Patterns of nitrogen and phosphorus pools in terrestrial ecosystems in China

Yi-Wei Zhang,Zhiyao Tang,Guoyi Zhou,Yuhao Feng,Zongqiang Xie,Yanpei Guo,Yongfei Bai,Jingyun Fang,Xinrong Zhu,Wenting Xu

doi:10.5194/essd-13-5337-2021

Abstract

Abstract. Recent increases in atmospheric carbon dioxide (CO2) and temperature relieve their limitations on terrestrial ecosystem productivity, while nutrient availability constrains the increasing plant photosynthesis more intensively. Nitrogen (N) and phosphorus (P) are critical for plant physiological activities and consequently regulate ecosystem productivity. Here, for the first time, we mapped N and P densities and concentrations of leaves, woody stems, roots, litter, and soil in forest, shrubland, and grassland ecosystems across China based on an intensive investigation at 4868 sites, covering species composition, biomass, and nutrient concentrations of different tissues of living plants, litter, and soil. Forest, shrubland, and grassland ecosystems in China stored 6803.6 Tg N, with 6635.2 Tg N (97.5 %) fixed in soil (to a depth of 1 m) and 27.7 (0.4 %), 57.8 (0.8 %), 71.2 (1 %), and 11.7 Tg N (0.2 %) in leaves, stems, roots, and litter, respectively. The forest, shrubland, and grassland ecosystems in China stored 2806.0 Tg P, with 2786.1 Tg P (99.3 %) fixed in soil (to a depth of 1 m) and 2.7 (0.1 %), 9.4 (0.3 %), 6.7 (0.2 %), and 1.0 Tg P (< 0.1 %) in leaves, stems, roots, and litter, respectively. Our estimation showed that N pools were low in northern China, except in the Changbai Mountains, Mount Tianshan, and Mount Alta, while relatively higher values existed in the eastern Qinghai–Tibetan Plateau and Yunnan. P densities in vegetation were higher towards the southern and north-eastern part of China, while soil P density was higher towards the northern and western part of China. The estimated N and P density and concentration datasets, “Patterns of nitrogen and phosphorus pools in terrestrial ecosystems in China” (https://doi.org/10.5061/dryad.6hdr7sqzx), are available from the Dryad digital repository (Zhang et al., 2021). These patterns of N and P densities could potentially improve existing earth system models and large-scale research on ecosystem nutrients.

Highlights

Nitrogen (N) and phosphorus (P) play fundamental roles in plant physiological activities and functioning, such as photosynthesis, resource utilization, and reproductive behaviours (Fernández-Martínez et al, 2019; Lovelock et al, 2004; Raaimakers et al, 1995), regulating plant growth and carbon (C) sequestration efficiency (Terrer et al, 2019; Sun et al, 2017)
There are a few studies on the spatial patterns of soil nutrient storages in China (Shangguan et al, 2013; Xu et al, 2020; Yang et al, 2007; Zhang et al, 2005), a thorough study on the distribution of N and P pools of all the ecosystems is still lacking as vegetation composes the most active part of the nutrient stocks. To fill this knowledge gap, here we identified N and P density patterns in China based on an intensive field investigation, covering all components of the entire ecosystem, including different plant organs, litter, and soil
The present study aims to provide high-resolution maps of nutrient densities in different ecosystem components and to answer the following questions

Summary

Introduction

Nitrogen (N) and phosphorus (P) play fundamental roles in plant physiological activities and functioning, such as photosynthesis, resource utilization, and reproductive behaviours (Fernández-Martínez et al, 2019; Lovelock et al, 2004; Raaimakers et al, 1995), regulating plant growth and carbon (C) sequestration efficiency (Terrer et al, 2019; Sun et al, 2017). Under the background of global warming, the limiting factors for the plant growth, such as carbon dioxide (CO2) and temperature, are becoming less restrictive for terrestrial ecosystem productivity (Norby et al, 2009; Fatichi et al, 2019), while nutrient availability tends to constrain the increasing plant photosynthesis more intensively P shortage could lower photosynthetic C-assimilation rates (Lovelock et al, 2006)

Methods

Results

Conclusion