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Climate warming enhances microbial network complexity and stability

Nature Climate Change volume 11, pages 343–348 (2021)Cite this article

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Abstract

Unravelling the relationships between network complexity and stability under changing climate is a challenging topic in theoretical ecology that remains understudied in the field of microbial ecology. Here, we examined the effects of long-term experimental warming on the complexity and stability of molecular ecological networks in grassland soil microbial communities. Warming significantly increased network complexity, including network size, connectivity, connectance, average clustering coefficient, relative modularity and number of keystone species, as compared with the ambient control. Molecular ecological networks under warming became significantly more robust, with network stability strongly correlated with network complexity, supporting the central ecological belief that complexity begets stability. Furthermore, warming significantly strengthened the relationships of network structure to community functional potentials and key ecosystem functioning. These results indicate that preserving microbial ‘interactions’ is critical for ecosystem management and for projecting ecological consequences of future climate warming.

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Fig. 1: Succession of soil microbial networks over time.
Fig. 2: Correlations between relative modularity and network topological properties.
Fig. 3: Temporal dynamics of network stability linking to network structure.
Fig. 4: Relationships between the network structures and environmental variables, ecosystem processes and community functional traits under warming.

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Data accessibility

16S rRNA gene sequences were deposited to the National Center for Biotechnology Information (NCBI) under the project accession number PRJNA331185. The OTU table and OTU representative sequences, soil physical and chemical attributes, and plant biomass and richness are downloadable online at http://www.ou.edu/ieg/publications/datasets. GeoChip signal intensity data can be accessed through the URL (https://www.ou.edu/ieg/publications/datasets). Source data are provided with this paper.

Code availability

The R scripts and Python 3 scripts are publicly available on GitHub at https://github.com/Mengting-Maggie-Yuan/warming-network-complexity-stability with the identifier https://doi.org/10.5281/zenodo.4383469.

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Acknowledgements

We thank numerous former and current members in the Institute for Environmental Genomics for their help in maintaining the long-term field experiment. This work was supported by the US Department of Energy, Office of Science, Genomic Science Program under award numbers DE-SC0004601 and DE-SC0010715, and the Office of the Vice President for Research at the University of Oklahoma. X.G. and X.Z. were generously supported by China Scholarship Council (CSC) to visit the University of Oklahoma. The statistical analyses performed by X.G. were also supported by the China Postdoctoral Science Foundation (2018M641327 and 2019T120101).

Author information

Author notes

  1. These authors contributed equally: Mengting Maggie Yuan, Xue Guo, Linwei Wu, Ya Zhang.

Authors and Affiliations

  1. Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, USA

    Mengting Maggie Yuan

  2. Institute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, USA

    Mengting Maggie Yuan, Xue Guo, Linwei Wu, Ya Zhang, Naijia Xiao, Daliang Ning, Zhou Shi, Xishu Zhou, Liyou Wu & Jizhong Zhou

  3. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China

    Xue Guo & Yunfeng Yang

  4. Gladstone Institutes and Chan-Zuckerberg Biohub, San Francisco, CA, USA

    Zhou Shi

  5. Key Laboratory of Biometallurgy of Ministry of Education, School of Minerals Processing and Bioengineering, Central South University, Changsha, China

    Xishu Zhou

  6. Center for Microbial Ecology, Michigan State University, East Lansing, MI, USA

    James M. Tiedje

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

    Jizhong Zhou

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

    Jizhong Zhou

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Contributions

All authors contributed intellectual input and assistance to this study. The original concepts were conceived by J.Z. and J.M.T. Field management was carried out by M.M.Y., X.G., Linwei W., Y.Z., Z.S., D.N. and Liyou W. Sampling collection, soil chemical and microbial characterization were carried out by M.M.Y., X.G. and X.Z. Data analyses were done by M.Y., X.G., Linwei W., Z.S. and N.X. with the assistance provided by D.N. and J.Z. All data analysis and integration were guided by J.Z. The manuscript was prepared by J.Z, M.M.Y., X.G., Linwei W. and Y.Z. with substantial input from J.M.T. and Y.Y. Considering their contributions in terms of site management, data collection, analyses and/or integration, M.Y., X.G., Linwei W. and Y.Z. were listed as co-first authors.

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Correspondence to Jizhong Zhou.

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Peer review information Nature Climate Change thanks Johannes Bjork, Dongmei Xue 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 Succession and environmental drivers of the networked community structure.

a, Detrended correspondence analysis (DCA) of the structure of networked communities. The community structure is significantly different by both treatment and year. b, Canonical correspondence analysis (CCA) of the links between networked community structure and environmental drivers. The ordination plot shows the CCA model with each networked microbial community and constraining variables, including spatial distance (Distance), soil temperature (Temp.), soil moisture (Moisture), soil pH (pH), soil total N (TN), soil nitrate (NO3−N) and ammonia (NH4-N) contents, plant biomass and richness. The model is significant with p = 0.001 tested by ANOVA. c, Variation partitioning analysis (VPA) separating the variation of community structure explained by the CCA model. Soil category includes soil temperature (Temp.), soil moisture (Moisture), soil pH (pH), soil total N (TN), soil nitrate (NO3-N) and ammonia (NH4-N) contents; plant category includes plant biomass and richness.

Source data

Extended Data Fig. 2 Cohesion of bacterial communities and its relationships with network complexity and stability indices.

a, Changes in positive cohesion of bactera community over time. b, Changes in negative cohesion of the commuity over time. In (a) and (b), filled red circles with solid line represent communities under warming, and open blue circles with dashed line represent communities under control condition. Each error bar corresponds to the standard deviation of cohesion in 24 plots. The slopes (b from Y=a+bX), adjusted r2 and p values of the linear model fittings are shown. c, Pearson correlations of cohesion with various network complexity and stability indices under warming (framed in red) or control (framed in blue) condition. The cells highlighted in red indicate significant positive correlations (p ≤ 0.05) and those in blue indicate significant negative correlations. Numbers inside of the cells are correlation coefficients. Correlations with p > 0.05 are in gray.

Source data

Extended Data Fig. 3 Modules preserved across time and treatments.

a, Large modules (that is, those with ≥ 5 nodes) shown in circular layout for the 11 networks. Colors of nodes indicate major taxa. Red links indicate positive correlations between nodes. Blue links indicate negative correlations between nodes. The bar underneath each network shows the proportions of positive and negative links. The label nearby each module represents its ID. b, Preserved module pairs highlighted and connected in the same module layout as (a). Modules are in the same color if they are in the same module cluster (that is, a cluster of modules consisting all the directly paired and indirectly linked modules). Note that two clusters of modules (the red and the blue clusters) were preserved over time consistently between Year 1 (2010) and Year 5 (2014). More details are in Supplementary text C.

Source data

Extended Data Fig. 4 Keystone taxa and their relative abundances in bacterial communities.

a,b, Putative keystone taxa identified based on the node topological roles in networks under control (a) and warming (b). Each symbol represents a node in one of the networks. A node was identified as a module hub if its Zi ≥ 2.5, as a connector if its Pi ≥ 0.62, and as a network hub if it had Zi ≥ 2.5 and Pi ≥ 0.62. Detailed taxonomic information for module hubs, connectors and network hubs is listed in Supplementary Table 6. c–f, The relative abundances of module hubs (c, d) and connectors (e, f) in the networks under control (c, e) and warming (d, f). The relative abundance of an OTU was estimated as the percentage of its number of sequences in the total number of squences detected for the community. g, A maximum likelihood phylogenetic tree of keystone nodes in all networks. Green, red, and blue dots represent the taxa of keystone nodes that occurred in both warming and control networks, only under warming, and only in control, respectively. Branches are colored based on bacterial phyla identified using RDP classifier.

Source data

Extended Data Fig. 5 Temporal variations (that is, constancy) of network nodes and links.

a, Network node constancy. Each box shows the constancy distribution of all the nodes, averaged across experimental plots, present in the networks under warming (n = 603) or control (n = 601). Mann-Whitney U test results are shown. b, The number of overlapping nodes under warming and control among different numbers of networks (that is, orders). For example, for order=2, the overlapping nodes were between any two pairs of networks; for order=3, they were among any three networks. The nodes consistently present in all the time points are listed in Supplementary Table 3. c, The number of overlapping nodes among multiple networks from different gap times. The datapoints include orders 2 to 6. Linear regression results are shown. d, Unweighted network link constancy. Each box shows the constancy distribution of the links in the networks under warming (n = 4,661) or control (n = 3,526). Mann-Whitney U test results are shown here.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–10, text and references.

Reporting Summary

Supplementary Data 1

Supplementary Tables 1–9.

Source data

Source Data Fig. 1

Source data for overall network.

Source Data Fig. 2

Source data for correlations.

Source Data Fig. 3

Source data for stability.

Source Data Fig. 4

Source data for ecosystem processes and functioning.

Source Data Extended Data Fig. 1

Statistical source data for DCA, CCA and VPA.

Source Data Extended Data Fig. 2

Statistical source data for cohesion.

Source Data Extended Data Fig. 3

Source data for module preservation.

Source Data Extended Data Fig. 4

Source data for node types.

Source Data Extended Data Fig. 5

Source data for node overlap and constancy.

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Yuan, M.M., Guo, X., Wu, L. et al. Climate warming enhances microbial network complexity and stability. Nat. Clim. Chang. 11, 343–348 (2021). https://doi.org/10.1038/s41558-021-00989-9

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