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

   https://doi.org/10.3390/forecast4030033  

   Moore, Tadhg N.; Thomas, R. Quinn; Woelmer, Whitney M.; Carey, Cayelan C. (September 2022, 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. 

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2. Collective motion of driven semiflexible filaments tuned by soft repulsion and stiffness

   https://doi.org/10.1039/D0SM01036G  

   Moore, Jeffrey M.; Thompson, Tyler N.; Glaser, Matthew A.; Betterton, Meredith D. (October 2020, Soft Matter)

   null (Ed.)

   In active matter systems, self-propelled particles can self-organize to undergo collective motion, leading to persistent dynamical behavior out of equilibrium. In cells, cytoskeletal filaments and motor proteins form complex structures important for cell mechanics, motility, and division. Collective dynamics of cytoskeletal systems can be reconstituted using filament gliding experiments, in which cytoskeletal filaments are propelled by surface-bound motor proteins. These experiments have observed diverse dynamical states, including flocks, polar streams, swirling vortices, and single-filament spirals. Recent experiments with microtubules and kinesin motor proteins found that the collective behavior of gliding filaments can be tuned by altering the concentration of the crowding macromolecule methylcellulose in solution. Increasing the methylcellulose concentration reduced filament crossing, promoted alignment, and led to a transition from active, isotropically oriented filaments to locally aligned polar streams. This emergence of collective motion is typically explained as an increase in alignment interactions by Vicsek-type models of active polar particles. However, it is not yet understood how steric interactions and bending stiffness modify the collective behavior of active semiflexible filaments. Here we use simulations of driven filaments with tunable soft repulsion and rigidity in order to better understand how the interplay between filament flexibility and steric effects can lead to different active dynamic states. We find that increasing filament stiffness decreases the probability of filament alignment, yet increases collective motion and long-range order, in contrast to the assumptions of a Vicsek-type model. We identify swirling flocks, polar streams, buckling bands, and spirals, and describe the physics that govern transitions between these states. In addition to repulsion and driving, tuning filament stiffness can promote collective behavior, and controls the transition between active isotropic filaments, locally aligned flocks, and polar streams. 

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3. An innovative active learning module on snow and climate modeling

   https://doi.org/10.3389/frwa.2022.912776  

   Roundy, Joshua K.; Gallagher, Melissa A.; Byrd, Jenny L. (July 2022, Frontiers in Water)

   The interaction between climate and the hydrologic cycle is complex due to intricate feedback mechanisms that can have multiple impacts on key hydrologic variables. Under a changing climate, it is becoming increasingly important for undergraduate engineering students to have a better understanding of climate and the hydrologic cycle to ensure future engineering systems are more climate resilient. One way of teaching undergraduate students about these key interactions between climate and the hydrologic cycle is through numerical models that mimic these relationships. However, this is difficult to do in an undergraduate engineering course because these models are complex, and it is not feasible to devote class time and resources to teaching students the knowledge base required to run and analyze these numerical models. In addition, the recent COVID-19 pandemic required a rapid change to flexible teaching methods that can be implemented in online, hybrid, or in-person courses. To overcome these limitations, a backward design and constructive alignment approach was used to develop an active learning module in the HydroLearn framework that allows students to explore the connection between snow processes and streamflow and how this will change under different climate scenarios using numerical models and analysis. This learning module provides learning activities and tools that help the student develop a basic knowledge of snow formation and terminology, snow measurements, numerical models of snow processes, and changes in snow and streamflow under future climate. This module is particularly innovative in that it uses Google Colabs and an interactive user interface to facilitate the students' active learning in an environment that is accessible for all students and is sustainable for continued use and adaptation. This paper describes the approach, best practices and lessons learned in developing and implementing this active learning module in a remote and in-person course. In addition, it presents the results from motivation and student self-assessment surveys and discusses opportunities for improvement and further implementation that have implications for the future of hydrologic education. 

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4. Macrosystems EDDIE teaching modules significantly increase ecology students' proficiency and confidence working with ecosystem models and use of systems thinking

   https://doi.org/10.1002/ece3.6757  

   Carey, Cayelan C.; Farrell, Kaitlin J.; Hounshell, Alexandria G.; O'Connell, Kristin (October 2020, Ecology and Evolution)

   Abstract Simulation models are increasingly used by ecologists to study complex, ecosystem‐scale phenomena, but integrating ecosystem simulation modeling into ecology undergraduate and graduate curricula remains rare. Engaging ecology students with ecosystem simulation models may enable students to conduct hypothesis‐driven scientific inquiry while also promoting their use of systems thinking, but it remains unknown how using hands‐on modeling activities in the classroom affects student learning. Here, we developed short (3‐hr) teaching modules as part of the Macrosystems EDDIE (Environmental Data‐Driven Inquiry & Exploration) program that engage students with hands‐on ecosystem modeling in the R statistical environment. We embedded the modules into in‐person ecology courses at 17 colleges and universities and assessed student perceptions of their proficiency and confidence before and after working with models. Across all 277 undergraduate and graduate students who participated in our study, completing one Macrosystems EDDIE teaching module significantly increased students' self‐reported proficiency, confidence, and likely future use of simulation models, as well as their perceived knowledge of ecosystem simulation models. Further, students were significantly more likely to describe that an important benefit of ecosystem models was their “ease of use” after completing a module. Interestingly, students were significantly more likely to provide evidence of systems thinking in their assessment responses about the benefits of ecosystem models after completing a module, suggesting that these hands‐on ecosystem modeling activities may increase students’ awareness of how individual components interact to affect system‐level dynamics. Overall, Macrosystems EDDIE modules help students gain confidence in their ability to use ecosystem models and provide a useful method for ecology educators to introduce undergraduate and graduate students to ecosystem simulation modeling using in‐person, hybrid, or virtual modes of instruction. 

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5. Synchronous and Fully Steerable Active Particle Systems for Enhanced Mimicking of Collective Motion in Nature

   https://doi.org/10.1002/adma.202304759  

   Chen, Zhihan; Ding, Hongru; Kollipara, Pavana_Siddhartha; Li, Jingang; Zheng, Yuebing (December 2023, Advanced Materials)

   Abstract The collective motion observed in living active matter, such as fish schools and bird flocks, is characterized by its dynamic and complex nature, involving various moving states and transitions. By tailoring physical interactions or incorporating information exchange capabilities, inanimate active particles can exhibit similar behavior. However, the lack of synchronous and arbitrary control over individual particles hinders their use as a test system for the study of more intricate collective motions in living species. Herein, a novel optical feedback control system that enables the mimicry of collective motion observed in living objects using active particles is proposed. This system allows for the experimental investigation of the velocity alignment, a seminal model of collective motion (known as the Vicsek model), in a microscale perturbed environment with controllable and realistic conditions. The spontaneous formation of different moving states and dynamic transitions between these states is observed. Additionally, the high robustness of the active‐particle group at the critical density under the influence of different perturbations is quantitatively validated. These findings support the effectiveness of velocity alignment in real perturbed environments, thereby providing a versatile platform for fundamental studies on collective motion and the development of innovative swarm microrobotics. 

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