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Meta-analysis reveals the effects of microbial inoculants on the biomass and diversity of soil microbial communities

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Abstract

Microbial inoculation involves transplanting microorganisms from their natural habitat to new plants or soils to improve plant performance, and it is being increasingly used in agriculture and ecological restoration. However, microbial inoculants can invade and alter the composition of native microbial communities; thus, a comprehensive analysis is urgently needed to understand the overall impact of microbial inoculants on the biomass, diversity, structure and network complexity of native communities. Here we provide a meta-analysis of 335 studies revealing a positive effect of microbial inoculants on soil microbial biomass. This positive effect was weakened by environmental stress and enhanced by the use of fertilizers and native inoculants. Although microbial inoculants did not alter microbial diversity, they induced major changes in the structure and bacterial composition of soil microbial communities, reducing the complexity of bacterial networks and increasing network stability. Finally, higher initial levels of soil nutrients amplified the positive impact of microbial inoculants on fungal biomass, actinobacterial biomass, microbial biomass carbon and microbial biomass nitrogen. Together, our results highlight the positive effects of microbial inoculants on soil microbial biomass, emphasizing the benefits of native inoculants and the important regulatory roles of soil nutrient levels and environmental stress.

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Fig. 1: The effects of microbial inoculants on soil microbial attributes.
Fig. 2: The effects of microbial inoculants on soil microbial biomass.
Fig. 3: The effects of microbial inoculants on soil microbial alpha diversity.
Fig. 4: The effects of microbial inoculants on soil microbial community structure and beta diversity.
Fig. 5: Global co-occurrence networks of bacterial ASVs in the CK and microbial inoculant treatments.

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Data availability

The sequences used in this study consist of publicly available published data and can be downloaded using the provided accession numbers. All accession numbers, Supplementary Dataset and other relevant data featured in this Article are available via GitHub at https://github.com/aijingjing1314/Microbial-inoculants_Meta-analysis. Source data are provided with this paper.

Code availability

The code used in this study is available via GitHub at https://github.com/aijingjing1314/Microbial-inoculants_Meta-analysis.

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Acknowledgements

We sincerely thank all the researchers whose valuable data were included in this global synthesis. We would like to express our gratitude to Y. Wu, who contributed to the project development through valuable discussions. C.L. is grateful for the partial financial support from the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX21_0915) and the China Scholarship Council (202108320300). J.Z. acknowledges the funding support from Jiangsu Science and Technology Plan Project (BE2022420), the Innovation and Promotion of Forestry Science and Technology Program of Jiangsu Province (LYKJ[2021]30), the Scientific Research Project of Baishanzu National Park (2021ZDLY01) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

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Author notes

  1. Xinli Chen

    Present address: State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, China

Authors and Affiliations

  1. Co-Innovation Center for Sustainable Forestry in Southern China, Jiangsu Province Key Laboratory of Soil and Water Conservation and Ecological Restoration, Nanjing Forestry University, Nanjing, China

    Chong Li, Zhaohui Jia, Shilin Ma, Xin Liu & Jinchi Zhang

  2. Institute of Plant Ecology, Justus-Liebig University Giessen, Giessen, Germany

    Chong Li, Uwe Grüters & Christoph Müller

  3. Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada

    Xinli Chen

  4. Department of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, OK, USA

    Lu Zhai

  5. Department of Integrative Biology, Oklahoma State University, Stillwater, OK, USA

    Bo Zhang

  6. Yangzhou China Grand Canal Museum, Yangzhou, China

    Jing Qian

  7. School of Biology and Environmental Science and Earth Institute, University College Dublin, Dublin, Ireland

    Christoph Müller

  8. Liebig Centre for Agroecology and Climate Impact Research, Justus-Liebig University, Giessen, Germany

    Christoph Müller

Authors
  1. Chong Li
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  2. Xinli Chen
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  3. Zhaohui Jia
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  4. Lu Zhai
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  5. Bo Zhang
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  6. Uwe Grüters
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  7. Shilin Ma
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  8. Jing Qian
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  9. Xin Liu
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  10. Jinchi Zhang
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  11. Christoph Müller
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Contributions

C.L., Z.J., B.Z., X.L. and J.Z. conceived the study. C.L., S.M. and J.Q. collected and organized the data. C.L., X.C. and L.Z. analysed the data. C.L. wrote the first draft of the paper. X.C., Z.J., L.Z., B.Z., U.G., X.L., J.Z. and C.M. reviewed the paper before submission. The authors have approved the final paper for publication.

Corresponding authors

Correspondence to Xin Liu or Jinchi Zhang.

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The authors declare no competing interests.

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Nature Ecology & Evolution thanks Elly Morriën, Xavier Roux and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 The effects of microbial inoculants on soil microbial biomass.

a, bacterial biomass; b, fungal biomass; c, actinobacterial biomass; d, diazotrophic biomass. Bars around the means denote 95% confidence intervals (CIs). Mean values < 0 indicate a higher value in control treatment (yellow dots), while mean values > 0 indicate a higher value in microbial inoculant treatment (blue dots). Bacteria, fungi, and AMF in front of each subgroup name represent bacterial inoculants, fungal inoculants, and arbuscular mycorrhizal fungi (AMF) inoculants, respectively. The number of observations is beside each attribute. The between-group heterogeneity (Qbetween) statistic is computed using the one-sided chi-square test. A significance level is set at Prandom < 0.05 to determine the significance of Qbetween. Differences among subgroups are deemed significant when their CIs do not overlap. Source data are provided as a Source Data file.

Source data

Extended Data Fig. 2 Effects of microbial inoculant on soil microbial biomass associated with soil background physicochemical properties.

Points signify the values predicted by the partial regressions of soil background physicochemical properties. Black lines represent the average responses with their 95% confidence intervals (CIs) shaded in grey. n represents the number of observations. The one-sided F-test is used to calculate P values.

Extended Data Fig. 3 The effects of microbial inoculants on soil microbial alpha diversity.

a, Bacterial Shannon diversity; b, bacterial richness diversity; c, fungal Shannon diversity; d, fungal richness diversity. Bars around the means denote 95% confidence intervals (CIs). Bacteria, fungi, and AMF in front of each subgroup name represent bacterial inoculants, fungal inoculants, and arbuscular mycorrhizal fungi (AMF) inoculants, respectively. The number of observations is beside each attribute. The between-group heterogeneity (Qbetween) statistic is computed using the one-sided chi-square test. A significance level is set at Prandom < 0.05 to determine the significance of Qbetween. Differences among subgroups are deemed significant when their CIs do not overlap. Source data are provided as a Source Data file.

Source data

Extended Data Fig. 4 The effects of microbial inoculants on soil bacterial alpha diversity and community structure based on reanalysis of amplicon data.

a, bacterial alpha diversity, including Shannon, Pielou, ACE, Chao, and Richness diversity. Error bars on the columns represent standard errors (SD). Statistical comparisons are assessed through two-tailed Wilcoxon’s rank sum tests. P values are adjusted using the Benjamini–Hochberg false discovery rate (FDR) correction. All dots signify the change in response ratio between the control and microbial inoculant bacterial diversity at 95% confidence intervals (CIs). The number of observations is provided beside each attribute. b, Principal Coordinate Analysis (PCoA) plots depict the Bray-Curtis distance of bacterial communities in CK and microbial inoculant treatments (CK n = 453 vs. microbial inoculant n = 1076) c, Three non-parametric multivariate analyses, including non-parametric multivariate analysis of variance (Adonis), analysis of similarity (ANOSIM), and multi-response permutation procedure (MRPP), consistently support the significant alteration of bacterial community structure by microbial inoculants. P values are adjusted using the Benjamini–Hochberg method with sequentially modified Bonferroni correction. Source data are provided as a Source Data file.

Source data

Extended Data Fig. 5 The effects of microbial inoculants on soil microbial community.

a, bacterial community structure; b, bacterial beta diversity; c, fungal community structure; d, fungal beta diversity. Bars around the means denote 95% confidence intervals (CIs). Mean values < 0 indicate a higher value in control treatment (yellow dots), while mean values > 0 indicate a higher value in microbial inoculant treatment (blue dots). Bacteria, fungi, and AMF in front of each subgroup name represent bacterial inoculants, fungal inoculants, and arbuscular mycorrhizal fungi (AMF) inoculants, respectively. RRStructure < 0 indicates that microbial inoculant has no effect on microbial community structure, and a greater positive value of RRStructure indicates a greater magnitude of change in the community structure. The number of observations is beside each attribute. The between-group heterogeneity (Qbetween) statistic is computed using the one-sided chi-square test. A significance level is set at Prandom < 0.05 to determine the significance of Qbetween. Differences among subgroups are deemed significant when their CIs do not overlap. Source data are provided as a Source Data file.

Source data

Extended Data Fig. 6 Effects of microbial inoculant on soil microbial diversity associated with soil background physicochemical properties.

Points signify the values predicted by the partial regressions of soil background physicochemical properties. n represents the number of observations. The one-sided F-test is used to calculate P values.

Extended Data Fig. 7 Phylogenetic tree showing the top 200 bacterial amplicon sequences variants (ASVs) with the highest cumulative relative abundance.

The color of the inner ring represents the taxonomy at the phylum level, while the name of the inner ring corresponds to the genus. Medium ring 1 illustrates the relative abundance of 200 ASVs in the CK treatment, and Medium ring 2 depicts the relative abundance of 200 ASVs in the microbial inoculant treatment. Medium ring 3 displays the relative abundance of ASVs across different treatments, with yellow indicating higher enrichment in the CK treatment and blue indicating higher enrichment in the microbial inoculant treatment. The symbol * represents a significance level of P < 0.05, determined through two-tailed Wilcoxon’s rank sum tests. P values are adjusted using the Benjamini–Hochberg false discovery rate (FDR) correction. The outer ring represents taxonomy at the class level.

Source data

Extended Data Fig. 8 The composition of bacterial communities under CK and microbial inoculant treatments at the phylum level.

a, the composition of bacterial communities under CK and microbial inoculant treatments at the phylum level; b, differences at the phylum level caused by microbial inoculants; c, differences at the phylum level caused by bacterial inoculants; d, differences at the phylum level caused by AMF inoculants; e, differences at the phylum level caused by fungal inoculants; f, differences at the phylum level caused by Mix inoculants. ▲ represents the increase of taxa under microbial inoculant treatments; ▼represents the decrease of taxa under microbial inoculant treatments. Statistical comparisons are assessed through two-tailed Wilcoxon’s rank sum tests. P values are adjusted using the Benjamini–Hochberg false discovery rate (FDR) correction.

Source data

Extended Data Fig. 9 The composition of bacterial communities under CK and microbial inoculant treatments at the class level.

a, the composition of bacterial communities under CK and microbial inoculant treatments at the class level; b, differences at the class level caused by microbial inoculants; c, differences at the class level caused by bacterial inoculants; d, differences at the class level caused by AMF inoculants; e, differences at the class level caused by fungal inoculants; f, differences at the class level caused by Mix inoculants. ▲ represents the increase of taxa under microbial inoculant treatments; ▼represents the decrease of taxa under microbial inoculant treatments. Statistical comparisons are assessed through two-tailed Wilcoxon’s rank sum tests. P values are adjusted using the Benjamini–Hochberg false discovery rate (FDR) correction.

Source data

Extended Data Fig. 10 The experimental locations in this meta-analysis.

a, the experimental locations of 335 publications included in this meta-analysis (microbial attributes); b, the experimental locations of 48 publications included in this meta-analysis (reanalysis of amplicon data based on upper microbial attributes database).

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–19 and Tables 1–3.

Reporting Summary

Peer Review File

Source data

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Li, C., Chen, X., Jia, Z. et al. Meta-analysis reveals the effects of microbial inoculants on the biomass and diversity of soil microbial communities. Nat Ecol Evol 8, 1270–1284 (2024). https://doi.org/10.1038/s41559-024-02437-1

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