to GitHub

The advantage of accessing the module as an RProject via GitHub is that you now have version control, which means you can track (and revert if needed) your changes over time. You also have a copy of the project stored remotely (on GitHub) as a backup if your computer is lost or broken. However, in order to benefit from these advantages, you will need to commit and push any changes you make to the module files locally on your computer back to GitHub. To do this from RStudio:

1. Navigate to the "Git" pane in the top right panel of RStudio.
2. Click the check box next to each file you have changed.
3. Click "Commit".
4. In the top right corner of the pop-up window, type a brief but informative note to your future self documenting the changes you have made.
5. Under your message, click "Commit".
6. Once the changes are committed, click "Push".
7. RStudio will ask for your GitHub credentials to verify that you have rights to push changes to the remote code repository. Enter the email you used to create your GitHub account under 'username' as well as your GitHub token under 'password', then push your changes. Instructions for obtaining a token can be found here.