CAREER: Advancements in Spatio-temporal Modeling and Education in Support of NEON and Large-scale and Long-term Ecological Research

职业：支持 NEON 和大规模长期生态研究的时空建模和教育进展

• 批准号: 1253225
• 负责人: Andrew Finley
• 金额: $ 99.63万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1253225&HistoricalAwards=false
• 关键词: CAREER; Advancements; Spatio; temporal; Modeling

The scientific community is moving into an era where open-access data-rich environments provide extraordinary opportunities to understand the spatial and temporal complexity of ecological processes at regional to continental scales. Investment to collect, develop, and distribute data and tools to further large-scale and long-term science is exemplified by the National Ecological Observatory Network (NEON) and Data Observation Network for Earth (DataONE) initiatives. These, and similar initiatives, represent a paradigm shift in the way future scientific discovery will occur. This Career award will develop theoretical, methodological, software, and instructional advancements that will allow current and future scientists and educators to draw valid inference about large and complex ecological systems by: assimilating disparate sources and types of data; accommodating spatial and temporal dependence to satisfy statistical model assumptions and improve predictive inference; partitioning and propagating sources of uncertainty through fine spatial scale predictions over large domains, and; scaling to effectively exploit information in massive datasets. The research will develop new flexible spatio-temporal modeling frameworks tailored to enable assessment of NEON's Grand Challenges in the areas of climate change, land use, invasive species, biogeochemistry, biodiversity, ecohydrology, and infectious diseases. Although development of the proposed methods is motivated by substantive questions related to NEON's mission, potential advancements in spatio-temporal data modeling will find use in fields such as public and environmental health, meteorology, engineering, and geosciences where the fundamental goal is the same -- use new findings to help improve society.The proposed educational objectives will enable students to explore their particular research interests, exploit complex data to build new understanding, and learn to collaborate to address challenges and opportunities within and across their respective disciplines. The award will develop and deliver several integrative education activities including: i) the development and implementation of cross-college undergraduate and graduate degree programs in Geo- and Eco-Informatics; ii) an undergraduate senior-level course in applied environmental data modeling; iii) a graduate-level course focused on more advanced topics in hierarchical Bayesian spatio-temporal modeling; iv) enrichment of science instruction in 23 K-12 schools in 13 districts in southwestern Michigan, and; v) graduate research symposia focused on contemporary topics in environmental data analysis that will engage students and experts from multiple institutions and serve as an opportunity for the graduates to share their research, network, garner specialized skills, and learn about NEON data products. Education activities will allow future and current scientists to extend themselves in innovative ways and collaborate on problems.

科学界正在进入一个时代，开放获取的数据丰富的环境为了解区域到大陆尺度的生态过程的空间和时间复杂性提供了绝佳的机会。国家生态观测站网络 (NEON) 和地球数据观测网络 (DataONE) 计划就是为收集、开发和分发数据和工具以进一步开展大规模和长期科学而进行的投资的例证。这些以及类似的举措代表了未来科学发现发生方式的范式转变。该职业奖将发展理论、方法、软件和教学方面的进步，使当前和未来的科学家和教育工作者能够通过以下方式对大型复杂的生态系统做出有效的推论：同化不同来源和类型的数据；适应空间和时间依赖性，以满足统计模型假设并改进预测推理；通过大域的精细空间尺度预测来划分和传播不确定性来源；扩展以有效地利用海量数据集中的信息。该研究将开发新的灵活的时空建模框架，以评估 NEON 在气候变化、土地利用、入侵物种、生物地球化学、生物多样性、生态水文学和传染病等领域面临的巨大挑战。尽管所提出的方法的开发是由与 NEON 任务相关的实质性问题推动的，但时空数据建模的潜在进步将在公共和环境健康、气象学、工程和地球科学等领域得到应用，这些领域的基本目标是相同的 - - 利用新发现帮助改善社会。拟议的教育目标将使学生能够探索他们特定的研究兴趣，利用复杂的数据建立新的理解，并学会合作应对各自学科内和跨学科的挑战和机遇。该奖项将开发和开展多项综合教育活动，包括：i）开发和实施地理和生态信息学方面的跨学院本科和研究生学位课程； ii) 应用环境数据建模本科高级课程； iii) 研究生课程，重点关注分层贝叶斯时空建模中更高级的主题； iv) 丰富密歇根州西南部 13 个地区 23 所 K-12 学校的科学教学； v) 研究生研究研讨会重点关注环境数据分析领域的当代主题，该研讨会将吸引来自多个机构的学生和专家，并为毕业生提供分享他们的研究、建立人际网络、获得专业技能和了解 NEON 数据产品的机会。教育活动将使未来和现在的科学家能够以创新的方式扩展自己并就问题进行合作。