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1. 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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2. Macrosystems EDDIE Teaching Modules Increase Students’ Ability to Define, Interpret, and Apply Concepts in Macrosystems Ecology

   https://doi.org/10.3390/educsci11080382  

   Hounshell, Alexandria G.; Farrell, Kaitlin J.; Carey, Cayelan C. (August 2021, Education Sciences)

   Ecologists are increasingly using macrosystems approaches to understand population, community, and ecosystem dynamics across interconnected spatial and temporal scales. Consequently, integrating macrosystems skills, including simulation modeling and sensor data analysis, into undergraduate and graduate curricula is needed to train future environmental biologists. Through the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration) program, we developed four teaching modules to introduce macrosystems ecology to ecology and biology students. Modules combine high-frequency sensor data from GLEON (Global Lake Ecological Observatory Network) and NEON (National Ecological Observatory Network) sites with ecosystem simulation models. Pre- and post-module assessments of 319 students across 24 classrooms indicate that hands-on, inquiry-based modules increase students’ understanding of macrosystems ecology, including complex processes that occur across multiple spatial and temporal scales. Following module use, students were more likely to correctly define macrosystems concepts, interpret complex data visualizations and apply macrosystems approaches in new contexts. In addition, there was an increase in student’s self-perceived proficiency and confidence using both long-term and high-frequency data; key macrosystems ecology techniques. Our results suggest that integrating short (1–3 h) macrosystems activities into ecology courses can improve students’ ability to interpret complex and non-linear ecological processes. In addition, our study serves as one of the first documented instances for directly incorporating concepts in macrosystems ecology into undergraduate and graduate ecology and biology curricula. 

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3. 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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4. Coordinated Distributed Experiments in Higher Education: A Low Barrier to Entry and Potential for Student Projects

   https://doi.org/10.1002/bes2.2101  

   Koomen, Michele; Ross, Jake; Hinrichs, Emelia; Hofstede, Ellen; Grimes, Jolie; Mohl, Emily (October 2023, The Bulletin of the Ecological Society of America)

   Abstract This mixed‐methods research focused on the implementation of a coordinated distributed experiment (CDE) investigating local adaptation in common milkweed (Asclepias syriaca), a host plant for the monarch butterfly population. Faculty participants were recruited from the Ecological Research as Education Network (EREN) who recruited their former undergraduate students. Quantitative data were drawn from the Milkweed Local Adaptation (MLA) CDE database across the three project years. Qualitative data included faculty survey responses, semi‐structured interviews of faculty and former undergraduates, and review of undergraduate research posters, papers, and curricula using rubrics aligned with 4DEE and Next Generation Science Standards (NGSS) benchmarks. Analysis of the MLA CDE database illustrates a decline in both participating institutions and in counts of milkweed stems over the project (2018–2020). Qualitative data analysis revealed that CDEs: (1) offer opportunities for higher education faculty and their students to be part of research including developing skills of data collection, analysis, and interpretation; (2) have unexpected challenges; and (3) can inspire undergraduate students to develop independent research projects or curricular modules for use in formal 6–12 classrooms. Broader ecological educational implications of our outcomes for higher education faculty and their undergraduate students include: (1) recommendation that faculty members involved ought to be proactively informed about potential challenges and provided with guidance on how to mitigate them; (2) mitigating challenges with model studies to try to estimate the sample size and redundancy likely to produce robust data; and (3) proactive use of the educational network to understand institutional use of the CDE project with undergraduates. 

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5. 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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