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Biogeographic Patterns in Members of Globally Distributed and Dominant Taxa Found in Port Microbial Communities

机译:在港口微生物社区中发现的全球分布和主导分类群成员的生物地理图案

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We conducted a global characterization of the microbial communities of shipping ports to serve as a novel system to investigate microbial biogeography. The community structures of port microbes from marine and freshwater habitats house relatively similar phyla, despite spanning large spatial scales. As part of this project, we collected 1,218 surface water samples from 604 locations across eight countries and three continents to catalogue a total of 20 shipping ports distributed across the East and West Coast of the United States, Europe, and Asia to represent the largest study of port-associated microbial communities to date. Here, we demonstrated the utility of machine learning to leverage this robust system to characterize microbial biogeography by identifying trends in biodiversity across broad spatial scales. We found that for geographic locations sharing similar environmental conditions, subpopulations from the dominant phyla of these habitats ( Actinobacteria , Bacteroidetes , Cyanobacteria , and Proteobacteria ) can be used to differentiate 20 geographic locations distributed globally. These results suggest that despite the overwhelming diversity within microbial communities, members of the most abundant and ubiquitous microbial groups in the system can be used to differentiate a geospatial location across global spatial scales. Our study provides insight into how microbes are dispersed spatially and robust methods whereby we can interrogate microbial biogeography. IMPORTANCE Microbes are ubiquitous throughout the world and are highly diverse. Characterizing the extent of variation in the microbial diversity across large geographic spatial scales is a challenge yet can reveal a lot about what biogeography can tell us about microbial populations and their behavior. Machine learning approaches have been used mostly to examine the human microbiome and, to some extent, microbial communities from the environment. Here, we display how supervised machine learning approaches can be useful to understand microbial biodiversity and biogeography using microbes from globally distributed shipping ports. Our findings indicate that the members of globally dominant phyla are important for differentiating locations, which reduces the reliance on rare taxa to probe geography. Further, this study displays how global biogeographic patterning of aquatic microbial communities (and other systems) can be assessed through populations of the highly abundant and ubiquitous taxa that dominant the system.
机译:我们对运输港的微生物社区进行了全球性,以作为调查微生物生物地理学的新型系统。尽管跨越了大型空间尺度,海洋和淡水栖息地房屋的港口微生物的社区结构相对相似。作为该项目的一部分,我们从8个国家的604个地点收集了1,218个地表水样,并在美国,欧洲和亚洲东部和西海岸分布了20个运输端口的目录,以代表最大的研究迄今为止港口相关的微生物社区。在这里,我们展示了机器学习的效用,利用这种稳健的系统来表征微生物生物地理,通过识别宽空间尺度的生物多样性趋势。我们发现,对于共享类似的环境条件的地理位置,这些栖息地(抗菌菌,菌株,蓝细菌和植物和植物)的主要植物的群体可用于区分全球分布的20个地理位置。这些结果表明,尽管微生物社区内的压倒性多样化,但系统中最丰富和无处不在的微生物组的成员可用于区分全局空间尺度的地理空间位置。我们的研究提供了对微生物分散的空间和鲁棒方法的洞察,从而我们可以询问微生物生物地理学。重要性微生物在全世界普遍存在,非常多样化。表征在大型地理空间尺度上微生物多样性的变化程度是一个挑战,但可以揭示很多关于生物地理可以告诉我们关于微生物种群及其行为的挑战。机器学习方法主要用于检查人类微生物组,在某种程度上从环境中进行微生物群落。在这里,我们展示监督机器学习方法如何使用来自全球分布式运输端口的微生物来了解微生物生物多样性和生物地理。我们的研究结果表明,全球占主导地位的成员对于区分地点来说很重要,这降低了对初步探测地理的依赖。此外,该研究显示了通过高度和无处不在的分类群的群体来评估水生微生物社区(和其他系统)的全球生物地理图案化程度如何。

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