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

Abstract

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.

The NEON Ecological Forecasting Challenge

“…Teaching ecological modeling in undergraduate classrooms improves students' system thinking skills and quantitative literacy (Carey et al 2020). Similarly, we have found that integrating ecological forecasting into classrooms expands students' understanding of complex ecological concepts (Moore et al 2022). Challenge submissions are an ideal project for students in undergraduate and graduate courses and workshops, as we have discovered by training students in several university courses how to submit forecasts in collaborative teams .…”

Progress and opportunities in advancing near‐term forecasting of freshwater quality

“…To help train new forecasters, forecasting communities of practice should help create and facilitate sharing of resources, such as teaching modules focused on fundamental forecasting concepts (Moore et al, 2022), curated lists of freely available forecasting educational resources (Willson, 2022), and community-based development of software (Boettiger et al, 2015). In addition, education in freshwater quality forecasting would be enhanced by introducing forecasting (and uncertainty) at earlier educational stages (e.g., in K-12 education; Rosenberg et al, 2022) and development of formal curricula in freshwater forecasting specifically (Moore et al, 2022).…”

“…Ultimately, generating accurate freshwater quality forecasts requires extensive training of the forecasting team. Obtaining training in a multidisciplinary, emerging field like ecological forecasting can be challenging (Woelmer et al, 2021), motivating the need for broad sharing of educational materials (Moore et al, 2022;Willson, 2022) and opensource tools and software (e.g., Boettiger et al, 2015;Daneshmand et al, 2021;Hipsey et al, 2019;Moore et al, 2021) within active communities of practice (Figure 2: educational resources; communities of practice). Communities of practice may occur within government agencies, originate from a specific project such as the Hydrological Ensemble Prediction EXperiment (HEPEX; Schaake et al, 2007), take the form of grassroots networks such as the Ecological Forecasting Initiative (EFI; Dietze & Lynch, 2019), exist as formal professional societies, or be housed at academic institutions.…”

Progress and opportunities in advancing near-term forecasting of freshwater quality

“…Ultimately, generating accurate freshwater quality forecasts requires extensive training of the forecasting team. Obtaining training in a multi-disciplinary, emerging field like ecological forecasting can be challenging (Woelmer et al, 2021), motivating the need for broad sharing of educational materials (Moore et al, 2022;Willson, 2022) and open-source tools and software (e.g., Boettiger et al, 2015;Daneshmand et al, 2021;Hipsey et al, 2019;Moore et al, 2021) within active communities of practice (Fig. 2: educational resources; communities of practice).…”

“…To help train new forecasters, forecasting communities of practice should help create and facilitate sharing of resources, such as teaching modules focused on fundamental forecasting concepts (Moore et al, 2022), curated lists of freely available forecasting educational resources (Willson, 2022), and community-based development of software (Boettiger et al, 2015). In addition, education in freshwater quality forecasting would be enhanced by introducing forecasting (and uncertainty) at earlier educational stages (e.g., in K-12 education; Rosenberg et al, 2022) and development of formal curricula in freshwater forecasting specifically .…”