The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index.
Disentangling the mechanisms shaping the surface ocean microbiota
Open accessAbstract
Background: The ocean microbiota modulates global biogeochemical cycles and changes in its configuration may have large-scale consequences. Yet, the underlying ecological mechanisms structuring it are unclear. Here, we investigate how fundamental ecological mechanisms ( selection , dispersal and ecological drift ) shape the smallest members of the tropical and subtropical surface-ocean microbiota: prokaryotes and minute eukaryotes (picoeukaryotes). Furthermore, we investigate the agents exerting abiotic selection on this assemblage as well as the spatial patterns emerging from the action of ecological mechanisms. To explore this, we analysed the composition of surface-ocean prokaryotic and picoeukaryotic communities using DNA-sequence data (16S- and 18S-rRNA genes) collected during the circumglobal expeditions Malaspina - 2010 and TARA - Oceans . Results: We found that the two main components of the tropical and subtropical surface-ocean microbiota, prokaryotes and picoeukaryotes, appear to be structured by different ecological mechanisms. Picoeukaryotic communities were predominantly structured by dispersal-limitation, while prokaryotic counterparts appeared to be shaped by the combined action of dispersal-limitation, selection and drift. Temperature-driven selection appeared as a major factor, out of a few selected factors, influencing species co-occurrence networks in prokaryotes but not in picoeukaryotes, indicating that association patterns may contribute to understand ocean microbiota structure and response to selection. Other measured abiotic variables seemed to have limited selective effects on community structure in the tropical and subtropical ocean. Picoeukaryotes displayed a higher spatial differentiation between communities and a higher distance decay when compared to prokaryotes, consistent with a scenario of higher dispersal limitation in the former after considering environmental heterogeneity. Lastly, random dynamics or drift seemed to have a more important role in structuring prokaryotic communities than picoeukaryotic counterparts. Conclusions: The differential action of ecological mechanisms seems to cause contrasting biogeography, in the tropical and subtropical ocean, among the smallest surface plankton, prokaryotes and picoeukaryotes. This suggests that the idiosyncrasy of the main constituents of the ocean microbiota should be considered in order to understand its current and future configuration, which is especially relevant in a context of global change, where the reaction of surface ocean plankton to temperature increase is still unclear.
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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
DADA2: High–resolution sample inference from Illumina amplicon data
Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016) · Nature Methods · doi:10.1038/nmeth.3869 · Cited by 35,698
The SILVA ribosomal RNA gene database project: improved data processing and web-based tools
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., & Glöckner, F. O. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1219 · Cited by 33,055
vegan: Community Ecology Package
Oksanen, J., Simpson, G. L., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O'Hara, R. B., Solymos, P., Stevens, M. H. H., Szoecs, E., et al. (2001) · CRAN: Contributed Packages · doi:10.32614/cran.package.vegan · Cited by 22,615
Search and clustering orders of magnitude faster than BLAST
Edgar, R. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq461 · Cited by 20,826
Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy
Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007) · Applied and Environmental Microbiology · doi:10.1128/aem.00062-07 · Cited by 19,885
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., et al. (2009) · Applied and Environmental Microbiology · doi:10.1128/aem.01541-09 · Cited by 19,549
UPARSE: highly accurate OTU sequences from microbial amplicon reads
Edgar, R. C. (2013) · Nature Methods · doi:10.1038/nmeth.2604 · Cited by 16,707
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Salinity controls soil microbial community structure and function in coastal estuarine wetlands
Zhang, G., Bai, J., Tebbe, C. C., Zhao, Q., Jia, J., Wang, W., Wang, X., & Yu, L. (2021) · Environmental Microbiology · doi:10.1111/1462-2920.15281 · Cited by 316
Metadata
- Authors
- Logares, R., Deutschmann, I. M., Junger, P. C., Giner, C. R., Krabberød, A. K., Schmidt, T. S. B., Rubinat-Ripoll, L., Mestre, M., Salazar, G., Ruiz-González, C., Sebastián, M., de Vargas, C., Acinas, S. G., Duarte, C. M., Gasol, J. M., & Massana, R.
- Venue
- Microbiome
- Published
- 2020-04-20
- Volume
- 8
- Issue
- 1
- Pages
- 55-55
- ISSN
- 2049-2618 (ISSN-L)
- OpenAlex
- W3017156868
- Cited by
- 285
- References
- 110
- FWCI
- 22.50
- Type
- journal article
- Language
- en
- Open access
- gold
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- Logares, R., Deutschmann, I. M., Junger, P. C., Giner, C. R., Krabberød, A. K., Schmidt, T. S. B., Rubinat-Ripoll, L., Mestre, M., Salazar, G., Ruiz-González, C., Sebastián, M., de Vargas, C., Acinas, S. G., Duarte, C. M., Gasol, J. M., & Massana, R. (2020). Disentangling the mechanisms shaping the surface ocean microbiota. Microbiome. https://doi.org/10.1186/s40168-020-00827-8
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