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:
- 金额:$ 30万
- 依托单位:
- 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:
- 关键词:
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,以预测生态系统的未来健康状况。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课堂上教授。为了克服这一挑战,该项目将扩大现有的,成功的培训计划(宏观系统EDDIE:环境数据驱动的调查探索),教学生基本的生态概念,因为他们创造的湖泊和森林在美国各地的预测。通过宏观系统EDDIE,学生和教师将学习如何使用模型,用观测数据评估预测准确性,并将预测传达给管理者和决策者。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的经验水平。宏观系统EDDIE为宏观系统生态学的教学提供了一种创新的新方法,并有可能促进整个美国的本科科学教育。通过加强学生的定量技能和对宏观系统生态学的理解,该项目将有助于发展多样化,具有全球竞争力的科学劳动力和增强宏观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,要求生态学家使用宏观系统科学的方法来预测人口,社区和生态系统的未来状态。宏观系统EDDIE将提供所需的培训,使生态预测访问所有生态学家,从本科生到高级研究人员,通过提取复杂的主题和技能,通过互动工具和可视化的入门级。教学模块将分析来自国家生态观测网络(氖)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生态系统之间和不同的空间尺度上变化。所有的教学模块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,生态预测倡议的研究人员将使用模块教材,从而支持跨多个机构的协作,跨学科科学。通过教授具有多种经验水平的生态学家如何检索、分析和可视化氖和GLEON数据,我们的目标是在推进宏观系统生态学和生态预测的同时,实现计算研究方法的广泛应用。该奖项反映了NSF的法定使命,并通过使用基金会的智力价值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:
- 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:
- 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:
- 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
- DOI:10.1007/s00027-021-00784-w
- 发表时间:2021-02
- 期刊:
- 影响因子:2.4
- 作者:Dexter W. Howard;A. Hounshell;M. Lofton;W. Woelmer;P. Hanson;C. Carey
- 通讯作者:Dexter W. Howard;A. Hounshell;M. Lofton;W. Woelmer;P. Hanson;C. Carey
Macrosystems EDDIE Module 8: Using Ecological Forecasts to Guide Decision-Making (Instructor Materials)
Macrosystems EDDIE 模块 8:使用生态预测来指导决策(讲师材料)
- DOI:10.6073/pasta/8bf4a076433f0e9f74f1d764d5bd4c3f
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Woelmer, Whitney M.;Thomas, R. Quinn;Moore, Tadhg N.;Carey, Cayelan C.
- 通讯作者:Carey, Cayelan C.
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Cayelan Carey其他文献
Cayelan Carey的其他文献
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{{ truncateString('Cayelan Carey', 18)}}的其他基金
LTREB: Integrating real-time open data pipelines and forecasting to quantify ecosystem predictability at day to decadal scales
LTREB:集成实时开放数据管道和预测,以量化每日到十年尺度的生态系统可预测性
- 批准号:
2327030 - 财政年份:2024
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
Global Centers Track 2: Building the Global Center for Forecasting Freshwater Futures
全球中心轨道 2:建立全球淡水未来预测中心
- 批准号:
2330211 - 财政年份:2023
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Collaborative Research: URoL:ASC: Applying rules of life to forecast emergent behavior of phytoplankton and advance water quality management
合作研究:URoL:ASC:应用生命规则预测浮游植物的紧急行为并推进水质管理
- 批准号:
2318861 - 财政年份:2023
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Collaborative Research: Elements: EdgeVPN: Seamless Secure VirtualNetworking for Edge and Fog Computing
协作研究:要素:EdgeVPN:用于边缘和雾计算的无缝安全虚拟网络
- 批准号:
2004323 - 财政年份:2020
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Collaborative Research: CIBR: Cyberinfrastructure Enabling End-to-End Workflows for Aquatic Ecosystem Forecasting
合作研究:CIBR:网络基础设施支持水生生态系统预测的端到端工作流程
- 批准号:
1933016 - 财政年份:2020
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Collaborative Research: Consequences of changing oxygen availability for carbon cycling in freshwater ecosystems
合作研究:改变淡水生态系统中碳循环的氧气可用性的后果
- 批准号:
1753639 - 财政年份:2018
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
SCC-IRG Track 2: Resilient Water Systems: Integrating Environmental Sensor Networks and Real-Time Forecasting to Adaptively Manage Drinking Water Quality and Build Social Trust
SCC-IRG 第 2 轨道:弹性水系统:集成环境传感器网络和实时预测,自适应管理饮用水质量并建立社会信任
- 批准号:
1737424 - 财政年份:2018
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
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 - 财政年份:2017
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
DISSERTATION RESEARCH: Hypoxia-induced trade-offs on zooplankton vertical distribution and community structure in freshwaters
论文研究:缺氧引起的淡水浮游动物垂直分布和群落结构的权衡
- 批准号:
1601061 - 财政年份:2016
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
相似海外基金
Conference: Macrosystems Biology and NEON-Enabled Science Investigator Meeting 2024
会议:2024 年宏观系统生物学和 NEON 科学研究者会议
- 批准号:
2334784 - 财政年份:2024
- 资助金额:
$ 30万 - 项目类别:
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A Prescriptive Study on the Mechanism of Overconcentration into Tokyo Based on Big Data and Macrosystems Ecology
基于大数据和宏观系统生态学的东京过度集中机制的规范性研究
- 批准号:
23K17775 - 财政年份:2023
- 资助金额:
$ 30万 - 项目类别:
Grant-in-Aid for Challenging Research (Exploratory)
CAREER: Advancing a macrosystems framework for climate-phenology coupling through integrated research and education
职业:通过综合研究和教育推进气候物候耦合的宏观系统框架
- 批准号:
2306198 - 财政年份:2022
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
RCN: The Macrosystems Ecology For All (MEFA) Research Coordination Network
RCN:全民宏观系统生态 (MEFA) 研究协调网络
- 批准号:
2213541 - 财政年份:2022
- 资助金额:
$ 30万 - 项目类别:
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MSA: A Macrosystems Perspective on River Eutrophication from Chlorophyll Abundance Patterns
MSA:从叶绿素丰度模式对河流富营养化的宏观系统视角
- 批准号:
2213574 - 财政年份:2022
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$ 30万 - 项目类别:
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Collaborative Proposal: MSB-FRA: A macrosystems ecology framework for continental-scale prediction and understanding of lakes
合作提案:MSB-FRA:用于大陆尺度预测和湖泊理解的宏观系统生态学框架
- 批准号:
2306364 - 财政年份:2022
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
CAREER: Advancing a macrosystems framework for climate-phenology coupling through integrated research and education
职业:通过综合研究和教育推进气候物候耦合的宏观系统框架
- 批准号:
2045309 - 财政年份:2021
- 资助金额:
$ 30万 - 项目类别:
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Macrosystems Biology and NEON enabled science investigator meeting
宏观系统生物学和 NEON 促成科学研究人员会议
- 批准号:
2022791 - 财政年份:2020
- 资助金额:
$ 30万 - 项目类别:
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MSA: Macrosystems Biology and Early NEON Science Investigator Meeting; May 15-17, 2019; Boulder, CO
MSA:宏观系统生物学和早期 NEON 科学研究者会议;
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1928375 - 财政年份:2019
- 资助金额:
$ 30万 - 项目类别:
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LTER: From Microbes to Macrosystems: Understanding the response of ecological systems to global change drivers and their interactions
LTER:从微生物到宏观系统:了解生态系统对全球变化驱动因素及其相互作用的响应
- 批准号:
1832210 - 财政年份:2019
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