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

1
Department of Biological Sciences, Virginia Tech, 926 West Campus Drive, Blacksburg, VA 24061, USA
2
Forest Resources and Environmental Conservation, 1015 Life Science Circle, Virginia Tech, Blacksburg, VA 24061, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Sonia Leva
Forecasting 2022, 4(3), 604-633; https://doi.org/10.3390/forecast4030033
Received: 26 April 2022 / Revised: 23 June 2022 / Accepted: 27 June 2022 / Published: 30 June 2022
(This article belongs to the Special Issue Near-Term Ecological 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. View Full-Text
Keywords: ecosystem modeling; ecological forecasting; macrosystems biology; Macrosystems EDDIE; NEON; R Shiny; sensor data; teaching modules; undergraduate education ecosystem modeling; ecological forecasting; macrosystems biology; Macrosystems EDDIE; NEON; R Shiny; sensor data; teaching modules; undergraduate education
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MDPI and ACS Style

Moore, T.N.; Thomas, R.Q.; Woelmer, W.M.; Carey, C.C. Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module. Forecasting 2022, 4, 604-633. https://doi.org/10.3390/forecast4030033

AMA Style

Moore TN, Thomas RQ, Woelmer WM, Carey CC. Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module. Forecasting. 2022; 4(3):604-633. https://doi.org/10.3390/forecast4030033

Chicago/Turabian Style

Moore, Tadhg N., R. Quinn Thomas, Whitney M. Woelmer, and Cayelan C. Carey. 2022. "Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module" Forecasting 4, no. 3: 604-633. https://doi.org/10.3390/forecast4030033

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