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.

Global trait–environment relationships of plant communities

Abstract

Plant functional traits directly affect ecosystem functions. At the species level, trait combinations depend on trade-offs representing different ecological strategies, but at the community level trait combinations are expected to be decoupled from these trade-offs because different strategies can facilitate co-existence within communities. A key question is to what extent community-level trait composition is globally filtered and how well it is related to global versus local environmental drivers. Here, we perform a global, plot-level analysis of trait–environment relationships, using a database with more than 1.1 million vegetation plots and 26,632 plant species with trait information. Although we found a strong filtering of 17 functional traits, similar climate and soil conditions support communities differing greatly in mean trait values. The two main community trait axes that capture half of the global trait variation (plant stature and resource acquisitiveness) reflect the trade-offs at the species level but are weakly associated with climate and soil conditions at the global scale. Similarly, within-plot trait variation does not vary systematically with macro-environment. Our results indicate that, at fine spatial grain, macro-environmental drivers are much less important for functional trait composition than has been assumed from floristic analyses restricted to co-occurrence in large grid cells. Instead, trait combinations seem to be predominantly filtered by local-scale factors such as disturbance, fine-scale soil conditions, niche partitioning and biotic interactions.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Conceptual figure to illustrate Hypothesis 1.
Fig. 2: Principal component analysis of global plot-level trait means (CWMs).
Fig. 3: Principal component analysis of global within-plot trait variances (CWVs).
Fig. 4: The two strongest relationships found for global plot-level trait means (CWMs) in the sPlot dataset.

Data availability

The data contained in sPlot (the vegetation-plot data complemented by trait and environmental information) are available on request, by contacting any of the sPlot consortium members, for submission of a paper proposal. The proposals should follow the Governance and Data Property Rules of the sPlot Working Group, which are available on the sPlot website (www.idiv.de/sPlot).

References

  1. 1.

    Warming, E. Lehrbuch der ökologischen Pflanzengeographie – Eine Einführung in die Kenntnis der Pflanzenvereine (Borntraeger, Berlin, 1896).

    Google Scholar 

  2. 2.

    Garnier, E. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, 2630–2637 (2004).

    Article  Google Scholar 

  3. 3.

    Ordoñez, J. C. A global study of relationships between leaf traits, climate and soil measures of nutrient fertility. Glob. Ecol. Biogeogr. 18, 137–149 (2009).

    Article  Google Scholar 

  4. 4.

    Garnier, E., Navas, M.-L. & Grigulis, K. Plant Functional Diversity – Organism Traits, Community Structure, and Ecosystem Properties (Oxford Univ. Press, Oxford, 2016).

  5. 5.

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

    Article  Google Scholar 

  6. 6.

    Moles, A. T. et al. Global patterns in plant height. J. Ecol. 97, 923–932 (2009).

    Article  Google Scholar 

  7. 7.

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

    Article  CAS  Google Scholar 

  8. 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. 9.

    Adler, P. B. et al. Functional traits explain variation in plant life history strategies. Proc. Natl Acad. Sci. USA 111, 740–745 (2014).

    Article  CAS  PubMed  Google Scholar 

  10. 10.

    Marks, C. O. & Lechowicz, M. J. Alternative designs and the evolution of functional diversity. Am. Nat. 167, 55–67 (2006).

    Article  PubMed  Google Scholar 

  11. 11.

    Grime, J. P. Trait convergence and trait divergence in herbaceous plant communities: mechanisms and consequences. J. Veg. Sci. 17, 255–260 (2006).

    Article  Google Scholar 

  12. 12.

    Muscarella, R. & Uriarte, M. Do community-weighted mean functional traits reflect optimal strategies?. Proc. R. Soc. B 283, 20152434 (2016).

    Article  PubMed  Google Scholar 

  13. 13.

    Swenson, N. G. & Weiser, M. D. Plant geography upon the basis of functional traits: an example from eastern North American trees. Ecology 91, 2234–2241 (2010).

    Article  PubMed  Google Scholar 

  14. 14.

    Fyllas, N. M. et al. Basin-wide variations in foliar properties of Amazonian forest: phylogeny, soils and climate. Biogeosciences 6, 2677–2708 (2009).

    Article  Google Scholar 

  15. 15.

    Swenson, N. G. et al. Phylogeny and the prediction of tree functional diversity across novel continental settings. Glob. Ecol. Biogeogr. 26, 553–562 (2017).

    Article  Google Scholar 

  16. 16.

    Swenson, N. G. et al. The biogeography and filtering of woody plant functional diversity in North and South America. Glob. Ecol. Biogeogr. 21, 798–808 (2012).

    Article  Google Scholar 

  17. 17.

    Wright, I. J. et al. Global climatic drivers of leaf size. Science 357, 917–921 (2017).

    Article  CAS  Google Scholar 

  18. 18.

    Mayfield, M. M. & Levine, J. M. Opposing effects of competitive exclusion on the phylogenetic structure of communities. Ecol. Lett. 13, 1085–1093 (2010).

    Article  Google Scholar 

  19. 19.

    Kraft, N. J. B. et al. Community assembly, coexistence and the environmental filtering metaphor. Funct. Ecol. 29, 592–599 (2015).

    Article  Google Scholar 

  20. 20.

    Barboni, D. et al. Relationships between plant traits and climate in the Mediterranean region: a pollen data analysis. J. Veg. Sci 15, 635–646 (2004).

    Article  Google Scholar 

  21. 21.

    Borgy, B. et al. Plant community structure and nitrogen inputs modulate the climate signal on leaf traits. Glob. Ecol. Biogeogr. 26, 1138–1152 (2017).

    Article  Google Scholar 

  22. 22.

    van Bodegom, P. M., Douma, J. C. & Verheijen, L. M. A fully traits-based approach to modeling global vegetation distribution. Proc. Natl Acad. Sci. USA 111, 13733–13738 (2014).

    Article  CAS  PubMed  Google Scholar 

  23. 23.

    Moles, A. T. et al. Which is a better predictor of plant traits: temperature or precipitation? J. Veg. Sci. 25, 1167–1180 (2014).

    Article  Google Scholar 

  24. 24.

    Ordoñez, J. C. et al. Plant strategies in relation to resource supply in mesic to wet environments: does theory mirror nature?. Am. Nat. 175, 225–239 (2010).

    Article  PubMed  Google Scholar 

  25. 25.

    Simpson, A. J., Richardson, S. J. & Laughlin, D. C. Soil–climate interactions explain variation in foliar, stem, root and reproductive traits across temperate forests. Glob. Ecol. Biogeogr. 25, 964–978 (2016).

    Article  Google Scholar 

  26. 26.

    Lienin, P. & Kleyer, M. Plant leaf economics and reproductive investment are responsive to gradients of land use intensity. Agric. Ecosyst. Environ. 145, 67–76 (2011).

    Article  Google Scholar 

  27. 27.

    Maire, V. et al. Habitat filtering and niche differentiation jointly explain species relative abundance within grassland communities along fertility and disturbance gradients. New Phytol. 196, 497–509 (2012).

    Article  PubMed  Google Scholar 

  28. 28.

    Craine, J. M. et al. Global patterns of foliar nitrogen isotopes and their relationships with climate, mycorrhizal fungi, foliar nutrient concentrations, and nitrogen availability. New Phytol. 183, 980–992 (2009).

    Article  CAS  PubMed  Google Scholar 

  29. 29.

    Güsewell, S. N:P ratios in terrestrial plants: variation and functional significance. New Phytol. 164, 243–266 (2004).

    Article  Google Scholar 

  30. 30.

    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  Google Scholar 

  31. 31.

    Scheiter, S., Langan, L. & Higgins, S. I. Next generation dynamic global vegetation models: learning from community ecology. New Phytol. 198, 957–969 (2013).

    Article  PubMed  Google Scholar 

  32. 32.

    Boyle, B. et al. The Taxonomic Name Resolution Service: an online tool for automated standardization of plant names. BMC Bioinform. 14, 16 (2013).

    Article  Google Scholar 

  33. 33.

    Bremer, B. et al. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG III. Bot. J. Linn. Soc. 161, 105–121 (2009).

    Article  Google Scholar 

  34. 34.

    Schrodt, F. et al. BHPMF – a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography. Glob. Ecol. Biogeogr. 24, 1510–1521 (2015).

    Article  Google Scholar 

  35. 35.

    Kattge, J. et al. TRY ‒ a global database of plant traits. Glob. Change Biol. 17, 2905–2935 (2011).

    Article  Google Scholar 

  36. 36.

    Shan, H. et al. Gap filling in the plant kingdom – trait prediction using hierarchical probabilistic matrix factorization. In Proc. 29th Int. Conf. Machine Learning (ICML 2012) 1303−1310 (Omnipress, Madison, 2012).

  37. 37.

    Fazayeli, F. et al. Uncertainty quantified matrix completion using Bayesian Hierarchical Matrix factorization. In Proc. 13th Int. Conf. Machine Learning and Applications (ICMLA 2014) 312−317 (Institute of Electrical and Electronics Engineers, Danvers, 2014).

  38. 38.

    Borgy, B. et al. Sensitivity of community-level trait–environment relationships to data representativeness: a test for functional biogeography. Glob. Ecol. Biogeogr. 26, 729–739 (2017).

    Article  Google Scholar 

  39. 39.

    Herz, K. et al. Drivers of intraspecific trait variation of grass and forb species in German meadows and pastures. J. Veg. Sci. 28, 705–716 (2017).

    Article  Google Scholar 

  40. 40.

    Karger, D. N. et al. Climatologies at high resolution for the Earth’s land surface areas. Sci. Data 4, 170122 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Karger, D. N. et al. Climatologies at High Resolution for the Earth Land Surface Areas (Version 1.1) (World Data Center for Climate (WDCC) at DKRZ, 2016); http://chelsa-climate.org/downloads/

  42. 42.

    Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc 137, 553–597 (2011).

    Article  Google Scholar 

  43. 43.

    Synes, N. W. & Osborne, P. E. Choice of predictor variables as a source of uncertainty in continental-scale species distribution modelling under climate change. Glob. Ecol. Biogeogr. 20, 904–914 (2011).

    Article  Google Scholar 

  44. 44.

    Enquist, B. et al. Scaling from traits to ecosystems: developing a general trait driver theory via integrating trait-based and metabolic scaling theories. Adv. Ecol. Res. 52, 249–318 (2015).

    Article  Google Scholar 

  45. 45.

    Buzzard, V. et al. Re-growing a tropical dry forest: functional plant trait composition and community assembly during succession. Funct. Ecol. 30, 1006–1013 (2016).

    Article  Google Scholar 

  46. 46.

    Rao, C. R. Diversity and dissimilarity coefficients: a unified approach. Theor. Popul. Biol. 21, 24–43 (1982).

    Article  Google Scholar 

  47. 47.

    Champely, S. & Chessel, D. Measuring biological diversity using Euclidean metrics. Environ. Ecol. Stat 9, 167–177 (2002).

    Article  Google Scholar 

  48. 48.

    Dowle, M. et al. data.table: Extension of data.frame. R Package Version 1.9.6 (2015); https://CRAN.R-project.org/package=data.table

  49. 49.

    Hawkins, B. A. et al. Structural bias in aggregated species-level variables driven by repeated species co-occurrences: a pervasive problem in community and assemblage data. J. Biogeogr. 44, 1199–1211 (2017).

    Article  Google Scholar 

  50. 50.

    Knijnenburg, T. A. et al. Fewer permutations, more accurate P-values. Bioinformatics 25, i161–i168 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Friendly, M. Corrgrams: exploratory displays for correlation matrices. Am. Stat. 56, 316–324 (2002).

    Article  Google Scholar 

  52. 52.

    Oksanen, J. et al. vegan: Community Ecology Package. R Package Version 2.3-3 (2016); https://CRAN.R-project.org/package=vegan

  53. 53.

    Lengyel, A., Chytrý, M. & Tichý, L. Heterogeneity-constrained random resampling of phytosociological databases. J. Veg. Sci 22, 175–183 (2011).

    Article  Google Scholar 

  54. 54.

    Baselga, A. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Glob. Ecol. Biogeogr. 21, 1223–1232 (2012).

    Article  Google Scholar 

  55. 55.

    Garnier, E. et al. Towards a thesaurus of plant characteristics: an ecological contribution. J. Ecol. 105, 298–309 (2017).

    Article  Google Scholar 

Download references

Acknowledgements

The sPlot was initiated by sDiv, the Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (FZT 118) and is now a platform of iDiv. H.B., J.De., O.Pu, U.J., B.J.-A., J.K., D.C., F.M.S., M.W. and C.W. appreciate the direct funding through iDiv. For all further acknowledgements see the Supplementary Information.

Author information

Affiliations

Authors

Contributions

H.B. and U.J. wrote the first draft of the manuscript, with considerable input by B.J.-A. and R.F. Most of the statistical analyses and the production of the graphs were carried out by H.B.. H.B., O.Pu. and U.J. initiated sPlot as an sDiv working group and iDiv platform. J.De. compiled the plot databases globally. J.De., S.M.H., U.J., O.Pu. and F.J. harmonized vegetation databases. J.De. and B.J.-A. coordinated the sPlot consortium. J.K. provided the trait data from TRY. F.S. performed the trait data gap filling. O.Pu. produced the taxonomic backbone. B.J.-A., G.S. and E. Welk compiled environmental data and produced the global maps. S.M.H. wrote the Turboveg v3 software, which holds the sPlot database. J.L. and T.H. wrote the resampling algorithm. Many authors participated in one or more of the three sPlot workshops at iDiv where the sPlot initiative was conceived and planned, and evaluation of the data and first drafts were discussed. All other authors contributed data. All authors contributed to writing the manuscript.

Corresponding author

Correspondence to Helge Bruelheide.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2, Supplementary Figures 1–11, and Supplementary Acknowledgements

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bruelheide, H., Dengler, J., Purschke, O. et al. Global trait–environment relationships of plant communities. Nat Ecol Evol 2, 1906–1917 (2018). https://doi.org/10.1038/s41559-018-0699-8

Download citation

Further reading

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