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The global distribution and environmental drivers of aboveground versus belowground plant biomass

Nature Ecology & Evolution volume 5, pages 1110–1122 (2021)Cite this article

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Abstract

A poor understanding of the fraction of global plant biomass occurring belowground as roots limits our understanding of present and future ecosystem function and carbon pools. Here we create a database of root-mass fractions (RMFs), an index of plant below- versus aboveground biomass distributions, and generate quantitative, spatially explicit global maps of RMFs in trees, shrubs and grasses. Our analyses reveal large gradients in RMFs both across and within vegetation types that can be attributed to resource availability. High RMFs occur in cold and dry ecosystems, while low RMFs dominate in warm and wet regions. Across all vegetation types, the directional effect of temperature on RMFs depends on water availability, suggesting feedbacks between heat, water and nutrient supply. By integrating our RMF maps with existing aboveground plant biomass information, we estimate that in forests, shrublands and grasslands, respectively, 22%, 47% and 67% of plant biomass exists belowground, with a total global belowground fraction of 24% (20–28%), that is, 113 (90–135) Gt carbon. By documenting the environmental correlates of root biomass allocation, our results can inform model projections of global vegetation dynamics under current and future climate scenarios.

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Fig. 1: RMF sample locations in forests, shrublands and grasslands.
Fig. 2: RMF variation and model validation.
Fig. 3: The global distribution of RMFs in forests, grasslands and shrublands.
Fig. 4: Relationships between environmental variables and RMFs.
Fig. 5: Spatial variations in the environmental correlates of RMFs.
Fig. 6: The global distribution of belowground plant biomass.

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Data availability

The root–shoot ratio data underlying this study are available at https://github.com/haozhima95/Global_mapping_root_shoot_ratio/tree/master/RSR_data. Citations for the root–shoot ratio data are provided in the methods.

Code availability

The code used for this study is available at https://github.com/haozhima95/Global_mapping_root_shoot_ratio.git.

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Acknowledgements

We thank J.-F. Bastin, P. B. Reich, R. B. Jackson and Y. Zeng for their constructive comments on this study. This work was supported by grants to C.M.Z. from the ETH Zurich Postdoctoral Fellowship programme, L.M. from the China Scholarship Council and T.W.C. from DOB Ecology. B.D.S. was funded by the Swiss National Science Foundation grant no. PCEFP2_181115. C.T. was supported by a Lawrence Fellow award through the Lawrence Livermore National Laboratory, the US Department of Energy under contract DE-AC52-07NA27344 and the Lawrence Livermore National Laboratory LDRD (Laboratory Directed Research & Development) Program under project no. 20-ERD-055.

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  1. These authors contributed equally: Haozhi Ma, Lidong Mo.

Authors and Affiliations

  1. Institute of Integrative Biology, ETH Zurich (Swiss Federal Institute of Technology), Zurich, Switzerland

    Haozhi Ma, Lidong Mo, Thomas W. Crowther, Daniel S. Maynard, Johan van den Hoogen, Benjamin D. Stocker & Constantin M. Zohner

  2. Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland

    Benjamin D. Stocker

  3. Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA

    César Terrer

  4. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    César Terrer

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Contributions

H.M., L.M., T.W.C. and C.M.Z. conceived and developed the study and wrote the manuscript. H.M. and L.M. collected the data. H.M. and L.M. performed the analyses. D.S.M., J.v.d.H., B.S. and C.T. gave input on the manuscript.

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Extended data

Extended Data Fig. 1

Information on the 63 selected covariate layers used to model root mass fractions194,195,196,197,198,199,200,201,202,203,204,205.

Extended Data Fig. 2 Random 10-fold cross-validation (RCV) of the spatial root-mass fraction models and spatial autocorrelation of model residuals.

a–c, Heat plots showing the relationships between predicted and observed RMFs in forests (a), shrublands (b), and grasslands (c) based on RCV. Solid lines indicate fitted relationships based on ordinary least squares regression [coefficient of determination values relative to the 1:1 line (equation 2) shown in the bottom right corner], dashed diagonal lines indicate a 1:1 relationship between observed and predicted points. d–f, The standard errors of the observed (black) and predicted (grey) mean values of root mass fractions decrease with increasing sample size. The operation was repeated with 1,000 random seeds for the observed and predicted mean values, and the calculated standard errors of the mean are shown. Note, ‘sample size’ in D–F refers to the number of pixels, and thus denotes square kilometres. g–i, Semivariograms illustrating spatial autocorrelation of model residuals in forests (g), shrublands (h) and grasslands (i). Semivariances of residuals were computed based on random 10-fold cross validation (blue) and spatial leave-one-out cross-validation (LOO-CV) with buffer radii of 150km (dark green), 250km (green) and 500km (light green). Dashed vertical lines indicate the buffer radii of the final validation model reported throughout the text.

Extended Data Fig. 3 Partial regression coefficients for the effects of 8 environmental covariates from linear multiple regression models.

To reduce the influence of spatial autocorrelation, a bootstrapping procedure was applied for the forest data (see Methods). Red dots indicate positive effects on RMFs, blue dots indicate negative effects. Error bars reflect two standard errors either side of the mean partial regression coefficient.

Extended Data Fig. 4 Recursive partitioning trees for the univariate effects of annual mean temperature (a), soil moisture (b), NDVI (c), and sand content (d) on RMFs in forests.

These four variables were chosen on basis of the random forest variable importance metric (Fig. 3a) and, for each model, the remaining three variables were evaluated as potential split points. The number of independent observations contained in each terminal node was constrained to ≥10% of the total data (500 observations). Regression plots show slopes and 95% confidence intervals.

Extended Data Fig. 5 Recursive partitioning trees for the univariate effects of annual mean temperature (a), soil moisture (b), aridity index (c), and NDVI (d) on RMFs in shrublands.

These four variables were chosen on basis of the random forest variable importance metric (Fig. 3b) and, for each model, the remaining three variables were evaluated as potential split points. The number of independent observations contained in each terminal node was constrained to ≥10% of the total data (30 observations). Regression plots show slopes and 95% confidence intervals.

Extended Data Fig. 6 Recursive partitioning trees for the univariate effects of annual mean temperature (a), soil moisture (b), aridity index (c), and NDVI (d) on RMFs in grasslands.

These four variables were chosen on basis of the random forest variable importance metric (Fig. 3c) and, for each model, the remaining three variables were evaluated as potential split points. The number of independent observations contained in each terminal node was constrained to ≥10% of the total data (120 observations). Regression plots show slopes and 95% confidence intervals.

Extended Data Fig. 7 Comparison of observed forest RMFs with predicted RMFs from dynamic global vegetation models and a current-generation biomass map.

The blue bars represent histograms of predicted RMF values based on our LOO-CV procedure (a), current-generation biomass estimates6 (b), and the vegetation models CABLE-POP (c), CLASS-CTEM (d), ISAM (e) and ORCHIDEE (f). Yellow bars represent observed values. Insets show scatter plots of predicted versus observed RMFs with solid lines indicating fitted relationships, dashed diagonal lines indicating a 1:1 relationship between observed and predicted points. For the vegetation models, forest was defined as pixels with a tree cover fraction higher than 50%.

Extended Data Fig. 8 The global distribution of belowground plant biomass and associated uncertainties in forests (a, b), grasslands (c, d), and shrublands (e, f).

a, c, e, Belowground plant biomass (in tons carbon per hectare). b, d, f Associated uncertainties in belowground carbon, calculated as the predicted biomass range (based on 2.5% and 97.5% RMF quantiles derived from the bootstrapped RMF models) divided by the mean predicted biomass in each pixel. Maps are projected at 30 arc-seconds (~1 km2) resolution.

Extended Data Fig. 9 Root mass fraction inter-model consistency in forests (a), grasslands (b) and shrublands (c).

Inter-model consistency was calculated as the coefficient of variation (standard deviation divided by mean, in %) of the predictions of the 10 best models. Maps are projected at 30 arc-seconds (~1 km2) resolution.

Extended Data Fig. 10 The extent of interpolation and extrapolation across all terrestrial pixels in which the respective vegetation type, forest (a), grassland (b) and shrubland (c) occurs.

Values represent the percentage of interpolation based on principal component analysis, that is, the percentage of bands that fall into the convex hull space.

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Ma, H., Mo, L., Crowther, T.W. et al. The global distribution and environmental drivers of aboveground versus belowground plant biomass. Nat Ecol Evol 5, 1110–1122 (2021). https://doi.org/10.1038/s41559-021-01485-1

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