Beatty Deanna S, Aoki Lillian R, Graham Olivia J, Yang Bo
Evolution and Ecology Department, University of California, Davis, California, USA.
Data Science Initiative, University of Oregon, Eugene, Oregon, USA.
mSystems. 2021 Dec 21;6(6):e0110621. doi: 10.1128/mSystems.01106-21. Epub 2021 Nov 2.
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.
将遥感技术与基于微生物组学的方法相结合,为科学家在时空尺度上拓展微生物相互作用的研究提供了一个充满希望的新领域。这些数据丰富的跨学科方法使我们能够更好地理解微生物群落与其环境之间的相互作用,进而了解它们对生态系统结构和功能的影响。在这里,我们重点介绍将遥感、机器学习、空间统计学和组学数据方法应用于海洋、水生和陆地系统的当前实例和新实例。我们强调整合生化和时空环境数据对于构建微生物群落相互作用及其生态系统水平效应的预测框架的重要性。最后,我们强调从合作研究中吸取的经验教训,并提出促进富有成效的跨学科团队合作的建议。