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MSA: Macrosystems EDDIE: An undergraduate training program in macrosystems science and ecological forecasting

MSA：宏观系统 EDDIE：宏观系统科学和生态预测的本科培训项目

基本信息

批准号：
1926050
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：
2020
资助国家：
美国
起止时间：
2020-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1926050&HistoricalAwards=false
关键词：
MSA Macrosystems EDDIE undergraduate training

项目摘要

Ecologists are increasingly analyzing big environmental datasets to make forecasts about the future health of ecosystems. However, the data analysis and modeling skills needed to successfully develop ecological forecasts are rarely taught in undergraduate classrooms. To overcome this challenge, this project will expand an existing, successful training program (Macrosystems EDDIE: Environmental Data-Driven Inquiry & Exploration) to teach students fundamental ecological concepts as they create forecasts for lakes and forests across the United States. Through Macrosystems EDDIE, students and instructors will learn how to use models, assess forecast accuracy with observational data, and communicate forecasts to managers and decision-makers. These skills will be embedded in stand-alone teaching modules that will be widely applicable to multiple disciplines and student experience levels. Macrosystems EDDIE provides an innovative new approach for teaching macrosystems ecology and has the potential to advance undergraduate science education across the U.S. By strengthening both students' quantitative skillsets and understanding of macrosystems ecology, this project will help develop a diverse, globally-competitive scientific workforce and enhanced infrastructure for macrosystems research and education.The biosphere is changing at unprecedented rates, requiring ecologists to use macrosystems science approaches to make forecasts about the future state of populations, communities, and ecosystems. Macrosystems EDDIE will provide the training needed to make ecological forecasting accessible to all ecologists, from undergraduates to senior researchers, by distilling complex topics and skills to an introductory level via interactive tools and visualizations. The teaching modules will analyze aquatic and terrestrial data from the National Ecological Observatory Network (NEON) and Global Lake Ecological Observatory Network (GLEON) to explore how the predictability of ecological dynamics varies among ecosystems and across different spatial scales. All teaching modules will be rigorously assessed, revised, and disseminated broadly for maximum impact through the Science Education Research Center (SERC). Beyond undergraduate classrooms, module teaching materials will be used by Ecological Forecasting Initiative researchers, thereby supporting collaborative, interdisciplinary science across multiple institutions. By teaching ecologists at multiple experience levels how to retrieve, analyze, and visualize NEON and GLEON data, our goal is to enable widespread use of computational research approaches while advancing macrosystems ecology and ecological forecasting.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

生态学家越来越多地分析大型环境数据集，以预测未来生态系统的健康状况。然而，成功开发生态预测所需的数据分析和建模技能很少在本科生课堂上教授。为了克服这一挑战，该项目将扩展现有的、成功的培训计划(宏观系统EDDIE：环境数据驱动的查询和探索)，向学生传授基本的生态概念，同时预测美国各地的湖泊和森林。通过宏观系统Eddie，学生和教师将学习如何使用模型，使用观测数据评估预测准确性，并将预测传达给经理和决策者。这些技能将嵌入到独立的教学模块中，这些模块将广泛适用于多个学科和学生体验水平。大系统Eddie为教授大系统生态学提供了一种创新的新方法，并有可能推动全美的本科生科学教育。通过加强学生的数量技能和对大系统生态学的理解，该项目将有助于发展一支多样化的、具有全球竞争力的科学队伍，并加强大系统研究和教育的基础设施。生物圈正在以前所未有的速度变化，要求生态学家使用大系统科学方法来预测人口、群落和生态系统的未来状态。宏观系统公司Eddie将提供必要的培训，通过互动工具和可视化将复杂的主题和技能提炼到入门水平，使所有生态学家，从本科生到高级研究人员，都能获得生态预测。教学模块将分析来自国家生态观测网络(NEON)和全球湖泊生态观测网络(GLEON)的水生和陆地数据，以探索生态动态的可预测性如何在不同的生态系统和不同的空间尺度上变化。所有教学模块都将通过科学教育研究中心(SERC)进行严格的评估、修订和广泛传播，以产生最大的影响。除了本科课堂外，生态预测倡议的研究人员还将使用模块教材，从而支持跨多个机构的协作、跨学科科学。通过教授不同经验水平的生态学家如何检索、分析和可视化霓虹灯和GLEON数据，我们的目标是在推进大系统生态学和生态预测的同时，使计算研究方法得到广泛使用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（24）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Eddy Covariance Data Reveal That a Small Freshwater Reservoir Emits a Substantial Amount of Carbon Dioxide and Methane

涡度协方差数据显示小型淡水水库排放大量二氧化碳和甲烷

DOI：
10.1029/2022jg007091
发表时间：
2023
期刊：
Journal of Geophysical Research: Biogeosciences
影响因子：
0
作者：
Hounshell, Alexandria G.;D’Acunha, Brenda M.;Breef‐Pilz, Adrienne;Johnson, Mark S.;Thomas, R. Quinn;Carey, Cayelan C.
通讯作者：
Carey, Cayelan C.

A Near‐Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time

近期迭代预测系统成功地实时预测储层水动力和分区不确定性

DOI：
10.1029/2019wr026138
发表时间：
2020
期刊：
Water Resources Research
影响因子：
5.4
作者：
Thomas, R. Quinn;Figueiredo, Renato J.;Daneshmand, Vahid;Bookout, Bethany J.;Puckett, Laura K.;Carey, Cayelan C.
通讯作者：
Carey, Cayelan C.

The importance of time and space in biogeochemical heterogeneity and processing along the reservoir ecosystem continuum

DOI：
10.1007/s00027-023-00959-7
发表时间：
2023-04
期刊：
Aquatic Sciences
影响因子：
2.4
作者：
W. Woelmer;A. Hounshell;M. Lofton;H. Wander;Abigail S. L. Lewis;D. Scott;C. Carey
通讯作者：
W. Woelmer;A. Hounshell;M. Lofton;H. Wander;Abigail S. L. Lewis;D. Scott;C. Carey

Variability in fluorescent dissolved organic matter concentrations across diel to seasonal time scales is driven by water temperature and meteorology in a eutrophic reservoir