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:

Tree phytochemical diversity and herbivory are higher in the tropics

Nature Ecology & Evolution (2024)Cite this article

Subjects

Abstract

A long-standing but poorly tested hypothesis in plant ecology and evolution is that biotic interactions play a more important role in producing and maintaining species diversity in the tropics than in the temperate zone. A core prediction of this hypothesis is that tropical plants deploy a higher diversity of phytochemicals within and across communities because they experience more herbivore pressure than temperate plants. However, simultaneous comparisons of phytochemical diversity and herbivore pressure in plant communities from the tropical to the temperate zone are lacking. Here we provide clear support for this prediction by examining phytochemical diversity and herbivory in 60 tree communities ranging from species-rich tropical rainforests to species-poor subalpine forests. Using a community metabolomics approach, we show that phytochemical diversity is higher within and among tropical tree communities than within and among subtropical and subalpine communities, and that herbivore pressure and specialization are highest in the tropics. Furthermore, we show that the phytochemical similarity of trees has little phylogenetic signal, indicating rapid divergence between closely related species. In sum, we provide several lines of evidence from entire tree communities showing that biotic interactions probably play an increasingly important role in generating and maintaining tree diversity in the lower latitudes.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Geographic location, forest landscapes, and phylogenetic and elevational distributions of chemical profiles across the three forest types.
Fig. 2: Phytochemical variation and diversity (alpha and beta) among three forest types for whole metabolite profiles.
Fig. 3: Herbivory damage and specialization across the elevational gradient.

Similar content being viewed by others

Data availability

The MS data (.mzML) have been deposited in the MassIVE public repository and are available under accession number MSV000092950. The datasets analysed in the current study, including the molecular network, sample–sample chemical structural and compositional similarity, plot-species-abundance community data, phytochemical richness and the phylogenetic tree of 206 tree species, are available via Figshare at https://doi.org/10.6084/m9.figshare.22758269 (ref. 69).

Code availability

The R code used in the current study is available via Figshare at https://doi.org/10.6084/m9.figshare.22758269 (ref. 69).

References

  1. Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).

    Article  PubMed  Google Scholar 

  2. Willig, M. R., Kaufman, D. M. & Stevens, R. D. Latitudinal gradients of biodiversity: pattern, process, scale, and synthesis. Annu. Rev. Ecol. Evol. Syst. 34, 273–309 (2003).

    Article  Google Scholar 

  3. Gentry, A. H. Tree species richness of upper Amazonian forests. Proc. Natl Acad. Sci. USA 85, 156–159 (1988).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Valencia, R. et al. in Tropical Forest Diversity and Dynamism: Findings from a Large-Scale Plot Network (eds Losos, E. C. & Leigh, E. G.) 609–628 (Univ. Chicago Press, 2004).

  5. Dobzhansky, T. Evolution in the tropics. Am. Sci. 38, 209–221 (1950).

    Google Scholar 

  6. Fischer, A. G. Latitudinal variations in organic diversity. Evolution 14, 64–81 (1960).

    Article  Google Scholar 

  7. Janzen, D. H. Herbivores and the number of tree species in tropical forests. Am. Nat. 104, 501–528 (1970).

    Article  Google Scholar 

  8. Connell, J. H. in Dynamics of Numbers in Populations (eds Den Boer, P. J. & Gradwell, G. R.) 298–312 (PUDOC, 1971).

  9. Ehrlich, P. R. & Raven, P. H. Butterflies and plants—a study in coevolution. Evolution 18, 586–608 (1964).

    Article  Google Scholar 

  10. Van Valen, L. The red queen. Am. Nat. 111, 809–810 (1977).

    Article  Google Scholar 

  11. Coley, P. D. & Kursar, T. A. On tropical forests and their pests. Science 343, 35–36 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. Zvereva, E. L. & Kozlov, M. V. Latitudinal gradient in the intensity of biotic interactions in terrestrial ecosystems: sources of variation and differences from the diversity gradient revealed by meta-analysis. Ecol. Lett. 24, 2506–2520 (2021).

    Article  PubMed  Google Scholar 

  13. Roslin, T. et al. Higher predation risk for insect prey at low latitudes and elevations. Science 356, 742–744 (2017).

    Article  CAS  PubMed  Google Scholar 

  14. Moles, A. T. & Ollerton, J. Is the notion that species interactions are stronger and more specialized in the tropics a zombie idea? Biotropica 48, 141–145 (2016).

    Article  Google Scholar 

  15. Coley, P. D. & Barone, J. A. Herbivory and plant defenses in tropical forests. Annu. Rev. Ecol. Evol. Syst. 27, 305–335 (1996).

    Article  Google Scholar 

  16. Schemske, D. W., Mittelbach, G. G., Cornell, H. V., Sobel, J. M. & Roy, K. Is there a latitudinal gradient in the importance of biotic interactions? Annu. Rev. Ecol. Evol. Syst. 40, 245–269 (2009).

    Article  Google Scholar 

  17. Ali, J. G. & Agrawal, A. A. Specialist versus generalist insect herbivores and plant defense. Trends Plant Sci. 17, 293–302 (2012).

    Article  CAS  PubMed  Google Scholar 

  18. Carmona, D., Lajeunesse, M. J. & Johnson, M. T. J. Plant traits that predict resistance to herbivores. Funct. Ecol. 25, 358–367 (2011).

    Article  Google Scholar 

  19. Jones, C. G. & Firn, R. D. On the evolution of plant secondary chemical diversity. Phil. Trans. R. Soc. Lond. B. 333, 273–280 (1991).

    Article  Google Scholar 

  20. Endara, M. J., Forrister, D. L. & Coley, P. D. The evolutionary ecology of plant chemical defenses: from molecules to communities. Annu. Rev. Ecol. Evol. Syst. 54, 107–127 (2023).

    Article  Google Scholar 

  21. Kessler, A. & Kalske, A. Plant secondary metabolite diversity and species interactions. Annu. Rev. Ecol. Evol. Syst. 49, 115–138 (2018).

    Article  Google Scholar 

  22. Wang, S., Alseekh, S., Fernie, A. R. & Luo, J. The structure and function of major plant metabolite modifications. Mol. Plant 12, 899–919 (2019).

    Article  CAS  PubMed  Google Scholar 

  23. Iason, G. R., Dicke, M. & Hartley, S. E. The Ecology of Plant Secondary Metabolites: From Genes to Global Processes (Cambridge Univ. Press, 2012).

  24. Sedio, B. E. Recent breakthroughs in metabolomics promise to reveal the cryptic chemical traits that mediate plant community composition, character evolution and lineage diversification. N. Phytol. 214, 952–958 (2017).

    Article  CAS  Google Scholar 

  25. Wetzel, W. C. & Whitehead, S. R. The many dimensions of phytochemical diversity: linking theory to practice. Ecol. Lett. 23, 16–32 (2020).

    Article  PubMed  Google Scholar 

  26. Sedio, B. E., Parker, J. D., McMahon, S. M. & Wright, S. J. Comparative foliar metabolomics of a tropical and a temperate forest community. Ecology 99, 2647–2653 (2018).

    Article  PubMed  Google Scholar 

  27. Defossez, E. et al. Spatial and evolutionary predictability of phytochemical diversity. Proc. Natl Acad. Sci. USA 118, e2013344118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Forrister, D. L. et al. Diversity and divergence: evolution of secondary metabolism in the tropical tree genus Inga. N. Phytol. 237, 63–642 (2023).

    Article  Google Scholar 

  29. Qian, L. S., Chen, J. H., Deng, T. & Sun, H. Plant diversity in Yunnan: current status and future directions. Plant Divers. 42, 281–291 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Zhu, H. & Tan, Y. H. Flora and vegetation of Yunnan, southwestern China: diversity, origin and evolution. Diversity 14, 340 (2022).

    Article  Google Scholar 

  31. Sedio, B. E., Boya P, C. A. & Rojas Echeverri, J. C. A protocol for high-throughput, untargeted forest community metabolomics using mass spectrometry molecular networks. Appl. Plant Sci. 6, e1033 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  32. GNPS: Global Natural Products Social Molecular Networking. UCSD https://gnps.ucsd.edu (2023).

  33. Wang, M. X. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Aron, A. T. et al. Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nat. Protoc. 15, 1954–1991 (2020).

    Article  CAS  PubMed  Google Scholar 

  35. Kim, H. W. et al. NPClassifier: a deep neural network-based structural classification tool for natural products. J. Nat. Prod. 84, 2795–2807 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Chao, A. et al. An attribute-diversity approach to functional diversity, functional beta diversity, and related (dis)similarity measures. Ecol. Monogr. 89, e01343 (2019).

    Article  Google Scholar 

  37. Swenson, N. G. Functional and Phylogenetic Ecology in R (Springer, 2014).

  38. Kraft, N. J. B. et al. Disentangling the drivers of β diversity along latitudinal and elevational gradients. Science 333, 1755–1758 (2011).

    Article  CAS  PubMed  Google Scholar 

  39. Wang, X. Z. et al. Niche differentiation along multiple functional-trait dimensions contributes to high local diversity of Euphorbiaceae in a tropical tree assemblage. J. Ecol. 110, 2731–2744 (2022).

    Article  Google Scholar 

  40. Labandeira, C. C., Wilf, P., Johnson, K. R. & Marsh, F. Guide to insect (and other) damage types on compressed plant fossils v.3.0. figshare https://doi.org/10.6084/m9.figshare.16571441.v1 (2007).

  41. Kursar, T. A. et al. Linking bioprospecting with sustainable development and conservation: the Panama case. Biodivers. Conserv. 16, 2789–2800 (2007).

    Article  Google Scholar 

  42. Kursar, T. A. et al. The evolution of antiherbivore defenses and their contribution to species coexistence in the tropical tree genus Inga. Proc. Natl Acad. Sci. USA 106, 18073–18078 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Song, X. Y. et al. Different environmental factors drive tree species diversity along elevation gradients in three climatic zones in Yunnan, southern China. Plant Divers. 43, 433–443 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Song, X. Y., Nakamura, A., Sun, Z. H., Tang, Y. & Cao, M. Elevational distribution of adult trees and seedlings in a tropical montane transect, Southwest China. Mt. Res. Dev. 36, 342–354 (2016).

    Article  Google Scholar 

  45. Endara, M. J. et al. Divergent evolution in antiherbivore defences within species complexes at a single Amazonian site. J. Ecol. 103, 1107–1118 (2015).

    Article  Google Scholar 

  46. Richards, L. A. et al. Phytochemical diversity drives plant–insect community diversity. Proc. Natl Acad. Sci. USA 112, 10973–10978 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Salazar, D. et al. Origin and maintenance of chemical diversity in a species-rich tropical tree lineage. Nat. Ecol. Evol. 2, 983–990 (2018).

    Article  PubMed  Google Scholar 

  48. Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sedio, B. E., Rojas Echeverri, J. C., Boya P, C. A. & Wright, S. J. Sources of variation in foliar secondary chemistry in a tropical forest tree community. Ecology 98, 616–623 (2017).

    Article  PubMed  Google Scholar 

  50. Oksanen, J. et al. vegan: community ecology package. R package v.2.6-4 CRAN https://CRAN.R-project.org/package=vegan (2022).

  51. Magneville, C. et al. mFD: an R package to compute and illustrate the multiple facets of functional diversity. Ecography 2022, e05904 (2022).

    Article  Google Scholar 

  52. Millar, R. B., Anderson, M. J. & Tolimieri, N. Much ado about nothings: using zero similarity points in distance–decay curves. Ecology 92, 1717–1722 (2011).

    Article  PubMed  Google Scholar 

  53. Graco-Roza, C. et al. Distance decay 2.0—a global synthesis of taxonomic and functional turnover in ecological communities. Glob. Ecol. Biogeogr. 31, 1399–1421 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Anderson, M. J. et al. Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist. Ecol. Lett. 14, 19–28 (2011).

    Article  PubMed  Google Scholar 

  55. Haan, J. & Avery, M. ciTools: confidence or prediction intervals, quantiles, and probabilities for statistical models. R package v.0.6.1 CRAN https://CRAN.R-project.org/package=ciTools (2020).

  56. Kurokawa, H. et al. Plant characteristics drive ontogenetic changes in herbivory damage in a temperate forest. J. Ecol. 110, 2772–2784 (2022).

    Article  Google Scholar 

  57. Katabuchi, M. LeafArea: an R package for rapid digital image analysis of leaf area. Ecol. Res. 30, 1073–1077 (2015).

    Article  Google Scholar 

  58. Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).

    Article  PubMed  Google Scholar 

  59. Carvalho, M. R. et al. Insect leaf-chewing damage tracks herbivore richness in modern and ancient forests. PLoS ONE 9, e94950 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Smith, D. M. & Nufio, C. R. Levels of herbivory in two Costa Rican rain forests: implications for studies of fossil herbivory. Biotropica 36, 318–326 (2004).

    Google Scholar 

  61. Azevedo-Schmidt, L., Meineke, E. K. & Currano, E. D. Insect herbivory within modern forests is greater than fossil localities. Proc. Natl Acad. Sci. USA 119, e2202852119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Wang, X. Z. et al. Phytochemical diversity impacts herbivory in a tropical rainforest tree community. Ecol. Lett. 26, 1898–1910 (2023).

    Article  PubMed  Google Scholar 

  63. Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article  Google Scholar 

  64. Jin, Y. & Qian, H. V. PhyloMaker: an R package that can generate very large phylogenies for vascular plants. Ecography 42, 1353–1359 (2019).

    Article  Google Scholar 

  65. Adams, D. C. A generalized K statistic for estimating phylogenetic signal from shape and other high-dimensional multivariate data. Syst. Biol. 63, 685–697 (2014).

    Article  PubMed  Google Scholar 

  66. Simon, P. B., Theodore Garland, J. R. & Anthony, R. I. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution 57, 717–745 (2003).

    Google Scholar 

  67. Letunic, I. & Bork, P. Interactive Tree of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022). https://www.R-project.org/

  69. Sun, L. et al. Tree phytochemical diversity and herbivory are higher in the tropics. figshare https://doi.org/10.6084/m9.figshare.22758269 (2024).

Download references

Acknowledgements

This research was supported by the NSFC China–US Dimensions of Biodiversity Grant (DEB: no. 32061123003 to M.C.); the National Natural Science Foundation of China (grant nos. 32201318 to L.S. and 31870410 to J.Y.); the Chinese Academy of Sciences Youth Innovation Promotion Association (grant no. Y202080 to J.Y.); the Distinguished Youth Scholar of Yunnan (grant no. 202101AV070005 to J.Y.); the Ten Thousand Talent Plans for Young Top-Notch Talents of Yunnan Province (grant no. YNWR-QNBJ-2018-309 to J.Y.); a Postdoctoral Fellowship of Xishuangbanna Tropical Botanical Garden, CAS, to L.S.; the Postdoctoral Science Foundation of Yunnan Province to L.S.; the 14th Five-Year Plan of the Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences (grant nos. XTBG-1450101 and E3ZKFF2B01 to J.Y.); and an NSF US–China Dimensions of Biodiversity Grant (DEB: no. 2124466) to N.G.S. We acknowledge support from Xishuangbanna Station for Tropical Rain Forest Ecosystem Studies, Ailaoshan Station for Subtropical Forest Ecosystem Studies and Lijiang Forest Ecosystem Research Station. We thank the Molecular Biology Experiment Center in Germplasm Bank of Wild Species, Chinese Academy of Sciences, for facilitating the extraction of plant metabolites, and the State Key Laboratory of Phytochemistry and Plant Resources in West China, Chinese Academy of Sciences, for performing the UHPLC–MS/MS analysis. We thank J. Wang, C. Xu, P. Song, T. Liang and many local residents for their assistance in collecting leaf samples. We also thank J. Yang, H. Liu and Y. Tan for their kind assistance during extracting plant metabolites and metabolite analysis.

Author information

Authors and Affiliations

  1. CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, China

    Lu Sun, Yunyun He, Min Cao, Xuezhao Wang & Jie Yang

  2. University of Chinese Academy Sciences, Beijing, China

    Yunyun He & Xuezhao Wang

  3. School of Ethnic Medicine, Key Lab of Chemistry in Ethnic Medicinal Resources, State Ethnic Affairs Commission & Ministry of Education of China, Yunnan Minzu University, Kunming, China

    Xiang Zhou

  4. Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA

    Nathan G. Swenson

Authors
  1. Lu Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yunyun He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Min Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xuezhao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jie Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Nathan G. Swenson
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.Y., L.S. and N.G.S. designed the study. M.C. set up the forest inventory plots. L.S., X.Z., Y.H. and X.W. collected and processed the metabolomics data. L.S., Y.H. and X.W. collected and processed the leaf samples. L.S. and J.Y. analysed the data with input from all authors. J.Y., N.G.S. and L.S. wrote the paper. All authors provided feedback on the final version of the paper.

Corresponding author

Correspondence to Jie Yang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks Susan Whitehead and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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 Observed phytochemical alpha diversity for seven biosynthetic pathway categories within each climatic zone (tropical, sub-tropical and sub-alpine) with diverse q exponents (Qorder = 0, 1, 2).

(a) terpenoids (n = 20 in tropical zone, n = 20 in sub-tropical zone, n = 20 in sub-alpine zone, for one of q exponents), (b) shikimates and phenylpropanoids (n = 20 in tropical zone, n = 20 in sub-tropical zone, n = 20 in sub-alpine zone, for one of q exponents), (c) polyketides (n = 20 in tropical zone, n = 20 in sub-tropical zone, n = 20 in sub-alpine zone, for one of q exponents), (d) alkaloids (n = 20 in tropical zone, n = 20 in sub-tropical zone, n = 20 in sub-alpine zone, for one of q exponents), (e) fatty acids (n = 20 in tropical zone, n = 20 in sub-tropical zone, n = 17 in sub-alpine zone, for one of q exponents), (f) amino acids/peptides (n = 18 in tropical zone, n = 19 in sub-tropical zone, n = 12 in sub-alpine zone, for one of q exponents), (g) carbohydrates (n = 17 in tropical zone, n = 20 in sub-tropical zone, n = 17 in sub-alpine zone, for one of q exponents). In all panels, the significance of difference of phytochemical alpha diversity across forest type pairs were tested using a one-way ANOVA with a post-hoc Tukey test. In boxplots: the centre line represents the median; the lower and upper hinges correspond to the 25th and 75th percentiles; the lower and upper whiskers extend to the lowest and highest points to a limit of 1.5× the interquartile range from the closest hinge.

Extended Data Fig. 2 Observed phytochemical beta diversity for seven biosynthetic pathway categories within each climatic zone (tropical, sub-tropical and sub-alpine) with diverse q exponents (Qorder = 0, 1, 2).

(a) terpenoids (n = 190 in tropical zone, n = 190 in sub-tropical zone, n = 190 in sub-alpine zone, for one of q exponents), (b) shikimates and phenylpropanoids (n = 190 in tropical zone, n = 190 in sub-tropical zone, n = 190 in sub-alpine zone, for one of q exponents), (c) polyketides (n = 190 in tropical zone, n = 190 in sub-tropical zone, n = 190 in sub-alpine zone, for one of q exponents), (d) alkaloids (n = 190 in tropical zone, n = 190 in sub-tropical zone, n = 190 in sub-alpine zone, for one of q exponents), (e) fatty acids (n = 190 in tropical zone, n = 190 in sub-tropical zone, n = 136 in sub-alpine zone, for one of q exponents), (f) amino acids/peptides (n = 153 in tropical zone, n = 171 in sub-tropical zone, n = 66 in sub-alpine zone, for one of q exponents), (g) carbohydrates (n = 136 in tropical zone, n = 190 in sub-tropical zone, n = 136 in sub-alpine zone, for one of q exponents). In all panels, the significance of difference of phytochemical beta diversity across forest type pairs were tested using a one-way ANOVA with a post-hoc Tukey test. In boxplots: the centre line represents the median; the lower and upper hinges correspond to the 25th and 75th percentiles; the lower and upper whiskers extend to the lowest and highest points to a limit of 1.5× the interquartile range from the closest hinge.

Extended Data Fig. 3 Distance-decay curves for the whole plant specialized metabolites with diverse q exponents of 0, 1, 2.

The rate of decay (slope) and corresponding significance level were estimated by regressing the chemical similarity against elevational distance via generalized linear model with link log and a quasi-binomial family. The trend lines represent linear fits from regressions, and coloured shaded areas indicate 95% confidence interval (CI) of the prediction. Colours denote whole study region (grey), tropical zone (red), sub-tropical zone (blue) and sub-alpine zone (yellow). Panels, a, d, g and j show the slope of the relationship when q exponents is 0. Panels, b, e, h and k. show the slope of the relationship when q exponents is 1. Panels, c, f, i and l show the slope of the relationship when q exponents is 2.

Extended Data Fig. 4 Distance-decay curves for the plant specialized metabolites on terpenoids with diverse q exponents of 0, 1, 2.

The rate of decay (slope) and corresponding significance level were estimated by regressing the chemical similarity against elevational distance via generalized linear model with link log and a quasi-binomial family. The trend lines represent linear fits from regressions, and coloured shaded areas indicate 95% confidence interval (CI) of the prediction. Colours denote whole study region (grey), tropical zone (red), sub-tropical zone (blue) and sub-alpine zone (yellow). Panels, a, d, g and j show the slope of the relationship when q exponents is 0. Panels, b, e, h and k. show the slope of the relationship when q exponents is 1. Panels, c, f, i and l show the slope of the relationship when q exponents is 2.

Extended Data Fig. 5 Distance-decay curves for the plant specialized metabolites on shikimates and phenylpropanoids with diverse q exponents of 0, 1, 2.

The rate of decay (slope) and corresponding significance level were estimated by regressing the chemical similarity against elevational distance via generalized linear model with link log and a quasi-binomial family. The trend lines represent linear fits from regressions, and coloured shaded areas indicate 95% confidence interval (CI) of the prediction. Colours denote whole study region (grey), tropical zone (red), sub-tropical zone (blue) and sub-alpine zone (yellow). Panels, a, d, g and j show the slope of the relationship when q exponents is 0. Panels, b, e, h and k. show the slope of the relationship when q exponents is 1. Panels, c, f, i and l show the slope of the relationship when q exponents is 2.

Extended Data Fig. 6 Distance-decay curves for the plant specialized metabolites on polyketides with diverse q exponents of 0, 1, 2.

The rate of decay (slope) and corresponding significance level were estimated by regressing the chemical similarity against elevational distance via generalized linear model with link log and a quasi-binomial family. The trend lines represent linear fits from regressions, and coloured shaded areas indicate 95% confidence interval (CI) of the prediction. Colours denote whole study region (grey), tropical zone (red), sub-tropical zone (blue), sub-alpine zone (yellow). Panels, a, d, g and j show the slope of the relationship when q exponents is 0. Panels, b, e, h and k. show the slope of the relationship when q exponents is 1. Panels, c, f, i and l show the slope of the relationship when q exponents is 2.

Extended Data Fig. 7 Distance-decay curves for the plant specialized metabolites on alkaloids with diverse q exponents of 0, 1, 2.

The rate of decay (slope) and corresponding significance level were estimated by regressing the chemical similarity against elevational distance via generalized linear model with link log and a quasi-binomial family. The trend lines represent linear fits from regressions, and coloured shaded areas indicate 95% confidence interval (CI) of the prediction. Colours denote whole study region (grey), tropical zone (red), sub-tropical zone (blue) and sub-alpine zone (yellow). Panels, a, d, g and j show the slope of the relationship when q exponents is 0. Panels, b, e, h and k. show the slope of the relationship when q exponents is 1. Panels, c, f, i and l show the slope of the relationship when q exponents is 2.

Extended Data Fig. 8 Distance-decay curves for the plant specialized metabolites on fatty acids with diverse q exponents of 0, 1, 2.

The rate of decay (slope) and corresponding significance level were estimated by regressing the chemical similarity against elevational distance via generalized linear model with link log and a quasi-binomial family. The trend lines represent linear fits from regressions, and coloured shaded areas indicate 95% confidence interval (CI) of the prediction. Colours denote whole study region (grey), tropical zone (red), sub-tropical zone (blue) and sub-alpine zone (yellow). Panels, a, d, g and j show the slope of the relationship when q exponents is 0. Panels, b, e, h and k. show the slope of the relationship when q exponents is 1. Panels, c, f, i and l show the slope of the relationship when q exponents is 2.

Extended Data Fig. 9 Distance-decay curves for the plant specialized metabolites on amino acids/ peptides with diverse q exponents of 0, 1, 2.

The rate of decay (slope) and corresponding significance level were estimated by regressing the chemical similarity against elevational distance via generalized linear model with link log and a quasi-binomial family. The trend lines represent linear fits from regressions, and coloured shaded areas indicate 95% confidence interval (CI) of the prediction. Colours denote whole study region (grey), tropical zone (red), sub-tropical zone (blue) and sub-alpine zone (yellow). Panels, a, d, g and j show the slope of the relationship when q exponents is 0. Panels, b, e, h and k. show the slope of the relationship when q exponents is 1. Panels, c, f, i and l show the slope of the relationship when q exponents is 2.

Extended Data Fig. 10 Distance-decay curves for the plant specialized metabolites on carbohydrates with diverse q exponents of 0, 1, 2.

The rate of decay (slope) and corresponding significance level were estimated by regressing the chemical similarity against elevational distance via generalized linear model with link log and a quasi-binomial family. The trend lines represent linear fits from regressions, and coloured shaded areas indicate 95% confidence interval (CI) of the prediction. Colours denote whole study region (grey), tropical zone (red), sub-tropical zone (blue) and sub-alpine zone (yellow). Panels, a, d, g and j show the slope of the relationship when q exponents is 0. Panels, b, e, h and k. show the slope of the relationship when q exponents is 1. Panels, c, f, i and l show the slope of the relationship when q exponents is 2.

Supplementary information

Supplementary Information

Supplementary Figs. 1–6, Tables 1–7, materials and methods.

Reporting Summary

Peer Review File

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

Sun, L., He, Y., Cao, M. et al. Tree phytochemical diversity and herbivory are higher in the tropics. Nat Ecol Evol (2024). https://doi.org/10.1038/s41559-024-02444-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41559-024-02444-2

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