The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning
Open accessAbstract
The soil microbiome is highly diverse and comprises up to one quarter of Earth’s diversity. Yet, how such a diverse and functionally complex microbiome influences ecosystem functioning remains unclear. Here we manipulated the soil microbiome in experimental grassland ecosystems and observed that microbiome diversity and microbial network complexity positively influenced multiple ecosystem functions related to nutrient cycling (e.g. multifunctionality). Grassland microcosms with poorly developed microbial networks and reduced microbial richness had the lowest multifunctionality due to fewer taxa present that support the same function (redundancy) and lower diversity of taxa that support different functions (reduced functional uniqueness). Moreover, different microbial taxa explained different ecosystem functions pointing to the significance of functional diversity in microbial communities. These findings indicate the importance of microbial interactions within and among fungal and bacterial communities for enhancing ecosystem performance and demonstrate that the extinction of complex ecological associations belowground can impair ecosystem functioning.
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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
Structure, function and diversity of the healthy human microbiome
The Human Microbiome Project Consortium (2012) · Nature · doi:10.1038/nature11234 · Cited by 11,726
Van Der Heijden, M. G. A., Bardgett, R. D., & Van Straalen, N. M. (2008) · Ecology Letters · doi:10.1111/j.1461-0248.2007.01139.x · Cited by 5,053
Prokaryotes: The unseen majority
Whitman, W. B., Coleman, D. C., & Wiebe, W. J. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.12.6578 · Cited by 4,871
Structure and functions of the bacterial microbiota of plants
Bulgarelli, D., Schlaeppi, K., Spaepen, S., van Themaat, E. V. L., & Schulze-Lefert, P. (2013) · Annual Review of Plant Biology · doi:10.1146/annurev-arplant-050312-120106 · Cited by 3,308
Self-citationBiodiversity and ecosystem productivity in a fluctuating environment: The insurance hypothesis
Yachi, S., & Loreau, M. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.4.1463 · Cited by 3,211
Using network analysis to explore co-occurrence patterns in soil microbial communities
Barberán, A., Bates, S. T., Casamayor, E. O., & Fierer, N. (2012) · The ISME Journal · doi:10.1038/ismej.2011.119 · Cited by 3,044
Microbial diversity drives multifunctionality in terrestrial ecosystems
Delgado-Baquerizo, M., Maestre, F. T., Reich, P. B., Jeffries, T. C., Gaitan, J. J., Encinar, D., Berdugo, M., Campbell, C. D., & Singh, B. K. (2016) · Nature Communications · doi:10.1038/ncomms10541 · Cited by 2,624
Test of an Ascorbic Acid Method for Determining Phosphorus in Water and NaHCO 3 Extracts from Soil
Watanabe, F. S., & Olsen, S. R. (1965) · Soil Science Society of America Journal · doi:10.2136/sssaj1965.03615995002900060025x · Cited by 2,485
Sparse and Compositionally Robust Inference of Microbial Ecological Networks
Kurtz, Z. D., Müller, C. L., Miraldi, E. R., Littman, D. R., Blaser, M. J., & Bonneau, R. A. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004226 · Cited by 1,799
Cited by
RESCRIPt: Reproducible sequence taxonomy reference database management
(2021) · doi:10.1371/journal.pcbi.1009581 · Cited by 983
Host selection shapes crop microbiome assembly and network complexity
Xiong, C., Zhu, Y., Wang, J., Singh, B., Han, L., Shen, J., Li, P., Wang, G., Wu, C., Ge, A., et al. (2021) · New Phytologist · doi:10.1111/nph.16890 · Cited by 710
Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions
Narunsky-Haziza, L., Sepich-Poore, G. D., Livyatan, I., Asraf, O., Martino, C., Nejman, D., Gavert, N., Stajich, J. E., Amit, G., González, A., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.005 · Cited by 538
“It remains unclear whether a permissive phenotype is passively allowed by immunosuppressed, nutrient-rich TMEs (Hinshaw and Shevde, 2019) or denotes active synergy for greater ecosystemmultifunctionality (Wagg et al., 2019) or a selection advantage for tumors (Aykut et al.”
BackgroundDisease-induced changes in plant microbiome assembly and functional adaptation
Gao, M., Xiong, C., Gao, C., Tsui, C. K. M., Wang, M.-M., Zhou, X., Zhang, A.-M., & Cai, L. (2021) · Microbiome · doi:10.1186/s40168-021-01138-2 · Cited by 478
Ding, Y., Gao, X., Shu, D., Siddique, K. H. M., Song, X., Wu, P., Li, C., & Zhao, X. (2024) · Science of The Total Environment · doi:10.1016/j.scitotenv.2024.171332 · Cited by 107
Metadata
- Authors
- Wagg, C., Schlaeppi, K., Banerjee, S., Kuramae, E. E., & van der Heijden, M. G. A.
- Venue
- Nature Communications
- Published
- 2019-10-24
- Volume
- 10
- Issue
- 1
- Pages
- 4841-4841
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2981774387
- Cited by
- 1,836
- References
- 16
- FWCI
- 87.83
- Type
- journal article
- Language
- en
- Open access
- gold
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