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Quantifying community assembly processes and identifying features that impose them
Open accessAbstract
Spatial turnover in the composition of biological communities is governed by (ecological) Drift, Selection and Dispersal. Commonly applied statistical tools cannot quantitatively estimate these processes, nor identify abiotic features that impose these processes. For interrogation of subsurface microbial communities distributed across two geologically distinct formations of the unconfined aquifer underlying the Hanford Site in southeastern Washington State, we developed an analytical framework that advances ecological understanding in two primary ways. First, we quantitatively estimate influences of Drift, Selection and Dispersal. Second, ecological patterns are used to characterize measured and unmeasured abiotic variables that impose Selection or that result in low levels of Dispersal. We find that (i) Drift alone consistently governs ∼25% of spatial turnover in community composition; (ii) in deeper, finer-grained sediments, Selection is strong (governing ∼60% of turnover), being imposed by an unmeasured but spatially structured environmental variable; (iii) in shallower, coarser-grained sediments, Selection is weaker (governing ∼30% of turnover), being imposed by vertically and horizontally structured hydrological factors;(iv) low levels of Dispersal can govern nearly 30% of turnover and be caused primarily by spatial isolation resulting from limited exchange between finer and coarser-grain sediments; and (v) highly permeable sediments are associated with high levels of Dispersal that homogenize community composition and govern over 20% of turnover. We further show that our framework provides inferences that cannot be achieved using preexisting approaches, and suggest that their broad application will facilitate a unified understanding of microbial communities.
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Cites
QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887
The merging of community ecology and phylogenetic biology
Cavender‐Bares, J., Kozak, K. H., Fine, P. V. A., & Kembel, S. W. (2009) · Ecology Letters · doi:10.1111/j.1461-0248.2009.01314.x · Cited by 2,321
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
A general framework for quantitatively assessing ecological stochasticity
Ning, D., Deng, Y., Tiedje, J. M., & Zhou, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1904623116 · Cited by 1,255
Jiao, S., Yang, Y., Xu, Y., Zhang, J., & Lu, Y. (2020) · The ISME Journal · doi:10.1038/s41396-019-0522-9 · Cited by 1,052
Martínez, I., Stegen, J. C., Maldonado-Gómez, M. X., Eren, A. M., Siba, P. M., Greenhill, A. R., & Walter, J. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.03.049 · Cited by 576
Self-citationThe 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
Metadata
- Authors
- Stegen, J. C., Lin, X., Fredrickson, J. K., Chen, X., Kennedy, D. W., Murray, C. J., Rockhold, M. L., & Konopka, A.
- Venue
- The ISME Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-06-06
- Volume
- 7
- Issue
- 11
- Pages
- 2069-2079
- ISSN
- 1751-7362 (ISSN-L)
- 1751-7370
- OpenAlex
- W2007929767
- Cited by
- 2,535
- References
- 2
- FWCI
- 11.51
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Stegen, J. C., Lin, X., Fredrickson, J. K., Chen, X., Kennedy, D. W., Murray, C. J., Rockhold, M. L., & Konopka, A. (2013). Quantifying community assembly processes and identifying features that impose them. The ISME Journal. https://doi.org/10.1038/ismej.2013.93
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