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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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.
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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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).
The authors declare no competing interests.
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.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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
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
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
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
Source data for overall network.
Source data for correlations.
Source data for stability.
Source data for ecosystem processes and functioning.
Statistical source data for DCA, CCA and VPA.
Statistical source data for cohesion.
Source data for module preservation.
Source data for node types.
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. (2021). https://doi.org/10.1038/s41558-021-00989-9