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Molecular trade-offs in soil organic carbon composition at continental scale

Nature Geoscience volume 13, pages 687–692 (2020)Cite this article

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Abstract

The molecular composition of soil organic carbon remains contentious. Microbial-, plant- and fire-derived compounds may each contribute, but whether they vary predictably among ecosystems remains unclear. Here we present carbon functional groups and molecules from a diverse spectrum of North American surface mineral soils, collected primarily from the National Ecological Observatory Network and quantified by nuclear magnetic resonance spectroscopy and a molecular mixing model. We find that soils vary widely in relative contributions of carbohydrate, lipid, protein, lignin and char-like carbon, but each compound class has similar overall abundance. Ninety percent of the variance in carbon composition can be explained by three principal component axes representing a trade-off between lignin and protein, a trade-off between carbohydrate and char, and lipids. Reactive aluminium, crystalline iron oxides and pH plus overlying organic horizon thickness—predictors that are all related to climate—best explain variation along each respective axis. Together, our data point to continental-scale trade-offs in soil carbon molecular composition that are linked to environmental and geochemical variables known to predict carbon mass concentrations. Controversies regarding the genesis of soil carbon and its potential responses to global change can be partially reconciled by considering diverse ecosystem properties that drive complementary persistence mechanisms.

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Fig. 1: Box plots of carbon abundance as the fraction of total SOC in each sample.
Fig. 2: Rotated principal components analysis of SOC molecules.
Fig. 3: Heatmap of correlations (r) between SOC molecules and biogeochemical predictors.
Fig. 4: Parsimonious structural equation models of SOC molecular composition.
Fig. 5: Conceptual model of three-dimensional trade-offs in SOC composition linked to complementary persistence mechanisms as supported by our data.

Data availability

Summarized NMR data are available in the Supplementary Information, and raw NMR spectra data and sample biogeochemical characteristics are available in the Environmental Data Initiative digital repository: https://doi.org/10.6073/pasta/2284825ecb8460f056ae5b0e7d355cc8.

Code availability

R scripts used for post-processing data are available in the Environmental Data Initiative digital repository: https://doi.org/10.6073/pasta/2284825ecb8460f056ae5b0e7d355cc8.

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Acknowledgements

We gratefully acknowledge the efforts of NEON and NRCS staff in conducting soil sampling and analyses, and E. Ayres for providing access to ‘Megapit’ samples. This research was supported in part by NSF projects DEB 1802745 and EAR 1132124. The National Ecological Observatory Network is a programme sponsored by the National Science Foundation and operated under cooperative agreement by Battelle Memorial Institute.

Author information

Author notes

  1. These authors contributed equally: Steven J. Hall, Chenglong Ye.

Authors and Affiliations

  1. Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA

    Steven J. Hall & Chenglong Ye

  2. Ecosystem Ecology Lab, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, China

    Chenglong Ye

  3. National Ecological Observatory Network, Battelle, Boulder, CO, USA

    Samantha R. Weintraub

  4. Department of Geosciences, Baylor University, Waco, TX, USA

    William C. Hockaday

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Contributions

S.J.H., S.R.W. and W.C.H. developed the research concepts, C.Y. and W.C.H. conducted the NMR analyses, S.J.H., C.Y., S.R.W. and W.C.H. analysed data, and S.J.H. and C.Y. wrote the paper with contributions from all authors.

Corresponding authors

Correspondence to Steven J. Hall or William C. Hockaday.

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Extended data

Extended Data Fig. 1 Soil sampling locations for this study.

Upper-case letters denote NEON sites and lower-case letters denote other sites as defined in Supplementary Table 1. Map tiles by Stamen Design, under CC BY 3.0. Map data by OpenStreetMap, under ODbL.

Extended Data Fig. 2 Summary boxplots of biogeochemical characteristics of sampled soils or nearby plant material.

Thick lines indicate medians, boxes denote upper and lower quantiles, and whiskers denote samples within 1.5x the interquartile range.

Extended Data Fig. 3 13C CPMAS NMR spectra of all samples following demineralization.

Colors indicate soil order in the US Department of Agriculture soil taxonomy.

Extended Data Fig. 4 Boxplots of SOC functional group fractional abundance as a function of vegetation type (a) and prescribed fire regime (b).

Lignin was significantly greater in forest than grassland/shrubland vegetation (0.23 vs. 0.16, P = 0.011), and protein was significantly greater in grassland/shrubland vegetation than in forest (0.23 vs. 0.15, P = 0.025). Char was significantly greater in sites with prescribed fire than without (0.22 vs. 0.16, P = 0.046). Thick lines indicate medians, boxes denote upper and lower quantiles, and whiskers denote samples within 1.5x the interquartile range.

Extended Data Fig. 5 Correlation heatmap of SOC molecule relative abundance within samples.

The symbols ** and **** indicate corrected P < 0.01 and P < 0.0001, respectively.

Extended Data Fig. 6 Pearson correlations between SOC molecule relative abundance and rotated principal components.

RC1, RC2, and RC3 refer to the rotated principal component axes 1–3.

Extended Data Fig. 7

A description of biogeochemical predictor variables used in this study.

Extended Data Fig. 8 Heatmap of correlations between SOC functional groups and biogeophysical predictors.

The symbols *, **, ***, and **** indicate corrected P < 0.05, P < 0.01, P < 0.001, and P < 0.0001, respectively.

Extended Data Fig. 9 Optimal linear regression models for each rotated principal component (RC) shown in Fig. 2 fit using backwards elimination.

Models are reported for three different datasets/significance criteria: all samples with α = 0.01, all samples with α = 0.05, and all NEON samples with α = 0.05. Model parameter values were calculated using variables standardized by subtracting the mean and dividing by one standard deviation. Values in parentheses are standard errors. Abbreviations for predictors are described in Extended Data Fig. 7.

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Supplementary Information

Supplementary Discussion, Tables 1–7 and Figs. 1–5.

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Hall, S.J., Ye, C., Weintraub, S.R. et al. Molecular trade-offs in soil organic carbon composition at continental scale. Nat. Geosci. 13, 687–692 (2020). https://doi.org/10.1038/s41561-020-0634-x

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