Functional trait diversity maximizes ecosystem multifunctionality


Understanding the relationship between biodiversity and ecosystem functioning has been a core ecological research topic over the past decades. Although a key hypothesis is that the diversity of functional traits determines ecosystem functioning, we do not know how much trait diversity is needed to maintain multiple ecosystem functions simultaneously (multifunctionality). Here, we uncovered a scaling relationship between the abundance distribution of two key plant functional traits (specific leaf area, maximum plant height) and multifunctionality in 124 dryland plant communities spread over all continents except Antarctica. For each trait, we found a strong empirical relationship between the skewness and the kurtosis of the trait distributions that cannot be explained by chance. This relationship predicted a strikingly high trait diversity within dryland plant communities, which was associated with a local maximization of multifunctionality. Skewness and kurtosis had a much stronger impact on multifunctionality than other important multifunctionality drivers such as species richness and aridity. The scaling relationship identified here quantifies how much trait diversity is required to maximize multifunctionality locally. Trait distributions can be used to predict the functional consequences of biodiversity loss in terrestrial ecosystems.

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Figure 1: Map of the 124 sampled drylands used in this study.
Figure 2: SKR represented in the Cullen and Frey graph37.
Figure 3: Observed SKRs and deviation of from null expectations.
Figure 4: Effect of abiotic and biotic factors on ecosystem multifunctionality.
Figure 5: Scaling relationship between the empirical SKRs and ecosystem multifunctionality.


  1. 1

    Schmid, B . et al. in Biodiversity, Ecosystem Functioning, and Human Wellbeing: An Ecological and Economic Perspective (eds Naeem, S ., Bunker, D. E ., Hector, A ., Loreau, M . & Perrings, C. ) 14–29 (Oxford Univ. Press, 2009).

    Google Scholar 

  2. 2

    Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).

    Article  Google Scholar 

  3. 3

    Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).

    CAS  Article  Google Scholar 

  4. 4

    Gamfeldt, L., Hillebrand, H. & Jonsson, P. R. Multiple functions increase the importance of biodiversity for overall ecosystem functioning. Ecology 89, 1223–1231 (2008).

    Article  Google Scholar 

  5. 5

    Maestre, F. T. et al. Plant species richness and ecosystem multifunctionality in global drylands. Science 335, 214–218 (2012).

    CAS  Article  Google Scholar 

  6. 6

    Grace, J. B. et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature 529, 390–393 (2016).

    CAS  Article  Google Scholar 

  7. 7

    Gamfeldt, L. et al. Marine biodiversity and ecosystem functioning: what’s known and what’s next? Oikos 124, 252–265 (2015).

    Article  Google Scholar 

  8. 8

    Lavorel, S. & Garnier, E. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. Funct. Ecol. 16, 545–556 (2002).

    Article  Google Scholar 

  9. 9

    Naeem, S. & Wright, J. P. Disentangling biodiversity effects on ecosystem functioning: deriving solutions to a seemingly insurmountable problem. Ecol. Lett. 6, 567–579 (2003).

    Article  Google Scholar 

  10. 10

    Díaz, S. et al. Incorporating plant functional diversity effects in ecosystem service assessments. Proc. Natl Acad. Sci. USA 104, 20684–20689 (2007).

    Article  Google Scholar 

  11. 11

    Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).

    Article  Google Scholar 

  12. 12

    McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).

    Article  Google Scholar 

  13. 13

    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 

  14. 14

    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  Google Scholar 

  15. 15

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

    Article  Google Scholar 

  16. 16

    Naeem, S. et al. Declining biodiversity can alter the performance of ecosystems. Nature 368, 734–737 (1994).

    Article  Google Scholar 

  17. 17

    de Bello, F. et al. Towards an assessment of multiple ecosystem processes and services via functional traits. Biodivers. Conserv. 19, 2873–2893 (2010).

    Article  Google Scholar 

  18. 18

    Gross, N., Suding, K. N., Lavorel, S. & Roumet, C. Complementarity as a mechanism of coexistence between functional groups of grasses. J. Ecol. 95, 1296–1305 (2007).

    Article  Google Scholar 

  19. 19

    Mouillot, D., Villéger, S., Scherer-Lorenzen, M. & Mason, N. W. Functional structure of biological communities predicts ecosystem multifunctionality. PLoS ONE 6, e17476 (2011).

    CAS  Article  Google Scholar 

  20. 20

    Funk, J. L . et al. Revisiting the Holy Grail: using plant functional traits to understand ecological processes. Biol. Rev. (2016).

  21. 21

    Byrnes, J. E. et al. Investigating the relationship between biodiversity and ecosystem multifunctionality: challenges and solutions. Methods Ecol. Evol. 5, 111–124 (2014).

    Article  Google Scholar 

  22. 22

    Lefcheck, J. S. et al. Biodiversity enhances ecosystem multifunctionality across trophic levels and habitats. Nat. Commun. 6, 6936 (2015).

    CAS  Article  Google Scholar 

  23. 23

    Valencia, E. et al. Functional diversity enhances the resistance of ecosystem multifunctionality to aridity in Mediterranean drylands. New Phytol. 206, 660–671 (2015).

    Article  Google Scholar 

  24. 24

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

    Article  Google Scholar 

  25. 25

    Enquist, B. J. 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 

  26. 26

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

    Article  Google Scholar 

  27. 27

    Grime, J. P. Plant Strategies, Vegetation Processes, and Ecosystem Properties. (John Wiley & Sons, 2006).

    Google Scholar 

  28. 28

    Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 10541 (2016).

    CAS  Article  Google Scholar 

  29. 29

    Cristelli, M. Complexity in Financial Markets 141–150 (Springer, 2014).

    Google Scholar 

  30. 30

    Cornwell, W. K. & Ackerly, D. D. Community assembly and shifts in plant trait distributions across an environmental gradient in coastal California. Ecol. Monogr. 79, 109–126 (2009).

    Article  Google Scholar 

  31. 31

    Kraft, N. J., Valencia, R. & Ackerly, D. D. Functional traits and niche-based tree community assembly in an Amazonian forest. Science 322, 580–582 (2008).

    CAS  Article  Google Scholar 

  32. 32

    Le Bagousse-Pinguet, Y. et al. Testing the environmental filtering concept in global drylands. J. Ecol. (2017).

  33. 33

    Schamp, B. S., Chau, J. & Aarssen, L. W. Dispersion of traits related to competitive ability in an old-field plant community. J. Ecol. 96, 204–212 (2008).

    Google Scholar 

  34. 34

    Gross, N. et al. Linking individual response to biotic interactions with community structure: a trait-based framework. Funct. Ecol. 23, 1167–1178 (2009).

    Article  Google Scholar 

  35. 35

    MacArthur, R. & Levins, R. The limiting similarity, convergence, and divergence of coexisting species. Am. Nat. 101, 377–385 (1967).

    Article  Google Scholar 

  36. 36

    Chesson, P. General theory of competitive coexistence in spatially-varying environments. Theor. Popul. Biol. 58, 211–237 (2000).

    CAS  Article  Google Scholar 

  37. 37

    Cullen, A. C. & Frey, H. C. Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and Inputs (Springer, 1999).

    Google Scholar 

  38. 38

    Keddy, P. A. Assembly and response rules: two goals for predictive community ecology. J. Veg. Sci. 3, 157–164 (1992).

    Article  Google Scholar 

  39. 39

    Weiher, E. et al. Advances, challenges and a developing synthesis of ecological community assembly theory. Phil. Trans. R. Soc. B 366, 2403–2413 (2011).

    Article  Google Scholar 

  40. 40

    Levine, J. M. & HilleRisLambers, J. The importance of niches for the maintenance of species diversity. Nature 461, 254–257 (2009).

    CAS  Article  Google Scholar 

  41. 41

    Gross, N., Liancourt, P., Butters, R., Duncan, R. P. & Hulme, P. E. Functional equivalence, competitive hierarchy and facilitation determine species coexistence in highly invaded grasslands. New Phytol. 206, 175–186 (2015).

    Article  Google Scholar 

  42. 42

    Bardgett, R. D., Mommer, L. & De Vries, F. T. Going underground: root traits as drivers of ecosystem processes. Trends Ecol. Evol. 29, 692–699 (2014).

    Article  Google Scholar 

  43. 43

    Delgado-Baquerizo, M. et al. Decoupling of soil nutrient cycles as a function of aridity in global drylands. Nature 502, 672–676 (2013).

    CAS  Article  Google Scholar 

  44. 44

    García-Palacios, P ., McKie, B. G ., Handa, I. T ., Frainer, A & Hättenschwiler, S. The importance of litter traits and decomposers for litter decomposition: a comparison of aquatic and terrestrial ecosystems within and across biomes. Funct. Ecol. 30, 819–829 (2016).

    Article  Google Scholar 

  45. 45

    Bolnick, D. I. et al. Why intraspecific trait variation matters in community ecology. Trends Ecol. Evol. 26, 183–192 (2011).

    Article  Google Scholar 

  46. 46

    Siefert, A. et al. A global meta-analysis of the relative extent of intraspecific trait variation in plant communities. Ecol. Lett. 18, 1406–1419 (2015).

    Article  Google Scholar 

  47. 47

    Crutsinger, G. M. et al. Plant genotypic diversity predicts community structure and governs an ecosystem process. Science 313, 966–968 (2006).

    CAS  Article  Google Scholar 

  48. 48

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

    CAS  Article  Google Scholar 

  49. 49

    Enquist, B. J. Universal scaling in tree and vascular plant allometry: toward a general quantitative theory linking plant form and function from cells to ecosystems. Tree Physiol. 22, 1045–1064 (2002).

    Article  Google Scholar 

  50. 50

    Pakeman, R. J. & Quested, H. M. Sampling plant functional traits: what proportion of the species need to be measured? Appl. Veg. Sci. 10, 91–96 (2007).

    Article  Google Scholar 

  51. 51

    Grime, J. P. Benefits of plant diversity to ecosystems: immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).

    Article  Google Scholar 

  52. 52

    Reiss, J., Bridle, J. R., Montoya, J. M. & Woodward, G. Emerging horizons in biodiversity and ecosystem functioning research. Trends Ecol. Evol. 24, 505–514 (2009).

    Article  Google Scholar 

  53. 53

    Jax, K. Ecosystem Functioning (Cambridge Univ. Press, 2010).

    Google Scholar 

  54. 54

    Whitford, W. G. Ecology of Desert Systems (Academic, 2002).

    Google Scholar 

  55. 55

    Reynolds, J. F. et al. Global desertification: building a science for dryland development. Science 316, 847–851 (2007).

    CAS  Article  Google Scholar 

  56. 56

    Pettorelli, N. et al. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol. 20, 503–510 (2005).

    Article  Google Scholar 

  57. 57

    Loik, M. E., Breshears, D. D., Lauenroth, W. K. & Belnap, J. A multi-scale perspective of water pulses in dryland ecosystems: climatology and ecohydrology of the western USA. Oecologia 141, 269–281 (2004).

    Article  Google Scholar 

  58. 58

    Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).

    Article  Google Scholar 

  59. 59

    Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).

    CAS  Article  Google Scholar 

  60. 60

    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014).

  61. 61

    Gotelli, N. J. & Entsminger, G. L. Swap and fill algorithms in null model analysis: rethinking the knight’s tour. Oecologia 129, 281–291 (2001).

    Article  Google Scholar 

  62. 62

    Májeková, M. et al. Evaluating functional diversity: missing trait data and the importance of species abundance structure and data transformation. PLoS ONE 11, e0149270 (2016).

    Article  Google Scholar 

  63. 63

    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).

    Google Scholar 

  64. 64

    Bartoń, K. MuMIn: multi-model inference. R package v. 1 (R Foundation for Statistical Computing, 2013).

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This work was funded by the European Research Council (ERC) under the European Community’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement 242658 (BIOCOM). Y.L.B.P was supported by a Marie Sklodowska-Curie Actions Individual Fellowship within the European programme Horizon 2020 (DRYFUN project 656035). N.G. was support by the AgreenSkills+ fellowship programme, which has received funding from the EU’s Seventh Framework Programme under grant agreement FP7-609398 (AgreenSkills+ contract). F.T.M. acknowledges support from the ERC (grant agreement 647038; BIODESERT). N.J.G. was supported by the US NSF DEB 1257625, NSF DEB 1144055 and NSF DEB 1136644. M.B. was supported by an FPU fellowship from the Spanish Ministry of Education, Culture and Sports (ref. AP2010-0759). We are very grateful to L. Börger, D. Eldridge, M. García-Gómez, V. Maire, M. Robson, H. Saiz and C. Violle for providing comments on previous versions of the manuscript, and to C. Mañá for explaining the statistical background on the skewness–kurtosis relationship.

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N.G., Y.L.B.P. and P.L. developed the original idea. F.T.M. designed and collected the ‘drylands’ data set. N.G., Y.L.B.P., N.J.G. and M.B. conducted the statistical analyses. N.G., Y.L.B.P. and P.L. wrote the article, with major contributions from F.T.M., M.B. and N.J.G.

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Correspondence to Nicolas Gross or Yoann Le Bagousse-Pinguet or Pierre Liancourt.

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The authors declare no competing financial interests.

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Supplementary Notes 1–5; Supplementary References; Supplementary Tables 1–3; Supplementary Figures 1–3 (PDF 2964 kb)

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Gross, N., Bagousse-Pinguet, Y., Liancourt, P. et al. Functional trait diversity maximizes ecosystem multifunctionality. Nat Ecol Evol 1, 0132 (2017).

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