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1. Macrosystems EDDIE Module 7: Using Data to Improve Ecological Forecasts (Instructor Materials)

   https://doi.org/10.6073/pasta/6c8478d9aa04eeab55646ffa8e62b278  

   Lofton, Mary E; Moore, Tadhg N; Thomas, R Quinn; Carey, Cayelan C (April 2024, Environmental Data Initiative)

   This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 7: Using Data to Improve Ecological Forecasts, a ~3-hour educational module for undergraduates. 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. To be useful for management, ecological forecasts need to be both accurate enough for managers to be able to rely on them for decision-making and include a representation of forecast uncertainty, so managers can properly interpret the probability of future events. To improve forecast accuracy, forecasts can be updated with observational data once they become available, a process known as data assimilation. Recent improvements in environmental sensor technology and an increase in the number of sensors deployed in ecosystems have increased the availability of data for assimilation to develop and improve forecasts for natural resource management. In this module, students will explore how assimilating data with different amounts of observation uncertainty and at different temporal frequencies affects forecasts of lake water quality, using data from the U.S. National Ecological Observatory Network (NEON). The flexible, three-part (A-B-C) structure of this module makes it adaptable to a range of student levels and course structures. There are two versions of the module: an R Shiny application which does not require students to code, and an RMarkdown version which requires students to read and alter R code to complete module activities. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module7/. GitHub repositories are available for both the R Shiny (https://github.com/MacrosystemsEDDIE/module7) and RMarkdown versions (https://github.com/MacrosystemsEDDIE/module7_R) of the module, and both code repositories have been published with DOIs to Zenodo (R Shiny version at DOI 10.5281/zenodo.10903839 and RMarkdown version at DOI 10.5281/zenodo.10909589). Readers are referred to the module landing page for additional information (https://serc.carleton.edu/eddie/teaching_materials/modules/module7.html). 

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2. Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts (Instructor Materials)

   https://doi.org/10.6073/pasta/1ce758925388a9273083c24ed0ee0c05  

   Moore, Tadhg N.; Lofton, Mary E.; Carey, Cayelan C.; Thomas, R. Quinn (December 2023, Environmental Data Initiative)

   This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts, a ~3-hour educational module for undergraduates. 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. 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 that students develop for a lake ecosystem, using data from the National Ecological Observatory Network (NEON). Students will visualize data, build a model, generate a forecast with uncertainty, and then compare the contributions of various sources of forecast uncertainty to total forecast uncertainty. The flexible, three-part (A-B-C) structure of this module makes it adaptable to a range of student levels and course structures. There are two versions of the module: an R Shiny application which does not require students to code, and an RMarkdown version which requires students to read and alter R code to complete module activities. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module6/. GitHub repositories are available for both the R Shiny (https://github.com/MacrosystemsEDDIE/module6) and RMarkdown versions (https://github.com/MacrosystemsEDDIE/module6_R) of the module, and both code repositories have been published with DOIs to Zenodo (R Shiny version at https://zenodo.org/doi/10.5281/zenodo.10380759 and RMarkdown version at https://zenodo.org/doi/10.5281/zenodo.10380339). Readers are referred to the module landing page for additional information (https://serc.carleton.edu/eddie/teaching_materials/modules/module6.html). 

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3. Macrosystems EDDIE Module 5 version 2: Introduction to Ecological Forecasting (Instructor Materials)

   https://doi.org/10.6073/pasta/b3953d81b3b4158e0ec5375c04774bd2  

   Moore, Tadhg N; Lofton, Mary E; Carey, Cayelan C; Thomas, R Quinn (March 2024, Environmental Data Initiative)

   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 necessary to teach the module. Intructional materials (instructor manual, introductory presentation for the module, and a presentation to introduce students and instructors to R Shiny) are provided in both pdf and editable formats within a compressed file. The module R Shiny application is available at https://macrosystemseddie.shinyapps.io/module5/. 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. 2024; DOI: 10.5281/zenodo.10733117) for the R Shiny application code. 

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4. Embedding communication concepts in forecasting training increases students' understanding of ecological uncertainty

   https://doi.org/10.1002/ecs2.4628  

   Woelmer, Whitney M.; Moore, Tadhg N.; Lofton, Mary E.; Thomas, R. Quinn; Carey, Cayelan C. (August 2023, Ecosphere)

   Abstract Communicating and interpreting uncertainty in ecological model predictions is notoriously challenging, motivating the need for new educational tools, which introduce ecology students to core concepts in uncertainty communication. Ecological forecasting, an emerging approach to estimate future states of ecological systems with uncertainty, provides a relevant and engaging framework for introducing uncertainty communication to undergraduate students, as forecasts can be used as decision support tools for addressing real‐world ecological problems and are inherently uncertain. To provide critical training on uncertainty communication and introduce undergraduate students to the use of ecological forecasts for guiding decision‐making, we developed a hands‐on teaching module within the Macrosystems Environmental Data‐Driven Inquiry and Exploration (EDDIE;MacrosystemsEDDIE.org) educational program. Our module used an active learning approach by embedding forecasting activities in an R Shiny application to engage ecology students in introductory data science, ecological modeling, and forecasting concepts without needing advanced computational or programming skills. Pre‐ and post‐module assessment data from more than 250 undergraduate students enrolled in ecology, freshwater ecology, and zoology courses indicate that the module significantly increased students' ability to interpret forecast visualizations with uncertainty, identify different ways to communicate forecast uncertainty for diverse users, and correctly define ecological forecasting terms. Specifically, students were more likely to describe visual, numeric, and probabilistic methods of uncertainty communication following module completion. Students were also able to identify more benefits of ecological forecasting following module completion, with the key benefits of using forecasts for prediction and decision‐making most commonly described. These results show promise for introducing ecological model uncertainty, data visualizations, and forecasting into undergraduate ecology curricula via software‐based learning, which can increase students' ability to engage and understand complex ecological concepts. 

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5. Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module

   https://doi.org/10.3390/forecast4030033  

   Moore, Tadhg N.; Thomas, R. Quinn; Woelmer, Whitney M.; Carey, Cayelan C. (September 2022, Forecasting)

   Ecological forecasting is an emerging approach to estimate the future state of an ecological system with uncertainty, allowing society to better manage ecosystem services. Ecological forecasting is a core mission of the U.S. National Ecological Observatory Network (NEON) and several federal agencies, yet, to date, forecasting training has focused on graduate students, representing a gap in undergraduate ecology curricula. In response, we developed a teaching module for the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational program to introduce ecological forecasting to undergraduate students through an interactive online tool built with R Shiny. To date, we have assessed this module, “Introduction to Ecological Forecasting,” at ten universities and two conference workshops with both undergraduate and graduate students (N = 136 total) and found that the module significantly increased undergraduate students’ ability to correctly define ecological forecasting terms and identify steps in the ecological forecasting cycle. Undergraduate and graduate students who completed the module showed increased familiarity with ecological forecasts and forecast uncertainty. These results suggest that integrating ecological forecasting into undergraduate ecology curricula will enhance students’ abilities to engage and understand complex ecological concepts. 

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