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Browsing by Author "Moore, Tadhg N."

Now showing 1 - 11 of 11
  • A modular curriculum to teach undergraduates ecological forecasting improves student and instructor confidence in their data science skills
    Lofton, Mary E.; Moore, Tadhg N.; Woelmer, Whitney M.; Thomas, R. Quinn; Carey, Cayelan C. (Oxford University Press, 2024-10-10)
    Data science skills (e.g., analyzing, modeling, and visualizing large data sets) are increasingly needed by undergraduates in the life sciences. However, a lack of both student and instructor confidence in data science skills presents a barrier to their inclusion in undergraduate curricula. To reduce this barrier, we developed four teaching modules in the Macrosystems EDDIE (for environmental data-driven inquiry and exploration) program to introduce undergraduate students and instructors to ecological forecasting, an emerging subdiscipline that integrates multiple data science skills. Ecological forecasting aims to improve natural resource management by providing future predictions of ecosystems with uncertainty. We assessed module efficacy with 596 students and 26 instructors over 3 years and found that module completion increased students’ confidence in their understanding of ecological forecasting and instructors’ likelihood to work with long-term, high-frequency sensor network data. Our modules constitute one of the first formalized data science curricula on ecological forecasting for undergraduates.
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    Annual water residence time effects on thermal structure: A potential lake restoration measure?
    Olsson, Freya; Mackay, Eleanor B.; Moore, Tadhg N.; Barker, Phil; Davies, Sian; Hall, Ruth; Spears, Bryan; Wilkinson, Jayne; Jones, Ian D. (Academic Press - Elsevier, 2022-07-15)
    Innovative methods to combat internal loading issues in eutrophic lakes are urgently needed to speed recovery and restore systems within legislative deadlines. In stratifying lakes, internal phosphorus loading is particularly problematic during the summer stratified period when anoxia persists in the hypolimnion, promoting phos-phorus release from the sediment. A novel method to inhibit stratification by reducing residence times is pro-posed as a way of controlling the length of the hypolimnetic anoxic period, thus reducing the loading of nutrients from the sediments into the water column. However, residence time effects on stratification length in natural lakes are not well understood. We used a systematic modelling approach to investigate the viability of changes to annual water residence time in affecting lake stratification and thermal dynamics in Elterwater, a small strati-fying eutrophic lake in the northwest of England. We found that reducing annual water residence times shortened and weakened summer stratification. Based on finer-scale dynamics of lake heat fluxes and water column sta-bility we propose seasonal or sub-seasonal management of water residence time is needed for the method to be most effective at reducing stratification as a means of controlling internal nutrient loading.
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    Assessing opportunities and inequities in undergraduate ecological forecasting education
    Willson, Alyssa M.; Gallo, Hayden; Peters, Jody A.; Abeyta, Antoinette; Watts, Nievita Bueno; Carey, Cayelan C.; Moore, Tadhg N.; Smies, Georgia; Thomas, R. Quinn; Woelmer, Whitney M.; McLachlan, Jason S. (Wiley, 2023-05)
    Conducting ecological research in a way that addresses complex, real-world problems requires a diverse, interdisciplinary and quantitatively trained ecology and environmental science workforce. This begins with equitably training students in ecology, interdisciplinary science, and quantitative skills at the undergraduate level. Understanding the current undergraduate curriculum landscape in ecology and environmental sciences allows for targeted interventions to improve equitable educational opportunities. Ecological forecasting is a sub-discipline of ecology with roots in interdisciplinary and quantitative science. We use ecological forecasting to show how ecology and environmental science undergraduate curriculum could be evaluated and ultimately restructured to address the needs of the 21(st) century workforce. To characterize the current state of ecological forecasting education, we compiled existing resources for teaching and learning ecological forecasting at three curriculum levels: online resources; US university courses on ecological forecasting; and US university courses on topics related to ecological forecasting. We found persistent patterns (1) in what topics are taught to US undergraduate students at each of the curriculum levels; and (2) in the accessibility of resources, in terms of course availability at higher education institutions in the United States. We developed and implemented programs to increase the accessibility and comprehensiveness of ecological forecasting undergraduate education, including initiatives to engage specifically with Native American undergraduates and online resources for learning quantitative concepts at the undergraduate level. Such steps enhance the capacity of ecological forecasting to be more inclusive to undergraduate students from diverse backgrounds and expose more students to quantitative training.
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    Embedding communication concepts in forecasting training increases students' understanding of ecological uncertainty
    Woelmer, Whitney M.; Moore, Tadhg N.; Lofton, Mary E.; Thomas, R. Quinn; Carey, Cayelan C. (Wiley, 2023-08)
    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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    Ensemble of models shows coherent response of a reservoir's stratification and ice cover to climate warming
    Feldbauer, Johannes; Ladwig, Robert; Mesman, Jorrit P.; Moore, Tadhg N.; Zundorf, Hilke; Berendonk, Thomas U.; Petzoldt, Thomas (Springer, 2022-10)
    Water temperature, ice cover, and lake stratification are important physical properties of lakes and reservoirs that control mixing as well as bio-geo-chemical processes and thus influence the water quality. We used an ensemble of vertical one-dimensional hydrodynamic lake models driven with regional climate projections to calculate water temperature, stratification, and ice cover under the A1B emission scenario for the German drinking water reservoir Lichtenberg. We used an analysis of variance method to estimate the contributions of the considered sources of uncertainty on the ensemble output. For all simulated variables, epistemic uncertainty, which is related to the model structure, is the dominant source throughout the simulation period. Nonetheless, the calculated trends are coherent among the five models and in line with historical observations. The ensemble predicts an increase in surface water temperature of 0.34 K per decade, a lengthening of the summer stratification of 3.2 days per decade, as well as decreased probabilities of the occurrence of ice cover and winter inverse stratification by 2100. These expected changes are likely to influence the water quality of the reservoir. Similar trends are to be expected in other reservoirs and lakes in comparable regions.
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    Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module
    Moore, Tadhg N.; Thomas, R. Quinn; Woelmer, Whitney M.; Carey, Cayelan C. (MDPI, 2022-06-30)
    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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    Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the US
    Thomas, R. Quinn; McClure, Ryan P.; Moore, Tadhg N.; Woelmer, Whitney M.; Boettiger, Carl; Figueiredo, Renato J.; Hensley, Robert T.; Carey, Cayelan C. (Wiley, 2023-04)
    The US National Ecological Observatory Network's (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 all six NEON lakes in the conterminous US. We generated 1-day-ahead to 35-days-ahead forecasts using a process-based hydrodynamic model that was updated with observations as they became available. Among lakes, forecasts were more accurate than a null model up to 35-days-ahead, with an aggregated 1-day-ahead root-mean square error (RMSE) of 0.61 degrees C and a 35-days-ahead RMSE of 2.17 degrees C. Water temperature forecast accuracy was positively associated with lake depth and water clarity, and negatively associated with fetch and catchment size. The results of our analysis 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.
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    Opportunities for seasonal forecasting to support water management outside the tropics
    Jackson-Blake, Leah A.; Clayer, Francois; de Eyto, Elvira; French, Andrew S.; Dolores Frias, Maria; Mercado-Bettin, Daniel; Moore, Tadhg N.; Puertolas, Laura; Poole, Russell; Rinke, Karsten; Shikhani, Muhammed; van der Linden, Leon; Marce, Rafael (Copernicus, 2022-03-14)
    Advance warning of seasonal conditions has the potential to assist water management in planning and risk mitigation, with large potential social, economic, and ecological benefits. In this study, we explore the value of seasonal forecasting for decision-making at five case study sites located in extratropical regions. The forecasting tools used integrate seasonal climate model forecasts with freshwater impact models of catchment hydrology, lake conditions (temperature, water level, chemistry, and ecology), and fish migration timing and were co-developed together with water managers. To explore the decision-making value of forecasts, we carried out a qualitative assessment of (1) how useful forecasts would have been for a problematic past season and (2) the relevance of any windows of opportunity (seasons and variables where forecasts are thought to perform well) for management. Overall, water managers were optimistic about the potential for improved decision-making and identified actions that could be taken based on forecasts. However, there was often a mismatch between those variables that could best be predicted and those which would be most useful for management. Reductions in forecast uncertainty and a need to develop practical, hands-on experience were identified as key requirements before forecasts would be used in operational decision-making. Seasonal climate forecasts provided little added value to freshwater forecasts in these extratropical study sites, and we discuss the conditions under which seasonal climate forecasts with only limited skill are most likely to be worth incorporating into freshwater forecasting workflows.
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    Sources of skill in lake temperature, discharge and ice-off seasonal forecasting tools
    Clayer, Francois; Jackson-Blake, Leah; Mercado-Bettin, Daniel; Shikhani, Muhammed; French, Andrew; Moore, Tadhg N.; Sample, James; Norling, Magnus; Frias, Maria-Dolores; Herrera, Sixto; de Eyto, Elvira; Jennings, Eleanor; Rinke, Karsten; van der Linden, Leon; Marce, Rafael (Copernicus, 2023-03)
    Despite high potential benefits, the development of seasonal forecasting tools in the water sector has been slower than in other sectors. Here we assess the skill of seasonal forecasting tools for lakes and reservoirs set up at four sites in Australia and Europe. These tools consist of coupled hydrological catchment and lake models forced with seasonal meteorological forecast ensembles to provide probabilistic predictions of seasonal anomalies in water discharge, temperature and ice-off. Successful implementation requires a rigorous assessment of the tools' predictive skill and an apportionment of the predictability between legacy effects and input forcing data. To this end, models were forced with two meteorological datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF), the seasonal forecasting system, SEAS5, with 3-month lead times and the ERA5 reanalysis. Historical skill was assessed by comparing both model outputs, i.e. seasonal lake hindcasts (forced with SEAS5), and pseudo-observations (forced with ERA5). The skill of the seasonal lake hindcasts was generally low although higher than the reference hindcasts, i.e. pseudo-observations, at some sites for certain combinations of season and variable. The SEAS5 meteorological predictions showed less skill than the lake hindcasts. In fact, skilful lake hindcasts identified for selected seasons and variables were not always synchronous with skilful SEAS5 meteorological hindcasts, raising questions on the source of the predictability. A set of sensitivity analyses showed that most of the forecasting skill originates from legacy effects, although during winter and spring in Norway some skill was coming from SEAS5 over the 3-month target season. When SEAS5 hindcasts were skilful, additional predictive skill originates from the interaction between legacy and SEAS5 skill. We conclude that lake forecasts forced with an ensemble of boundary conditions resampled from historical meteorology are currently likely to yield higher-quality forecasts in most cases.
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    Uncertainty in projections of future lake thermal dynamics is differentially driven by lake and global climate models
    Wynne, Jacob H.; Woelmer, Whitney M.; Moore, Tadhg N.; Thomas, R. Quinn; Weathers, Kathleen C.; Carey, Cayelan C. (PeerJ, 2023-06-02)
    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.
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    Virtual Growing Pains: Initial Lessons Learned from Organizing Virtual Workshops, Summits, Conferences, and Networking Events during a Global Pandemic
    Meyer, Michael F.; Ladwig, Robert; Dugan, Hilary A.; Anderson, Alyssa; Bah, Abdou R.; Boehrer, Bertram; Borre, Lisa; Chapina, Rosaura J.; Doyle, Chris; Favot, Elizbaeth J.; Flaim, Giobanna; Forsberg, Philip; Hanson, Paul C.; Ibelings, Bas W.; Isles, Peter; Lin, Fang-Pang; Lofton, Dendy; Moore, Tadhg N.; Peel, Sara; Peters, Jody A.; Pierson, Don; de Senerpont Domis, Lisette N.; Schloss, Jeffrey A.; Shikhani, Muhammed; Smagula, Amy P.; Stockwell, Jason D.; Thomas, Perry; Thomas, R. Quinn; Tietjen, Todd; Weathers, Kathleen C. (Wiley, 2021-02-01)
    For many, 2020 was a year of abrupt professional and personal change. For the aquatic sciences community, many were adapting to virtual formats for conducting and sharing science, while simultaneously learning to live in a socially distanced world. Understandably, the aquatic sciences community postponed or canceled most in-person scientific meetings. Still, many scientific communities either transitioned annual meetings to a virtual format or inaugurated new virtual meetings. Fortunately, increased use of video conferencing platforms, networking and communication applications, and a general comfort with conducting science virtually helped bring the in-person meeting experience to scientists worldwide. Yet, the transition to conducting science virtually revealed new barriers to participation whereas others were lowered. The combined lessons learned from organizing a meeting constitute a necessary knowledge base that will prove useful, as virtual conferences are likely to continue in some form. To concentrate and synthesize these experiences, we showcase how six scientific societies and communities planned, organized, and conducted virtual meetings in 2020. With this consolidated information in hand, we look forward to a future, where scientific meetings embrace a virtual component, so to as help make science more inclusive and global.