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 leaf trait estimates biased due to plasticity in the shade

Abstract

The study of leaf functional trait relationships, the so-called leaf economics spectrum1,2, is based on the assumption of high-light conditions (as experienced by sunlit leaves). Owing to the exponential decrease of light availability through canopies, however, the vast majority of the world's vegetation exists in at least partial shade. Plant functional traits vary in direct dependence of light availability3, with different traits varying to different degrees, sometimes in conflict with expectations from the economic spectrum3. This means that the derived trait relationships of the global leaf economic spectrum are probably dependent on the extent to which observed data in existing large-scale plant databases represent high-light conditions. Here, using an extensive worldwide database of within-canopy gradients of key physiological, structural and chemical traits3, along with three different global trait databases4,5, we show that: (1) accounting for light-driven trait plasticity can reveal novel trait relationships, particularly for highly plastic traits (for example, the relationship between net assimilation rate per area (Aa) and leaf mass per area (LMA)); and (2) a large proportion of leaf traits in current global plant databases reported as measured in full sun were probably measured in the shade. The results show that even though the majority of leaves exist in the shade, along with a large proportion of observations, our current understanding is too focused on conditions in the sun.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Comparison of trait values from different databases.
Figure 2: The effective shading by trait and plant functional type.
Figure 3: The effect of plasticity on the leaf economic spectrum.

References

  1. 1

    Reich, P. B. The world-wide ‘fast-slow’ plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).

    Article  Google Scholar 

  2. 2

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

    CAS  Article  Google Scholar 

  3. 3

    Niinemets, Ü., Keenan, T. F. & Hallik, L. A worldwide analysis of within-canopy variations in leaf structural, chemical and physiological traits across plant functional types. New Phytol. 205, 973–993 (2015).

    CAS  Article  Google Scholar 

  4. 4

    Poorter, H., Niinemets, U., Poorter, L., Wright, I. J. & Villar, R. Causes and consequences of variation in leaf mass per area (LMA): a meta-analysis. New Phytol. 182, 565–588 (2009).

    Article  Google Scholar 

  5. 5

    Niinemets, Ü. Components of leaf dry mass per area thickness and density alter leaf photosynthetic capacity in reverse directions in woody plants. New Phytol. 144, 35–47 (1999).

    Article  Google Scholar 

  6. 6

    Westoby, M. A leaf-height-seed (LHS) plant ecology strategy scheme. Plant Soil 199, 213–227 (1998).

    CAS  Article  Google Scholar 

  7. 7

    Craine, J. M. Resource strategies of wild plants (Princeton Univ. Press, 2009).

    Book  Google Scholar 

  8. 8

    Bonan, G. B., Oleson, K. W., Fisher, R. A., Lasslop, G. & Reichstein, M. Reconciling leaf physiological traits and canopy flux data: use of the TRY and FLUXNET databases in the Community Land Model version 4. J. Geophys. Res. Biogeosci. 117, 1–19 (2012).

    Article  Google Scholar 

  9. 9

    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, 13733–8 (2014).

    CAS  Article  Google Scholar 

  10. 10

    Hirose, T., Werger, M. J. A., Pons, T. L. & Vanrheenen, J. W. A. Canopy structure and leaf nitrogen distribution in a stand of lysimachia-vulgaris L as influenced by stand density. Oecologia 77, 145–150 (1988).

    CAS  Article  Google Scholar 

  11. 11

    Joffre, R., Rambal, S. & Damesin, C. in Handbook of Functional Plant Ecology (eds Pugnaire, F. I. & Valladares, F. ) 285–312 (CRC, 2007).

    Google Scholar 

  12. 12

    Niinemets, Ü. in Canopy Photosynthesis: From Basics to Applications (eds Hikosaka, K., Niinemets, Ü. & Anter, N. P. R. ) 101–141 (Springer, 2016).

    Book  Google Scholar 

  13. 13

    Hirose, T . & Werger, M. J. A. Maximizing dialy canopy phoytosynthesis with respect to the leaf nitrogen allocation pattern in the canopy. Oecologia 72, 520–526 (1987).

    CAS  Article  Google Scholar 

  14. 14

    Ellsworth, D. S. & Reich, P. B. Canopy structure and vertical patterns of photosynthesis and related leaf traits in a deciduous forest. Oecologia 96, 169–178 (1993).

    CAS  Article  Google Scholar 

  15. 15

    Anten, N. P. R. Modelling canopy photosynthesis using parameters determined from simple non-destructive measurements. Ecol. Res. 12, 77–88 (1997).

    Article  Google Scholar 

  16. 16

    Pons, T. L. & Anten, N. P. R. Is plasticity in partitioning of photosynthetic resources between and within leaves important for whole-plant carbon gain in canopies? Funct. Ecol. 18, 802–811 (2004).

    Article  Google Scholar 

  17. 17

    Niinemets, Ü. & Anten, N. P. R. in Photosynthesis In Silico: Understanding Complexity from Molecules to Ecosystems (eds Laisk, A., Nedbal, L. & Govindjee, J. ) 363–399 (Springer, 2009).

    Book  Google Scholar 

  18. 18

    Jetz, W. et al. Monitoring plant functional diversity from space. Nat. Plants 2, 16024 (2016).

    Article  Google Scholar 

  19. 19

    Ollinger, S. V. Sources of variability in canopy reflectance and the convergent properties of plants. New Phytol. 189, 375–94 (2011).

    CAS  Article  Google Scholar 

  20. 20

    Keenan, T. F. & Niinemets, Ü . The canopy trait plasticity (CANTRIP) database V1.0.0. (Zenodo, 2016).

  21. 21

    Niinemets, Ü., Kull, O. & Tenhunen, J. D. Within-canopy variation in the rate of development of photosynthetic capacity is proportional to integrated quantum flux density in temperate deciduous trees. Plant Cell Environ. 27, 293–313 (2004).

    CAS  Article  Google Scholar 

  22. 22

    Niinemets, Ü. & Keenan, T. F. Measures of light in studies on light-driven plant plasticity in artificial environments. Front. Plant Sci. 3, 156 (2012).

    Article  Google Scholar 

  23. 23

    Niinemets, Ü. Leaf age dependent changes in within-canopy variation in leaf functional traits: a meta-analysis. J. Plant Res. 129, 313–338 (2016).

    Article  Google Scholar 

  24. 24

    Niinemets, Ü. Is there a species spectrum within the world-wide leaf economics spectrum? major variations in leaf functional traits in the Mediterranean sclerophyll Quercus ilex. New Phytol. 205, 79–96 (2015).

    Article  Google Scholar 

  25. 25

    Hoover, C. M . Field Measurements for Forest Carbon Monitoring (Springer, 2008).

    Book  Google Scholar 

  26. 26

    Ollinger, S. V. et al. Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: functional relations and potential climate feedbacks. Proc. Natl Acad. Sci. USA 105, 19336–19341 (2008).

    CAS  Article  Google Scholar 

  27. 27

    Lloyd, J., Bloomfield, K., Domingues, T. F. & Farquhar, G. D. Photosynthetically relevant foliar traits correlating better on a mass vs an area basis: of ecophysiological relevance or just a case of mathematical imperatives and statistical quicksand? New Phytol. 199, 311–321 (2013).

    CAS  Article  Google Scholar 

  28. 28

    Osnas, J. L. D., Lichstein, J. W., Reich, P. B. & Pacala, S. W. Global leaf trait relationships: mass, area, and the leaf economics spectrum. Science 340, 741–744 (2013).

    CAS  Article  Google Scholar 

  29. 29

    Poorter, H., Lambers, H. & Evans, J. R. Trait correlation networks—a whole-plant perspective on the recently criticized leaf economic spectrum. New Phytol. 201, 378–382 (2013).

    Article  Google Scholar 

  30. 30

    Westoby, M., Reich, P. B. & Wright, I. J. Understanding ecological variation across species: area-based vs mass-based expression of leaf traits. New Phytol. 199, 322–323 (2013).

    Article  Google Scholar 

  31. 31

    Poorter, H., Niinemets, Ü., Walter, A., Fiorani, F. & Schurr, U. A method to construct dose-response curves for a wide range of environmental factors and plant traits by means of a meta-analysis of phenotypic data. J. Exp. Bot. 61, 2043–2055 (2010).

    CAS  Article  Google Scholar 

  32. 32

    Moorthy, I. et al. Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data. Agric. For. Meteorol. 151, 204–214 (2011).

    Article  Google Scholar 

  33. 33

    Widlowski, J. L. et al. The fourth radiation transfer model intercomparison (RAMI-IV): proficiency testing of canopy reflectance models with ISO-13528. J. Geophys. Res. Atmos. 118, 6869–6890 (2013).

    Article  Google Scholar 

  34. 34

    Cescatti, A. & Niinemets, Ü. in Photosynthetic Adaptation. Chloroplast to Landscape (eds Smith, W. K., Vogelmann, T. C. & Chritchley, C. ) 42–85 (Springer, 2004).

    Book  Google Scholar 

  35. 35

    Prévost, M. & Raymond, P. Effect of gap size, aspect and slope on available light and soil temperature after patch-selection cutting in yellow birch–conifer stands, Quebec, Canada. For. Ecol. Manage. 274, 210–221 (2012).

    Article  Google Scholar 

Download references

Acknowledgements

T.F.K. acknowledges the financial support from the Laboratory Directed Research and Development (LDRD) fund under the auspices of Department of Energy, Biological and Environmental Research Office of Science at Lawrence Berkeley National Laboratory. Ü.N. acknowledges the European Regional Development Fund (Centre of Excellence EcolChange) and the Estonian Ministry of Science and Education (institutional grant IUT-8-3). The authors acknowledge useful feedback from W. Han on an earlier version of the manuscript.

Author information

Affiliations

Authors

Contributions

T.F.K. and Ü.N. conceived the analysis. T.F.K. performed the analysis and both authors participated in drafting the manuscript.

Corresponding author

Correspondence to Trevor F. Keenan.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Figures 1–6. (PDF 1869 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Keenan, T., Niinemets, Ü. Global leaf trait estimates biased due to plasticity in the shade. Nature Plants 3, 16201 (2017). https://doi.org/10.1038/nplants.2016.201

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