Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance Article Swipe
Tor Einar Møller
,
Sven Le Moine Bauer
,
Bjarte Hannisdal
,
Rui Zhao
,
Tamara Baumberger
,
Desiree L. Roerdink
,
Amandine Dupuis
,
Ingunn H. Thorseth
,
Rolf B. Pedersen
,
Steffen L. Jørgensen
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fmicb.2022.804575
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fmicb.2022.804575
Oxygen constitutes one of the strongest factors explaining microbial taxonomic variability in deep-sea sediments. However, deep-sea microbiome studies often lack the spatial resolution to study the oxygen gradient and transition zone beyond the oxic-anoxic dichotomy, thus leaving important questions regarding the microbial response to changing conditions unanswered. Here, we use machine learning and differential abundance analysis on 184 samples from 11 sediment cores retrieved along the Arctic Mid-Ocean Ridge to study how changing oxygen concentrations (1) are predicted by the relative abundance of higher taxa and (2) influence the distribution of individual Operational Taxonomic Units. We find that some of the most abundant classes of microorganisms can be used to classify samples according to oxygen concentration. At the level of Operational Taxonomic Units, however, representatives of common classes are not differentially abundant from high-oxic to low-oxic conditions. This weakened response to changing oxygen concentration suggests that the abundance and prevalence of highly abundant OTUs may be better explained by other variables than oxygen. Our results suggest that a relatively homogeneous microbiome is recruited to the benthos, and that the microbiome then becomes more heterogeneous as oxygen drops below 25 μM. Our analytical approach takes into account the oft-ignored compositional nature of relative abundance data, and provides a framework for extracting biologically meaningful associations from datasets spanning multiple sedimentary cores.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fmicb.2022.804575
- OA Status
- gold
- Cited By
- 7
- References
- 83
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4280573710
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4280573710Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fmicb.2022.804575Digital Object Identifier
- Title
-
Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential AbundanceWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-05-18Full publication date if available
- Authors
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Tor Einar Møller, Sven Le Moine Bauer, Bjarte Hannisdal, Rui Zhao, Tamara Baumberger, Desiree L. Roerdink, Amandine Dupuis, Ingunn H. Thorseth, Rolf B. Pedersen, Steffen L. JørgensenList of authors in order
- Landing page
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https://doi.org/10.3389/fmicb.2022.804575Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3389/fmicb.2022.804575Direct OA link when available
- Concepts
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Abundance (ecology), Anoxic waters, Relative species abundance, Deep sea, Ecology, Microbial population biology, Sediment, Oceanography, Taxonomic rank, Biology, Microbiome, Environmental science, Taxon, Geology, Paleontology, Bioinformatics, BacteriaTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 3, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
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83Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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