Leaf carbon and nitrogen stoichiometric variation along environmental gradients

Abstract

Leaf stoichiometric traits are central to ecosystem function and biogeochemical cycling, yet no accepted theory predicts their variation along environmental gradients. Using data in the China Plant Trait Database version 2, we aimed to characterize variation in leaf carbon and nitrogen per unit mass (Cmass, Nmass) and their ratio and to test an eco-evolutionary optimality model for Nmass. Community-mean trait values were related to climate variables by multiple linear regression. Climatic optima and tolerances of major genera were estimated; Pagel's λ was used to quantify phylogenetic controls, and Bayesian phylogenetic linear mixed models to assess the contributions of climate, species identity, and phylogeny. Optimality-based predictions of community-mean Nmass were compared to observed values. All traits showed strong phylogenetic signals. Climate explained only 18g% of C:N ratio variation among species but 45g% among communities, highlighting the role of taxonomic replacement in mediating community-level responses. Geographic distributions of deciduous taxa were separated primarily by moisture and evergreens by temperature. Cmass increased with irradiance but decreased with moisture and temperature. Nmass declined with all three variables. C:N ratio variations were dominated by Nmass. The coefficients relating Nmass to the ratio of maximum carboxylation capacity at 25ggC (Vcmax25) and leaf mass per area (Ma) were influenced by leaf area index. The optimality model captured 68g% and 53g% of variation between communities for Vcmax25 and Ma, respectively, and 21g% for Nmass. We conclude that stoichiometric variations along climate gradients are achieved largely by environmental selection among species and clades with different intraspecific trait values. Variations in leaf C:N ratio are mainly determined by Nmass, and optimality-based modelling shows useful predictive ability for community-mean Nmass. These findings should help to improve the representation of C:N coupling in ecosystem models.