Biogeographic Patterns of Sulfur in the Vegetation of the Tibetan Plateau

灌木丛 高原(数学) 植被(病理学) 地理 自然地理学 环境科学 生态学 地质学 大气科学 生态系统 生物 数学 医学 数学分析 病理
作者
Wenzong Zhao,Chunwang Xiao,Mingxu Li,Li Xu,Xin Li,Xingyu Zhu,Changjin Cheng,Nianpeng He
出处
期刊:Journal Of Geophysical Research: Biogeosciences [Wiley]
卷期号:128 (3) 被引量:2
标识
DOI:10.1029/2022jg007051
摘要

Abstract Sulfur (S) plays an important role in plant growth and development. However, due to climatic conditions and limited data availability, the variation and allocation of S are largely unknown at regional or global scales. In this study, we systematically evaluated the S distribution patterns and storage in vegetation on the Tibetan Plateau for the first time, based on consistent field‐measured data of 2,040 plant communities. The mean S contents of leaves, branches, trunks, and roots were 1.68, 0.40, 0.19, and 1.45 g kg −1 , respectively; corresponding to S densities of 0.40 × 10 −2 (9.57%), 1.18 × 10 −2 (28.38%), 1.37 × 10 −2 (32.90%) and 1.21 × 10 −2 t hm −2 (29.15%), respectively. The mean S densities for forests (5.59 ± 0.26 × 10 −2 t hm −2 ) were higher than that of shrublands (4.54 ± 0.51 × 10 −2 t hm −2 ), grasslands (1.03 ± 0.07 × 10 −2 t hm −2 ), and deserts (1.32 ± 0.28 × 10 −2 t hm −2 ). The S density was generally lower in the northwest and higher in the southeast of the Tibetan Plateau and had divergent allocation patterns between different plant organs and vegetation types. Furthermore, we found that S in leaves and roots was more strongly influenced by environmental factors and was particularly sensitive to radiation and atmospheric pressure. Moreover, the total S storage in the vegetation of the Tibetan Plateau was estimated to be 337.32 × 10 4 t, with 114.32 × 10 4 (33.89%), 92.36 × 10 4 (27.38%), 128.77 × 10 4 (38.17%), and 1.86 × 10 4 t (0.55%) in the forests, shrublands, grasslands, and deserts, respectively. Our study clarified the S densities of different plant organs and ecosystems on the Tibetan Plateau and compiled 1 × 1 km vegetation S density data sets, which could help determine the key parameters for regional S cycle models in the future.

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