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:

Arbuscular mycorrhizal association regulates global root–seed coordination

Nature Plants volume 11, pages 1759–1768 (2025)Cite this article

Subjects

Abstract

Terrestrial plants exhibit immense variation in their form and function among species. Coordination between resource acquisition by roots and reproduction through seeds could promote the fitness of plant populations. How root and seed traits covary has remained unclear until our analysis of the largest-ever compiled joint global dataset of root traits and seed mass. Here we demonstrate that seed mass and seed phosphorus mass scale positively with root diameter in arbuscular mycorrhizal (AM) plants, depending on variation in root cortical thickness instead of root vessel size. These findings suggest a dual role of AM association in phosphorus uptake and pathogen resistance which drives the global root–seed coordination, instead of initially expected resource transport via root vessels as the main driver. In contrast, we found no relationship between root traits and seed mass in ectomycorrhizal plants. Overall, our study reveals coordination between roots and seeds in AM plants, which is probably regulated by root–mycorrhizal symbiosis, and may be crucial in shaping global plant diversity and species distributions.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

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

Fig. 1: Three hypotheses regarding the relationships between roots and seeds.
Fig. 2: Integration of seed mass within the above- and belowground plant trait space.
Fig. 3: Global relationship between root diameter and seed mass.
Fig. 4: Relationships between root anatomical traits and seed mass.
Fig. 5: Relationships between soil pathogen richness and root traits.
Fig. 6: Evolutionary trends of absorptive root diameter and seed mass.

Similar content being viewed by others

Data availability

The raw data in this study are available in figshare (https://doi.org/10.6084/m9.figshare.28300658)74. Literature data were extracted from the Global Root Trait database (https://groot-database.github.io/GRooT/)40.

Code availability

The code utilized for this study is publicly available and is hosted in figshare (https://doi.org/10.6084/m9.figshare.28300658)74.

References

  1. Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).

    Article  PubMed  Google Scholar 

  2. Carmona, C. P. et al. Fine-root traits in the global spectrum of plant form and function. Nature 597, 683–687 (2021).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  4. Li, L. et al. Leaf economics and hydraulic traits are decoupled in five species-rich tropical-subtropical forests. Ecol. Lett. 18, 899–906 (2015).

    Article  CAS  PubMed  Google Scholar 

  5. Zhang, Y. et al. The origin of bi-dimensionality in plant root traits. Trends Ecol. Evol. 39, 78–88 (2024).

    Article  CAS  PubMed  Google Scholar 

  6. Bergmann, J. et al. The fungal collaboration gradient dominates the root economics space in plants. Sci. Adv. 6, eaba3756 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Corner, E. J. H. The Durian theory or the origin of the modern tree. Ann. Bot. 13, 367–414 (1949).

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Leishman, M., Wright, I., Moles, A. & Westoby, M. in Seeds: The Ecology of Regeneration in Plant Communities (ed. Fenner, M.) 31–57 (CABI, 2000).

  10. Ma, Z. et al. The determination of leaf size on the basis of developmental traits. New Phytol. 246, 461–480 (2025).

    Article  PubMed  Google Scholar 

  11. Weigelt, A. et al. The importance of trait selection in ecology. Nature 618, E29–E30 (2023).

    Article  CAS  PubMed  Google Scholar 

  12. Weigelt, A. et al. An integrated framework of plant form and function: the belowground perspective. New Phytol. 232, 42–59 (2021).

    Article  PubMed  Google Scholar 

  13. Zhang, P. P. et al. Contrasting coordination of non-structural carbohydrates with leaf and root economic strategies of alpine coniferous forests. New Phytol. 243, 580–590 (2024).

    Article  CAS  PubMed  Google Scholar 

  14. Joswig, J. S. et al. Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation. Nat. Ecol. Evol. 6, 36–50 (2022).

    Article  PubMed  Google Scholar 

  15. Moles, A. T. et al. Global patterns in seed size. Glob. Ecol. Biogeogr. 16, 109–116 (2007).

    Article  Google Scholar 

  16. Zheng, J., Guo, Z. & Wang, X. Seed mass of angiosperm woody plants better explained by life history traits than climate across China. Sci. Rep. 7, 2741 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Fricke, E. C., Ordonez, A., Rogers, H. S. & Svenning, J.-C. The effects of defaunation on plants’ capacity to track climate change. Science 375, 210–214 (2022).

    Article  CAS  PubMed  Google Scholar 

  18. Leslie, A. B., Beaulieu, J. M. & Mathews, S. Variation in seed size is structured by dispersal syndrome and cone morphology in conifers and other nonflowering seed plants. New Phytol. 216, 429–437 (2017).

    Article  CAS  PubMed  Google Scholar 

  19. Marchand, P. et al. Seed-to-seedling transitions exhibit distance-dependent mortality but no strong spacing effects in a Neotropical forest. Ecology 101, e02926 (2020).

    Article  PubMed  Google Scholar 

  20. Bergmann, J., Ryo, M., Prati, D., Hempel, S. & Rillig, M. C. Root traits are more than analogues of leaf traits: the case for diaspore mass. New Phytol. 216, 1130–1139 (2017).

    Article  PubMed  Google Scholar 

  21. Copeland, L. O. & McDonald, M. B. in Principles of Seed Science and Technology (eds Copeland, L. O. & McDonald, M. B.) 58–71 (Springer, 2001).

  22. Van Der Heijden, M. G. A. in Mycorrhizal Ecology (eds van der Heijden, M. G. A. & Sanders, I. R.) 243–265 (Springer, 2003).

  23. Nallathambi, P. et al. in Seed-borne Diseases of Agricultural Crops: Detection, Diagnosis and Management (eds Kumar, R. & Gupta, A.) 749–791 (Springer, 2020).

  24. Kong, D. et al. Leading dimensions in absorptive root trait variation across 96 subtropical forest species. New Phytol. 203, 863–872 (2014).

    Article  PubMed  Google Scholar 

  25. Liang, M. et al. Arbuscular mycorrhizal fungi counteract the Janzen-Connell effect of soil pathogens. Ecology 96, 562–574 (2015).

    Article  PubMed  Google Scholar 

  26. Zangaro, W., Nishidate, F. R., Camargo, F. R. S., Romagnoli, G. G. & Vandressen, J. Relationships among arbuscular mycorrhizas, root morphology and seedling growth of tropical native woody species in southern Brazil. J. Trop. Ecol. 21, 529–540 (2005).

    Article  Google Scholar 

  27. Laughlin, D. C., Leppert, J. J., Moore, M. M. & Sieg, C. H. A multi-trait test of the leaf-height-seed plant strategy scheme with 133 species from a pine forest flora. Funct. Ecol. 24, 493–501 (2010).

    Article  Google Scholar 

  28. Siqueira, J. O. & Saggin-Júnior, O. J. Dependency on arbuscular mycorrhizal fungi and responsiveness of some Brazilian native woody species. Mycorrhiza 11, 245–255 (2001).

    Article  CAS  Google Scholar 

  29. West, G. B., Brown, J. H. & Enquist, B. J. A general model for the structure and allometry of plant vascular systems. Nature 400, 664–667 (1999).

    Article  CAS  Google Scholar 

  30. Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).

    Article  Google Scholar 

  31. Kong, D. et al. Nonlinearity of root trait relationships and the root economics spectrum. Nat. Commun. 10, 2203 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Swamy, V. et al. Are all seeds equal? Spatially explicit comparisons of seed fall and sapling recruitment in a tropical forest. Ecol. Lett. 14, 195–201 (2011).

    Article  PubMed  Google Scholar 

  33. Puerta-Piñero, C., Muller-Landau, H. C., Calderón, O. & Wright, S. J. Seed arrival in tropical forest tree fall gaps. Ecology 94, 1552–1562 (2013).

    Article  PubMed  Google Scholar 

  34. Im, C., Chung, J., Kim, H. S., Chung, S. & Yoon, T. K. Are seed dispersal and seedling establishment distance- and/or density-dependent in naturally regenerating larch patches? A within-patch scale analysis using an eigenvector spatial filtering approach. For. Ecol. Manage. 531, 120763 (2023).

    Article  Google Scholar 

  35. Connell, J. H. in Dynamics of Numbers in Populations (eds den Boer, P. J & Gradwell, G. R.) 298–312 (Centre for Agricultural Publishing and Documentation, 1971).

  36. Hülsmann, L. et al. Latitudinal patterns in stabilizing density dependence of forest communities. Nature 627, 564–571 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Hou, S. et al. A microbiota-root-shoot circuit favours Arabidopsis growth over defence under suboptimal light. Nat. Plants 7, 1078–1092 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Marx, D. H. Ectomycorrhizae as biological deterrents to pathogenic root infections. Annu. Rev. Phytopathol. 10, 429–454 (1972).

    Article  CAS  PubMed  Google Scholar 

  39. Brundrett, M., Murase, G. & Kendrick, B. Comparative anatomy of roots and mycorrhizae of common Ontario trees. Botany 68, 551–578 (1990).

    Google Scholar 

  40. Guerrero-Ramírez, N. R. et al. Global root traits (GRooT) database. Glob. Ecol. Biogeogr. 30, 25–37 (2021).

    Article  Google Scholar 

  41. Ma, Z. et al. Evolutionary history resolves global organization of root functional traits. Nature 555, 94–97 (2018).

    Article  CAS  PubMed  Google Scholar 

  42. Janos, D. P. Vesicular-arbuscular mycorrhizae affect lowland tropical rain forest plant growth. Ecology 61, 151–162 (1980).

    Article  Google Scholar 

  43. Peat, H. J. & Fitter, A. H. The distribution of arbuscular mycorrhizas in the British flora. New Phytol. 125, 845–854 (1993).

    Article  CAS  PubMed  Google Scholar 

  44. Hennecke, J. et al. Responses of rhizosphere fungi to the root economics space in grassland monocultures of different age. New Phytol. 240, 2035–2048 (2023).

    Article  CAS  PubMed  Google Scholar 

  45. Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).

    Article  PubMed  Google Scholar 

  46. Hu, Y. et al. Contrasting latitudinal diversity and co-occurrence patterns of soil fungi and plants in forest ecosystems. Soil Biol. Biochem. 131, 100–110 (2019).

    Article  CAS  Google Scholar 

  47. Reich, P. B. & Oleksyn, J. Global patterns of plant leaf N and P in relation to temperature and latitude. Proc. Natl Acad. Sci. USA 101, 11001–11006 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Wang, M. et al. Molecular-level carbon traits underlie the multidimensional fine root economics space. Nat. Plants 10, 901–909 (2024).

    Article  CAS  PubMed  Google Scholar 

  49. Misra, V. & Mall, A. K. in Plant Endophytes and Secondary Metabolites (eds Egamberdieva, D. et al.) 81–94 (Academic Press, 2024).

  50. Newsham, K. K., Fitter, A. H. & Watkinson, A. R. Multi-functionality and biodiversity in arbuscular mycorrhizas. Trends Ecol. Evol. 10, 407–411 (1995).

    Article  CAS  PubMed  Google Scholar 

  51. Schneider, G. F., Salazar, D., Hildreth, S. B., Helm, R. F. & Whitehead, S. R. Comparative metabolomics of fruits and leaves in a hyperdiverse lineage suggests fruits are a key incubator of phytochemical diversification. Front. Plant Sci. 12, 693739 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Wang, B., Phillips, J. S. & Tomlinson, K. W. Tradeoff between physical and chemical defense in plant seeds is mediated by seed mass. Oikos 127, 440–447 (2018).

    Article  CAS  Google Scholar 

  53. Li, Y.-L. et al. Seed traits of reintroduced invasive populations of Triadica sebifera show few differences in comparison with those of native populations. Plant Ecol. 224, 697–703 (2023).

    Article  Google Scholar 

  54. Cheng, J. et al. Seed traits and burial state affect plant seed secondary dispersal mediated by rodents. Heliyon 10, e32612 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Kong, D. et al. Plant functional group removal alters root biomass and nutrient cycling in a typical steppe in Inner Mongolia, China. Plant Soil 316, 133–144 (2011).

    Article  Google Scholar 

  56. Long, Y., Kong, D., Chen, Z. & Zeng, H. Variation of the linkage of root function with root branch order. PLoS ONE 8, e57153 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Guo, D. et al. Anatomical traits associated with absorption and mycorrhizal colonization are linked to root branch order in twenty-three Chinese temperate tree species. New Phytol. 180, 673–683 (2008).

    Article  PubMed  Google Scholar 

  58. Kattge, J. et al. TRY plant trait database – enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).

    Article  Google Scholar 

  59. Wright, I. J. & Westoby, M. Differences in seedling growth behaviour among species: trait correlations across species, and trait shifts along nutrient compared to rainfall gradients. J. Ecol. 87, 85–97 (1999).

    Article  Google Scholar 

  60. Mueller, K. E., Kray, J. A. & Blumenthal, D. M. Coordination of leaf, root, and seed traits shows the importance of whole plant economics in two semiarid grasslands. New Phytol. 241, 2410–2422 (2024).

    Article  CAS  PubMed  Google Scholar 

  61. Wang, B. & Qiu, Y. L. Phylogenetic distribution and evolution of mycorrhizas in land plants. Mycorrhiza 16, 299–363 (2006).

    Article  CAS  PubMed  Google Scholar 

  62. Akhmetzhanova, A. A. et al. A rediscovered treasure: mycorrhizal intensity database for 3000 vascular plant species across the former Soviet Union. Ecology 93, 689–690 (2012).

    Article  Google Scholar 

  63. Maherali, H., Oberle, B., Stevens, P. F., Cornwell, W. K. & McGlinn, D. J. Mutualism persistence and abandonment during the evolution of the mycorrhizal symbiosis. Am. Nat. 188, E113–E125 (2016).

    Article  PubMed  Google Scholar 

  64. Brundrett, M. & Tedersoo, L. Misdiagnosis of mycorrhizas and inappropriate recycling of data can lead to false conclusions. New Phytol. 221, 18–24 (2019).

    Article  PubMed  Google Scholar 

  65. Soudzilovskaia, N. A. et al. FungalRoot: global online database of plant mycorrhizal associations. New Phytol. 227, 955–966 (2020).

    Article  PubMed  Google Scholar 

  66. Valverde-Barrantes, O. J., Freschet, G. T., Roumet, C. & Blackwood, C. B. A worldview of root traits: the influence of ancestry, growth form, climate and mycorrhizal association on the functional trait variation of fine-root tissues in seed plants. New Phytol. 215, 1562–1573 (2017).

    Article  PubMed  Google Scholar 

  67. Sun, T., Zhang, H. & Wang, Z. Reply to Tedersoo et al.: Plant species within the same family or genus can have different mycorrhizal types? Proc. Natl Acad. Sci. USA 116, 12141–12142 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Jin, Y. & Qian, H. U. PhyloMaker: an R package that can generate large phylogenetic trees for plants and animals. Plant Divers. 45, 347–352 (2023).

    Article  CAS  PubMed  Google Scholar 

  69. The Angiosperm Phylogeny Group et al. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG IV. Bot. J. Linn. Soc. 181, 1–20 (2016).

    Article  Google Scholar 

  70. Blomberg, S. P. & Garland Jr, T. Tempo and mode in evolution: phylogenetic inertia, adaptation and comparative methods. J. Evol. Biol. 15, 899–910 (2002).

    Article  Google Scholar 

  71. Orme, D. et al. CAPER: comparative analyses of phylogenetics and evolution in R. R package version 0.5.2 https://github.com/davidorme/caper (2012).

  72. Carmona, C. P., Pavanetto, N. & Puglielli, G. funspace: an R package to build, analyse and plot functional trait spaces. Divers. Distrib. 30, e13820 (2024).

    Article  Google Scholar 

  73. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013); http://www.R-project.org/

  74. Yang, Q. et al. Arbuscular mycorrhizal association regulates global root-seed coordination. figshare https://doi.org/10.6084/m9.figshare.28300658 (2025).

Download references

Acknowledgements

We thank S. Chen for comments on an early draft of the manuscript; J. Bergmann for discussion of the idea regarding hypothesis 2 in the early draft; J. Chen, J. Cao, M. Han, H. Wang, Y. Hou, Y. Tian, Y. Dong, M. Liu, D. Mo, C. Xiang, Y. Jiang, Y. Liang, S. Dong, Z. Meng, L. Zhao and C. Hu for assistance in field sampling. We also thank the following field research stations and government agencies for their kind support: Xishuangbanna Station for Tropical Forest Studies of Xishuangbanna Tropical Botanical Garden; Dinghushan Station for Subtropical Forest Studies of South China Botany Garden; Gutianshan Biodiversity Science Research Station; Shennongjia National Park Administration; Forest Ecosystem Research Station, Institute of Botany, Chinese Academy of Sciences; Beijing Forest Ecosystem Research Station, Chinese Academy of Sciences; Liangshui Experimental Forest Farm, Northeast Forestry University; Changbai Mountain Forest Ecosystem Positioning Station, Chinese Academy of Sciences. This study was supported by the National Natural Science Foundation of China (32471824, 32171746 and 31870522) to D.K., the leading talents of basic research in Henan Province (24XM0375) to D.K., Excellent Youth Creative Research Group Project in Henan Province (252300421002) to D.K., Foreign Scientists Studio in Henan Province (GZS2025011) to D.K., the Scientific Research Foundation of Henan Agricultural University (30500854) to D.K., the National Natural Science Foundation of China (42122054 and 42477227) to J.W., the Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control (2023B1212060002) to J.W., the High-level University Special Fund (G030290001) to J.W., the National Natural Science Foundation of China (42077450) to Y. Z. and the Estonian Research Council (PRG2142) to C.P.C.

Author information

Authors and Affiliations

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

    Qingpei Yang, Binglin Guo, Yuxin Pei, Qinwen Han, Zhenjiang Li, Huifang Wu, Yue Zhang, Weizheng Ren, Yong Zhao, Xitian Yang, Guoqiang Fan & Deliang Kong

  2. Department of Environmental Studies, New York University, New York, NY, USA

    Mingzhen Lu

  3. Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China

    Yanjie Liu

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

    Paul Kardol

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

    Paul Kardol

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

    Peter B. Reich

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

    Peter B. Reich

  8. Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia

    Peter B. Reich & Ian J. Wright

  9. Lancaster Environment Centre, Lancaster University, Lancaster, UK

    Richard D. Bardgett

  10. Systems Ecology, A-LIFE, Vrije Universiteit, Amsterdam, the Netherlands

    Johannes H. C. Cornelissen

  11. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA

    Nathan J. B. Kraft

  12. Instituto Multidisciplinario de Biología Vegetal (IMBIV), CONICET, Córdoba, Argentina

    Sandra Díaz

  13. FCEFyN, Universidad Nacional de Córdoba, Córdoba, Argentina

    Sandra Díaz

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

    Ian J. Wright

  15. Key Laboratory of Sustainable Forest Ecosystem Management - Ministry of Education, Northeast Forest University, Harbin, China

    Nianpeng He

  16. Department of Ecology and Conservation Biology, Texas A&M University, College Station, TX, USA

    J. Aaron Hogan

  17. College of Landscape Architecture and Art, Henan Agricultural University, Zhengzhou, China

    Zheng Wang

  18. School of Life Sciences, Taizhou University, Taizhou, China

    Wanqin Yang

  19. College of Ecology and Environment, Zhengzhou University, Zhengzhou, China

    Junxiang Ding

  20. School of Life Sciences, Henan University, Kaifeng, China

    Zhongling Yang & Guoyong Li

  21. Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia

    Carlos P. Carmona

  22. International Center of Tropical Botany, Institute of Environment, Department of Biology, Florida International University, Miami, FL, USA

    Oscar J. Valverde-Barrantes

  23. Center for Interdisciplinary Biodiversity Research and College of Forestry, Shandong Agricultural University, Tai’an, China

    Dezhu Li

  24. Germplasm Bank of Wild Species and Yunnan Key Laboratory of Crop Wild Relatives Omics, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China

    Dezhu Li & Jie Cai

  25. College of Urban and Environmental Sciences, Peking University, Beijing, China

    Hui Zeng

  26. State Key Laboratory of Soil Pollution Control and Safety, Southern University of Science and Technology, Shenzhen, China

    Junjian Wang

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

    Junjian Wang

Authors
  1. Qingpei Yang
    View author publications

    Search author on:PubMed Google Scholar

  2. Binglin Guo
    View author publications

    Search author on:PubMed Google Scholar

  3. Mingzhen Lu
    View author publications

    Search author on:PubMed Google Scholar

  4. Yanjie Liu
    View author publications

    Search author on:PubMed Google Scholar

  5. Paul Kardol
    View author publications

    Search author on:PubMed Google Scholar

  6. Peter B. Reich
    View author publications

    Search author on:PubMed Google Scholar

  7. Richard D. Bardgett
    View author publications

    Search author on:PubMed Google Scholar

  8. Johannes H. C. Cornelissen
    View author publications

    Search author on:PubMed Google Scholar

  9. Nathan J. B. Kraft
    View author publications

    Search author on:PubMed Google Scholar

  10. Sandra Díaz
    View author publications

    Search author on:PubMed Google Scholar

  11. Ian J. Wright
    View author publications

    Search author on:PubMed Google Scholar

  12. Nianpeng He
    View author publications

    Search author on:PubMed Google Scholar

  13. J. Aaron Hogan
    View author publications

    Search author on:PubMed Google Scholar

  14. Yuxin Pei
    View author publications

    Search author on:PubMed Google Scholar

  15. Qinwen Han
    View author publications

    Search author on:PubMed Google Scholar

  16. Zhenjiang Li
    View author publications

    Search author on:PubMed Google Scholar

  17. Zheng Wang
    View author publications

    Search author on:PubMed Google Scholar

  18. Wanqin Yang
    View author publications

    Search author on:PubMed Google Scholar

  19. Junxiang Ding
    View author publications

    Search author on:PubMed Google Scholar

  20. Zhongling Yang
    View author publications

    Search author on:PubMed Google Scholar

  21. Huifang Wu
    View author publications

    Search author on:PubMed Google Scholar

  22. Carlos P. Carmona
    View author publications

    Search author on:PubMed Google Scholar

  23. Oscar J. Valverde-Barrantes
    View author publications

    Search author on:PubMed Google Scholar

  24. Dezhu Li
    View author publications

    Search author on:PubMed Google Scholar

  25. Jie Cai
    View author publications

    Search author on:PubMed Google Scholar

  26. Hui Zeng
    View author publications

    Search author on:PubMed Google Scholar

  27. Yue Zhang
    View author publications

    Search author on:PubMed Google Scholar

  28. Weizheng Ren
    View author publications

    Search author on:PubMed Google Scholar

  29. Yong Zhao
    View author publications

    Search author on:PubMed Google Scholar

  30. Xitian Yang
    View author publications

    Search author on:PubMed Google Scholar

  31. Guoqiang Fan
    View author publications

    Search author on:PubMed Google Scholar

  32. Junjian Wang
    View author publications

    Search author on:PubMed Google Scholar

  33. Guoyong Li
    View author publications

    Search author on:PubMed Google Scholar

  34. Deliang Kong
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Q.Y. and D.K. conceived of the idea. Q.Y. and B.G. completed the creation of figures. Q.Y., D.K., M.L. and P.B.R. conducted the statistical analyses. Q.Y., D.K., J.W., G.L., H.W. and Y.L. discussed and contributed to the final framework of this study. Q.Y and D.K. wrote the first draft of the paper with significant help from P.K., R.D.B., J.H.C.C., S.D., I.J.W. and J.A.H. Q.Y., B.G., M.L., Y.L., P.K., P.B.R., R.D.B., J.H.C.C., N.J.B.K., S.D., I.J.W., N.H., J.A.H., Y.P., Q.H., Z.L., Z.W., W.Y., J.D., Z.Y., H.W., C.P.C., O.J.V.-B., D.L., J.C., H.Z., Y. Zhang, W.R., Y. Zhao, X.Y., G.F., J.W., G.L. and D.K. contributed to the completion and revision of the paper.

Corresponding authors

Correspondence to Junjian Wang, Guoyong Li or Deliang Kong.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Plants thanks Wenming Bai, Christina Birnbaum and Thomas Kuyper for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Phylogenetic tree of 626 taxa in the study.

The internal purple section of the phylogeny corresponds to the Cornales, the red section to the Ranunculales, the green section to the Magnoliales, the coffee-colored section to the Gymnosperms, the yellow section to the Rosales, and the blue section to the Saxifragales. The various color bars on the outside of the phylogeny’s inner circle correspond to the mycorrhizal group, growth form, and climatic zone (in that order from inside to outside) of the respective plant species (the tips of the phylogeny). AM = arbuscular mycorrhizal; ECM = ectomycorrhizal; ERM = ericoid mycorrhizal; Non = unknown / non-mycorrhizal. Due to the limited number of ERM species, we only use AM and ECM species in our analysis.

Extended Data Fig. 2 Relationships between root diameter and seed mass.

Analyses were performed using our field-measured trait data of this study (a, c) and existing data from the literature (b, d) using the standardized major axis (SMA) regression. For arbuscular mycorrhizal (AM) plant species, a significant positive correlation is shown: Field-measured data (a): Green filled circles; regression equation: log10(y) = 5.80 log10(x) + 4.46, r = 0.43, P = 2.2 × 10−16, n = 489. Literature data: (b): Tan filled circles; regression equation: log10(y) = 6.10 log10(x) + 4.41, r = 0.43, P = 2.2 × 10−16, n = 506. The scaling exponent for the field-measured data (a) is 5.80 (95% confidence interval (CI) = 5.35−6.28), equivalent to the exponent derived from the literature data (b) (6.10; 95% CI = 5.61−6.57) (P = 2.22 × 10−16; see the Method section). In contrast, no significant correlation was found for ectomycorrhizal (ECM) plant species: Field-measured data (c): Tan filled circles; r = 0.01, P = 0.91, n = 78. Literature data (d): Tan filled circles; r = 0.24, P = 0.07, n = 55. Significance was tested using a two-sided t-test. All data are plotted on logarithmic scales (log10) for both axes, with each point representing a single plant species.

Extended Data Fig. 3 Relationships between root anatomical traits and seed mass for different plant growth forms and mycorrhizal types.

Analyses were performed using field-measured data using the standardized major axis (SMA) regression. There is a significant positive relationship between seed mass and root diameter in arbuscular mycorrhizal (AM) tree and shrub species (green filled circles; a, log10(y) = 5.75 log10(x) + 4.51, r = 0.40, P = 2.24 × 10−13, 95% CI = 5.19−6.38, n = 303; tan filled circles, d, log10(y) = 5.55 log10(x) + 4.21, r = 0.38, P = 6.70 × 10−6, 95% CI = 4.85−6.34, n = 186). The cortical thickness and seed mass of AM plant species show a positive relationship across two growth forms (green filled circles, b, log10(y) = 4.73 log10(x) - 7.34, r = 0.33, P = 1.26 × 10−9, 95% CI = 4.25−5.26, n = 303; tan filled circles; e, log10(y) = 4.63 log10(x) - 7.40, r = 0.26, P = 0.0003, 95% CI = 4.02−5.33, n = 185). The vessel diameter and seed mass is positively correlated only in tree species (green filled circles, c, log10(y) = 7.82 log10(x) - 4.37, r = 0.20, P = 0.0006, 95% CI = 6.98−8.78, n = 283), but not in shrub species (tan filled circles, f, r = 0.05, P = 0.51, n = 175). Conditional correlations by considering the significant relationship between cortical thickness and vessel diameter (r = 0.52, P < 0.01) shows no correlation between vessel diameter and seed mass (r = 0.03, P = 0.49) but a positive correlation between cortical thickness and seed mass (r = 0.22, P < 0.01). In contrast, no correlation is observed for ectomycorrhizal (ECM) species (green open circles, a, r = 0.05, P = 0.73, n = 62; b, r = 0.18, P = 0.15, n = 62; c, r = 0.01, P = 0.92, n = 57; tan open circles, d, r = 0.09, P = 0.73, n = 16; e, r = 0.44, P = 0.09, n = 16; f, r = 0.13, P = 0.67, n = 14). Significance was tested using a two-sided t-test. Data are plotted on a logarithmic scale (log10) for both axes, with each point representing one species.

Extended Data Fig. 4 Relationships between root traits and seed mass of different mycorrhizal types across different climatic zones.

Analyses were performed using field-measured data using the standardized major axis (SMA) regression. Seed mass with both root diameter and cortical thickness of arbuscular mycorrhiza (AM) plant species are positively correlated in tropics and subtropics (green filled circles, a, log10(y) = 5.72 log10(x) + 4.42, r = 0.38, P = 1.83 × 10−8, 95% CI = 5.04−6.50, n = 206; d, log10(y) = 5.80 log10(x) + 4.50, r = 0.44, P = 5.88 × 10−10, 95% CI = 5.08−6.63, n = 177; tan filled circles, b, log10(y) = 4.40 log10(x) −6.61, r = 0.31, P = 4.58 × 10−6, 95% CI = 3.86−5.01, n = 206; e, log10(y) = 4.76 log10(x) −7.56, r = 0.38, P = 1.92 × 10−7, 95% CI = 4.15−5.46, n = 177), but not in temperate (green filled circles, g, r = 0.17, P = 0.09, n = 106. tan filled circles, h, r = 0.1, P = 0.31, n = 105). In contrast, no correlation is observed for ectomycorrhizal (ECM) plant species (green open circles, a, R2 = 0.27, P = 0.45, n = 10; d, r = 0.12, P = 0.57, n = 25; g, r = 0.07, P = 0.65, n = 43; tan open circles, b, r = 0.48, P = 0.16, n = 10; e, r = 0.08, P = 0.71, n = 25; h, r = 0.29, P = 0.06, n = 43). The vessel diameter and seed mass of AM and ECM plant species show no correlation across three climatic zones (blue filled circles, c, r = 0.13, P = 0.07, n = 192; f, r = 0.15, P = 0.052, n = 170; i, r = 0.002, P = 0.95, n = 96; blue open circles, c, r = 0.62, P = 0.10, n = 8; f, r = 0.06, P = 0.79, n = 24; i, r = 0.06, P = 0.68, n = 39). Significance was tested using a two-sided t-test. Data are plotted on a logarithmic scale (log10) for both axes, with each point representing one species.

Extended Data Fig. 5 Relationships between root diameter and seed mass for global woody and non-woody species.

Analyses were performed using field-measured data of this study and literature data using the standardized major axis (SMA) regression. There is a positive correlation between root diameter and seed mass in global woody arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) plant species (AM + ECM) plant species (Total, a, log10(y) = 5.93 log10(x) + 4.52, r = 0.26, P = 2.2 × 10−16, CI = 5.56−6.33, n = 854). The positive correlation between root diameter and seed mass persists for both global woody and non-woody AM plant species (green filled circles, a, log10(y) = 5.55 log10(x) + 4.27, r = 0.31, P = 2.2 × 10−16, n = 729; tan filled circles, b, log10(y) = 4.76 log10(x) + 4.26, r = 0.41, P = 2.79 × 10−12, n = 266). For global woody AM plant species, the scaling exponent is 5.55 (95% (CI) = 5.18−5.95), which is significantly different from that of global non-woody AM plant species (exponent = 4.76, 95% CI = 4.26−5.31) (P = 2.22 × 10−16, see the Methods section). In contrast, no correlation is observed for ECM woody plant species (green open circles, a, r = 0.14, P = 0.11, n = 137). Non-woody ECM plant species are not analyzed due to the limited number of species. Significance was tested using a two-sided t-test. Data are presented on logarithmic scales (log10) for both axes, with each point representing one species.

Extended Data Fig. 6 Pairwise correlations for all plant traits used in this study.

Analyses were performed using field-measured data of this study. In each scatter plot, the blue scatter is represented by the raw data, and the red scatter is represented by PGLS data (removal of the phylogenetic signal). SM - seed mass, RD - root diameter, SRL - specific root length, RTD - root tissue density, RN- root nitrogen concentration, CT - root cortical thickness, VD – root vessel diameter, VDen – root vessel density, LA - single leaf area, SLA – specific leaf area, LN - leaf nitrogen concentration, and H - height of mature plants. Regression lines represent raw data correlations (blue) and phylogenetically-corrected bivariate relationships calculated by fitting Phylogenetic Generalized Least Square models (red). Correlation coefficients are obtained using Pearson correlation, with the raw data shown in blue and the phylogenetic correction data in red (* 0.05 > P > 0.01; ** 0.01 > P > 0.001; *** P < 0.001) Note that the two regression lines and scatter points are too close to be distinguished by the naked eye.

Extended Data Fig. 7 Relationships between mature plant root diameter and seedling root diameter of a same plant species.

Pearson correlation is conducted using our field-measured data and literature data (Siqueira and Saggin-Júnior28). There is a positive relationship between root diameter of mature plants and root diameter of seedlings (y = 1.04x + 0.02, r = 0.89, P = 2.14 × 10−12, n = 39). Shaded areas indicates 95% confidence intervals of the regression lines. Significance was tested using a two-sided t-test. Each point represents one species.

Extended Data Fig. 8 Indirect evidence supporting the Pathogen Resistance Hypothesis.

Analyses were performed using field-measured data of this study and literature data. a, there are differences in arbuscular mycorrhizal (AM) colonization rates across different climatic zones, with colonization rates in the tropics (blue) and subtropics (red) being higher than colonization rates in the temperate zone (green) (n = 237; *** P < 0.001). Pearson correlation indicates a positive correlation between soil pathogen richness and both root diameter and cortical thickness in arbuscular mycorrhizal (AM) plant species (b, y = 208.43x−22.41, r = 0.67, P = 6.59 × 10−9; d, y = 1.43x−70.55, r = 0.85, P = 2.2 × 10−16), but not in ectomycorrhizal (ECM) plant species (c, r = 0.14, P = 0.25; e, r = 0.17, P = 0.20). Shaded areas indicates 95% confidence intervals of the regression lines. The soil pathogen richness here were obtained through sequencing of soil samples collected from XSBN, DHS, SNJ, JGS, CBS, HZ sites (see the Methods section for details of these sites). The arbuscular mycorrhizal colonization data were collected from the GRooT database. Violin plots indicate the median value (solid line), the 25th and 75th percentiles (box), and the density of the data (width of the violin).

Extended Data Fig. 9 Relationships between arbuscular mycorrhizal colonization rates and root diameter in woody and non-woody plants.

Analyses were performed using field-measured data (a), as well as data from GRooT and literature (b, c). a, root diameter is positively related with seed mass in temperate non-woody species using the standardized major axis (SMA) regression (non-woody; a, log10(y) = 3.94 log10(x) + 1.01, r = 0.46, P = 2.55 × 10-11, 95% CI = 3.46−4.50, n = 285), but not in temperate woody species (woody, a, r = 0.06, P = 0.32, n = 284). b, Pearson correlation indicates a significantly positive relationship between arbuscular mycorrhizal (AM) colonization and root diameter in both woody and non-woody plants (woody, y = 55.88x + 33.25, r = 0.34, P = 9.66 × 10-7, n = 202; non-woody, y = 57.24x + 1.44, r = 0.53, P = 0.001, n = 32). Shaded areas indicates 95% confidence intervals of the regression lines. Mixed linear model showed no effect of plant growth form (woody versus non-woody) on the linear regression between arbuscular mycorrhizal colonization and root diameter (P = 0.51). c, boxplots of AM colonization rates by plant growth form; the red points represent woody plants, and the blue points represent non-woody plants. Horizontal lines in the middle of the box plots are the median AM colonization rates. There is a difference in AM colonization rate between woody and non-woody plants (n = 234, P = 0.003, F = 50.57****). Each point representing one species. Box plots indicate the median value (solid line), 25th and 75th percentiles (box), and the data range (whiskers).

Extended Data Table 1 Summary of sampling information for all Chinese field sites where root traits were measured
Full size table

Supplementary information

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

Yang, Q., Guo, B., Lu, M. et al. Arbuscular mycorrhizal association regulates global root–seed coordination. Nat. Plants 11, 1759–1768 (2025). https://doi.org/10.1038/s41477-025-02089-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41477-025-02089-4

This article is cited by

Search

Advanced 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