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Macrosystems EDDIE Module 5: Introduction to Ecological Forecasting (Instructor Materials)

General Information
Data Package:
Local Identifier:edi.1097.1
Title:Macrosystems EDDIE Module 5: Introduction to Ecological Forecasting (Instructor Materials)
Alternate Identifier:DOI PLACE HOLDER
Abstract:

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. 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 Macrosystems EDDIE (Environmental Data-Driven Inquiry & Exploration) 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.

This EDI data package contains instructional materials and the files necessary to teach the module. Readers are referred to the module landing page for additional information (https://serc.carleton.edu/eddie/teaching_materials/modules/module5.html) and GitHub repo (https://github.com/MacrosystemsEDDIE/module5) and/or Zenodo data package (Moore et al. 2023; DOI: XXXXXXXXXXX) for the R Shiny application code needed to run the module locally.

Publication Date:2023-12-01
For more information:
Visit: DOI PLACE HOLDER

Time Period
Begin:
2022-05-28
End:
2023-12-01

People and Organizations
Contact:Carey, Cayelan C. (Virginia Tech) [  email ]
Creator:Moore, Tadhg N. (Virginia Tech)
Creator:Lofton, Mary E. (Virginia Tech)
Creator:Carey, Cayelan C. (Virginia Tech)
Creator:Thomas, R. Quinn (Virginia Tech)

Data Entities
Other Name:
instructor_materials
Description:
This zip folder contains materials for instructors to teach the Macrosystems EDDIE module in their classroom. See README file for file types and descriptions
Detailed Metadata

Data Entities

Non-Categorized Data Resource

Name:instructor_materials
Entity Type:unknown
Description:This zip folder contains materials for instructors to teach the Macrosystems EDDIE module in their classroom. See README file for file types and descriptions
Physical Structure Description:
Object Name:instructor_materials.zip
Size:7478763 bytes
Authentication:d40faeab9eb04949f53f5c2537fabc47 Calculated By MD5
Externally Defined Format:
Format Name:application/zip
Data:https://pasta-s.lternet.edu/package/data/eml/edi/1097/1/a24794498ee9638a12c2844de7c5daf5

Data Package Usage Rights

This information is released under the Creative Commons license - Attribution - CC BY (https://creativecommons.org/licenses/by/4.0/). The consumer of these data ("Data User" herein) is required to cite it appropriately in any publication that results from its use. The Data User should realize that these data may be actively used by others for ongoing research and that coordination may be necessary to prevent duplicate publication. The Data User is urged to contact the authors of these data if any questions about methodology or results occur. Where appropriate, the Data User is encouraged to consider collaboration or co-authorship with the authors. The Data User should realize that misinterpretation of data may occur if used out of context of the original study. While substantial efforts are made to ensure the accuracy of data and associated documentation, complete accuracy of data sets cannot be guaranteed. All data are made available "as is." The Data User should be aware, however, that data are updated periodically and it is the responsibility of the Data User to check for new versions of the data. The data authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data. Thank you.

Methods and Protocols

These methods, instrumentation and/or protocols apply to all data in this dataset:

Methods and protocols used in the collection of this data package
Description:

MODULE DEVELOPMENT AND TESTING

Module teaching materials were developed by T.N. Moore, M.E. Lofton, C.C. Carey, and R.Q. Thomas to provide instructors of undergraduate ecology courses with a ready-to-use, adaptable module that could be implemented in a 3-4 hour time period to introduce undergraduates to ecological forecasting.

As the fifth module within the suite of Macrosystems EDDIE (www.macrosystemseddie.org) teaching materials, this module was developed to teach students macrosystems ecology and ecolgoical forecasting. 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 work with an R Shiny application to visualize data, build a model, generate a forecast with uncertainty, and then compare the forecast with observations. As a secondary goal, Macrosystems EDDIE modules introduce students to advanced computational tools as a way to manage, analyze, visualize, and interpret high-frequency and long-term ecological data sets; conduct ecosystem modeling; and generate ecological forecasts with quantified uncertainty.

The specific student learning goals for this module are that by the end of the module, students will be able to: - Describe an ecological forecast and the iterative forecasting cycle. - Explore and visualize NEON data (Activity A). - Construct a simple ecological model to generate forecasts of ecosystem primary productivity with uncertainty (Activity B). - Adjust model parameters and inputs to study how they affect forecast performance relative to observations (Activity B). - Compare productivity forecasts among NEON sites in different ecoclimatic regions (Activity C). The module was assessed by volunteer faculty testers during the 2021-2022 academic year. Faculty testers provided feedback that was used to update and optimize teaching materials. Moore, Carey, and Thomas also used student pre- and post-module assessment questions to gauge effectiveness of teaching materials for achieving module learning goals. Pedagogical specialists with the Science Education Resource Center at Carleton College assisted with assessment development and implementation.

MODULE WORKFLOW

Workflow for this module: - Give students their module report ahead of time to read over prior to class or distribute reports when they arrive to class. There is also an option to download the report directly from the Shiny app. The report includes pre-class readings, videos, and a short pre-class activity introducing some current examples of ecological forecasts. Students will also answer the questions embedded throughout the Shiny app in the module report, which could be submitted to the instructor for grading if desired. - Instructor gives a brief PowerPoint presentation that introduces ecological forecasting, the iterative forecasting cycle, and a basic ecosystem productivity model (~30mins). - After the presentation, the students divide into pairs. Each pair selects their own NEON site and visualizes their site's data, which is used to build and calibrate an ecosystem productivity model (Activity A). The two students within a pair each build their own model with unique inputs and parameters to compare the performance of two different models for the same ecosystem. For virtual instruction, we recommend putting two pairs together (n=4 students) into separate Zoom breakout rooms during this activity so the two pairs can compare results. - The instructor then introduces Activities B and C, potentially revisiting some of the slides from the introductory presentation as a reminder to students about the next steps. For virtual instruction, this would entail having the students come back to the main Zoom room for a short check-in. - The students work in their pairs to forecast primary productivity at their chosen site using each model, and investigate how the forecast uncertainty changes with different model inputs and parameters (Activity B). At the end of Activity B, students assess their forecasts. They may also compare their forecasts with their partner's. For virtual instruction, we recommend putting the two pairs back into the same Zoom breakout rooms. Optionally, instructors may bring the class back together at the end of Activity B to discuss performance of students' initial forecasts before beginning Activity C. - Student pairs then update their forecast models and generate a second forecast, thus completing and recommencing the iterative forecast cycle (Activity C). The students work together in a group to present the results from their site and different models to the rest of the class. The class may discuss why the forecasts are similar or different among the different sites and models.

For more information, we refer users to the website and R Shiny application listed below.

WEBSITE & PUBLICATIONS

Moore, T.N., Lofton, M.E., Carey, C.C., and Thomas, R.Q. 2023. Macrosystems EDDIE Module 5: Introduction to Ecological Forecasting. http://module5.macrosystemseddie.org.

Moore, T.N., Lofton, M.E., Carey, C.C., and Thomas, R.Q. 2023. Macrosystems EDDIE Module 5: Introduction to Ecological Forecasting (R Shiny application) (v1.X). Zenodo. https://doi.org/XXXX

People and Organizations

Publishers:
Organization:Environmental Data Initiative
Email Address:
info@edirepository.org
Web Address:
https://edirepository.org
Id:https://ror.org/0330j0z60
Creators:
Individual: Tadhg N. Moore
Organization:Virginia Tech
Email Address:
tadhgm@vt.edu
Id:https://orcid.org/0000-0002-3834-8868
Individual: Mary E. Lofton
Organization:Virginia Tech
Email Address:
melofton@vt.edu
Id:https://orcid.org/0000-0003-3270-1330
Individual: Cayelan C. Carey
Organization:Virginia Tech
Email Address:
Cayelan@vt.edu
Id:https://orcid.org/0000-0001-8835-4476
Individual: R. Quinn Thomas
Organization:Virginia Tech
Email Address:
rqthomas@vt.edu
Id:https://orcid.org/0000-0003-1282-7825
Contacts:
Individual: Cayelan C. Carey
Organization:Virginia Tech
Email Address:
Cayelan@vt.edu
Id:https://orcid.org/0000-0001-8835-4476

Temporal, Geographic and Taxonomic Coverage

Temporal, Geographic and/or Taxonomic information that applies to all data in this dataset:

Time Period
Begin:
2022-05-28
End:
2023-12-01
Geographic Region:
Description:The Department of Biological Sciences at Virginia Tech is located in Blacksburg, Virginia, USA
Bounding Coordinates:
Northern:  37.229596Southern:  37.22854
Western:  -80.426228Eastern:  -80.424863

Project

Parent Project Information:

Title:MSA: Macrosystems EDDIE: An undergraduate training program in macrosystems science and ecological forecasting
Personnel:
Individual: Cayelan C. Carey
Organization:Virginia Tech
Email Address:
Cayelan@vt.edu
Id:https://orcid.org/0000-0001-8835-4476
Role:Principal Investigator
Funding: National Science Foundation EF 1926050
Related Project:
Title:Collaborative Research: CIBR: Cyberinfrastructure Enabling End-to-End Workflows for Aquatic Ecosystem Forecasting
Personnel:
Individual: Cayelan C. Carey
Organization:Virginia Tech
Email Address:
Cayelan@vt.edu
Id:https://orcid.org/0000-0001-8835-4476
Role:Principal Investigator
Funding: National Science Foundation EF 1933016

Maintenance

Maintenance:
Description:Ongoing
Frequency:

Additional Info

Additional Information:
 

AUTHORSHIP STATEMENT this needs to be finalized in Google Drive first

ACKNOWLEDGEMENTS We thank Whitney Woelmer for providing friendly reviews and feedback on version 1 of this module.

Other Metadata

Additional Metadata

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