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Leaf litter carbon,nitrogen, and phosphorus stoichiometric patterns as related to climatic factors and leaf habits across Chinese broad-leaved tree species
Authors:Jielin?Ge  Email author" target="_blank">Zongqiang?XieEmail author
Affiliation:1.State Key Laboratory of Vegetation and Environmental Change,Institute of Botany, Chinese Academy of Sciences,Beijing,China
Abstract:Our understanding of how climate and leaf habit (evergreen vs. deciduous) drive leaf litter carbon (C), nitrogen (N), and phosphorus (P) stoichiometric patterns is largely limited but is particularly important for broad-leaved forests, since the forest is sensitive to climate change. Here, we investigated leaf litter C, N, and P stoichiometric ratios of broad-leaved tree species in relation to climate and leaf habit using previous publications and our additional samplings across China. We found that mean leaf litter C:N across Chinese broad-leaved tree species was within the range of the global flora, whereas C:P was lower and N:P higher. Evergreen species displayed higher leaf litter C:N, C:P, and N:P than their deciduous counterparts. Both leaf litter C:P and N:P for all species pooled were negatively correlated with latitude, driven by mean annual precipitation (MAP) and mean annual temperature, respectively, while leaf litter C:N displayed no clear latitudinal trend. The direction and magnitude of leaf litter C, N, and P stoichiometric ratios in response to climate diverged between leaf habits. For example, evergreen leaf litter C:N was negatively correlated with MAP, while deciduous counterparts did not respond significantly to MAP. We conclude that leaf litter C, N, and P stoichiometric ratios shifted along the climatic gradient, and the strength of such shifts differed between leaf habits. Therefore, leaf litter stoichiometric patterns across leaf habits suggest that any climate change-driven shift in species distribution may potentially alter the ecosystem’s nutrient cycling processes of evergreen- and deciduous-dominated broad-leaved forests differentially.
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