NEON RCN: The Ecological Forecasting Initiative RCN: Using NEON-enabled near-term forecasting to synthesize our understanding of predictability across ecological systems and scales

NEON RCN：生态预测计划 RCN：使用 NEON 支持的近期预测来综合我们对跨生态系统和规模的可预测性的理解

• 批准号: 1926388
• 负责人: Robert Thomas
• 金额: $ 50万
• 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=1926388&HistoricalAwards=false
• 关键词: NEON; RCN; Ecological; Forecasting; Initiative

The 21st century is characterized by major environmental changes that alter the ecosystems society depends on for supplying clean water, storing carbon, and supporting plants and animals. Predicting and forecasting the future of these systems can help resource managers anticipate and respond to these changes. Ecological forecasts also advance scientific understanding by developing and testing models and testing alternative views of how ecological systems operate. To harness the power of forecasting, a community of practice is needed. This community will establish standards for developing and communicating forecasts. It will share best practices for generating forecasts with known uncertainties. The group will create tools and educational materials to enable ecologists to begin forecasting. The Ecological Forecasting Initiative Research Coordination Network award will build this community of practice around forecasting data from the continental-scale National Ecological Observatory Network (NEON). By focusing on data from NEON, the community will be challenged to develop and evaluate forecasts for a diversity of environments that exist across the United States. The award will train hundreds of early career researchers and graduate students in ecological forecasting.The Ecological Forecasting Initiative Research Coordination Network will use a set of network workshops, conferences, and collaborative software development events to address the following objectives: (1) develop best practices for archiving and sharing NEON-enabled ecological forecasts, (2) increase the number, diversity, and quality of NEON-enabled forecasts, (3) train the ecological community to produce NEON-enabled forecasts by developing open tutorials in ecological forecasting methods, (4) facilitate and support collaborative interactions dedicated to forecasting knowledge transfer among ecologists, (5) cultivate connections between the academic ecological forecasting community and mission-driven agencies that use forecasts to guide decision-making, and (6) synthesize existing forecasts and new NEON-enabled forecasts to evaluate limits to forecastability across ecological systems and scales. Overall, these objectives are focused on solving the grand challenge of understanding the predictability of nature.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.

21世纪的特征是改变生态系统社会的重大环境变化依赖于提供清洁水，储存碳和支持动植物的动植物。预测和预测这些系统的未来可以帮助资源经理预测并响应这些变化。生态预测还通过开发和测试模型以及测试生态系统的运作方式来提高科学理解。为了利用预测的力量，需要一个实践社区。这个社区将建立制定和沟通预测的标准。它将分享具有已知不确定性的预测的最佳实践。该小组将创建工具和教育材料，以使生态学家能够开始预测。生态预测计划研究协调网络奖将围绕来自大陆级国家生态观测网络（NEON）的预测数据建立这个实践社区。通过关注霓虹灯的数据，将挑战社区，以开发和评估美国存在各种环境的预测。 The award will tr​​ain hundreds of early career researchers and graduate students in ecological forecasting.The Ecological Forecasting Initiative Research Coordination Network will use a set of network workshops, conferences, and collaborative software development events to address the following objectives: (1) develop best practices for archiving and sharing NEON-enabled ecological forecasts, (2) increase the number, diversity, and quality of NEON-enabled forecasts, (3) train生态界通过开发开放的生态预测方法中的开放教程来产生霓虹灯的预测，（4）促进和支持协作互动，致力于预测知识转移之间的知识转移，（5）建立与现有的社区和传教士预测的学术预测和综合构成的社区预测和（6）之间的联系（5）预测评估对生态系统和量表之间可预测性的限制。总体而言，这些目标的重点是解决理解自然可预测性的巨大挑战。该奖项反映了NSF的法定使命，并被认为是值得通过基金会的知识分子优点和更广泛影响的评论标准来评估值得支持的。

项目成果

Going Virtual: What we Learned from the Ecological Forecasting Initiative Research Coordination Network Virtual Workshop

走向虚拟：我们从生态预测倡议研究协调网络虚拟研讨会中学到了什么

• DOI: 10.1002/bes2.1828
• 发表时间: 2021
• 期刊: The Bulletin of the Ecological Society of America
• 作者: Peters, Jody A.;Quinn Thomas, R.
• 通讯作者: Quinn Thomas, R.

Progress and opportunities in advancing near‐term forecasting of freshwater quality

• DOI: 10.1111/gcb.16590
• 发表时间: 2023-01
• 期刊: Global Change Biology
• 影响因子: 11.6
• 作者: M. Lofton;Dexter W. Howard;R. Q. Thomas;C. Carey

Assessing Ecosystem State Space Models: Identifiability and Estimation

• DOI: 10.1007/s13253-023-00531-8
• 发表时间: 2023-03-09
• 期刊: JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS
• 影响因子: 1.4
• 作者: Smith Jr，J. W.;Johnson，L. R.;Thomas，R. Q.
• 通讯作者: Thomas，R. Q.

Virtual Growing Pains: Initial Lessons Learned from Organizing Virtual Workshops, Summits, Conferences, and Networking Events during a Global Pandemic

虚拟成长的烦恼：在全球大流行期间组织虚拟研讨会、峰会、会议和社交活动的初步经验教训

• DOI: 10.1002/lob.10431
• 发表时间: 2021
• 期刊: Limnology and Oceanography Bulletin
• 作者: 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.
• 通讯作者: Favot, Elizbaeth J.

The power of forecasts to advance ecological theory

• DOI: 10.1111/2041-210x.13955
• 发表时间: 2022-08
• 期刊: Methods in Ecology and Evolution
• 影响因子: 6.6
• 作者: Abigail S. L. Lewis;C. Rollinson;A. Allyn;Jaime Ashander;S. Brodie;Cole B. Brookson;Elyssa L. Collins-Elyssa-L.-Coll