Widespread controls of leaf nutrient resorption by nutrient limitation and stoichiometry
Sun, Xibin et al. (2023), Widespread controls of leaf nutrient resorption by nutrient limitation and stoichiometry, Dryad, Dataset, https://doi.org/10.5061/dryad.6m905qg24
Leaf nutrient resorption is a key process in nutrient cycles, but fundamental knowledge regarding its control mechanisms remains limited. Among the three proposed basic control mechanisms on leaf nutrient resorption, namely nutrient concentration control, nutrient limitation control, and stoichiometry control, only the first has been demonstrated to exist globally, while the latter two have not been systematically evaluated.
Here, we conducted a global data synthesis to explore nutrient limitation and stoichiometry control on leaf resorption of carbon, nitrogen, phosphorus, sulfur, potassium, calcium, and magnesium, based on 3,395 data points from 109 peer-reviewed studies.
Results showed that the nutrient limitation control existed globally, but was only applicable for the resorption of nitrogen, phosphorus, and potassium. The stoichiometry control existed globally for carbon and all studied nutrients, and coexisted with nutrient limitation control. Conifers’ resorption relied primarily on stoichiometry control rather than nutrient limitation control. Nutrient limitation control was stronger in evergreen angiosperms than in deciduous angiosperms.
Our findings support the ubiquity of both nutrient limitation and stoichiometry in controlling leaf resorption for multiple nutrients, such that these controls should be considered in Earth system models to better predict biogeochemical cycles in terrestrial ecosystems.
National Natural Science Foundation of China, Award: 3210130173, 42007086
Special Foundation for Science and Technology Base and Talents in Guangxi Province of China, Award: GuikeAD20297037
Young Teachers Team Project of Fundamental Research Funds for the Central Universities, Sun Yat-sen University, Award: Project no. 22qntd2702
Shenzhen Science and Technology Program, Award: JCYJ20220530150015035