Department of Biological Sciences, Michigan Technological University, Houghton, Michigan, USA.
Department of Biological Sciences, Michigan Technological University, Houghton, Michigan, USA
mSphere. 2020 Jan 29;5(1):e00481-19. doi: 10.1128/mSphere.00481-19.
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 (, , , and ) 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. 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.
我们对航运港口的微生物群落进行了全球特征描述，将其作为一种新的系统来研究微生物生物地理学。尽管跨越了很大的空间尺度，但来自海洋和淡水生境的港口微生物的群落结构却容纳了相对相似的门。作为该项目的一部分，我们从八个国家和三个大洲的 604 个地点收集了 1218 个地表水样本，总共对 20 个航运港口进行了编目，这些港口分布在美国东海岸和西海岸、欧洲和亚洲，代表了迄今为止对港口相关微生物群落的最大研究。在这里，我们展示了机器学习的实用性，通过识别大空间尺度上生物多样性的趋势，利用这一强大系统来描述微生物生物地理学。我们发现，对于具有相似环境条件的地理位置，可以使用这些栖息地的主要门的亚群（、、、和）来区分分布在全球的 20 个地理位置。这些结果表明，尽管微生物群落中存在着压倒性的多样性，但系统中最丰富和最普遍的微生物群体的成员可以用来区分全球空间尺度上的地理位置。我们的研究提供了有关微生物如何在空间上扩散的见解，以及我们可以用来探究微生物生物地理学的稳健方法。微生物在世界各地无处不在，具有高度多样性。描述大地理空间尺度上微生物多样性的变化程度是一项挑战，但可以揭示很多关于生物地理学可以告诉我们的微生物种群及其行为的信息。机器学习方法主要用于研究人类微生物组，在某种程度上也用于研究环境中的微生物群落。在这里，我们展示了如何使用来自全球分布的航运港口的微生物，通过监督机器学习方法来理解微生物生物多样性和生物地理学。我们的研究结果表明，全球优势门的成员对于区分位置很重要，这减少了对稀有分类群的依赖，以探测地理位置。此外，这项研究还表明，通过对系统中占主导地位的高度丰富和普遍存在的分类群的种群，可以评估水生微生物群落（和其他系统）的全球生物地理格局。
