Variation and evolution of C:N ratio among different organs enable plants to adapt to N‐limited environments

生物 温带气候 生态系统 比叶面积 分歧(语言学) 植物 分类单元 变化(天文学) 氮气 生态学 光合作用 化学 物理 有机化学 哲学 天体物理学 语言学
作者
Jiahui Zhang,Nianpeng He,Congcong Liu,Li Xu,Zhi Chen,Ying Li,Ruomeng Wang,Guirui Yu,Wei Sun,Chunwang Xiao,Han Y. H. Chen,Peter B. Reich
出处
期刊:Global Change Biology [Wiley]
卷期号:26 (4): 2534-2543 被引量:192
标识
DOI:10.1111/gcb.14973
摘要

Carbon (C) and nitrogen (N) are the primary elements involved in the growth and development of plants. The C:N ratio is an indicator of nitrogen use efficiency (NUE) and an input parameter for some ecological and ecosystem models. However, knowledge remains limited about the convergent or divergent variation in the C:N ratios among different plant organs (e.g., leaf, branch, trunk, and root) and how evolution and environment affect the coefficient shifts. Using systematic measurements of the leaf-branch-trunk-root of 2,139 species from tropical to cold-temperate forests, we comprehensively evaluated variation in C:N ratio in different organs in different taxa and forest types. The ratios showed convergence in the direction of change but divergence in the rate of change. Plants evolved toward lower C:N ratios in the leaf and branch, with N playing a more important role than C. The C:N ratio of plant organs (except for the leaf) was constrained by phylogeny, but not strongly. Both the change of C:N during evolution and its spatial variation (lower C:N ratio at midlatitudes) help develop the adaptive growth hypothesis. That is, plants with a higher C:N ratio promote NUE under strong N-limited conditions to ensure survival priority, whereas plants with a lower C:N ratio under less N-limited environments benefit growth priority. In nature, larger proportion of species with a high C:N ratio enabled communities to inhabit more N-limited conditions. Our results provide new insights on the evolution and drivers of C:N ratio among different plant organs, as well as provide a quantitative basis to optimize land surface process models.
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