MSB-ECA: A macrosystems science training program: developing undergraduates' simulation modeling, distributed computing, and collaborative skills

MSB-ECA：宏观系统科学培训计划：培养本科生的仿真建模、分布式计算和协作技能

• 批准号: 1702506
• 负责人: Cayelan Carey
• 金额: $ 30万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1702506&HistoricalAwards=false
• 关键词: MSB; ECA; macrosystems; science; training

Ecologists are increasingly using computer models, involving extensive observations obtained through environmental sensor networks, to study lakes and forests and predict future change. Conducting this modeling, as well as understanding the model results, requires skills in data analysis, quantitative reasoning, and computing. However, modeling and computational skills are rarely taught in undergraduate classrooms, representing a major gap in training students to tackle complex environmental challenges. This project will develop a training program that teaches thousands of students across the U.S. the foundations of macrosystems ecology through simulation modeling. Through the program, instructors and their students will learn how to quickly and efficiently run the models by using new resources emerging from computer science. Thus, students will simultaneously learn the core concepts of macrosystems science and develop the skills needed to conduct the next generation of environmental research. These concepts and skills will be embedded within teaching modules centered on the frontier of macrosystems ecology, enabling undergraduate students to contribute to high-level macrosystems research. Engaging undergraduate students in hands-on modeling activities with real-world applications translates into a workforce with increased data science, systems thinking, and quantitative skills.This project will create a training program (MacroSystems EDDIE: Environmental Data-Driven Inquiry & Exploration) that connects undergraduate students with new computing tools to teach them the foundations of macrosystems science using simulation modeling. These tools will be embedded in stand-alone teaching modules that will be widely applicable to a suite of disciplines and undergraduate student experience levels. The proposed modules represent the first formalized macrosystems science curriculum for undergraduates, which will be rigorously assessed, revised, and disseminated broadly for maximum impact through a partnership with the Science Education Research Center. The modules will be designed around areas of active research inquiry e.g., interacting local and regional effects on lake temperatures, carbon cycling, the incidence of algal blooms, continental-scale water policy and students will be encouraged to develop novel questions at the frontier of macrosystems science that they will answer using simulation models. As part of the training program, the students will share their results with Global Lake Ecological Observatory Network (GLEON) scientific working groups to advance ongoing macrosystems research. The proposed program represents an innovative approach for teaching macrosystems biology to undergraduates and has the potential to advance undergraduate STEM education across the U.S. A minimum of 750 students will participate in the MacroSystems EDDIE modules during this project, with the potential for several thousands more through dissemination by Science Education Research Center. student participation in the Macrosystems EDDIE modules will generate scientific discoveries that will advance our understanding of macrosystems bio

生态学家越来越多地使用计算机模型，包括通过环境传感器网络获得的广泛观测，来研究湖泊和森林并预测未来的变化。进行这种建模，以及理解模型结果，需要数据分析、定量推理和计算方面的技能。然而，建模和计算技能很少在本科生课堂上教授，这是培养学生应对复杂环境挑战的一个重大差距。该项目将开发一个培训计划，通过模拟建模向美国各地的数千名学生传授宏观系统生态学的基础。通过该计划，教师和他们的学生将学习如何通过使用计算机科学中出现的新资源来快速有效地运行模型。因此，学生将同时学习大系统科学的核心概念，并发展进行下一代环境研究所需的技能。这些概念和技能将被嵌入到以大系统生态学前沿为中心的教学模块中，使本科生能够为高水平的大系统研究做出贡献。让本科生参与实际应用的建模活动将转化为一支拥有更多数据科学、系统思维和量化技能的队伍。该项目将创建一个培训计划(MacroSystems Eddie：环境数据驱动的查询与探索)，将本科生与新的计算工具联系起来，教他们使用模拟建模的大系统科学基础。这些工具将嵌入到独立的教学模块中，这些模块将广泛适用于一系列学科和本科生经验水平。拟议的单元是面向本科生的第一个正式的大系统科学课程，将通过与科学教育研究中心的伙伴关系对其进行严格评估、修订和广泛传播，以产生最大影响。这些模块将围绕活跃的研究调查领域进行设计，例如，对湖泊温度、碳循环、藻华发生的本地和区域相互影响、大陆规模的水政策，并将鼓励学生在宏观系统科学的前沿开发新的问题，他们将使用模拟模型来回答这些问题。作为培训计划的一部分，学生们将与全球湖泊生态观测网络(GLEON)科学工作组分享他们的成果，以推动正在进行的宏观系统研究。拟议的计划代表了向本科生教授大系统生物学的一种创新方法，并有可能推动全美的本科STEM教育。在该项目期间，至少有750名学生将参加MacroSystems Eddie模块，通过科学教育研究中心的传播，可能还会有数千名学生参加。学生参与大系统Eddie模块将产生科学发现，这将促进我们对大系统生物学的理解。

项目成果

The Magnitude and Drivers of Methane Ebullition and Diffusion Vary on a Longitudinal Gradient in a Small Freshwater Reservoir

小型淡水水库中甲烷沸腾和扩散的幅度和驱动因素随纵向梯度的变化而变化

• DOI: 10.1029/2019jg005205
• 发表时间: 2020
• 期刊: Journal of Geophysical Research: Biogeosciences
• 作者: McClure, R. P.;Lofton, M. E.;Chen, S.;Krueger, K. M.;Little, J. C.;Carey, C. C.
• 通讯作者: Carey, C. C.

Relative importance of top‐down vs. bottom‐up control of lake phytoplankton vertical distributions varies among fluorescence‐based spectral groups

• DOI: 10.1002/lno.11465
• 发表时间: 2020-06
• 期刊: Limnology and Oceanography
• 影响因子: 4.5
• 作者: M. Lofton;Taylor H. Leach;B. Beisner;C. Carey

Effectiveness of a bubble-plume mixing system for managing phytoplankton in lakes and reservoirs

• DOI: 10.1016/j.ecoleng.2018.01.002
• 发表时间: 2018-04
• 期刊: Ecological Engineering
• 影响因子: 3.8
• 作者: Shengyang Chen;C. Carey;John C. Little;M. Lofton;R. McClure;C. Lei

Whole-Ecosystem Experiments Reveal Varying Responses of Phytoplankton Functional Groups to Epilimnetic Mixing in a Eutrophic Reservoir

• DOI: 10.3390/w11020222
• 发表时间: 2019-01
• 期刊: Water
• 影响因子: 3.4
• 作者: M. Lofton;R. McClure;Shengyang Chen;John C. Little;C. Carey

Hypolimnetic Hypoxia Increases the Biomass Variability and Compositional Variability of Crustacean Zooplankton Communities

低氧缺氧增加了甲壳类浮游动物群落的生物量变异性和成分变异性

• DOI: 10.3390/w11102179
• 发表时间: 2019
• 期刊: Water
• 影响因子: 3.4
• 作者: Doubek, Jonathan P.;Campbell, Kylie L.;Lofton, Mary E.;McClure, Ryan P.;Carey, Cayelan C.
• 通讯作者: Carey, Cayelan C.