The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Decoupling function and taxonomy in the global ocean microbiome
Abstract
Microbial metabolism powers biogeochemical cycling in Earth’s ecosystems. The taxonomic composition of microbial communities varies substantially between environments, but the ecological causes of this variation remain largely unknown. We analyzed taxonomic and functional community profiles to determine the factors that shape marine bacterial and archaeal communities across the global ocean. By classifying >30,000 marine microorganisms into metabolic functional groups, we were able to disentangle functional from taxonomic community variation. We find that environmental conditions strongly influence the distribution of functional groups in marine microbial communities by shaping metabolic niches, but only weakly influence taxonomic composition within individual functional groups. Hence, functional structure and composition within functional groups constitute complementary and roughly independent “axes of variation” shaped by markedly different processes.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A new method for non-parametric multivariate analysis of variance
Anderson, M. J. (2001) · Austral Ecology · doi:10.1111/j.1442-9993.2001.01070.pp.x · Cited by 13,828
Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB
DeSantis, T. Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E. L., Keller, K., Huber, T., Dalevi, D., Hu, P., & Andersen, G. L. (2006) · Applied and Environmental Microbiology · doi:10.1128/aem.03006-05 · Cited by 10,891
Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
Langille, M. G. I., Zaneveld, J., Caporaso, J. G., McDonald, D., Knights, D., Reyes, J. A., Clemente, J. C., Burkepile, D. E., Vega Thurber, R. L., Knight, R., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2676 · Cited by 9,116
The Microbial Engines That Drive Earth's Biogeochemical Cycles
Falkowski, P. G., Fenchel, T., & Delong, E. F. (2008) · Science · doi:10.1126/science.1153213 · Cited by 3,379
Cited by
Stochastic Community Assembly: Does It Matter in Microbial Ecology?
Zhou, J., & Ning, D. (2017) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.00002-17 · Cited by 2,773
The best practice for microbiome analysis using R
Wen, T., Niu, G., Chen, T., Shen, Q., Yuan, J., & Liu, Y.-X. (2023) · Protein & Cell · doi:10.1093/procel/pwad024 · Cited by 139
Hu, J., Kang, L., Li, Z., Feng, X., Liang, C., Wu, Z., Zhou, W., Liu, X., Yang, Y., & Chen, L. (2023) · Nature Communications · doi:10.1038/s41467-023-39432-2 · Cited by 114
Kane, J. L., Schartiger, R. G., Daniels, N. K., Freedman, Z. B., McDonald, L. M., Skousen, J. G., & Morrissey, E. M. (2023) · Soil Biology and Biochemistry · doi:10.1016/j.soilbio.2023.109178 · Cited by 59
Purkamo, L., Kietäväinen, R., Nuppunen-Puputti, M., Bomberg, M., & Cousins, C. (2020) · Life · doi:10.3390/life10010002 · Cited by 55
Metadata
- Authors
- Louca, S., Parfrey, L. W., & Doebeli, M.
- Venue
- Science
- Published
- 2016-09-16
- Volume
- 353
- Issue
- 6305
- Pages
- 1272-1277
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2520355709
- Cited by
- 3,510
- References
- 4
- FWCI
- 74.39
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Decoupling function and taxonomy in the global ocean microbiome. https://app.cochat.ai/papers/10.1126/science.aaf4507
- The paper
- Louca, S., Parfrey, L. W., & Doebeli, M. (2016). Decoupling function and taxonomy in the global ocean microbiome. Science. https://doi.org/10.1126/science.aaf4507
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
