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Tree diversity increases decadal forest soil carbon and nitrogen accrual

Nature (2023)Cite this article

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Abstract

Increasing soil carbon and nitrogen storage can help mitigate climate change and sustain soil fertility1,2. A large number of biodiversity-manipulation experiments collectively suggest that high plant diversity increases soil carbon and nitrogen stocks3,4. It remains debated, however, whether such conclusions hold in natural ecosystems5,6,7,8,9,10,11,12. Here we analyse Canada’s National Forest Inventory (NFI) database with the help of structural equation modelling (SEM) to explore the relationship between tree diversity and soil carbon and nitrogen accumulation in natural forests. We find that greater tree diversity is associated with higher soil carbon and nitrogen accumulation, validating inferences from biodiversity-manipulation experiments. Specifically, on a decadal scale, increasing species evenness from its minimum to maximum value increases soil carbon and nitrogen in the organic horizon by 30% and 42%, whereas increasing functional diversity enhances soil carbon and nitrogen in the mineral horizon by 32% and 50%, respectively. Our results highlight that conserving and promoting functionally diverse forests could promote soil carbon and nitrogen storage, enhancing both carbon sink capacity and soil nitrogen fertility.

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Fig. 1: Suggested causal pathways of direct and indirect effects of tree diversity, identity, stand age, climate and background soil condition on tree productivity and changes in soil C and N stocks.
Fig. 2: Structural equation model showing the effects of tree diversity and climatic and soil conditions on decadal changes in soil C and N stocks in the organic soil horizon (N = 361).
Fig. 3: Structural equation model showing the effects of tree diversity and climatic and soil conditions on decadal changes in soil C and N stocks in the mineral soil horizon (N = 245).

Data availability

The source data underlying Figs. 2 and 3 are provided as source data files and all data used in this study are archived in Figshare (https://doi.org/10.6084/m9.figshare.20988187.v2). Source data are provided with this paper.

Code availability

The R scripts needed to reproduce the analysis are archived in Figshare (https://doi.org/10.6084/m9.figshare.20988187.v2).

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Acknowledgements

We thank Natural Resources Canada, Canadian Forest Service for sharing data from the National Forest Inventory database and the Discovery Grants programme (grant no. RGPIN-2018-05700 to S.X.C.) of the Natural Sciences and Engineering Research Council of Canada (NSERC) for supporting this research. H.Y.H.C. acknowledges the support from NSERC (RGPIN-2019–05109 and STPGP428641) and the Canada Foundation for Innovation and Ontario Research Fund (CFI36014). X.C. wishes to thank NSERC and the Government of Canada for a Banting Postdoctoral Fellowship and P.B.R. acknowledges support by the U.S. National Science Foundation Biological Integration Institutes grant no. NSF-DBI-2021898.

Author information

Authors and Affiliations

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

    Xinli Chen & Scott X. Chang

  2. Institute for Global Change Biology, School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA

    Xinli Chen & Peter B. Reich

  3. Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, New Brunswick, Canada

    Anthony R. Taylor

  4. Department of Forest Resources, University of Minnesota, St. Paul, MN, USA

    Peter B. Reich

  5. Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia

    Peter B. Reich

  6. Department of Ecosystem Studies, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan

    Masumi Hisano

  7. Faculty of Natural Resources Management, Lakehead University, Thunder Bay, Ontario, Canada

    Han Y. H. Chen

  8. State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, China

    Scott X. Chang

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  1. Xinli Chen
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Contributions

X.C., P.B.R., H.Y.H.C. and S.X.C. were responsible for the conception and design of the project. X.C. and A.R.T. compiled data. X.C. analysed the data and wrote the first draft of the manuscript. X.C., A.R.T., P.B.R., M.H., H.Y.H.C. and S.X.C. contributed to reviewing and editing. All authors approved the final manuscript.

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Correspondence to Han Y. H. Chen or Scott X. Chang.

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Extended data figures and tables

Extended Data Fig. 1 The result of PCA showing permanent sampling plots and each functional identity.

CWM, community-weighted mean of trait value; CWMNmass, CWM of nitrogen content per leaf mass; CWMPmass, CWM of phosphorus content per leaf mass; CWMSLA, CWM of specific leaf area; CWMWD, CWM of wood density; CWMMH, CWM of maximum height. The first axis (PC1) represents traits associated with acquisitive versus conservative strategies, whereas the second axis (PC2) refers to traits associated with wood density (WD) versus the maximum height (MH) of trees.

Extended Data Fig. 2 The distributions of 406 ground plots from the Canadian NFI with climate and plant community characteristics information.

a, Long-term averages of mean annual temperature (MAT). b, Long-term averages of mean annual climate moisture index (CMI). c, Species richness. d, Species evenness. e, Functional diversity (FDis). f,g, CWM of trait value (CWMPC1, CWMPC2). h, Schematic diagram of the NFI ground plot.

Extended Data Fig. 3 The bivariate relationships between decadal changes in soil C and N stocks in the organic horizon (ΔSoil COrganic stock and ΔSoil NOrganic stock) and explanatory variables (n = 361) for all proposed causal paths in the structural equation model.

MAT, the long-term average of mean annual temperature; CMI, the long-term average of mean annual climate moisture index; FDis, functional diversity; Horizon thickness, initial organic horizon thickness; ΔThickness, decadal organic horizon soil thickness change. Higher CWMPC1 values indicate traits associated with acquisitive strategy, whereas lower values indicate conservative strategy. Higher CWMPC2 values indicate traits associated with lower tree maximum height (see Extended Data Fig. 1).

Extended Data Fig. 4 The bivariate relationships between decadal changes in soil thickness in the organic horizon (ΔThickness) and explanatory variables (n = 361) for all proposed causal paths in the structural equation model.

All fitted regressions are significant at P < 0.05. MAT, the long-term average of mean annual temperature; CMI, the long-term average of mean annual climate moisture index; FDis, functional diversity; Horizon thickness, initial organic horizon thickness. Higher CWMPC1 values indicate traits associated with acquisitive strategy, whereas lower values indicate conservative strategy. Higher CWMPC2 values indicate traits associated with lower tree maximum height (see Extended Data Fig. 1).

Extended Data Fig. 5 The bivariate relationships between decadal changes in soil C and N stocks in the mineral horizon (ΔSoil CMineral stock and ΔSoil NMineral stock) and explanatory variables (n = 245) for all proposed causal paths in the structural equation model.

All fitted regressions are significant at P < 0.05. MAT, the long-term average of mean annual temperature; CMI, the long-term average of mean annual climate moisture index; FDis, functional diversity. Higher CWMPC1 values indicate traits associated with acquisitive strategy, whereas lower values indicate conservative strategy. Higher CWMPC2 values indicate traits associated with lower tree maximum height (see Extended Data Fig. 1).

Extended Data Fig. 6 The bivariate relationships between decadal aboveground primary productivity and explanatory variables for all proposed causal paths in the structural equation model.

All fitted regressions are significant at P < 0.05. MAT, the long-term average of mean annual temperature; CMI, the long-term average of mean annual climate moisture index; FDis, functional diversity. Higher CWMPC1 values indicate traits associated with acquisitive strategy, whereas lower values indicate conservative strategy. Higher CWMPC2 values indicate traits associated with lower tree maximum height (see Extended Data Fig. 1).

Extended Data Fig. 7 The bivariate relationships between decadal relative changes in soil C and N stocks in the mineral horizon and mineral horizon soil C and N content, respectively.

All fitted regressions are significant at P < 0.05. Dotted vertical line represents the soil C or N content when relative changes in soil C and N stocks began to shift from positive to negative.

Extended Data Fig. 8 Structural equation models showing the effects of tree diversity, alternative climatic factors and soil conditions on decadal changes in soil C and N stocks.

a,b, Path diagrams of factors influencing changes in soil C and N stocks in the organic horizon (n = 361). b,d Path diagrams of factors influencing changes in soil C and N stocks in the mineral horizon (n = 245). Numbers adjacent to arrows are standardized path coefficients, analogous to relative regression weights. Solid and dashed arrows represent positive and negative relationships, respectively. Different colours represent different types of explanatory variable (see Fig. 1). Only significant pathways are shown (P < 0.05). The goodness-of-fit statistics for panels a–d are: GFI = 0.988, SRMR = 0.032, P = 0.249; GFI = 0.991, SRMR = 0.029, P = 0.477; GFI = 0.987, SRMR = 0.033, P = 0.367; and GFI = 0.986, SRMR = 0.038, P = 0.391, respectively, indicating close model-data fit. ΔSoil COrganic and ΔSoil NOrganic represent decadal changes in soil C and N stocks of the organic soil horizons, respectively. ΔSoil CMineral and ΔSoil NMineral represent decadal changes in soil C and N stocks of the mineral soil horizons, respectively. GDD, mean annual growing degree-days; AGP, mean annual precipitation at the growing season; FDis, functional diversity; ΔThickness, decadal changes in soil organic horizon thickness; CWMPC2, community-weighted mean of trait values; Horizon thickness, initial organic horizon thickness. Higher CWMPC2 values indicate traits associated with lower tree maximum height (see Extended Data Fig. 1).

Extended Data Table 1 Summary statistics (mean, s.d. and range) of the permanent sample plots across Canada (2002–2018)
Full size table
Extended Data Table 2 Functional trait values of major tree genus (>5% of the total basal area across all plots during the entire census) occurred from all the provinces
Full size table

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Chen, X., Taylor, A.R., Reich, P.B. et al. Tree diversity increases decadal forest soil carbon and nitrogen accrual. Nature (2023). https://doi.org/10.1038/s41586-023-05941-9

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