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Environmental stress mediates groundwater microbial community assembly

Nature Microbiology volume 9, pages 490–501 (2024)Cite this article

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Abstract

Community assembly describes how different ecological processes shape microbial community composition and structure. How environmental factors impact community assembly remains elusive. Here we sampled microbial communities and >200 biogeochemical variables in groundwater at the Oak Ridge Field Research Center, a former nuclear waste disposal site, and developed a theoretical framework to conceptualize the relationships between community assembly processes and environmental stresses. We found that stochastic assembly processes were critical (>60% on average) in shaping community structure, but their relative importance decreased as stress increased. Dispersal limitation and ‘drift’ related to random birth and death had negative correlations with stresses, whereas the selection processes leading to dissimilar communities increased with stresses, primarily related to pH, cobalt and molybdenum. Assembly mechanisms also varied greatly among different phylogenetic groups. Our findings highlight the importance of microbial dispersal limitation and environmental heterogeneity in ecosystem restoration and management.

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Fig. 1: Schematic representation of the relationships between community assembly processes and stress.
Fig. 2: Maps of sampling positions, stress and relative importance of community assembly processes.
Fig. 3: Variation in community assembly processes at different stress levels.
Fig. 4: Variations in assembly mechanisms across different phylogenetic groups.
Fig. 5: Environmental variables significantly correlated with each assembly process.

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Data availability

Data are accessible from a KBase narrative (https://narrative.kbase.us/narrative/145709), the current static version of which is https://kbase.us/n/145709/13/. The 16S rRNA gene sequencing data are from our prior study42 and are available in MG-RAST with accession code mgp8190. The taxonomy classifier trained on Silva SSU 138 is from QIIME2 (v.2021.2), available at https://data.qiime2.org/2021.2/common/silva-138-99-515-806-nb-classifier.qza. Source data are provided with this paper.

Code availability

All custom scripts and the latest version of the R package iCAMP are available from GitHub (https://github.com/DaliangNing/iCAMP1).

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Acknowledgements

This study by ENIGMA (Ecosystems and Networks Integrated with Genes and Molecular Assemblies; http://enigma.lbl.gov), a Science Focus Area Program at Lawrence Berkeley National Laboratory, is based on work supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research under contract number DE-AC02-05CH11231 (to P.D.A., A.P.A., J.Z., T.C.H., E.J.A., M.W.F., M.W.W.A., R.C. and D.A.S.). The development of the theoretical framework and statistical methods was partly supported by NSF Grants EF-2025558 and DEB-2129235 to J.Z. and D.N. The study was also supported by the Office of the Vice President for Research at the University of Oklahoma (to J.Z.).

Author information

Authors and Affiliations

  1. Institute for Environmental Genomics, University of Oklahoma, Norman, OK, USA

    Daliang Ning, Yajiao Wang, Yupeng Fan, Jianjun Wang, Joy D. Van Nostrand, Liyou Wu, Ping Zhang, Daniel J. Curtis, Renmao Tian, Zhili He & Jizhong Zhou

  2. School of Biological Sciences, University of Oklahoma, Norman, OK, USA

    Daliang Ning, Yajiao Wang, Yupeng Fan, Liyou Wu & Jizhong Zhou

  3. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China

    Jianjun Wang

  4. Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA

    Ping Zhang

  5. Institute for Food Safety and Health, Illinois Institute of Technology, Bedford Park, IL, USA

    Renmao Tian

  6. Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Lauren Lui, Paul D. Adams & Adam P. Arkin

  7. Department of Earth and Planetary Sciences, Bredesen Center, Department of Civil and Environmental Sciences, Center for Environmental Biotechnology, and Institute for a Secure and Sustainable Environment, University of Tennessee, Knoxville, TN, USA

    Terry C. Hazen

  8. Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Terry C. Hazen

  9. Department of Bioengineering, University of California, Berkeley, CA, USA

    Terry C. Hazen, Paul D. Adams & Adam P. Arkin

  10. Department of Biological Engineering, Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA

    Eric J. Alm

  11. Center for Biofilm Engineering and Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT, USA

    Matthew W. Fields

  12. Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, USA

    Farris Poole & Michael W. W. Adams

  13. Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Romy Chakraborty & Jizhong Zhou

  14. Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA

    David A. Stahl

  15. Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, China

    Zhili He

  16. School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK, USA

    Jizhong Zhou

  17. School of Computer Science, University of Oklahoma, Norman, OK, USA

    Jizhong Zhou

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  1. Daliang Ning
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Contributions

All authors contributed intellectual input and assistance to this study and the paper preparation. J.Z. conceived the research questions. J.Z. and D.N. developed the theoretical framework. E.J.A., T.C.H., M.W.F., M.W.W.A., R.C., Z.H., J.Z., P.D.A. and A.P.A. designed and organized the experiment. Y.W., Y.F., J.W., J.D.V.N., L.W., P.Z., D.J.C., R.T., L.L. and F.P. generated or collected the data. D.N. integrated the data and performed statistical analyses with the assistance of Y.W.; J.Z. and D.N. wrote the paper with inputs from D.A.S., Z.H., J.W., L.L. and D.J.C.

Corresponding author

Correspondence to Jizhong Zhou.

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The authors declare no competing interests.

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Peer review information

Nature Microbiology thanks Malak Tfaily, Michael Wilkins and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Maps of the concentrations of representative environmental variables in groundwater.

a, Nitrate-nitrogen. b, pH. c, Uranium (U). d, Dissolved oxygen (DO). The bubble size represents the value at each sampling position; R2CV is cross-validated R2 of the model used to generate each map; P values (one-sided) are based on permutational test; the uppercase A - D indicate hotspots of heterogeneous selection, the same as in Fig. 2.

Source data

Extended Data Fig. 2 Correlation between reference taxa number and log-transformed maximum stress index (ln MSI).

Each data point represents a sample. The line and shadow are the trendline and 95% confidence interval based on linear regression. r and P show Pearson correlation coefficient and significance (two-sided). The ‘reference taxa’ are defined as relatively abundant and common taxa in uncontaminated samples.

Source data

Extended Data Fig. 3 Variation of bacterial alpha diversity along the stress gradient.

a, Richness estimated by iChao1 index; b, Shannon index. The line and shadow show the trendline and 95% confidence interval based on linear regression. r and P are Pearson correlation coefficient and significance (two-sided). The significant Pearson correlation indicates a general decrease in bacterial richness as stress increased in the groundwater.

Source data

Extended Data Fig. 4 Determinism and stochasticity of groundwater bacterial assembly at different stress levels.

a, Abundance-weighted percentage of neutral taxa (NP) based on neutral theory model. b, Stochastic turnover ratio, that is, percentage of community turnovers governed by stochastic assembly processes based on a framework of entire-community null model analysis (QPEN). c, Relative importance of stochastic assembly processes based on a framework of phylogenetic-bin null model analysis (iCAMP). MSI, maximum stress index. The violin and box plots are based on bootstrapping results at each stress level (n = 13 in each level; bootstrapping 1000 times). Colors of violin plots indicate the stress levels. In box plots, center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; dots, outliers; triangle, mean value at each stress level. Black line, gray shadow, R2, and P are the trendline, 95% confidence interval, coefficient of determination, and significance based on linear regression of the mean values as a function of log-transformed MSI.

Source data

Extended Data Fig. 5 Variation of groundwater bacterial assembly processes along the stress gradient.

a, Homogeneous selection (HoS). b, Homogenizing dispersal (HD). The violin and box plots are based on bootstrapping results at each stress level (n = 13 in each level; bootstrapping 1000 times). Colors of violin/box plots indicate the stress levels. In box plots, center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; dots, outliers; triangle, mean value at each stress level. Black line, gray shadow, R2, and P values are the trendline, 95% confidence interval, coefficient of determination, and significance based on linear regression of the mean values as a function of log-transformed MSI.

Source data

Extended Data Fig. 6 Association network of environmental variables and relative importance of community assembly processes.

Links show significant correlation (cross-validated R2CV > 0.01 and P < 0.05); P values (one-sided) were calculated based on permutational test (1000 times) for correlations among environmental factors and Mantel test (permutated 1000 times) for correlations between assembly processes and environmental factors, then, adjusted by false discovery rate (FDR) method; line width reflecting the R2CV. Node area is related to its degree (associated node number). Nodes are grouped by modules based on greedy optimization. Dep. (T) and Dep. (B), depth of the screen top and bottom, respectively; geo.distance, geographic distance; cond., conductivity. Heterogeneous selection (HeS) showed much more strong associations with environmental variables and mainly with pH and metals in water phase (supernatant and suspended solid). The numerous gray links demonstrated the high density of strong associations among environmental variables.

Source data

Extended Data Fig. 7 Relationship between bacterial phylogenetic distance and niche preference difference (phylogenetic signal).

a, Mantel correlogram. b, Relationship curve and stepwise Mantel test. Mantel correlogram was performed as previously described. Stepwise Mantel test was used to evaluate the correlation between niche preference difference and phylogenetic distance within the phylogenetic distance from 0 to a certain value. On the relationship curves, each niche difference value is the mean value in each phylogenetic distance ‘class’ within a distance interval of 0.02. The niche preference difference was estimated based on nine representative environmental variables. In the nine variables, pH, U, and nitrate (NO3) are major stressors in the contaminated area; Yb-sup, Sulfide, 52Cr-ss, Cu-sup, Sr, and Rb-plt were identified as ‘centroid’ variables which showed the nearest distance to the centroids of six environmental variable clusters, to represent the variation of each cluster. The clusters were identified by hierarchical clustering based on pairwise correlation among environmental variables. The distances to centroid were calculated by multivariate homogeneity of groups dispersions using function ‘betadisper’ in R package ‘vegan’. P values (one-sided) are based on Mantel test (permutated 1000 times) and adjusted by false discovery rate (FDR) method. The Mantel correlograms and stepwise Mantel tests showed generally significant (P < 0.05) phylogenetic signal, validating the use of phylogenetic diversity to infer the influence of environmental selection. However, the trends in the relationship curves and the change of correlation coefficients and P values revealed the tipping points of phylogenetic signal at short phylogenetic distances around 0.2 to 0.6, supporting the necessity to use the phylogenetic-bin-based null model approach (iCAMP) which better exploits phylogenetic signal within relatively short phylogenetic distance.

Source data

Supplementary information

Supplementary Information

Supplementary Discussion A–D, Notes, References, Figs. 1–4 and Tables 1–9.

Reporting Summary

Supplementary Data 1

Source data for supplementary figures.

Source data

Source Data Fig. 2

Maps of sampling positions, stress and relative importance of community assembly processes.

Source Data Fig. 3

Variation in community assembly processes at different stress levels.

Source Data Fig. 4

Variation in assembly mechanisms across different phylogenetic groups.

Source Data Fig. 5

Correlation between environmental variables and assembly processes.

Source Data Extended Data Fig. 1

Maps of the concentrations of representative environmental variables in groundwater.

Source Data Extended Data Fig. 2

Correlation between reference taxa number and log-transformed maximum stress index.

Source Data Extended Data Fig. 3

Variation in bacterial alpha diversity along the stress gradient.

Source Data Extended Data Fig. 4

Determinism and stochasticity of groundwater bacterial assembly at different stress levels.

Source Data Extended Data Fig. 5

Variation in groundwater bacterial assembly processes along the stress gradient.

Source Data Extended Data Fig. 6

Associations among environmental variables and relative importance of community assembly processes.

Source Data Extended Data Fig. 7

Relationship between bacterial phylogenetic distance and niche preference difference.

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Ning, D., Wang, Y., Fan, Y. et al. Environmental stress mediates groundwater microbial community assembly. Nat Microbiol 9, 490–501 (2024). https://doi.org/10.1038/s41564-023-01573-x

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