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

Michigan State University, East Lansing MI

Investigators

Abstract

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.