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.

中文翻译：

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在港口微生物群落中发现的全球分布和主要类群成员的生物地理模式。

我们对航运港口的微生物群落进行了全球表征，以作为研究微生物生物地理学的新型系统。尽管空间分布较大，但海洋和淡水生境的港口微生物群落结构具有相对相似的门。作为该项目的一部分，我们从八个国家和三大洲的604个地点收集了1,218个地表水样本，对分布在美国，欧洲和亚洲东西海岸的20个航运港口进行了分类，这是最大的研究迄今为止，与港口相关的微生物群落数量。在这里，我们展示了机器学习的实用性，可通过识别广泛空间范围内生物多样性的趋势，利用这一强大的系统来表征微生物生物地理学。我们发现，对于共享类似环境条件的地理位置，这些栖息地的主要门（放线菌，拟杆菌，蓝细菌和变形杆菌）的亚种群可用于区分全球分布的20个地理位置。这些结果表明，尽管微生物群落内部存在压倒性的多样性，但系统中最丰富和普遍存在的微生物组的成员可用于区分全球空间尺度上的地理空间位置。我们的研究提供了有关微生物如何在空间上分散的见解以及可靠的方法，从而可以询问微生物的生物地理学。在较大的地理空间尺度上表征微生物多样性的变化程度是一个挑战，但可以揭示很多生物地理学可以告诉我们有关微生物种群及其行为的信息。机器学习方法主要用于检查人类微生物组以及某种程度上来自环境的微生物群落。在这里，我们展示有监督的机器学习方法如何使用来自全球分布的港口的微生物来了解微生物的生物多样性和生物地理。我们的发现表明，全球优势种群的成员对于区分地理位置非常重要，这减少了对稀有分类群进行地理探测的依赖。进一步，