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The worldwide leaf economics spectrum

Abstract

Bringing together leaf trait data spanning 2,548 species and 175 sites we describe, for the first time at global scale, a universal spectrum of leaf economics consisting of key chemical, structural and physiological properties. The spectrum runs from quick to slow return on investments of nutrients and dry mass in leaves, and operates largely independently of growth form, plant functional type or biome. Categories along the spectrum would, in general, describe leaf economic variation at the global scale better than plant functional types, because functional types overlap substantially in their leaf traits. Overall, modulation of leaf traits and trait relationships by climate is surprisingly modest, although some striking and significant patterns can be seen. Reliable quantification of the leaf economics spectrum and its interaction with climate will prove valuable for modelling nutrient fluxes and vegetation boundaries under changing land-use and climate.

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Figure 1: Mean annual rainfall (MAR) and mean annual temperature (MAT).
Figure 2: Three-way trait relationships among the six leaf traits with reference to LMA, one of the key traits in the leaf economics spectrum.
Figure 3: LMA as a function of MAT and MAR at the study sites (data for 2,370 species from 163 sites; rainfall and LMA are log10-scaled).
Figure 4: LL as a function of LMA and MAR (all axes are log10-scaled).

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Acknowledgements

All authors contributed (mostly unpublished) data and intellectual input to the project. The first three authors took the lead in the organization, analysis and writing-up of this work, and contributed 8% of the data, with 223 species from 11 sites. We also thank the many other researchers who provided additional information about their study sites and published data.

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Correspondence to Ian J. Wright.

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The authors declare that they have no competing financial interests.

Supplementary information

Supplementary Information

This consists of: 1. a list of published literature sources; 2. further details of bivariate trait relationships allowing formulation of predictive regression equations; 3. details of multiple regression analyses mentioned in the text; 4. PCA loadings for the area-based 5 trait analysis. (DOC 320 kb)

Supplementary Data

Glopnet dataset (XLS 747 kb)

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Wright, I., Reich, P., Westoby, M. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004). https://doi.org/10.1038/nature02403

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