Leaf carbon nitrogen and phosphorus concentrations in dominant trees across China’s forests from 2005 to 2020

Carbon (C), nitrogen (N), and phosphorus (P) are fundamental elements for plant growth and metabolism. Leaf C-N-P stoichiometry provides critical insights into plant nutrient use efficiency and ecosystem-scale carbon-nutrient interactions, particularly under global change. However, comprehensive long-term records of leaf C, N, and P concentrations remain scarce. Here we compiled and publicly released the Leaf Carbon-Nitrogen-Phosphorus Concentrations in China's Forests (CNP-China) dataset, containing 628 standardized records from 52 dominant tree species across 11 representative China's forest ecosystems from 2005 to 2020. All samples were collected and analyzed following the Chinese Ecosystem Research Network (CERN) protocols, with accompanying plot-level geographic and environmental data, species taxonomy and functional traits, and surface soil properties (0-20 cm depth). The dataset underwent rigorous quality control, including unit harmonization, error checking, and outlier detection, and is provided in accessible CSV format. CNP-China enables critical assessments of stoichiometric responses to environmental change, supports improved ecosystem modeling, and facilitates cross-biome comparisons of nutrient cycling dynamics, providing an essential foundation for understanding forest carbon-nutrient feedback under global change.

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Publication Details

Journal
Scientific Data
Published
2025-08-16
DOI
https://doi.org/10.1038/s41597-025-05719-w
Citations
1
Primary Topic
Forest ecology and management
Type
article
Field-Weighted Citation Impact
2.31

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article

Leaf carbon nitrogen and phosphorus concentrations in dominant trees across China’s forests from 2005 to 2020

Zongqiang Xie, Zining Lin, Qingkui Wang, Juxiu Liu et al.
1 citations
Scientific Data
Forest ecology and management
2.31
article

Leaf carbon nitrogen and phosphorus concentrations in dominant trees across China’s forests from 2005 to 2020

Zongqiang Xie, Zining Lin, Qingkui Wang, Juxiu Liu, Chenxi Li, Ze‐Xin Fan, Fusun Shi, Lingli Liu, Honglin He, Qian Xu, Ruiying Chang, Yongbiao Lin, Anzhi Wang, Xiaoli Ren, Luxiang Lin, S DONG
article en
1 citations

Abstract

Carbon (C), nitrogen (N), and phosphorus (P) are fundamental elements for plant growth and metabolism. Leaf C-N-P stoichiometry provides critical insights into plant nutrient use efficiency and ecosystem-scale carbon-nutrient interactions, particularly under global change. However, comprehensive long-term records of leaf C, N, and P concentrations remain scarce. Here we compiled and publicly released the Leaf Carbon-Nitrogen-Phosphorus Concentrations in China's Forests (CNP-China) dataset, containing 628 standardized records from 52 dominant tree species across 11 representative China's forest ecosystems from 2005 to 2020. All samples were collected and analyzed following the Chinese Ecosystem Research Network (CERN) protocols, with accompanying plot-level geographic and environmental data, species taxonomy and functional traits, and surface soil properties (0-20 cm depth). The dataset underwent rigorous quality control, including unit harmonization, error checking, and outlier detection, and is provided in accessible CSV format. CNP-China enables critical assessments of stoichiometric responses to environmental change, supports improved ecosystem modeling, and facilitates cross-biome comparisons of nutrient cycling dynamics, providing an essential foundation for understanding forest carbon-nutrient feedback under global change.

Scientific DataVol. 12(1)
Chinese Academy of Sciences (CN), Xishuangbanna Tropical Botanical Garden (CN), Beijing Institute of Big Data Research (CN), Institute of Applied Ecology (CN), South China Botanical Garden (CN), Institute of Mountain Hazards and Environment (CN), National Science Centre (PL), Chengdu Institute of Biology (CN), Institute of Botany (CN), Institute of Geographic Sciences and Natural Resources Research (CN), University of Chinese Academy of Sciences (CN), Renmin University of China (CN)
CERN, National Natural Science Foundation of China
Openalex Percentile: Top 11%
Forest ecology and management
2.31
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