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

科学界正在进入一个时代，在这个时代，开放获取的数据丰富的环境为了解区域到大陆尺度上生态过程的时空复杂性提供了非凡的机会。国家生态观测网（NEON）和地球数据观测网（DataONE）举措就是收集、开发和分发数据和工具以进一步开展大规模和长期科学研究的例证。这些以及类似的举措代表了未来科学发现方式的范式转变。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进行有效推断：吸收不同的来源和类型的数据；适应时空依赖性，满足统计模型假设，提高预测推理能力；通过在大范围内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，以便能够评估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、生态水文和传染病等领域的重大挑战。虽然所提出的方法的发展是由与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卫生、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——利用新的发现来帮助改善社会。拟议的教育目标将使学生能够探索他们特定的研究兴趣，利用复杂的数据来建立新的理解，并学会合作来应对各自学科内部和跨学科的挑战和机遇。该奖项将开发和提供若干综合教育活动，包括：i)开发和实施地理和生态信息学的跨学院本科和研究生学位课程；Ii)应用环境数据建模的本科高级课程；iii)一门研究生水平的课程，侧重于层次贝叶斯时空建模的更高级主题；iv)在密歇根州西南部13个地区的23所K-12学校中丰富科学教学；（五）研究生研究专题讨论会，重点讨论环境数据分析的当代主题，来自多个机构的学生和专家将参与其中，为毕业生提供分享他们的研究、网络、获得专业技能和了解NEON数据产品的机会。教育活动将允许未来和现在的科学家以创新的方式扩展自己，并在问题上进行合作。

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.