Plant functional traits have globally consistent effects on competition

Journal name:
Nature
Volume:
529,
Pages:
204–207
Date published:
DOI:
doi:10.1038/nature16476
Received
Accepted
Published online

Phenotypic traits and their associated trade-offs have been shown to have globally consistent effects on individual plant physiological functions1, 2, 3, but how these effects scale up to influence competition, a key driver of community assembly in terrestrial vegetation, has remained unclear4. Here we use growth data from more than 3 million trees in over 140,000 plots across the world to show how three key functional traits—wood density, specific leaf area and maximum height—consistently influence competitive interactions. Fast maximum growth of a species was correlated negatively with its wood density in all biomes, and positively with its specific leaf area in most biomes. Low wood density was also correlated with a low ability to tolerate competition and a low competitive effect on neighbours, while high specific leaf area was correlated with a low competitive effect. Thus, traits generate trade-offs between performance with competition versus performance without competition, a fundamental ingredient in the classical hypothesis that the coexistence of plant species is enabled via differentiation in their successional strategies5. Competition within species was stronger than between species, but an increase in trait dissimilarity between species had little influence in weakening competition. No benefit of dissimilarity was detected for specific leaf area or wood density, and only a weak benefit for maximum height. Our trait-based approach to modelling competition makes generalization possible across the forest ecosystems of the world and their highly diverse species composition.

At a glance

Figures

  1. Assessing competitive interactions at global scale.
    Figure 1: Assessing competitive interactions at global scale.

    a, Precipitation–temperature space occupied by each data set (LPP, large permanent plots data; NFI, national forest inventories data). For data with multiple plots, the range of climatic condition is represented by an ellipse covering 98% of the plots. Biomes are: 1, tundra; 2, taiga; 3, Mediterranean; 4, temperate forest; 5, temperate rainforest; 6, desert; 7, tropical seasonal forest; and 8, tropical rainforest (see Methods for details). b, Sampled patches vary in the abundance of competitors from species c around individuals of focal species f. c, We modelled how trait values of the focal tree (tf), and the abundance (measured as the sum of their basal areas) and traits values of competitor species (tc) influenced basal area growth of the focal tree. Species maximum growth (red) was influenced by trait of the focal tree (m0 + m1 tf, with m0 maximum growth independent of the trait). Reduction in growth per unit basal area of competitors (−αf,c, black) was modelled as the sum of growth reduction independent of the trait (blue) by conspecific (α0intra) and heterospecific (α0inter) competitors, the effect of competitor traits (tc) on their competitive effect (αe), the effect of the focal tree’s traits (tf) on its tolerance of competition (αt), and the effect of trait dissimilarity between the focal tree and its competitors (|tc − tf|) on competition (αd). The parameters m0, m1, α0intra, α0inter, αe, αt and αd are fitted from data using a maximum likelihood method.

  2. Trait-dependent and trait-independent effects on maximum growth and competition across the globe, and their variation among biomes.
    Figure 2: Trait-dependent and trait-independent effects on maximum growth and competition across the globe, and their variation among biomes.

    ac, Standardized regression coefficients for growth models, fitted separately for wood density (a), SLA (b) and maximum height (c) (points denote average estimates, lines denote 95% confidence intervals). Black points and lines represent global estimates, and coloured points and lines represent the biome level estimates. The parameter estimates represent: the effect of focal tree’s trait value on maximum growth m1, the effect of competitor trait values on their competitive effect αe (positive values indicate that higher trait values lead to a stronger reduction in growth of the focal tree), the effect of the focal tree’s trait value on its tolerance of competition αt (positive values indicate that greater trait values result in greater tolerance of competition), the effect on competition of trait dissimilarity between the focal tree and its competitors αd (negative values indicate that higher trait dissimilarity leads to a lower reduction of the growth of the focal tree), and the trait-independent competitive effect of conspecific (α0intra) and heterospecific (α0inter) competitors. Tropical rainforest and tropical seasonal forest were merged together as tropical forest, tundra was merged with taiga, and desert was not included as too few plots were available (see Fig. 1a for biomes definitions).

  3. Variation of maximum growth, competitive effects and competitive tolerance with wood density and SLA predicted by global traits models.
    Figure 3: Variation of maximum growth, competitive effects and competitive tolerance with wood density and SLA predicted by global traits models.

    af, Variation of maximum growth (m1 × tf) (a, d), tolerance of competition (αt × tf) (b, e) and competitive effect (αe × tc) (c, f) parameters with wood density (ac) and SLA (df). The shaded area represents the 95% confidence interval of the prediction (including uncertainty associated with α0 or m0).

  4. Map of the plot locations of all data sets analysed.
    Extended Data Fig. 1: Map of the plot locations of all data sets analysed.

    LPP plots are represented with a large points and NFI plots with small points (the Panama data set comprises both a 50 ha plot and a network of 1 ha plots). The world map is from the R package rworldmap131 using Natural Earth data.

  5. Average difference between interspecific and intraspecific competition predicted with estimates of trait-independent and trait-dependent processes influencing competition for models fitted for each trait.
    Extended Data Fig. 2: Average difference between interspecific and intraspecific competition predicted with estimates of trait-independent and trait-dependent processes influencing competition for models fitted for each trait.

    ac, Models were fitted for wood density (a), SLA (b) or maximum height (c). The average differences between interspecific and intraspecific competition are influenced by α0intra, α0inter and αd coefficients (see Methods for details). Negative values indicate that intraspecific competition is stronger than interspecific competition.

  6. Variation of trait-independent inter and intraspecific competition, trait dissimilarity (|tf − tc| × αd), competitive effect (tc × αe), tolerance to competition (tf × αt) and maximum growth (tf × m1) with wood density, SLA and maximum height.
    Extended Data Fig. 3: Variation of trait-independent inter and intraspecific competition, trait dissimilarity (|tf− tc| × αd), competitive effect (tc × αe), tolerance to competition (tf × αt) and maximum growth (tf × m1) with wood density, SLA and maximum height.

    ao, Wood density (ae), SLA (fj) and maximum height (ko). Trait varied from their quantile at 5% to their quantile at 95%. The shaded area represents the 95% confidence interval of the prediction (including uncertainty associated with α0 or m0). α0intra and α0inter, which do not vary with traits, are represented with their associated confidence intervals.

  7. Trait-dependent and trait-independent effects on maximum growth and competition across the globe and their variation among biomes for models without separation of α0 between intra and interspecific competition for wood density, SLA and maximum height.
    Extended Data Fig. 4: Trait-dependent and trait-independent effects on maximum growth and competition across the globe and their variation among biomes for models without separation of α0 between intra and interspecific competition for wood density, SLA and maximum height.

    a, Wood density. b, SLA. c, Maximum height. See Fig. 2 in the main text for parameters description, and see Fig. 1a in the main text for biome definition.

Tables

  1. Standardized coefficient estimates from models fitted for each trait
    Extended Data Table 1: Standardized coefficient estimates from models fitted for each trait
  2. Trees data description
    Extended Data Table 2: Trees data description
  3. Traits data description
    Extended Data Table 3: Traits data description
  4. Species traits pairwise correlations
    Extended Data Table 4: Species traits pairwise correlations

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Author information

  1. Present address: Forestry and Forest Products Research Institute, Tsukuba 305-8687, Japan.

    • Hiroko Kurokawa

Affiliations

  1. Irstea, UR EMGR, 2 rue de la Papeterie BP-76, F-38402, St-Martin-d’Hères, France

    • Georges Kunstler
  2. Univ. Grenoble Alpes, F-38402 Grenoble, France

    • Georges Kunstler
  3. Department of Biological Sciences, Macquarie University, New South Wales 2109, Australia

    • Georges Kunstler,
    • Daniel Falster,
    • Robert M. Kooyman &
    • Mark Westoby
  4. Forest Ecology and Conservation Group, Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK

    • David A. Coomes
  5. Mathematical Sciences Institute, The Australian National University, Canberra 0200, Australia

    • Francis Hui
  6. National Herbarium of New South Wales, Royal Botanic Gardens and Domain Trust, Sydney 2000, New South Wales, Australia

    • Robert M. Kooyman
  7. Environmental Research Institute, School of Science, University of Waikato, Hamilton 3240, New Zealand

    • Daniel C. Laughlin
  8. Forest Ecology and Forest Management Group, Wageningen University, 6708 PB Wageningen, The Netherlands

    • Lourens Poorter
  9. Department of Biology, University of Regina, 3737 Wascana Pkwy, Regina SK S4S 0A2, Canada

    • Mark Vanderwel
  10. Cirad, UPR BSEF, F-34398 Montpellier, France

    • Ghislain Vieilledent &
    • Sylvie Gourlet-Fleury
  11. Smithsonian Tropical Research Institute, Apartado 0843–03092, Balboa, Republic of Panama

    • S. Joseph Wright
  12. Graduate School of Life Sciences, Tohoku University, Sendai 980-8578, Japan

    • Masahiro Aiba &
    • Hiroko Kurokawa
  13. INRA, UMR Ecologie des Forêts de Guyane, BP 709, 97387 Kourou Cedex, France

    • Christopher Baraloto
  14. International Center for Tropical Botany, Department of Biological Sciences, Florida International University, Miami, Florida 33199, USA

    • Christopher Baraloto
  15. Faculty of Forestry, University of Toronto, 33 Willcocks Street, Toronto, Ontario M5S 3B3, Canada

    • John Caspersen &
    • Hongcheng Zeng
  16. Swiss Federal Research Institute WSL, Landscape Dynamics Unit, CH-8903 Birmensdorf, Switzerland

    • John Caspersen &
    • Niklaus E. Zimmermann
  17. Systems Ecology, Department of Ecological Science, Vrije Universiteit, Amsterdam 1081 HV, The Netherlands

    • J. Hans C. Cornelissen
  18. Swiss Federal Research Institute WSL, Forest Resources and Management Unit, CH-8903 Birmensdorf, Switzerland

    • Marc Hanewinkel
  19. University of Freiburg, Chair of Forestry Economics and Planning, D-79106 Freiburg, Germany

    • Marc Hanewinkel
  20. Cirad, UMR Ecologie des Forêts de Guyane, Campus Agronomique, BP 701, 97387 Kourou, France

    • Bruno Herault
  21. Max Planck Institute for Biogeochemistry, Hans Knöll Str. 10, 07745 Jena, Germany

    • Jens Kattge
  22. German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Deutscher Platz 5e 04103 Leipzig, Germany

    • Jens Kattge &
    • Christian Wirth
  23. Graduate School of Agriculture, Kyoto University, Kyoto, 606-8502 Japan

    • Yusuke Onoda
  24. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Cerdanyola del Vallès 08193, Catalonia, Spain

    • Josep Peñuelas
  25. CREAF, Cerdanyola del Vallès, 08193 Barcelona, Catalonia, Spain

    • Josep Peñuelas
  26. Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, D-52425 Jülich, Germany

    • Hendrik Poorter
  27. Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, New York 10027, USA

    • Maria Uriarte
  28. Landcare Research, PO Box 40, Lincoln 7640, New Zealand

    • Sarah Richardson
  29. Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, Stirling FK9 4LA, UK

    • Paloma Ruiz-Benito
  30. Forest Ecology and Restoration Group, Department of Life Sciences, Science Building, University of Alcala, Campus Universitario, 28805 Alcalá de Henares (Madrid), Spain

    • Paloma Ruiz-Benito &
    • Miguel A. Zavala
  31. Department of Natural Resources and Environmental Studies, National Dong Hwa University, Hualien 97401, Taiwan

    • I-Fang Sun
  32. Department of Forest Resource Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgränd, 901 83 Umeå, Sweden

    • Göran Ståhl &
    • Bertil Westerlund
  33. Department of Biology, University of Maryland, College Park, Maryland 20742, USA

    • Nathan G. Swenson
  34. Centre for Ecology and Hydrology, Bush Estate, Penicuik, Midlothian EH26 0QB, UK

    • Jill Thompson
  35. Department of Environmental Sciences, University of Puerto Rico, Río Piedras Campus PO Box 70377 San Juan, Puerto Rico 00936-8377, USA

    • Jill Thompson &
    • Jess K. Zimmerman
  36. Institute for Systematic, Botany and Functional Biodiversity, University of Leipzig, Johannisallee 21 04103 Leipzig, Germany

    • Christian Wirth

Contributions

G.K. and M.W. conceived the study, and with D.F. led a workshop with the participation of D.A.C., F.H., R.M.K., D.C.L., L.P., M.V., G.V. and S.J.W. G.K. wrote the manuscript with key inputs from all workshop participants and help from all authors. G.K., D.F. and F.H. wrote the computer code and processed the data. G.K. devised the main analytical approach and performed analyses with assistance from D.F. for the figures. G.K., D.A.C., D.F., F.H., R.M.K., D.C.L., M.V., G.V., S.J.W., M.A., C.B., J.C., J.H.C.C., S.G.-F., M.H., B.H., J.K., H.K., Y.O., J.P., H.P., M.U., S.R., P.R.-B., I.-F.S., G.S., N.G.S., J.T., B.W., C.W., M.A.Z., H.Z., J.K.Z. and N.E.Z. collected and processed the raw data.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Map of the plot locations of all data sets analysed. (202 KB)

    LPP plots are represented with a large points and NFI plots with small points (the Panama data set comprises both a 50 ha plot and a network of 1 ha plots). The world map is from the R package rworldmap131 using Natural Earth data.

  2. Extended Data Figure 2: Average difference between interspecific and intraspecific competition predicted with estimates of trait-independent and trait-dependent processes influencing competition for models fitted for each trait. (74 KB)

    ac, Models were fitted for wood density (a), SLA (b) or maximum height (c). The average differences between interspecific and intraspecific competition are influenced by α0intra, α0inter and αd coefficients (see Methods for details). Negative values indicate that intraspecific competition is stronger than interspecific competition.

  3. Extended Data Figure 3: Variation of trait-independent inter and intraspecific competition, trait dissimilarity (|tf− tc| × αd), competitive effect (tc × αe), tolerance to competition (tf × αt) and maximum growth (tf × m1) with wood density, SLA and maximum height. (343 KB)

    ao, Wood density (ae), SLA (fj) and maximum height (ko). Trait varied from their quantile at 5% to their quantile at 95%. The shaded area represents the 95% confidence interval of the prediction (including uncertainty associated with α0 or m0). α0intra and α0inter, which do not vary with traits, are represented with their associated confidence intervals.

  4. Extended Data Figure 4: Trait-dependent and trait-independent effects on maximum growth and competition across the globe and their variation among biomes for models without separation of α0 between intra and interspecific competition for wood density, SLA and maximum height. (159 KB)

    a, Wood density. b, SLA. c, Maximum height. See Fig. 2 in the main text for parameters description, and see Fig. 1a in the main text for biome definition.

Extended Data Tables

  1. Extended Data Table 1: Standardized coefficient estimates from models fitted for each trait (190 KB)
  2. Extended Data Table 2: Trees data description (108 KB)
  3. Extended Data Table 3: Traits data description (101 KB)
  4. Extended Data Table 4: Species traits pairwise correlations (38 KB)

Supplementary information

PDF files

  1. Supplementary Information (297 KB)

    This file contains Supplementary Data, a Supplementary Discussion, Supplementary Figures 1-3 and Supplementary References.

Additional data