EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Global patterns of leaf traits based on optimality theory 

Ning Dong1, Benjamin Dechant2,3, and Iain Colin Prentice1
Ning Dong et al.
  • 1Georgina Mace Centre for the Living Planet, Imperial College London, Department of Life Sciences, Silwood Park Campus, Ascot SL5 7PY, UK
  • 2German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstr. 4, D-04103 Germany
  • 3Leipzig University, Ritterstraße 26, 04109, Germany

Plant functional traits are a key component of land vegetation models. We present global maps of specific leaf area (SLA) and leaf nitrogen content (N) by mass and area, derived from optimality principles. Leaf N per unit area (Narea) is proposed to be determined primarily by the amount of leaf tissue (is related to LMA = 1/SLA) and its metabolic activity (is related to  carboxylation capacity at 25˚C, known as Vcmax,25). SLA is predicted via optimality hypothesis that LMA maximizes average net carbon over the life cycle of the leaf, with separate calculations for evergreen and deciduous plant types. Global maps then use a remote sensing-based land cover product to assign fractional coverage of each type. Vcmax,25  is predicted via the coordination hypothesis, which posits that Vcmax under current growth conditions tends towards a value that balances the Rubisco- and electron transport-limited rates of photosynthesis.  Predicted trait values are compared to in-situ observations, showing good agreement for all three traits. Predicted global distributions are further compared with recently developed, data-based global trait maps. This research indicates how an optimality perspective can help to improve our understanding of vegetation functional diversity and ecosystem function, and potentially enhance vegetation models.

How to cite: Dong, N., Dechant, B., and Prentice, I. C.: Global patterns of leaf traits based on optimality theory , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10673,, 2022.

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