Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Tree diversity increases decadal forest soil carbon and nitrogen accrual

A Publisher Correction to this article was published on 24 July 2023

This article has been updated

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

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).

Similar content being viewed by others

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).

Change history

References

  1. Lal, R. Soil carbon sequestration impacts on global climate change and food security. Science 304, 1623–1627 (2004).

    Article  ADS  CAS  PubMed  Google Scholar 

  2. Vitousek, P. M. & Howarth, R. W. Nitrogen limitation on land and in the sea: how can it occur?. Biogeochemistry 13, 87–115 (1991).

    Article  Google Scholar 

  3. Chen, X. L., Chen, H. Y. H., Searle, E. B., Chen, C. & Reich, P. B. Negative to positive shifts in diversity effects on soil nitrogen over time. Nat. Sustain. 4, 225–234 (2021).

    Article  Google Scholar 

  4. Chen, X. et al. Effects of plant diversity on soil carbon in diverse ecosystems: a global meta-analysis. Biol. Rev. 95, 167–183 (2020).

    Article  PubMed  Google Scholar 

  5. Chen, S. P. et al. Plant diversity enhances productivity and soil carbon storage. Proc. Natl Acad. Sci. USA 115, 4027–4032 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  6. Chen, X., Hisano, M., Taylor, A. R. & Chen, H. Y. H. The effects of functional diversity and identity (acquisitive versus conservative strategies) on soil carbon stocks are dependent on environmental contexts. For. Ecol. Manag. 503, 119820 (2022).

    Article  Google Scholar 

  7. Dawud, S. M. et al. Is tree species diversity or species identity the more important driver of soil carbon stocks, C/N ratio, and pH? Ecosystems 19, 645–660 (2016).

    Article  CAS  Google Scholar 

  8. Conti, G. & Diaz, S. Plant functional diversity and carbon storage – an empirical test in semi-arid forest ecosystems. J. Ecol. 101, 18–28 (2013).

    Article  CAS  Google Scholar 

  9. van der Plas, F. Biodiversity and ecosystem functioning in naturally assembled communities. Biol. Rev. 94, 1220–1245 (2019).

    PubMed  Google Scholar 

  10. Gamfeldt, L. et al. Higher levels of multiple ecosystem services are found in forests with more tree species. Nat. Commun. 4, 1340 (2013).

    Article  ADS  PubMed  Google Scholar 

  11. Dawud, S. M. et al. Tree species functional group is a more important driver of soil properties than tree species diversity across major European forest types. Funct. Ecol. 31, 1153–1162 (2017).

    Article  Google Scholar 

  12. Ratcliffe, S. et al. Biodiversity and ecosystem functioning relations in European forests depend on environmental context. Ecol. Lett. 20, 1414–1426 (2017).

    Article  PubMed  Google Scholar 

  13. Carvalhais, N. et al. Global covariation of carbon turnover times with climate in terrestrial ecosystems. Nature 514, 213–217 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  14. Lange, M. et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat. Commun. 6, 6707 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  15. Mao, Z. K. et al. Tree mycorrhizal associations mediate soil fertility effects on forest community structure in a temperate forest. New Phytol. 223, 475–486 (2019).

    Article  CAS  PubMed  Google Scholar 

  16. Zhang, Y., Chen, H. Y. H. & Taylor, A. R. Positive species diversity and above-ground biomass relationships are ubiquitous across forest strata despite interference from overstorey trees. Funct. Ecol. 31, 419–426 (2017).

    Article  Google Scholar 

  17. Post, W. M., Pastor, J., Zinke, P. J. & Stangenberger, A. G. Global patterns of soil nitrogen storage. Nature 317, 613–616 (1985).

    Article  ADS  Google Scholar 

  18. Jing, X. et al. Above- and below-ground complementarity rather than selection drive tree diversity–productivity relationships in European forests. Funct. Ecol. 35, 1756–1767 (2021).

    Article  CAS  Google Scholar 

  19. Jucker, T., Bouriaud, O., Avacaritei, D. & Coomes, D. A. Stabilizing effects of diversity on aboveground wood production in forest ecosystems: linking patterns and processes. Ecol. Lett. 17, 1560–1569 (2014).

    Article  PubMed  Google Scholar 

  20. Jucker, T. et al. Climate modulates the effects of tree diversity on forest productivity. J. Ecol. 104, 388–398 (2016).

    Article  Google Scholar 

  21. van der Voort, T. S. et al. Variability in 14C contents of soil organic matter at the plot and regional scale across climatic and geologic gradients. Biogeosciences 13, 3427–3439 (2016).

    Article  ADS  Google Scholar 

  22. Grace, J. B. et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature 529, 390–393 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

  23. Shovon, T. A., Kang, S., Scherer-Lorenzen, M. & Nock, C. A. Changes in the direction of the diversity–productivity relationship over 15 years of stand development in a planted temperate forest. J. Ecol. 110, 1125–1137 (2022).

    Article  Google Scholar 

  24. De Deyn, G. B., Cornelissen, J. H. & Bardgett, R. D. Plant functional traits and soil carbon sequestration in contrasting biomes. Ecol. Lett. 11, 516–531 (2008).

    Article  PubMed  Google Scholar 

  25. Paquette, A. & Messier, C. The effect of biodiversity on tree productivity: from temperate to boreal forests. Glob. Ecol. Biogeogr. 20, 170–180 (2011).

    Article  Google Scholar 

  26. Weedon, J. T. et al. Global meta-analysis of wood decomposition rates: a role for trait variation among tree species? Ecol. Lett. 12, 45–56 (2009).

    Article  PubMed  Google Scholar 

  27. Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).

    Article  PubMed  Google Scholar 

  28. Adair, E. C., Hooper, D. U., Paquette, A. & Hungate, B. A. Ecosystem context illuminates conflicting roles of plant diversity in carbon storage. Ecol. Lett. 21, 1604–1619 (2018).

    Article  Google Scholar 

  29. Hillebrand, H., Bennett, D. M. & Cadotte, M. W. Consequences of dominance: a review of evenness effects on local and regional ecosystem processes. Ecology 89, 1510–1520 (2008).

    Article  PubMed  Google Scholar 

  30. Jackson, R. B. et al. The ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls. Annu. Rev. Ecol. Evol. Syst. 48, 419–445 (2017).

    Article  Google Scholar 

  31. Chen, X. & Chen, H. Y. H. Global effects of plant litter alterations on soil CO2 to the atmosphere. Glob. Change Biol. 24, 3462–3471 (2018).

    Article  ADS  Google Scholar 

  32. Pietsch, K. A. et al. Global relationship of wood and leaf litter decomposability: the role of functional traits within and across plant organs. Glob. Ecol. Biogeogr. 23, 1046–1057 (2014).

    Article  Google Scholar 

  33. Rosell, J. A., Gleason, S., Mendez-Alonzo, R., Chang, Y. & Westoby, M. Bark functional ecology: evidence for tradeoffs, functional coordination, and environment producing bark diversity. New Phytol. 201, 486–497 (2014).

    Article  PubMed  Google Scholar 

  34. Kahl, T. et al. Wood decay rates of 13 temperate tree species in relation to wood properties, enzyme activities and organismic diversities. For. Ecol. Manag. 391, 86–95 (2017).

    Article  Google Scholar 

  35. Giardina, F. et al. Tall Amazonian forests are less sensitive to precipitation variability. Nat. Geosci. 11, 405–409 (2018).

    Article  ADS  CAS  Google Scholar 

  36. Park, J. H. & Matzner, E. Controls on the release of dissolved organic carbon and nitrogen from a deciduous forest floor investigated by manipulations of aboveground litter inputs and water flux. Biogeochemistry 66, 265–286 (2003).

    Article  CAS  Google Scholar 

  37. Olson, J. S. Energy storage and the balance of producers and decomposers in ecological systems. Ecology 44, 322–331 (1963).

    Article  Google Scholar 

  38. Hassink, J. The capacity of soils to preserve organic C and N by their association with clay and silt particles. Plant Soil 191, 77–87 (1997).

    Article  CAS  Google Scholar 

  39. Clemmensen, K. E. et al. Roots and associated fungi drive long-term carbon sequestration in boreal forest. Science 339, 1615–1618 (2013).

    Article  ADS  CAS  PubMed  Google Scholar 

  40. Preston, C. M., Bhatti, J. S., Flanagan, L. B. & Norris, C. Stocks, chemistry, and sensitivity to climate change of dead organic matter along the Canadian boreal forest transect case study. Clim. Change 74, 223–251 (2006).

    Article  ADS  CAS  Google Scholar 

  41. Michalzik, B., Kalbitz, K., Park, J. H., Solinger, S. & Matzner, E. Fluxes and concentrations of dissolved organic carbon and nitrogen – a synthesis for temperate forests. Biogeochemistry 52, 173–205 (2001).

    Article  Google Scholar 

  42. Pastore, M. A., Hobbie, S. E. & Reich, P. B. Sensitivity of grassland carbon pools to plant diversity, elevated CO2, and soil nitrogen addition over 19 years. Proc. Natl Acad. Sci. USA 118, e2016965118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Yemshanov, D., McKenney, D. W. & Pedlar, J. H. Mapping forest composition from the Canadian National Forest Inventory and land cover classification maps. Environ. Monit. Assess. 184, 4655–4669 (2012).

    Article  PubMed  Google Scholar 

  44. Baeten, L. et al. Identifying the tree species compositions that maximize ecosystem functioning in European forests. J. Appl. Ecol. 56, 733–744 (2019).

    Article  Google Scholar 

  45. Moles, A. T. et al. Global patterns in plant height. J. Ecol. 97, 923–932 (2009).

    Article  Google Scholar 

  46. Dyk, A. Forest composition across Canada. Canadian Forest Service https://cfs.nrcan.gc.ca/publications?id=35724 (2014).

  47. Butler, E. E. et al. Increasing functional diversity in a global land surface model illustrates uncertainties related to parameter simplification. J. Geophys. Res. Biogeosci. 127, e2021JG006606 (2022).

    Article  ADS  Google Scholar 

  48. Mason, R. E. et al. Evidence, causes, and consequences of declining nitrogen availability in terrestrial ecosystems. Science 376, eabh3767 (2022).

    Article  CAS  PubMed  Google Scholar 

  49. Canadian Forest Inventory Committee. Canada’s National Forest Inventory – Design Overview. Version 3.2 (Canadian Forest Service, 2004).

  50. Lambert, M. C., Ung, C. H. & Raulier, F. Canadian national tree aboveground biomass equations. Can. J. For. Res. 35, 1996–2018 (2005).

    Article  Google Scholar 

  51. Chen, H. Y. H. & Klinka, K. Aboveground productivity of western hemlock and western redcedar mixed-species stands in southern coastal British Columbia. For. Ecol. Manag. 184, 55–64 (2003).

    Article  Google Scholar 

  52. British Columbia Ministry of Forests and Range and British Columbia Ministry of Environment. Field Manual for Describing Terrestrial Ecosystems 2nd edn (Province of British Columbia, 2010).

  53. Gillis, M. D., Omule, A. Y. & Brierley, T. Monitoring Canada’s forests: the National Forest Inventory. For. Chron. 81, 214–221 (2005).

    Article  Google Scholar 

  54. Pearson, T. R. H., Brown, S. L. & Birdsey, R. A. Measurement Guidelines for the Sequestration of Forest Carbon (U.S. Department of Agriculture, Forest Service, Northern Research Station, 2007).

  55. Pielou, E. C. An Introduction to Mathematical Ecology (Wiley, 1969).

  56. Reich, P. B. The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).

    Article  Google Scholar 

  57. Kunstler, G. et al. Plant functional traits have globally consistent effects on competition. Nature 529, 204–207 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

  58. Hisano, M. & Chen, H. Y. H. Spatial variation in climate modifies effects of functional diversity on biomass dynamics in natural forests across Canada. Glob. Ecol. Biogeogr. 29, 682–695 (2020).

    Article  Google Scholar 

  59. Reich, P. B., Walters, M. B. & Ellsworth, D. S. From tropics to tundra: global convergence in plant functioning. Proc. Natl Acad. Sci. USA 94, 13730–13734 (1997).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  60. Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).

    Article  ADS  CAS  PubMed  Google Scholar 

  61. Kattge, J. et al. TRY – a global database of plant traits. Glob. Change Biol. 17, 2905–2935 (2011).

    Article  ADS  Google Scholar 

  62. Laliberte, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).

    Article  PubMed  Google Scholar 

  63. Ruiz-Benito, P. et al. Climate- and successional-related changes in functional composition of European forests are strongly driven by tree mortality. Glob. Change Biol. 23, 4162–4176 (2017).

    Article  ADS  Google Scholar 

  64. Hisano, M., Ryo, M., Chen, X. & Chen, H. Y. H. Rapid functional shifts across high latitude forests over the last 65 years. Glob. Change Biol. 27, 3846–3858 (2021).

    Article  CAS  Google Scholar 

  65. Diaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–173 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

  66. Zeugin, F., Potvin, C., Jansa, J. & Scherer-Lorenzen, M. Is tree diversity an important driver for phosphorus and nitrogen acquisition of a young tropical plantation? For. Ecol. Manag. 260, 1424–1433 (2010).

    Article  Google Scholar 

  67. Régnière, J., St-Amant, R. & Béchard, A. BioSIM 10 User’s manual. Report No. LAU-X-137E (Natural Resources Canada, Laurentian Forestry Centre, 2014).

  68. Hogg, E. H. Temporal scaling of moisture and the forest-grassland boundary in western Canada. Agr. For. Meteorol. 84, 115–122 (1997).

    Article  Google Scholar 

  69. Senici, D., Chen, H. Y. H., Bergeron, Y. & Cyr, D. Spatiotemporal variations of fire frequency in central boreal forest. Ecosystems 13, 1227–1238 (2010).

    Article  Google Scholar 

  70. Rosseel, Y. lavaan: an R package for structural equation modeling. J. Stat. Softw. 48, 1–36 (2012).

    Article  Google Scholar 

  71. Oberski, D. lavaan.survey: an R package for complex survey analysis of structural equation models. J. Stat. Softw. 57, 1–27 (2014).

    Article  Google Scholar 

  72. Kenny, D. A., Kaniskan, B. & McCoach, D. B. The performance of RMSEA in models with small degrees of freedom. Sociol. Methods Res. 44, 486–507 (2015).

    Article  MathSciNet  Google Scholar 

  73. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).

Download references

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

Authors

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.

Corresponding authors

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

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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 ad 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)
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

Supplementary information

Source data

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, X., Taylor, A.R., Reich, P.B. et al. Tree diversity increases decadal forest soil carbon and nitrogen accrual. Nature 618, 94–101 (2023). https://doi.org/10.1038/s41586-023-05941-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-023-05941-9

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing