TITLE : Embedding communication concepts in forecasting training increases students’ understanding of ecological uncertainty Submitted as an Article to Ecosphere , Eco-Education TrackAUTHOR LIST: Whitney M. Woelmera*, Tadhg N. Moorea,b11Present address: School of Biological, Earth and Environmental Sciences, University College Cork, Cork, Ireland, Mary E. Loftona, R. Quinn Thomasa,b, and Cayelan C. CareyaaDepartment of Biological Sciences, Virginia Tech, Blacksburg, VA, USAbDepartment of Forest Resources and Environmental Conservation, Virginia Tech, Blacksburg, VA, USA*Corresponding author: [email protected] RESEARCH STATEMENT : This study collected and analyzed human subject data and was approved by the Virginia Tech Institutional Review Board (19-669) and the Carleton College Institutional Review Board (19-20 065). Data for this study have been anonymized and aggregated and can be found at Woelmer (2023) along with all code to reproduce the analysis and figures within this study.Woelmer, W. 2023. Wwoelmer/module8_public_ecosphere: Ecosphere submission March 2023 (v1.0). Zenodo. https://doi.org/10.5281/zenodo.7733965KEYWORDS : active learning, ecology education, ecological forecast, Macrosystems EDDIE, R Shiny, teaching modules, translational ecology, undergraduate curricula, visualization literacyABSTRACT : 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 EDDIE (Environmental Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational program. Our module used an active learning approach by embedding forecasting activities in an R Shiny application to engage introductory students in data science, ecological modeling, and forecasting without needing advanced computational or programming skills. Pre- and post-module assessment data from >250 undergraduate ecology students 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.

Water temperature forecasting in lakes and reservoirs is a valuable tool to manage crucial freshwater resources in a changing and more variable climate, but previous efforts have yet to identify an optimal modelling approach. Here, we demonstrate the first multi-model ensemble (MME) reservoir water temperature forecast, a forecasting method that combines individual model strengths in a single forecasting framework. We developed two MMEs: a three-model process-based MME and a five-model MME that includes process-based and empirical models to forecast water temperature profiles at a temperate drinking water reservoir. Our results showed that the five-model MME improved forecast performance by 8-30% relative to individual models and the process-based MME, as quantified using an aggregated probabilistic skill score. This increase in performance was due to large improvements in forecast bias in the five-model MME, despite increases in forecast uncertainty. High correlation among the process-based models resulted in little improvement in forecast performance in the process-based MME relative to the individual process-based models. The utility of MMEs is highlighted by two results: 1) no individual model performed best at every depth and horizon (days in the future), and 2) MMEs avoided poor performances by rarely producing the worst forecast for any single forecasted period (<6% of the worst ranked forecasts over time). This work presents an example of how existing models can be combined to improve water temperature forecasting in lakes and reservoirs and discusses the value of utilising MMEs, rather than individual models, in operational forecasts.

Ecosystems around the globe are experiencing increased variability due to land use and climate change. In response, ecologists are increasingly using near-term, iterative ecological forecasts to predict how ecosystems will change in the future. To date, many near-term, iterative forecasting systems have been developed using high temporal frequency (minute to hourly resolution) data streams for assimilation. However, this approach may be cost-prohibitive or impossible for forecasting ecological variables that lack high-frequency sensors or have high data latency (i.e., a delay before data are available for modeling after collection). To explore the effects of data assimilation frequency on forecast skill, we developed water temperature forecasts for a eutrophic drinking water reservoir and conducted data assimilation experiments by selectively withholding observations to examine the effect of data availability on forecast accuracy. We used  in-situ sensors, manually collected data, and a calibrated water quality ecosystem model driven by forecasted weather data to generate future water temperature forecasts using FLARE (Forecasting Lake And Reservoir Ecosystems), an open-source water quality forecasting system. We tested the effect of daily, weekly, fortnightly, and monthly data assimilation on the skill of 1 to 35-day-ahead water temperature forecasts. We found that forecast skill varied depending on the season, forecast horizon, depth, and data assimilation frequency, but overall forecast performance was high, with a mean 1-day-ahead forecast root mean square error (RMSE) of 0.94°C, mean 7-day RMSE of 1.33°C, and mean 35-day RMSE of 2.15°C. Aggregated across the year, daily data assimilation yielded the most skillful forecasts at 1-7-day-ahead horizons, weekly data assimilation resulted in the most skillful forecasts at 8-35-day-ahead horizons. Within a year, daily to fortnightly data assimilation substantially outperformed monthly data assimilation in the stratified summer period, whereas all data assimilation frequencies resulted in skillful forecasts across depths in the mixed spring/autumn periods for shorter forecast horizons. Our results suggest that lower-frequency data (i.e., weekly) may be adequate for developing accurate forecasts in some applications, further enabling the development of forecasts broadly across ecosystems and ecological variables without high-frequency sensor data.

Freshwater ecosystems provide vital services, yet are facing increasing risks from global change. In particular, lake thermal dynamics have been altered around the world as a result of climate change, necessitating a predictive understanding of how climate will continue to alter lakes in the future as well as the associated uncertainty in these predictions. Numerous sources of uncertainty affect projections of future lake conditions but few are quantified, limiting the use of lake modeling projections as management tools. To quantify and evaluate the effects of two potentially important sources of uncertainty, lake model selection uncertainty and climate model selection uncertainty, we developed ensemble projections of lake thermal dynamics for a dimictic lake in New Hampshire, USA (Lake Sunapee). Our ensemble projections used four different climate models as inputs to five vertical one-dimensional (1-D) hydrodynamic lake models under three different climate change scenarios to simulate thermal metrics from 2006 to 2099. We found that almost all the lake thermal metrics modeled (surface water temperature, bottom water temperature, Schmidt stability, stratification duration, and ice cover, but not thermocline depth) are projected to change over the next century. Importantly, we found that the dominant source of uncertainty varied among the thermal metrics, as thermal metrics associated with the surface waters (surface water temperature, total ice duration) were driven primarily by climate model selection uncertainty, while metrics associated with deeper depths (bottom water temperature, stratification duration) were dominated by lake model selection uncertainty. Consequently, our results indicate that researchers generating projections of lake bottom water metrics should prioritize including multiple lake models for best capturing projection uncertainty, while those focusing on lake surface metrics should prioritize including multiple climate models. Overall, our ensemble modeling study reveals important information on how climate change will affect lake thermal properties, and also provides some of the first analyses on how climate model selection uncertainty and lake model selection uncertainty interact to affect projections of future lake dynamics.

The National Ecological Observatory Network (NEON)’s standardized monitoring program provides an unprecedented opportunity for comparing the predictability of ecosystems. To harness the power of NEON data for examining environmental predictability, we scaled a near-term, iterative water temperature forecasting system to six NEON lakes. We generated 1 to 35-day ahead forecasts using a process-based hydrodynamic model that was updated with observations as they became available. Forecasts were more accurate than a null model up to 35-days ahead among lakes, with an aggregated 1-day ahead RMSE (root-mean square error) of 0.60℃ and 35-days ahead RMSE of 2.17℃. Water temperature forecast accuracy was positively associated with lake depth and water clarity, and negatively associated with catchment size and fetch. Our results suggest that lake characteristics interact with weather to control the predictability of thermal structure. Our work provides some of the first probabilistic forecasts of NEON sites and a framework for examining continental-scale predictability.