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Molecular-level carbon traits underlie the multidimensional fine root economics space

Nature Plants (2024)Cite this article

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Abstract

Carbon influences the evolution and functioning of plants and their roots. Previous work examining a small number of commonly measured root traits has revealed a global multidimensionality of the resource economics traits in fine roots considering carbon as primary currency but without considering the diversity of carbon-related traits. To address this knowledge gap, we use data from 66 tree species from a tropical forest to illustrate that root economics space co-varies with a novel molecular-level traits space based on nuclear magnetic resonance. Thinner fine roots exhibit higher proportions of carbohydrates and lower diversity of molecular carbon than thicker roots. Mass-denser fine roots have more lignin and aromatic carbon compounds but less bioactive carbon compounds than lighter roots. Thus, the transition from thin to thick fine roots implies a shift in the root carbon economy from ‘do-it-yourself’ soil exploration to collaboration with mycorrhizal fungi, while the shift from light to dense fine roots emphasizes a shift from acquisitive to conservative root strategy. We reveal a previously undocumented role of molecular-level carbon traits that potentially undergird the multidimensional root economics space. This finding offers new molecular insight into the diversity of root form and function, which is fundamental to our understanding of plant evolution, species coexistence and adaptations to heterogeneous environments.

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Fig. 1: Phylogenetic relationships and 13 root traits for 66 tropical tree species.
Fig. 2: Coordination between the 2D root functional trait spaces and the molecular-level root carbon traits.
Fig. 3: Relationships between cell wall chemistry and morphology in fine roots.
Fig. 4: Conceptual framework of the 2D root functional traits space.

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Data availability

All data supporting the findings of this study are available within this paper and its Supplementary Information. The raw data in this study are available via Figshare at https://doi.org/10.6084/m9.figshare.24218970 (ref. 73). Correspondence and requests for materials should be addressed to J.W. (wangjj@sustech.edu.cn). Literature data were extracted from Fine-Root Ecology Database 3.0 (https://roots.ornl.gov/)62.

Code availability

The code utilized for this study is publicly available and is hosted in figshare at https://doi.org/10.6084/m9.figshare.24218970 (ref. 73).

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (42122054, 42192513, 42321004) to J.W.; the National Natural Science Foundation of China (32171746, 31670550) to D.K.; the Guangdong Basic and Applied Basic Research Foundation (2021B1515020082), Key Platform and Scientific Research Projects of Guangdong Provincial Education Department (2020KCXTD006), and Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control (2023B1212060002) to J.W.; the Leading Talents of Basic Research in Henan Province to D.K.; the Natural Sciences and Engineering Research Council (NSERC) of Canada for support via the Tier 1 Canada Research Chair in Integrative Molecular Biogeochemistry to M.J.S.; a Swedish Research Council (Vetenskapsrådet) grant (2015-04214) to P.K.; the National Science Foundation: Biological Integration Institutes Grant (NSF-DBI-2021898) and Long Term Ecological Research Grant (NSF-DEB-1831944) to P.B.R.

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Authors and Affiliations

  1. State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China

    Mengke Wang, Yinghui Wang & Junjian Wang

  2. Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China

    Mengke Wang, Yinghui Wang & Junjian Wang

  3. College of Forestry, Henan Agricultural University, Zhengzhou, Henan, China

    Deliang Kong & Qingpei Yang

  4. School of Urban Planning and Design, Peking University Shenzhen Graduate School, Peking University, Shenzhen, Guangdong, China

    Xiaohan Mo & Hui Zeng

  5. Department of Forest Mycology and Plant Pathology, Swedish University of Agricultural Sciences, Uppsala, Sweden

    Paul Kardol

  6. Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden

    Paul Kardol

  7. Department of Biological Sciences, International Center for Tropical Biodiversity, Florida International University, Miami, FL, USA

    Oscar J. Valverde-Barrantes

  8. Environmental NMR Centre and Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada

    Myrna J. Simpson

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

    Peter B. Reich

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

    Peter B. Reich

  11. Hawkesbury Institute for the Environment, Western Sydney University, Penrith, Australia

    Peter B. Reich

  12. Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany

    Joana Bergmann

  13. Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, USA

    Nishanth Tharayil

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  1. Mengke Wang
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Contributions

M.W., D.K. and J.W. conceptualized the project. M.W., D.K., J.W., X.M., Y.W. and Q.Y. developed the methodology. D.K., J.W., M.J.S., P.K. and P.B.R. acquired funding. D.K. and J.W. administered the project. M.W., D.K. and J.W. wrote the original draft. M.W., D.K., P.K., O.J.V.-B., M.J.S., H.Z., P.B.R., J.B., N.T. and J.W. reviewed and edited the paper.

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Correspondence to Deliang Kong or Junjian Wang.

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Nature Plants thanks J. Aaron Hogan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Phylogenetic relationships for 66 tree species spanning three major angiosperm lineages (40 rosids in blue, 7 asterids in red, and 19 magnoliids in green).

The phylogenetic relationships among plant species were extracted from a molecular phylogenic tree described in Zanne et al.74.

Extended Data Fig. 2 Matrix of Pearson’s correlation coefficients among 13 root traits for 66 tropical tree species.

The correlation matrix is arranged in the hierarchical clustering order. A bigger circle denotes a stronger correlation. Significance level of correlations is indicated; ***, P < 0.001; **, P < 0.01; *, P < 0.05. Significance was tested using a two-sided t-test.

Extended Data Fig. 3 Simulation analyses for coordination between the two-dimensional root functional trait spaces and the molecular-level root carbon traits.

Observed correlations (vertical lines) relative to the distribution when scores were randomly simulated (a) between PC1 scores of 13C-NMR-based carbon traits in the first principal component (including O-aromatic, aromatic, O2-alkyl, carbonyl and carboxyl, N-alkyl/methoxy, and alkyl C) and PC1 scores of root economics traits in the first principal component (including root tissue density and root nitrogen concentration); (b) between PC2 scores of 13C-NMR-based carbon traits in the second principal component (including O-alkyl and H’RSC) and PC2 scores of root economics traits in the second principal component (including root diameter and specific root length). Significance was tested using a two-sided permutation test.

Extended Data Fig. 4 Coordination between the two-dimensional root functional trait planes and the molecular-level root carbon traits based on phylogenetic-informed PCAs (pPCAs).

(a) The two-dimensional root economic traits space. (b) Root carbon traits space, including total root carbon and molecular-level carbon traits. (c) Integrating both commonly-measured root functional traits and molecular-level carbon traits in the two-dimensional root functional trait space. The color gradient indicates species occurrence probability in the trait space defined by PC1 and PC2, with red indicating high occurrence and white indicating low occurrence. Contour lines indicate 0.50 and 0.90 quantiles. The commonly-measured root functional traits are root diameter, specific root length, root tissue density, and root nitrogen concentration. The carbon traits include total root carbon concentration, and 8 molecular-level carbon traits (alkyl, N-alkyl/methoxy, O-alkyl, di-O-alkyl, aromatic, O-aromatic, carbonyl/carboxyl, and structural carbon trait diversity (H′RSC)).

Extended Data Fig. 5 Simulation analyses for coordination between the two-dimensional root functional trait spaces and the molecular-level root carbon traits after controlling for phylogeny.

Observed correlations (vertical lines) relative to the distribution when scores were randomly simulated (a) between PC1 scores of 13C-NMR-based carbon traits (including O-aromatic, aromatic, O2-alkyl, carbonyl and carboxyl, N-alkyl/methoxy, and alkyl C) and PC1 scores of root economics traits (including root tissue density and root nitrogen concentration) after controlling for phylogeny, and (b) between PC2 scores of 13C-NMR-based carbon traits in the second principal component (including O-alkyl and H’RSC) and PC2 scores of root economics traits (including root diameter and specific root length) after controlling for phylogeny. Significance was tested using a two-sided permutation test.

Extended Data Fig. 6 Different species in the two-dimensional root trait space.

Ordination of 66 tree species from three major clades, Asterids (in red), Magnolidds (in green) and Rosids (in blue), in the two-dimensional root trait space based on (a) principal component analysis and (b) phylogenetically-informed principal component analyses.

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Wang, M., Kong, D., Mo, X. et al. Molecular-level carbon traits underlie the multidimensional fine root economics space. Nat. Plants (2024). https://doi.org/10.1038/s41477-024-01700-4

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