草原
放牧
环境科学
强度(物理)
生态系统
生态学
生物多样性
地理
草地生态系统
生物
物理
量子力学
作者
Miao He,Guiyao Zhou,Tengfei Yuan,Kees Jan van Groenigen,Junjiong Shao,Xuhui Zhou
摘要
Abstract Aim Livestock grazing can alter carbon (C), nitrogen (N) and phosphorus (P) cycles, thereby affecting the C : N : P stoichiometry in grasslands. In this study, we aimed to examine mechanisms underlying the impacts of grazing on grassland C : N : P stoichiometry, focusing on belowground processes and their linkages with aboveground vegetation properties. Location Global. Time period 1900–2018. Major taxa studied Grassland ecosystems. Methods We conducted a meta‐analysis based on 129 published studies to synthesize the effects of grazing on the C : N : P stoichiometry of leaves, stems, litter, roots, microbial biomass, and soil in grassland ecosystems. Results Grazing significantly affected the C, N and P pools, and then the C : N : P stoichiometry in grassland ecosystems. Grazing effects on C : N : P stoichiometry varied strongly with grazing intensity. Specifically, heavy grazing decreased all C : N : P stoichiometry except litter N : P and root C : N ratios, while light and moderate grazing caused less negative or positive effects. Grazing effects on litter C : N ratio were negatively correlated with grazing effects on soil C : N ratios under light and moderate grazing, but this relationship was positive under heavy grazing. In contrast, grazing effects on root C : P and soil C : P were positively correlated under light and moderate grazing but negatively correlated under heavy grazing. Importantly, grazing significantly decreased the soil N pool by 10.0% but increased the soil P pool by 3.6%, indicating differential mechanisms for grazing impact on N and P cycles in grasslands. Main conclusions Our results strongly suggest that grazing intensity regulates the biogeochemical cycles of C, N and P in grassland ecosystems by affecting plant nutrient use efficiency and soil physicochemical processes. Therefore, incorporating grazing intensity into Earth system models may improve predictions of climate–grassland feedbacks in the Anthropocene.
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