The Future Is Big—and Small: Remote Sensing Enables Cross-Scale Comparisons of Microbiome Dynamics and Ecological Consequences

mSystems

Lillian R. Aoki¹  Olivia J. Graham²  Bo Yang³  Deanna S. Beatty⁴ 

[1] Data Science Initiative, University of Oregon, Eugene, Oregon, USA;Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, USA;Department of Urban and Regional Planning, San Jose State University, San Jose, California, USA;Evolution and Ecology Department, University of California, Davis, California, USA;

关键词: geographic information systems;    machine learning;    metabolomics;    microbiome;    modeling;    remote sensing;   

DOI  :  10.1128/mSystems.01106-21

来源: DOAJ

【 摘 要 】

ABSTRACT 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 ecosystem-level effects. Finally, we emphasize lessons learned from our collaborative research with recommendations to foster productive and interdisciplinary teamwork.

【 授权许可】

Unknown