in Ecology

The overlooked role of root water content in the root economics space

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Abstract

Root traits are fundamental to plant survival, growth and adaptation to environmental changes. Despite increasing attention to the root economics space, a quantitative understanding of global patterns and key drivers of root trait variation remains elusive. By combining metabolic theory with global trait datasets, we reveal universal nonlinear relationships of five key root traits with root water content regardless of plant growth form or climate zone. Root water content emerges as a stronger predictor of growth-related root traits and shows a closer association with the conservation gradient than the widely considered root nitrogen, thereby better defining ‘fast’ resource acquisition strategies. Moreover, replacing nitrogen with tissue water content in analyses reveals a closer alignment of leaf and fine-root traits than expected. Our findings highlight general quantitative biotic and abiotic controls on plant trait variation, offering broader insights into plant economics strategies, community dynamics and ecosystem functioning under changing climate and resource availability.

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Fig. 1: General nonlinear relationships between key root traits and dry-mass-based RWC.
Fig. 2: RWC is a more robust predictor of other root traits than RN.
Fig. 3: RWC better defines the fast end of the conservation gradient of the RES than the widely considered trait RN.
Fig. 4: Replacing nitrogen with plant water content improves the alignment between leaf and fine-root traits.

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Data availability

The TRY plant trait database is publicly available at https://www.try-db.org. The GRooT database is publicly available at https://github.com/GRooT-Database/GRooT-Data. The WorldClim version 2.1 database is publicly available at https://www.worldclim.org/. Global soil temperature maps are available via Zenodo at https://doi.org/10.5281/zenodo.4558732 (ref. 59). The Global Aridity Index and Potential Evapotranspiration Database version 3 is publicly available via figshare at https://doi.org/10.6084/m9.figshare.7504448.v6 (ref. 60). The datasets generated and analysed in this study are available via the Open Science Framework at https://osf.io/65s3c/.

Code availability

The R code used for data analysis in this study is available via the Open Science Framework at https://osf.io/65s3c/.

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Acknowledgements

This study was supported by the Young Talent Program from Guangdong Province (grant no. 77010-42150005), the Start-up Grant (no. 77010-12255006) and the Fundamental Research Funds for the Central Universities (grant no. 77010-13130003) from Sun Yat-sen University. C.C. was supported by the National Natural Science Foundation of China (grant no. 32330064). C.P.C. was supported by the Estonian Research Council (grant no. PRG2142) and the Spanish Ministry of Science, Innovation and Universities through the ATRAE Program 2024 (grant no. ATR2024-154934). J.P. and J.S. were supported by the Spanish government grant nos. PID2022-140808NB-I00 and PID2023-153125NB-I00 funded by the MICIU/AEI/10.13039/501100011033 and FEDER, EU.

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Authors and Affiliations

  1. State Key Laboratory of Biocontrol, School of Ecology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China

    Heng Li, Jing Wang, Ruixuan Liu, Jiahao Tan, Ying Xin, Chengjin Chu & Heng Huang

  2. Misión Biológica de Galicia, Consejo Superior de Investigaciones Científicas, Pontevedra, Spain

    Carlos P. Carmona

  3. Department of Botany, Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia

    Carlos P. Carmona

  4. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Shuli Niu

  5. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China

    Shuli Niu

  6. ARC Centre for Plant Success in Nature & Agriculture, Hawkesbury Institute for the Environment, Western Sydney University, Sydney, New South Wales, Australia

    Ian J. Wright

  7. School of Natural Sciences, Macquarie University, Sydney, New South Wales, Australia

    Ian J. Wright

  8. Department of Wildland Resources, Utah State University, Logan, UT, USA

    Yong Zhou

  9. Department of Ecology, Evolution, & Marine Biology, University of California, Santa Barbara, Santa Barbara, CA, USA

    Yong Zhou

  10. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain

    Josep Peñuelas & Jordi Sardans

  11. CREAF, Cerdanyola del Vallès, Spain

    Josep Peñuelas & Jordi Sardans

  12. Sichuan Zoige Alpine Wetland Ecosystem National Observation and Research Station, Southwest Minzu University, Chengdu, China

    Zhiqiang Wang

  13. Institute of Qinghai-Tibetan Plateau, Southwest Minzu University, Chengdu, China

    Zhiqiang Wang

  14. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, China

    Lei Deng

  15. Institute of Soil and Water Conservation, Northwest A&F University, Yangling, China

    Lei Deng

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  1. Heng Li
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Contributions

H.H. and H.L. conceived the study. H.L., J.T. and Y.X. collected the trait data and performed a literature search. H.L. performed the analyses. H.L., H.H., C.P.C., S.N., I.J.W., Y.Z., J.P., J.S., Z.W., L.D., J.W., R.L. and C.C. discussed the design and methods and interpreted the results. H.L. and H.H. wrote the paper with contributions from all authors.

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Heng Huang.

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Extended data

Extended Data Fig. 1 The quantitative effects of root water content on root traits across growth forms.

The nonlinear relationships of RRcor (a), SRAcor (b), SRLcor (c), RDcor (d), RTD (e) with RWC for non-woody (blue) and woody species (orange) are shown, respectively. The parameter values were obtained from the non-linear fitting regression results based on our theoretical framework (equations (5), (6) and (8)–(10)).

Extended Data Fig. 2 The quantitative effects of root water content on root traits across climate zones.

The nonlinear relationships of SRAcor (a), SRLcor (b), RDcor (c), RTD (d) with RWC for dry (defined as areas with aridity index (AI) ≤ 0.65; red points and lines) and humid climate zones (defined as areas with AI > 0.65; green points and lines) are shown, respectively. The parameter values were obtained from the non-linear fitting regression results based on our theoretical framework (equations (5), (6) and (8)–(10)).

Extended Data Fig. 3 Correlations of root traits with mean annual precipitation.

The scaling relationships of RR (a, nmol g−1 s−1), SRA (b, cm2 g−1), SRL (c, m g−1), RD (d, mm), RTD (e, g cm−3), and RWC (f, g g−1) with MAP across the pooled data are shown, respectively. The OLS regression lines (black lines) are shown.

Extended Data Fig. 4 Correlations of root traits with mean annual precipitation.

The scaling relationships of RR (a, nmol g−1 s−1), SRA (b, cm2 g−1), SRL (c, m g−1), RD (d, mm), RTD (e, g cm−3), and RWC (f, g g−1) with mean annual aridity index (AI) are shown across the pooled data, respectively. The OLS regression lines (black lines) are shown.

Extended Data Fig. 5 Pairwise correlations of all traits in the imputed dataset including 1657 species (blue) and complete dataset (red) including 150 species.

Scatterplots indicate the log10-transformed trait-trait relationships. The blue and red lines in the scatterplots represent the SMA regression fitting results. The correlation coefficients are shown in the upper-right triangle. The diagonals represent the probability density distributions of each trait.

Extended Data Fig. 6 The scaling relationships among root traits.

a, The scaling relationship of RR (nmol g−1 s−1) with SRA (cm2 g−1) is presented. b, The scaling relationship of SRL (m g−1) with RD (mm) are shown. c and d, The scaling relationships of RR (nmol g−1 s−1) with SRL (m g−1) and RD (mm) are shown, respectively. The SMA regression lines (black lines) are shown.

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Supplementary Figs. 1–10, Tables 1–10, Methods, Results and Data Sources.

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Li, H., Carmona, C.P., Niu, S. et al. The overlooked role of root water content in the root economics space.
Nat. Plants (2026). https://doi.org/10.1038/s41477-026-02232-9

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