NSF2026: EAGER: Identifying microbes' population-level environmental responses using Bayesian modeling

With support from the Directorate for Geosciences and the NSF 2026 Fund Program in the Office of Integrated Activities, Professors Dana Hunt, Mark Borsuk, and James Clark at Duke University conduct research that provides new insights into the factors that shape microbial productivity and function in the oceans as well as how this change during extreme events such as hurricanes. The driver of this research comes from the fact that marine microbes provide essential ecosystem services, including primary production (photosynthesis) and organic matter turnover, that sustains all marine organisms. That said, it still remains unclear as to what extent microbiomes are shaped by environmental factors, such as temperature and primary productivity, that can be altered by season, disturbances, global change, and other factors. This research combines long-term observations at a coastal site at Beaufort Island, North Carolina and uses these data to capture annual changes in microbiomes and their environments using high frequency measurements that were taken before and after hurricanes Florence (2018) and Dorian (2019). Examining the impact of hurricanes on marine biomes is important because hurricanes are multi-factor disturbances that introduce both foreign freshwater and terrestrial microbes into a stable system while altering salinity, nutrients, and organic matter in the coastal ocean. This work combines information from field observations and modeling to develop new approaches that will allow the differentiation of factors that often co-occur in field samples, such as warmer temperatures and higher primary production that occur during the summer months in the coastal Atlantic Ocean. By integrating multiple aspects of microbiome research, this work deepens current understanding of the coastal ocean microbiome system and its functionality. It also develops new testable hypothesis to guide future research. Broader impacts of the work include advanced training for undergraduate, graduate, and postdoctoral students, as well as translating research results into products for K-12 students and the public. Additional impacts include the production of detailed user manuals and training materials for software developed in the course of the project to facilitate the use of research results for future microbiome research and undergraduate education.

This research leverages an established decade-long microbial time-series, the Piver's Island Coastal Observatory (PICO, Beaufort Inlet, NC USA) to improve the modeling of microbial populations and their relationship to changing environments. With 10 years of weekly (or more frequent) microbial community SSU rRNA gene sequence datasets, coupled with the suite of sample, in-situ, and environmental parameters, the PICO dataset is one of the most complete, long-term datasets for coastal ocean microbiomes. The work carried out uses the application of Bayesian modeling to the PICO time series to improve understanding and predictions of microbiome responses to ocean conditions. Bayesian models are well suited to microbial systems because they have the ability to handle sparse datasets, capture non-linear responses to environmental changes, and include impacts of disturbances. This research integrates microbiome applications and the Bayesian model gjamTime. This combination has the potential to transform microbial ecology by leveraging advances in multivariate time-series methods that accommodate the dependence among individual taxa and their environment over time. One goal of the project is to test model predictions using time-series data from natural disturbances (i.e., hurricanes) at the Beaufort Inlet site and explore various key environmental parameters such as temperature (+3 °C) and primary production as key environmental parameters. Similar work will be done more broadly for the ocean. Impacts of the research extend beyond the targeted coastal dataset as, if successful, the approach can be applied to other diverse study systems such as soil and human microbiomes. It can also be used to address questions about environmental filtering, disturbance and stochasticity, each of which is critical to understanding the factors and processes that govern microbial responses to environmental change.

This project responds to the NSF2026 Idea Machine winning entries of 'Global Microbiome in a Changing Planet' and 'Imagine a Life with Clean Oceans'

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.