ArticleScientific data2025
Leaf carbon nitrogen and phosphorus concentrations in dominant trees across China's forests from 2005 to 2020.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Leaf carbon nitrogen and phosphorus concentrations in dominant trees across China's forests from 2005 to 2020.Scientific data · 2025Article
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Authors and funding
16 authors.
Funding
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.
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Registered trials
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