Coupling remote sensing with microbial omics-based approaches provides a promising new frontier for scientists to scale microbial interactions across space and time. These data-rich, interdisciplinary methods allow us to better understand interactions between microbial communities and their environments and, in turn, their impact on ecosystem structure and function. Here, we highlight current and novel examples of applying remote sensing, machine learning, spatial statistics, and omics data approaches to marine, aquatic, and terrestrial systems. We emphasize the importance of integrating biochemical and spatiotemporal environmental data to move toward a predictive framework of microbiome interactions and their ecosystemlevel effects. Finally, we emphasize lessons learned from our collaborative research with recommendations to foster productive and interdisciplinary teamwork.
National Science Foundation
Geographic information systems, Machine learning, Metabolomics, Microbiome, Modeling, Remote sensing, Spatial ecology, Unmanned aerial vehicle
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Urban and Regional Planning
Deanna S. Beatty, Lillian R. Aoki, Olivia J. Graham, and Bo Yang. "The future is big - And small: Remote sensing enables cross- scale comparisons of microbiome dynamics and ecological consequences" mSystems (2021). https://doi.org/10.1128/mSystems.01106-21