Predictability of leaf traits with climate and elevation: a case study in Gongga Mountain, China.

Xu, H., Wang, H., Prentice, I. C., Harrison, S. P. ORCID: https://orcid.org/0000-0001-5687-1903, Wang, G. and Sun, X. (2021) Predictability of leaf traits with climate and elevation: a case study in Gongga Mountain, China. Tree Physiology, 41 (8). pp. 1336-1352. ISSN 1758-4469 doi: 10.1093/treephys/tpab003

Abstract/Summary

Leaf mass per area (Ma), nitrogen content per unit leaf area (Narea), maximum carboxylation capacity (Vcmax) and the ratio of leaf-internal to ambient CO2 partial pressure (χ) are important traits related to photosynthetic function, and they show systematic variation along climatic and elevational gradients. Separating the effects of air pressure and climate along elevational gradients is challenging due to the covariation of elevation, pressure and climate. However, recently developed models based on optimality theory offer an independent way to predict leaf traits and thus to separate the contributions of different controls. We apply optimality theory to predict variation in leaf traits across 18 sites in the Gongga Mountain region. We show that the models explain 59% of trait variability on average, without site- or region-specific calibration. Temperature, photosynthetically active radiation, vapor pressure deficit, soil moisture and growing season length are all necessary to explain the observed patterns. The direct effect of air pressure is shown to have a relatively minor impact. These findings contribute to a growing body of research indicating that leaf-level traits vary with the physical environment in predictable ways, suggesting a promising direction for the improvement of terrestrial ecosystem models.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/117129
Item Type	Article
Refereed	Yes
Divisions	Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science
Publisher	Oxford University Press
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