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The tree growth–herbivory relationship depends on functional traits across forest biodiversity experiments

Nature Ecology & Evolution volume 9, pages 2014–2024 (2025)Cite this article

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Abstract

While studies have demonstrated that higher tree species richness can increase forest productivity, the relationships between tree species richness, tree growth and herbivore damage remain insufficiently explored. Here we investigate these linkages using data from 8,790 trees across 80 species in 9 biodiversity experiments, spanning temperate and subtropical biomes. Despite considerable geographic variation, we reveal an overall positive relationship between tree species richness and insect herbivory, as well as between tree growth and herbivory, at individual, species and community levels. The tree growth–herbivory relationship is further influenced by leaf functional traits. In particular, we show that tree species with a higher carbon to nitrogen ratio and, to a lesser extent, tougher leaves, experienced higher herbivory when their growth rate increased. The associations between tree growth and herbivory are further modulated by climatic and soil variation among the sites. Our study highlights the role of functional traits in shaping the relationship between tree growth and herbivory, supporting the resource availability and plant vigour hypotheses.

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Fig. 1: Overview of the study sites and hypotheses.
Fig. 2: Relationships between tree species richness and percentage herbivory.
Fig. 3: Relationships between tree growth–productivity and herbivory.
Fig. 4: Functional trait-dependent relationships between tree growth and herbivory.

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Data availability

Insect herbivory, tree growth and functional trait data that support the findings of this study are available via figshare at https://doi.org/10.6084/m9.figshare.26968534.v1 (ref. 71).

References

  1. Sharma, L. K. & Naik, R. in Conservation of Saline Wetland Ecosystems: An Initiative towards UN Decade of Ecological Restoration 255–266 (Springer, 2024).

  2. Sachs, J. D., Kroll, C., Lafortune, G., Fuller, G. & Woelm, F. Sustainable Development Report 2022 (Cambridge Univ. Press, 2022).

  3. Chazdon, R. & Brancalion, P. Restoring forests as a means to many ends. Science 365, 24–25 (2019).

    CAS  PubMed  Google Scholar 

  4. Edwards, D. P. et al. Upscaling tropical restoration to deliver environmental benefits and socially equitable outcomes. Curr. Biol. 31, R1326–1341 (2021).

    CAS  PubMed  Google Scholar 

  5. Huang, Y. et al. Impacts of species richness on productivity in a large-scale subtropical forest experiment. Science 362, 80–83 (2018).

    CAS  PubMed  Google Scholar 

  6. Schnabel, F. et al. Species richness stabilizes productivity via asynchrony and drought-tolerance diversity in a large-scale tree biodiversity experiment. Sci. Adv. 7, eabk1643 (2021).

    PubMed  PubMed Central  Google Scholar 

  7. Züst, T. & Agrawal, A. A. Trade-offs between plant growth and defense against insect herbivory: an emerging mechanistic synthesis. Annu. Rev. Plant Biol. 68, 513–534 (2017).

    PubMed  Google Scholar 

  8. Gianoli, E. & Salgado-Luarte, C. Tolerance to herbivory and the resource availability hypothesis. Biol. Lett. 13, 20170120 (2017).

    PubMed  PubMed Central  Google Scholar 

  9. Fine, P. V. et al. The growth–defense trade‐off and habitat specialization by plants in Amazonian forests. Ecology 87, S150–S162 (2006).

    PubMed  Google Scholar 

  10. Belovsky, G. E. & Slade, J. B. Insect herbivory accelerates nutrient cycling and increases plant production. Proc. Natl Acad. Sci. USA 97, 14412–14417 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Schuldt, A. et al. Early positive effects of tree species richness on herbivory in a large-scale forest biodiversity experiment influence tree growth. J. Ecol. 103, 563–571 (2015).

    PubMed  PubMed Central  Google Scholar 

  12. Brezzi, M., Schmid, B., Niklaus, P. A. & Schuldt, A. Tree diversity increases levels of herbivore damage in a subtropical forest canopy: evidence for dietary mixing by arthropods? J. Plant Ecol. 10, 13–27 (2017).

    Google Scholar 

  13. Jactel, H. et al. Tree diversity drives forest stand resistance to natural disturbances. Curr. For. Rep. 3, 223–243 (2017).

    Google Scholar 

  14. Castagneyrol, B., Jactel, H., Vacher, C., Brockerhoff, E. G. & Koricheva, J. Effects of plant phylogenetic diversity on herbivory depend on herbivore specialization. J. Appl. Ecol. 51, 134–141 (2014).

    Google Scholar 

  15. Jactel, H., Moreira, X. & Castagneyrol, B. Tree diversity and forest resistance to insect pests: patterns, mechanisms, and prospects. Annu. Rev. Entomol. 66, 277–296 (2021).

    CAS  PubMed  Google Scholar 

  16. Hambäck, P. A., Inouye, B. D., Andersson, P. & Underwood, N. Effects of plant neighborhoods on plant–herbivore interactions: resource dilution and associational effects. Ecology 95, 1370–1383 (2014).

    PubMed  Google Scholar 

  17. Castagneyrol, B., Giffard, B., Péré, C. & Jactel, H. Plant apparency, an overlooked driver of associational resistance to insect herbivory. J. Ecol. 101, 418–429 (2013).

    Google Scholar 

  18. Coley, P. D., Bryant, J. P. & Chapin, F. S. III Resource availability and plant antiherbivore defense. Science 230, 895–899 (1985).

    CAS  PubMed  Google Scholar 

  19. Price, P. W. The plant vigor hypothesis and herbivore attack. Oikos 62, 244–251 (1991).

  20. Barbosa, P. et al. Associational resistance and associational susceptibility: having right or wrong neighbors. Annu. Rev. Ecol. Evol. Syst. 40, 1–20 (2009).

    Google Scholar 

  21. Sato, Y. et al. Reducing herbivory in mixed planting by genomic prediction of neighbor effects in the field. Nat. Commun. 15, 8467 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Kambach, S., Kühn, I., Castagneyrol, B. & Bruelheide, H. The impact of tree diversity on different aspects of insect herbivory along a global temperature gradient—a meta-analysis. PLoS ONE 11, e0165815 (2016).

    PubMed  PubMed Central  Google Scholar 

  23. Coley, P. D. Interspecific variation in plant anti‐herbivore properties: the role of habitat quality and rate of disturbance. New Phytol. 106, 251–263 (1987).

    Google Scholar 

  24. Endara, M. J. & Coley, P. D. The resource availability hypothesis revisited: a meta‐analysis. Funct. Ecol. 25, 389–398 (2011).

    Google Scholar 

  25. Bryant, J., Kuropat, P., Cooper, S., Frisby, K. & Owen-Smith, N. Resource availability hypothesis of plant antiherbivore defence tested in a South African savanna ecosystem. Nature 340, 227–229 (1989).

    Google Scholar 

  26. Bongers, F. J. et al. Functional diversity effects on productivity increase with age in a forest biodiversity experiment. Nat. Ecol. Evol. 5, 1594–1603 (2021).

    PubMed  Google Scholar 

  27. Ma, L. et al. Species identity and composition effects on community productivity in a subtropical forest. Basic Appl. Ecol. 55, 87–97 (2021).

    Google Scholar 

  28. Dosanjos, L. et al. Key leaf traits indicative of photosynthetic plasticity in tropical tree species. Trees 29, 247–258 (2015).

    CAS  Google Scholar 

  29. Thompson, K., Parkinson, J. A., Band, S. R. & Spencer, R. E. A comparative study of leaf nutrient concentrations in a regional herbaceous flora. New Phytol. 136, 679–689 (1997).

    CAS  PubMed  Google Scholar 

  30. Castro Sánchez-Bermejo, P., Davrinche, A., Matesanz, S., Harpole, W. S. & Haider, S. Within-individual leaf trait variation increases with phenotypic integration in a subtropical tree diversity experiment. New Phytol. 240, 1390–1404 (2023).

    PubMed  Google Scholar 

  31. Castro Sánchez-Bermejo, P. et al. Tree and mycorrhizal fungal diversity drive intraspecific and intraindividual trait variation in temperate forests: evidence from a tree diversity experiment. Funct. Ecol. 38, 1089–1103 (2024).

    Google Scholar 

  32. Verheyen, K. et al. Contributions of a global network of tree diversity experiments to sustainable forest plantations. Ambio 45, 29–41 (2016).

    CAS  PubMed  Google Scholar 

  33. Paquette, A. et al. A million and more trees for science. Nat. Ecol. Evol. 2, 763–766 (2018).

    PubMed  Google Scholar 

  34. Schuldt, A. et al. Multiple plant diversity components drive consumer communities across ecosystems. Nat. Commun. 10, 1460 (2019).

    PubMed  PubMed Central  Google Scholar 

  35. Grossman, J. J. et al. Synthesis and future research directions linking tree diversity to growth, survival, and damage in a global network of tree diversity experiments. Environ. Exp. Bot. 152, 68–89 (2018).

    Google Scholar 

  36. Leonard, J. S. et al. Explaining variation in plant–herbivore associational effects in a tree biodiversity experiment. J. Ecol. 111, 2694–2709 (2023).

    Google Scholar 

  37. Zhang, J. et al. Tree diversity promotes generalist herbivore community patterns in a young subtropical forest experiment. Oecologia 183, 455–467 (2017).

    PubMed  Google Scholar 

  38. Azevedo-Schmidt, L., Swain, A., Shoemaker, L. G. & Currano, E. D. Landscape-level variability and insect herbivore outbreak captured within modern forests provides a framework for interpreting the fossil record. Sci. Rep. 13, 9701 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Loranger, H. et al. Invertebrate herbivory increases along an experimental gradient of grassland plant diversity. Oecologia 174, 183–193 (2014).

    PubMed  Google Scholar 

  40. Schuldt, A. et al. Tree diversity promotes insect herbivory in subtropical forests of south‐east China. J. Ecol. 98, 917–926 (2010).

    PubMed  PubMed Central  Google Scholar 

  41. Bernays, E. A. & Chapman, R. F. Plant secondary compounds and grasshoppers: beyond plant defenses. J. Chem. Ecol. 26, 1773–1794 (2000).

    CAS  Google Scholar 

  42. Wang, M. Q. et al. Host functional and phylogenetic composition rather than host diversity structure plant–herbivore networks. Mol. Ecol. 29, 2747–2762 (2020).

    PubMed  Google Scholar 

  43. Ray, T. et al. Tree diversity increases productivity through enhancing structural complexity across mycorrhizal types. Sci. Adv. 9, eadi2362 (2023).

    PubMed  PubMed Central  Google Scholar 

  44. Sagrario, G., Geoff, M. G., Anantanarayanan, R. & Helen, I. N. Plant diversity and habitat structure affect tree growth, herbivory and natural enemies in shelterbelts. Basic Appl. Ecol. 6, 542–549 (2010).

    Google Scholar 

  45. Vázquez‐González, C. et al. Tree diversity enhances predation by birds but not by arthropods across climate gradients. Ecol. Lett. 27, e14427 (2024).

    PubMed  Google Scholar 

  46. Lefcheck, J. S., Whalen, M. A., Davenport, T. M., Stone, J. P. & Duffy, J. E. Physiological effects of diet mixing on consumer fitness: a meta‐analysis. Ecology 94, 565–572 (2013).

    PubMed  Google Scholar 

  47. Hawkins, B. A., Cornell, H. V. & Hochberg, M. E. Predators, parasitoids, and pathogens as mortality agents in phytophagous insect populations. Ecology 78, 2145–2152 (1997).

    Google Scholar 

  48. Yi, H. et al. Tree diversity and mycorrhizal type co-determine multitrophic ecosystem functions. J. Ecol. 112, 528–546 (2024).

    CAS  Google Scholar 

  49. Yi, H. et al. Belowground energy fluxes determine tree diversity effects on above- and belowground food webs. Curr. Biol. 35, 1870–1882 (2025).

    CAS  PubMed  Google Scholar 

  50. Cyr, H. & Pace, M. L. Magnitude and patterns of herbivory in aquatic and terrestrial ecosystems. Nature 361, 148–150 (1993).

    Google Scholar 

  51. Wang, M. Q. et al. Multiple components of plant diversity loss determine herbivore phylogenetic diversity in a subtropical forest experiment. J. Ecol. 107, 2697–2712 (2019).

    Google Scholar 

  52. Peterson, A. T. et al. Ecological Niches and Geographic Distributions (Princeton Univ. Press, 2011).

  53. Ågren, G. I. The C:N:P stoichiometry of autotrophs—theory and observations. Ecol. Lett. 7, 185–191 (2004).

    Google Scholar 

  54. Zuo, X. et al. Plant functional diversity mediates the effects of vegetation and soil properties on community-level plant nitrogen use in the restoration of semiarid sandy grassland. Ecol. Indic. 64, 272–280 (2016).

    CAS  Google Scholar 

  55. Kleyer, M. et al. Assessing species and community functional responses to environmental gradients: which multivariate methods? J. Veg. Sci. 23, 805–821 (2012).

    Google Scholar 

  56. Pérez‐Harguindeguy, N. et al. Leaf traits and herbivore selection in the field and in cafeteria experiments. Austral Ecol. 28, 642–650 (2003).

    Google Scholar 

  57. Schuldt, A. et al. Functional and phylogenetic diversity of woody plants drive herbivory in a highly diverse forest. New Phytol. 202, 864–873 (2014).

    PubMed  PubMed Central  Google Scholar 

  58. Eichenberg, D., Purschke, O., Ristok, C., Wessjohann, L. & Bruelheide, H. Trade-offs between physical and chemical carbon-based leaf defence: of intraspecific variation and trait evolution. J. Ecol. 103, 1667–1679 (2015).

    CAS  Google Scholar 

  59. Honkanen, T., Haukioja, E. & Suomela, J. Effects of simulated defoliation and debudding on needle and shoot growth in scots pine (Pinus sylvestris): implications of plant source/sink relationships for plant–herbivore studies. Func. Ecol. 8, 631–639 (1994).

    Google Scholar 

  60. Lin, D., Lai, J., Muller-Landau, H. C., Mi, X. & Ma, K. Topographic variation in aboveground biomass in a subtropical evergreen broad-leaved forest in China. PLoS ONE 7, e48244 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Gonzalez-Akre, E. et al. allodb: an R package for biomass estimation at globally distributed extratropical forest plots. Methods Ecol. Evol. 13, 330–338 (2022).

    Google Scholar 

  62. Salgado-Luarte, C. & Gianoli, E. Herbivores modify selection on plant functional traits in a temperate rainforest understory. Am. Nat. 180, E42–E53 (2012).

    PubMed  Google Scholar 

  63. Ness, J. H., Rollinson, E. J. & Whitney, K. D. Phylogenetic distance can predict susceptibility to attack by natural enemies. Oikos 120, 1327–1334 (2011).

    Google Scholar 

  64. Serrano-León, H., Nitschke, R., Scherer-Lorenzen, M. & Forrester, D. I. Intra-specific leaf trait variability of F. sylvatica, Q. petraea and P. abies in response to inter-specific competition and implications for forest functioning. Tree Physiol. 42, 253–272 (2022).

    PubMed  Google Scholar 

  65. Haase, J. et al. Contrasting effects of tree diversity on young tree growth and resistance to insect herbivores across three biodiversity experiments. Oikos 124, 1674–1685 (2015).

    Google Scholar 

  66. Kröber, W., Zhang, S., Ehmig, M. & Bruelheide, H. Linking xylem hydraulic conductivity and vulnerability to the leaf economics spectrum—a cross-species study of 39 evergreen and deciduous broadleaved subtropical tree species. PLoS ONE 9, e109211 (2014).

    PubMed  PubMed Central  Google Scholar 

  67. Kröber, W., Heklau, H. & Bruelheide, H. Leaf morphology of 40 evergreen and deciduous broadleaved subtropical tree species and relationships to functional ecophysiological traits. Plant Biol. 17, 373–383 (2015).

    PubMed  Google Scholar 

  68. Barajas Barbosa, M. P. et al. Assembly of functional diversity in an oceanic island flora. Nature 619, 545–550 (2023).

    CAS  PubMed  Google Scholar 

  69. Beaudette, D. E., Skovlin, J. M. & Roecker, S. soilDB: Soil database interface. R package v.2.8.5. CRAN https://doi.org/10.32614/CRAN.package.soilDB (2024).

  70. Levins, R. in Evolution in Changing Environments: Some Theoretical Explorations 7–19 (Princeton Univ. Press, 1968).

  71. Li, Y. & Liu, X. LiEtAl tree growth and herbivory data. Dataset. figshare https://doi.org/10.6084/m9.figshare.26968534.v1 (2025).

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Acknowledgements

We thank the site managers of BEF-China, CADE-BEF, BiodiversiTREE_SERC, BIOTREE_SP (Kaltenborn), IDENT_AU (Auclair), IDENT_SSM (Sault-Ste-Marie), IDENT_FR (Freiburg) and MyDiv for their efforts in maintaining the experiments and the support of many students (including L. Thoma, E. Weindel and X. Zhu) and local staff for the measurements. This study was supported by the National Key Research and Development Programme of China (2022YFF0802300) and the National Natural Science Foundation of China (32222055 and 32301337). X.L. was funded by the Youth Innovation Promotion Association CAS (2023019). A.S., J.B., H.B., M.S.-L., M.S. and B.S. gratefully acknowledge the funding by the German Research Foundation (DFG FOR 891). A.S., H.B. and N.E. also received further support through the TreeDì International Training group (319936945/GRK2324) and A.S., H.B. and M.S. through the MultiTroph research unit (452861007/FOR5281). K.T.B. was supported by McIntire Stennis Project (7006895) through the Maryland Agricultural Experiment Station and NSF grant (DEB2044361). We thank the Helmholtz Centre for Environmental Research (UFZ) for providing the field site of the MyDiv experiment, J. Quosh and the technical staff at the Bad Lauchstädt field station of the UFZ. This work was supported by a grant from the Baden-Württemberg Stiftung (elite programme for postdoctorals, 1.16101.17) to J.F. We acknowledge funding by the Deutsche Forschungsgemeinschaft (German Centre for Integrative Biodiversity Research, FZT118; and Gottfried Wilhelm Leibniz Prize, Ei 862/29-1).

Author information

Authors and Affiliations

  1. State Key Laboratory of Forage Breeding-by-Design and Utilization, Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China

    Yi Li, Shan Li, Yu Liang, Keping Ma & Xiaojuan Liu

  2. Forest Nature Conservation, University of Göttingen, Göttingen, Germany

    Andreas Schuldt

  3. Chair of Silviculture, University of Freiburg, Freiburg, Germany

    Jürgen Bauhus

  4. Departement de Biology, University of Sherbrooke, Sherbrooke, Quebec, Canada

    Michaël Belluau & Dominique Gravel

  5. Biometry and Environmental System Analysis, University of Freiburg, Freiburg, Germany

    Sylvie Berthelot

  6. Department of Entomology, University of Maryland, College Park, MD, USA

    Karin T. Burghardt

  7. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

    Helge Bruelheide, Nico Eisenhauer & Olga Ferlian

  8. INRAE, University of Bordeaux, UMR Biogeco, Cestas, France

    Bastien Castagneyrol & Hervé Jactel

  9. School of Ecology and State Key Laboratory of Biological Control, Sun Yat-sen University, Guangzhou, China

    Chengjin Chu

  10. Institute of Biology, Leipzig University, Leipzig, Germany

    Nico Eisenhauer & Olga Ferlian

  11. Animal Network Ecology, Department of Biology, University of Hamburg, Hamburg, Germany

    Jochen Fründ

  12. Geobotany, Faculty of Biology, University of Freiburg, Freiburg, Germany

    Tobias Gebauer & Michael Scherer-Lorenzen

  13. Smithsonian Environmental Research Center, Smithsonian Institution, Edgewater, MD, USA

    John D. Parker

  14. Ontario Ministry of Natural Resources and Forestry, Sault Ste. Marie, Ontario, Canada

    William C. Parker

  15. Ecological Networks, Technical University of Darmstadt, Darmstadt, Germany

    Michael Staab

  16. Institute of Ecology, Leuphana University Lüneburg, Lüneburg, Germany

    Michael Staab

  17. Forest & Nature Lab, Department of Environment, Faculty of Biosciences Engineering, Ghent University, Gontrode-Melle, Belgium

    Kris Verheyen

  18. Remote Sensing Laboratories, Department of Geography, University of Zurich, Zurich, Switzerland

    Bernhard Schmid

  19. University of Chinese Academy of Sciences, Beijing, China

    Xiaojuan Liu

  20. Zhejiang Qianjiangyuan Forest Biodiversity National Observation and Research Station, Beijing, China

    Xiaojuan Liu

Authors
  1. Yi Li
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  2. Andreas Schuldt
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  25. Xiaojuan Liu
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Contributions

X.L. conceived the study. Y. Li, A.S., J.B., M.B., S.B., K.T.B., H.B., B.C., C.C., N.E., O.F., J.F., T.G., D.G., H.J., S.L., Y. Liang, J.D.P., W.C.P., M.S.-L., M.S., K.V. and K.M. contributed methodology and investigation. The initial paper was prepared by Y. Li, X.L. A.S. M.S. and B.S. All authors helped to improve the readability of the text.

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Correspondence to Xiaojuan Liu.

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Extended data

Extended Data Fig. 1 Relationships between tree species richness or growth and herbivory under different environmental conditions.

Relationships between tree species richness or tree growth and herbivory under different mean annual temperature (MAT) and soil conditions. (a–d) represent predictions based on individual tree, species, community, and individual levels, respectively. The transition from light to dark blue indicates an increase in MAT. The shift from dark to light green reflects an increasing proportion of clay, water, and fine sand in the soil.

Extended Data Fig. 2 Functional trait-dependent relationships between tree growth and herbivory.

Results of principal component analysis (PCA) for five functional traits across all experiments (a). The first and second PCA axis explained 75% (PC1: 54.2%, PC2: 19.9%) of the total variation among species. Relationships between relative growth rate and herbivory depends on leaf carbon: nitrogen ratio (PC1, b) and leaf texture (PC2, c). Note that the analysis herein only includes the functional traits of leaves obtained through direct measurement, and does not include the traits downloaded from the TRY database. The axes are on sqrt root-transformed for RGR and arcsin-transformed for herbivory.

Extended Data Fig. 3 Single functional trait-dependent relationships between tree growth and herbivory.

Relationships between relative tree growth rate and herbivory for tree species grouped by mean values of (a) SLA, (b) LDMC, (c) leaf N content, (d) leaf C:N ratio and (e) leaf habit. Fitted lines are shown for individual functional traits within discrete ranges of trait values of study species. Darker colors of fitted lines indicates higher mean trait values. In panel (e), the light line indicates deciduous species and the dark line indicates evergreen species.

Extended Data Fig. 4 Relationships between tree species richness and herbivory, and between tree growth and herbivory under different biomes.

Relationships between tree species richness and herbivory, and between tree growth/productivity (RGR/biomass increment) and herbivory at the individual (a, d), species (b, e), and community level (c, f) in subtropical and temperate biomes. Each point represents the raw data of one observation, and each regression line represents the slope of linear models that show change in herbivory with tree species richness based on different climatic zones (subtropical: yellow, and temperate: green) or all zones (black lines) or in herbivory with RGR/biomass increment based on different climatic zones (subtropical: yellow, and temperate: green) or all zones (black lines). Solid lines show significant (P ≤ 0.05) effects and dashed lines show non-significant (P > 0.1) relationships. All tests were two-sided, with no adjustments for multiple comparisons. The shaded areas in a–f represent the 95% confidence interval of the herbivory responses to tree species richness or tree growth in linear models. The axes are on log2-transformed for tree species richness, sqrt root-transformed for RGR/biomass increment and arcsin-transformed for herbivory.

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Li, Y., Schuldt, A., Bauhus, J. et al. The tree growth–herbivory relationship depends on functional traits across forest biodiversity experiments. Nat Ecol Evol 9, 2014–2024 (2025). https://doi.org/10.1038/s41559-025-02835-z

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