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Reduction of microbial diversity in grassland soil is driven by long-term climate warming

Abstract

Anthropogenic climate change threatens ecosystem functioning. Soil biodiversity is essential for maintaining the health of terrestrial systems, but how climate change affects the richness and abundance of soil microbial communities remains unresolved. We examined the effects of warming, altered precipitation and annual biomass removal on grassland soil bacterial, fungal and protistan communities over 7 years to determine how these representative climate changes impact microbial biodiversity and ecosystem functioning. We show that experimental warming and the concomitant reductions in soil moisture play a predominant role in shaping microbial biodiversity by decreasing the richness of bacteria (9.6%), fungi (14.5%) and protists (7.5%). Our results also show positive associations between microbial biodiversity and ecosystem functional processes, such as gross primary productivity and microbial biomass. We conclude that the detrimental effects of biodiversity loss might be more severe in a warmer world.

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Fig. 1: Effects of experimental warming on soil microbial communities.
Fig. 2: Effects of experimental warming on different microbial taxa.
Fig. 3: Environmental drivers of microbial diversity.

Data availability

The DNA sequences of the 16S rRNA gene, 18S rRNA gene and ITS amplicons are deposited in the National Center for Biotechnology Information (NCBI) under project accession number PRJNA331185. Raw shotgun metagenomic sequences are deposited in the European Nucleotide Archive (http://www.ebi.ac.uk/ena) under study no. PRJNA533082. Silva 138.1 Ref NR database is available at https://www.arb-silva.de/documentation/release-138/. Protist Ribosomal Reference database (PR2) databases are available at https://github.com/pr2database/pr2database. The ASV table and ASV representative sequences, soil physical and chemical attributes, and plant biomass and richness are downloadable online at http://www.ou.edu/ieg/publications/datasets. Source data are provided with this paper.

Code availability

R scripts for statistical analyses are available on GitHub at https://github.com/Linwei-Wu/warming_soil_biodiversity.

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Acknowledgements

We thank the acting director of the KAEFS field site, M. Bomgraars, and the numerous former and current members of 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 Number DE-SC0004601 and DE-SC0010715, and the Office of the Vice President for Research at the University of Oklahoma to J.Z. X.G. and X.Z. were generously supported by the China Scholarship Council (CSC) to visit the University of Oklahoma. The data analyses performed by X.G. were also supported by the China Postdoctoral Science Foundation (2018M641327 and 2019T120101).

Author information

Authors and Affiliations

Authors

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 Linwei Wu, Y.Z., X.G., J.F., M.M.Y., J.K., Y.F., A.Z., D.N., J.M., S.J., S.H., Z.Y., Y.O. and Liyou Wu. Sampling collection, soil chemical and microbial characterization were carried out by M.M.Y., X.G., Linwei Wu, J.G., Z.G. and X.Z. Data analysis were done by Linwei Wu, Y.Z., X.G., S.L. and N.X. with 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., Linwei Wu, Y.Z. and X.G., with significant input from J.M.T., Y.Y. and X.L. Considering their contributions in terms of site management, data collection, analyses and/or integration, Linwei Wu, Y. Z. and X.G. are listed as co-first authors.

Corresponding author

Correspondence to Jizhong Zhou.

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The authors declare no competing interests.

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Nature Microbiology thanks Pankaj Trivedi, Kirsten Hofmockel and Noah Fierer for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 A schematic map of the field experimental treatments.

In the long-term climate change experiment, warming (+3 °C), half precipitation (−50% precipitation), and double precipitation (+100% precipitation) are primary factors, which are nested with clipping as the secondary factor (all the 24 southern subplots are under clipping treatment). Thus, this experiment has twelve single and combined treatments as follows: Control (N), Warming (W), Half precipitation (H), Double precipitation (D), Clipping (C), Warming & Half precipitation (WH), Warming & Double precipitation (WD), Warming & Clipping (WC), Half precipitation & Clipping (HC), Double precipitation & Clipping (DC), Warming & Half precipitation & Clipping (WHC), and Warming & Double precipitation & Clipping (WDC). Each of these treatments has four replicates in four different blocks. The site was established in July, 2009. Surface (0-15 cm) soil samples were collected annually from all plots at approximately the date of peak plant biomass in fall (September or October) from 2009 to 2016.

Extended Data Fig. 2 Effects of experimental treatments on soil and plant variables by linear mixed-effects models (LMMs).

a, Soil temperature; b, Soil moisture; c, Soil pH; d, Soil NO3N; e, Soil NH4-N; f, Total plant biomass; and g, Plant richness. Data are presented as mean values ± standard errors of the estimated effect sizes. Statistical significance is based on Wald type II χ² tests (n = 360 independent soil samples). Significant effects are denoted by asterisks: *** P < 0.001, ** P < 0.01, * P < 0.05. W: Warming; P: Precipitation level; C: Clipping. Since the precipitation level is considered as a continuous variable in the LMM (0.5 for half precipitation, 1 for normal and 2 for double precipitation), only one regression coefficient of precipitation treatment would be derived by the LMM. The effect size of half precipitation (as compared to ambient precipitation) can be derived by multiplying the regression coefficient by −0.5, while the effect size of double precipitation (as compared to ambient precipitation) can be derived by multiplying the regression coefficient by 1.

Source data

Extended Data Fig. 3 Sequencing depth among different treatments for the bacterial community (a), fungal community (b), and protistan community (c).

A total of 360 soil samples over 8 years were analyzed with 16S ribosomal RNA (rRNA) gene for bacteria and archaea, the internal transcribed spacer (ITS) between 5.8S and 28S rRNA genes for fungi, and the 18S rRNA gene for protists. For 18S sequences, only those annotated as protists were selected for the subsequent analyses. An average of 56,182 ± 27,613, 23,569 ± 16,323, and 11,146 ± 10,528 sequence reads were obtained for bacteria, fungi, and protists, respectively. There was no significant difference between treatments in the number of sequences (that is, sequencing depth) for the bacteria, fungal, and protistan communities except half precipitation (H), double precipitation (D), clipping (C), and double precipitation & clipping (DC) for protists (p = 0.002-0.021). Groups: Control (N), Warming (W), Half precipitation (H), Double precipitation (D), Clipping (C), Warming & Half precipitation (WH), Warming & Double precipitation (WD), Warming & Clipping (WC), Half precipitation & Clipping (HC), Double precipitation & Clipping (DC), Warming & Half precipitation & Clipping (WHC), and Warming & Double precipitation & Clipping (WDC). In the box plots, hinges show the 25, 50, and 75 percentiles. The upper whisker extends to the largest value no further than 1.5 * IQR from the upper hinge, where IQR is the inter-quartile range between the 25% and 75% quartiles; the lower whisker extends to the smallest value at most 1.5 * IQR from the lower hinge.

Source data

Extended Data Fig. 4 Rarefaction curves.

The number of ASVs with an increasing number of sequences (a, c, e) and accumulation curves of the number of ASVs with an increasing number of samples (b, d, f) for the bacterial community (a, b), fungal community (c, d), and protistan community (e, f). The observed number of ASVs with warming treatment was lower compared with all those without warming treatment except warming & double precipitation & clipping (WDC) versus double precipitation & clipping (DC) for fungi and warming & clipping (WC) versus clipping (C) for protists in (a, c, e) (Paired t test, p < 0.0001). The number of samples did not have a substantial influence on the differences between warming and non-warming control as shown in (b, d, f). After removing global singletons and resampling, the rarefaction curves approached asymptotes for all treatments, indicating that the sequencing depth was sufficient for assessing the effects of various climate change factors on the diversity of these soil microbial communities.

Source data

Extended Data Fig. 5 Yearly differences of bacterial (a), fungal (b), and protistan (c) richness between warmed and unwarmed samples.

Data are presented as mean values ± SEM of the mean differences (warmed -unwarmed). For each year, the treatment effects are tested with linear mixed-effects models, and the significant treatment effects (p < 0.05, Wald type II χ² tests, n = 48 soils each year) are listed in the table. W: Warming; P: Precipitation level; C: Clipping.

Source data

Extended Data Fig. 6 Effects of experimental warming across microbial groups based on linear mixed-effects models.

a, Effect sizes of experimental warming on the (rescaled) phylogenetic diversity of major microbial groups based on linear mixed-effects models. b, Effect sizes of experimental warming on the (rescaled) relative abundance of major microbial groups based on linear mixed-effects models. Data are presented as mean values ± standard errors of the estimated effect sizes. Statistical significance is based on Wald type II χ² tests (n = 360 independent soil samples), which is denoted by asterisks: *** p < 0.001, ** p < 0.01, * p < 0.05.

Source data

Extended Data Fig. 7 Effects of experimental warming on sporulation families or genes of Firmicutes and Actinobacteria.

a, Number of Firmicutes families whose relative abundances increased, decreased or unchanged under warming. b, Number of Actinobacteria families whose relative abundances increased, decreased or unchanged under warming. Significant changes (p < 0.05) are based on Wald type II χ² tests (n = 360 independent soil samples) of the warming effects in linear mixed-effects models (relative abundance ~ warming × precipitation level × clipping + (1|Block) + (1 | year)). ‘Yspore’, ‘Nspore’, and ‘NAspore’ refer to known spore-formers, known non-spore-formers, and information not available on the ability to form spores, respectively. The spore-forming capability or different families within Firmicutes and Actinobacteria were mainly based on databases and literature. c, Effect sizes of experimental warming on the (rescaled) relative abundance of major sporulation genes in Firmicutes (spo0A gene) and Actinobacteria (bldD gene) based on linear mixed-effects models. The sporulation genes were retrieved from shotgun sequencing data. Data are presented as mean values ± standard errors of the estimated effect sizes. Statistical significance is based on Wald type II χ² tests (n = 64 independent soil metagenome samples).

Source data

Extended Data Fig. 8 Hypothesized conceptual models on the relationships between experimental treatments, environmental variables, and microbial diversity.

a, Bacteria and protists; b, Fungi. The environmental variables were selected based on their biological inference and collinearity, as detailed in the Methods and Supplementary Tables 37. We hypothesized that each experimental treatment would influence each environmental variable, and the environmental variables would all influence microbial diversity. We also assumed that microbial diversity would influence plant richness and biomass. In addition, we assumed interactions between bacteria and protists since there could be prey-predator relationships between them. In fact, the consumers, which include potential predators of bacteria, account for 84.6% of the total protist abundance. The richness of protistan consumers also highly correlated with the total protistan richness (Pearson’s r = 0.98). Protists was not included in the fungal model because the relative abundance of fungivorous protists is very low.

Extended Data Fig. 9 The structural equation model (SEM) showing the relationships among treatments, soil and plant variables, and fungal richness.

Blue and red arrows indicate positive and negative relationships, respectively. Solid or dashed lines indicate significant (p < 0.05) or nonsignificant relationships. Numbers near the pathway arrow indicate the standard path coefficients. R2 represents the proportion of variance explained for every dependent variable. χ2 = 28.70, df = 23, p = 0.19 (large p value indicates that the predicted model and observed data are equal, that is, good model fitting), CFI = 0.974. n = 48 biologically independent plots.

Source data

Extended Data Fig. 10 Effects of experimental warming on different ecosystem functions.

Data are presented as mean values ± standard errors of the estimated effect sizes. Statistical significance is based on Wald type II χ² tests (n = 360), which is indicated in the plot. GPP: gross primary productivity; ER: ecosystem respiration; NEE: net ecosystem exchange; Rh: heterotrophic respiration; Rs: soil total respiration.

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Supplementary information

Supplementary Information

Supplementary Fig. 1, Tables 1–7 and Notes A–F.

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Wu, L., Zhang, Y., Guo, X. et al. Reduction of microbial diversity in grassland soil is driven by long-term climate warming. Nat Microbiol 7, 1054–1062 (2022). https://doi.org/10.1038/s41564-022-01147-3

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