Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Forecasting |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-9394/4/3/33 |
| _version_ | 1797488411610710016 |
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| author | Tadhg N. Moore R. Quinn Thomas Whitney M. Woelmer Cayelan C. Carey |
| author_facet | Tadhg N. Moore R. Quinn Thomas Whitney M. Woelmer Cayelan C. Carey |
| author_sort | Tadhg N. Moore |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T00:01:47Z |
| format | Article |
| id | doaj.art-e24fdb0059b443019bc9e2e0fd25fab3 |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-03-10T00:01:47Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-e24fdb0059b443019bc9e2e0fd25fab32023-11-23T16:15:38ZengMDPI AGForecasting2571-93942022-06-014360463310.3390/forecast4030033Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching ModuleTadhg N. Moore0R. Quinn Thomas1Whitney M. Woelmer2Cayelan C. Carey3Department of Biological Sciences, Virginia Tech, 926 West Campus Drive, Blacksburg, VA 24061, USADepartment of Biological Sciences, Virginia Tech, 926 West Campus Drive, Blacksburg, VA 24061, USADepartment of Biological Sciences, Virginia Tech, 926 West Campus Drive, Blacksburg, VA 24061, USADepartment of Biological Sciences, Virginia Tech, 926 West Campus Drive, Blacksburg, VA 24061, USAEcological 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.https://www.mdpi.com/2571-9394/4/3/33ecosystem modelingecological forecastingmacrosystems biologyMacrosystems EDDIENEONR Shiny |
| spellingShingle | Tadhg N. Moore R. Quinn Thomas Whitney M. Woelmer Cayelan C. Carey Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module Forecasting ecosystem modeling ecological forecasting macrosystems biology Macrosystems EDDIE NEON R Shiny |
| title | Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module |
| title_full | Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module |
| title_fullStr | Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module |
| title_full_unstemmed | Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module |
| title_short | Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module |
| title_sort | integrating ecological forecasting into undergraduate ecology curricula with an r shiny application based teaching module |
| topic | ecosystem modeling ecological forecasting macrosystems biology Macrosystems EDDIE NEON R Shiny |
| url | https://www.mdpi.com/2571-9394/4/3/33 |
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