• EFI Education Working Group Publications & Blog Posts
• EFI2025 Conference Workshop Tutorials
• Ecological Forecasting Courses & Workshop Materials
• Macrosystems EDDIE Forecasting Resources
• A list of Inclusive Pedagogy Resources
• A compiled list of over one hundred educational resources related to Ecological Forecasting
• A compiled list of of articles, papers, and websites related to using AI in the classroom
• 2-page pdf overview of what ecological forecasting is and potential careers as well as examples of courses that is a starting point for students to learn more about the concepts and statistical and computational tools used in ecological forecasting
• Infographics to support teaching and learning biostats in R
• Example syllabi for ecological forecasting courses

Publications & Blog Posts

Resources for Reviewing Code Blog Post, posted February 27, 2025
This blog post, co-developed by the EFI Theory and Education working groups, provides

• An overview of why to review code or have your code reviewed
• The background for the blog post and resources presented
• A project overview template and a code review checklist template which were resources developed and tested by the blog co-authors
• Pain points to be aware of and suggestions for how to manage them in the review process
• Other resources from SORTEE (Society for Open, Reliable, and Transparent Eoclogy and Evolutionary Biology) and elsewhere

Papers

1. The paper on Ethics in Forecasting Educational Modules was published in both Teaching Issues and Experiments in Ecology and as a QUBES Open Educational Resource. This allows us to reach a broader audience. A DOI is available with the QUBES publication. Here are the two citations:

Lewis, A.S.L., D.W. Howard, G. Koren, C. Kowalski, J. McLachlan, J.A. Peters, G. Smies, and O. Tabares. 2023. Ethics in ecological forecasting: four case-based teaching modules. Teaching Issues and Experiments in Ecology, Vol. 19: Practice #13. https://tiee.esa.org/vol/v19/issues/case_studies/lewis/abstract.html

Lewis, A. S., Howard, D. W., Koren, G., Kowalski, C., McLachlan, J., Peters, J., Smies, G., Tabares, O. (2024). Ethics in ecological forecasting: four case-based teaching modules. EFI-RCN: Ecological Forecasting Initiative Research Coordination Network, QUBES Educational Resources. doi:10.25334/5D99-Y019

Module topics from the ethics paper

• Module 1: Flying foxes and uncertainty
• Module 2: Marine Fisheries and conflicts of interest
• Module 3: Water Quality and Indigenous Knowledge
• Module 4: Tropical forests and data availability

2. Willson, A. M., H. Gallo, J.A. Peters, A. Abeyta, N. Bueno Watts, C.C. Carey, T.N. Moore, G. Smies, R.Q. Thomas, W.M. Woelmer & J.S. McLachlan. 2023. Assessing opportunities and inequities in undergraduate ecological forecasting education. Ecology and Evolution, 13, e10001. https://doi.org/10.1002/ece3.10001

EFI2025 Conference Workshop Tutorials

The following are links to resources from eight training workshops held at the EFI2025 Conference. The goals of the workshops are to enhance skills in model development, data analysis, and stakeholder engagement.  See the summary and more details about the EFI2025 Conference here.

1. Create and automate real-time water quality forecasts 

Leads: Mary Lofton, Freya Olsson, Austin Delany, Adrienne Breef-Pilz, Rohit Shukla, Quinn Thomas, Cayelan Carey
Virginia Tech

Participants in this workshop created, submitted, and automated real-time forecasts for up to 40 freshwater physical, chemical, and biological variables in the Virginia Ecoforecast Reservoir Analysis (VERA) forecasting challenge.

● VERA forecast challenge website: https://www.ltreb-reservoirs.org/vera4cast/
● VERA forecast tutorial in R: https://github.com/LTREB-reservoirs/vera4cast-example
● VERA forecast tutorial in Python: https://github.com/LTREB-reservoirs/PY-VERA_EXAMPLE

2. Spatial forecast of post fire recovery using MODIS LAI 

Leads: Steven Hammond¹, David Durden², Chris Jones³, John Smith^1
1Montana State University, ²NEON, ³NC State University Center for Geospatial Analytics

Participants learned about creating a spatial forecast and how that differs from other non-spatial NEON forecast challenges. Forecasting wildfire recovery using MODIS LAI was used in this example. 

● Find the tutorial material at: https://github.com/eco4cast/modis-lai-forecast/tree/main/tutorials
● See the rendered html at: https://htmlpreview.github.io/?https://github.com/eco4cast/modis-lai-forecast/blob/main/tutorials/efi_2025_workshop.html

3. Hands-on introduction to the Beetle Communities NEON Ecological Forecasting Challenge 

Leads: Eric Sokol, Vicky Louangaphay
NEON

This workshop provided code-along instructions to submit forecasts for ground beetle abundance and richness across NEON’s terrestrial sites to provide a hands-on demonstration of how to participate in the NEON Forecasting Challenge. 

● https://www.neonscience.org/resources/learning-hub/tutorials/neon-beetle-forecasting

4. Introduction to Gaussian Process Modeling of time dependent ecological data in R 

Leads: Leah Johnson, Robert Gramacy, Parul Patil
Virginia Tech

This workshop introduced Gaussian Process (GP) modeling for forecasting time dependent ecological data and demonstrate applications to tick abundance in the NEON Forecasting Challenge. 

● Find workshop details, slides, and a tutorial at: https://lrjohnson0.github.io/QEDLab/training/EFI2025.html

5. Water Quality Modeling: Building aquatic ecosystem models with the modular AED framework 

Lead: Matthew Hipsey
The University of Western Australia

This workshop provided hands-on training in the simulation of aquatic ecosystem processes using the open-source Aquatic Ecosystem Dynamics (AED) platform. 

● Find workshop materials at: https://github.com/AquaticEcoDynamics/efi-workshop

6. Accessing and Using NEON Data 

Leads: Eric Sokol, David Durden, Vicky Louangaphay
NEON

This workshop included an overview of NEON data and how to use the Data Portal and the neonUtilities R package to access and work with selected datasets.
● Slides and notes from the workshop

7. Hands-on Introduction to Cloud-native, Event-driven Computing in R with FaaSr 

Lead: Renato Figueiredo, Ashish Ramrakhiani
Oregon State University

Participants in this workshop gained hands-on experience installing and running an example ecological forecasting workflow using FaaSr, an R package with cloud-native functions and workflows that execute on-demand. 

● Find workshop materials at: https://github.com/Ashish-Ramrakhiani/FaaSr_workshop

8. Evaluation, scoring, and synthesis of ecological forecasts using the NEON Forecasting Challenge Catalogue 

Leads: Freya Olsson¹, Caleb Robbins², Quinn Thomas^1
1Virginia Tech, ²Baylor University

This workshop introduced concepts and tools for forecast evaluation, scoring, and synthesis. Participants used the vast, open, catalogue of forecasts submitted to the EFI-NEON Forecasting Challenge to apply the tools for forecast evaluation and comparison.

● Find workshop materials at: https://github.com/OlssonF/Forecast-evaluation-EFI25

Ecological Forecasting Courses & Workshop Materials

• NEON Beetle Forecast Challenge Tutorials. This tutorial was presented in a workshop at the Ecological Society of America 2024 conference and as a virtual tutorial on March 4, 2025.
  • Here is the recording of the March 4 tutorial for forecasting Beetle abundance
  • Download and view template code from the March 4 tutorial as an R script, Jupyter notebook, or Google Colab 
  • View the original tutorial developed by EFI community and used at ESA 2024 
• Ecological Forecasting & Dynamics Course. A course on how ecological systems change over time and how to forecast how they will change in the future. Reading + discussion + R tutorials. This material can be used to independently learn about ecological forecasting or can be used and modified by instructors for course material. See Ernest et al. 2023, doi: 10.21105/jose.00198 for more details.
• Forecasting for Decision-Making: An Epidemiological & Ecological Perspective. A short course hosted by the Canadian EFI Chapter to build skills for producing more reliable and decision-relevant forecasts. Specific course topics covered included Bayesian model-fitting, forecasting with static vs. dynamic models, increasing model reproducibility, propagating uncertainties, incorporating decision-making priorities into forecasts, engaging with a lens of data sovereignty, and increasing effective communication.
  • In the spirit of having the course be as open as possible, the course materials are available including lectures, exercises, and forecast modelling materials for three case studies: Infectious Disease Control, Fisheries Management, Water Quality Monitoring. The playlist of the lectures is available on the EFI YouTube channel here and the exercises and case study materials are on GitHub here.
• NEON Forecast Challenge Workshop.  Workshop materials used in several national conferences to demonstrate how to participate in the NEON Ecological Forecasting Challenge with examples for the aquatics and terrestrial carbon Challenge themes. Learn how to submit forecasts, automate forecast workflows, and analyze forecast scores to understand how forecasts are performing. The material is dynamic and may change with future workshop presentations. The most recent archived version with a DOI is at: https://zenodo.org/records/8316966
• NEFI Short Courses. NSF-funded short courses adapted from the Ecological Forecasting book on topics of Bayesian statistics; fusing multiple data sources; forecast uncertainty propagation & assessment; iterative data assimilation; machine learning; decision science; and a range of ecological forecasting applications such as phenology, microbiomes, carbon, infectious disease, and aquatic productivity. These were in-person courses, but the hands-on activities from the courses are available here.

Macrosystems EDDIE Resources

Macrosystems EDDIE modules help students learn the foundations of macrosystems ecology through modeling and forecasting. Students simultaneously learn the core concepts of macrosystems science and develop the quantitative skill sets needed to conduct the next generation of environmental research.

Are you looking for fresh curriculum ideas? Consider including a one to three-hour active learning module on ecological forecasting that allows students to explore key forecasting concepts using real environmental data. The materials in the modules are in an RShiny format that introduces the concepts and gives hands-on applications for what is taught. Students can save their progress in the RShiny app so they do not need to complete the materials in one sitting.  Modules 6 and 7 are also available in an RMarkdown format for students with coding experience. This allows instructors who want to go a step further with their instruction to allow the students to understand and apply the code to new situations.

1) Module 5: Introduction to Ecological Forecasting (link to the module; link to the instructor materials)
2) Module 6: Understanding Uncertainty in Ecological Forecasts (link to the module; link to the instructor materials; link to the Rmarkdown material)
3) Module 7: Using Data to Improve Ecological Forecasts (link to the module; link to the instructor materials; link to the Rmarkdown material)
4) Module 8: Using Ecological Forecasts to Guide Decision Making (link to the module; link to the instructor materials)

These modules are part of the Macrosystems EDDIE series funded by the NSF Macrosystems Biology program. Each module uses NEON data to teach key forecasting concepts.

If you are interested in teaching these modules in your class, email Mary Lofton (melofton@vt.edu) or Cayelan Carey (cayelan@vt.edu).

QUBES Resources

A number of the Educational Resources are available as open educational resources on the QUBES platform. You can access the resources on QUBES individually below or find all of them compiled on QUBES here.

HERE is a list of over one hundred educational resources related to Ecological Forecasting. You can also access this resource on QUBES.

• Resources include course material, videos, code repositories, modules, and online textbooks.
• Resources are organized around concepts of forecasting, basics of R, Python, ecology, and statistics, data sources, data skills, model building, the forecast cycle, applying forecasts, and using decision science or interpreting forecasts as end-users.
• Resources are categorized as Introductory, Intermediate, and Advanced

• Alyssa Willson, Peters, J. (2021). Ecological Forecasting Repository of Online Educational Resources. EFI-RCN: Ecological Forecasting Initiative Research Coordination Network, QUBES Educational Resources. doi:10.25334/XAW7-JW16