Abstract
ABSTRACTCoupling 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.
Highlights
Coupling remote sensing with microbial omics-based approaches provides a promising new frontier for scientists to scale microbial interactions across space and time
We discuss the use of remote sensing and machine learning with omics-based technologies to create an atlas of host-associated and free-living microbes and their diverse ecologies
We present two case studies that illustrate how remote sensing—when coupled with omics approaches—can improve monitoring and prediction of macroscale phenomena
Summary
Coupling remote sensing with microbial omics-based approaches provides a promising new frontier for scientists to scale microbial interactions across space and time. Scientists can leverage powerful, cross-disciplinary approaches that pair omics tools with remote sensing and spatial statistics to study spatiotemporal variation in microbiome interactions. These methods measure cyanobacteria or algal biomass and can evaluate potential environmental drivers (auxiliary remotely sensed data products) (Fig. 2) but cannot detect toxin presence or differentiate species within blooms.
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