ArticlemSphere2020
Biogeographic Patterns in Members of Globally Distributed and Dominant Taxa Found in Port Microbial Communities.
Article in mSphere, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Exploring local and regional drivers of microbial biodiversity across freshwater ponds.FEMS microbiology ecology · 2026Article
- A benchmark analysis of feature selection and machine learning methods for environmental metabarcoding datasets.Computational and structural biotechnology journal · 2025Article
- Community- and genome-based evidence for a shaping influence of redox potential on bacterial protein evolution.mSystems · 2023Article
- Geochemistry and Multiomics Data Differentiate Streams in Pennsylvania Based on Unconventional Oil and Gas Activity.Microbiology spectrum · 2022Article
- Sampling across large-scale geological gradients to study geosphere-biosphere interactions.Frontiers in microbiology · 2022Article
- Machine Learning Predicts the Presence of 2,4,6-Trinitrotoluene in Sediments of a Baltic Sea Munitions Dumpsite Using Microbial Community Compositions.Frontiers in microbiology · 2021Article
- Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring.Computational and structural biotechnology journal · 2021Review
- Characteristics of microbial community of soil subjected to industrial production of antibiotics.Folia microbiologica · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.