Data from: Responses of subsoil organic carbon to climate warming and cooling is determined by microbial community rather than its molecular composition
Data files
Jun 02, 2025 version files 52.48 KB
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README.md
4.46 KB
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WarmingCooling_Data.zip
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Abstract
Little is currently known about how long-term climate changes modulate the relationship between soil organic carbon (SOC) molecular composition, microbial community, and SOC storage, and the mechanisms involved. Here, we show substantial changes in subsoil SOC in the Qinghai-Tibetan alpine grasslands over 16 years of soil warming and cooling. Warming reduced SOC content by 8.5%, while cooling increased it by 7.0%. Neither warming nor cooling affected plant- and microbial-derived molecular components. However, warming elevated the fungal-to-bacterial biomass ratio (F/B) and the gram-positive to gram-negative bacterial biomass ratio (G+/G-) by 15.0% and 8.6%, respectively, whereas cooling reduced them by 4.5% and 9.6%. Warming reduced SOC storage by directly increasing F/B and G+/G- and indirectly decreasing the soil carbon-to-nitrogen ratio, whereas cooling enhanced SOC storage primarily by decreasing F/B. Conventional warming experiments, which consider only climate warming and neglect cooling, may underestimate the negative impacts of warming on subsoil SOC pools in alpine grasslands.
Dataset DOI: 10.5061/dryad.vx0k6dk3v
Yuzhang Li1,2, Yao Wei1,2, Lile He1, Bo Fan1, Kunhe Liu1,2, Mingli Ding1,2, Yigang Hu3, Xin Jing4, Biao Zhu5, Shiping Wang6, Jin-Sheng He4,5, Xinquan Zhao1, Zhenhua Zhang1,2*
1 Qinghai Haibei National Field Research Station of Alpine Grassland Ecosystem, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, 810008, China
2 University of Chinese Academy of Science, Beijing, 100049, China
3 Key Laboratory of Land Resources Evaluation and Monitoring in Southwest, Ministry of Education, Sichuan Normal University, Chengdu, 610068, China
4 State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730000, China
5 Institute of Ecology and Ministry of Education Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
6 Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China
*Author of correspondence: Zhenhua Zhang (Email: [zhenhua@nwipb.cas.cn])
Description of the data and file structure
1. WarmingCooling_Data.zip:
(1) WarmCool_Data.xlsx: An Excel file containing raw data utilized in our study, along with variable abbreviations for the 28 variables included in the dataset. For detailed information on the generation of the raw data, please refer to the Materials and Methods section of the manuscript.
(2) Metadata.csv: A file containing the calculated values of the log-response ratio (yi) and its variance (vi), derived using the bias-corrected metric logarithmically transformed response ratio of all the 28 columns in the raw data (WarmCool_Data.xlsx). For further details on the generation of Metadata, please refer to the Statistical Analysis section (the calculation of effect sizes might take several minutes, however, these data tables can be directly read for the figure visualization).
(3) Standardized effects from SEM.csv: A file presenting the mean and 95% credible intervals (CIs) of the significant direct and indirect standardized path coefficients based on the Bayesian piecewise structural equation models (SEM) for the subsoil under warming conditions. For detailed information on the generation of standardized effects, please refer to the WarmCool_Code.R (Zenodo) from line 30 to line 1732 (the calculation of standardized path coefficients might take several minutes, however, these data tables can be directly read for the figure visualization).
(4) Initial_standing_SOC.csv: A file documenting soil organic carbon (SOC) changes (using data from 2016 and 2022) and the initial standing SOC content (using data from 2009) in the topsoil, calculated from the raw data (WarmCool_Data.xlsx) using Excel (the calculation of mean and standard error might take a few minutes, but you can directly read these data tables for the visualization of figures).
2.WarmCool_Code.R (Zenodo)
The R codes used for generating results presented in the Figures and Tables of the main text and supplementary information in our analysis.
3.Supplementary information.zip (Zenodo)
(1) Estimate: This folder contains two CSV files with calculated the posterior distribution data and separately for soil properties, plant community properties (biomass and diversity), plant-derived carbon, microbial-derived carbon, and microbial community properties. For further details on the generation of posterior distribution data, please refer to the WarmCool_Code.R (Zenodo) (the calculation of posterior distribution data may take several minutes; however, these data tables can be directly read for figure visualization).
(2) Main: This folder contains all the figures and tables in the main text generated by running the R code (WarmCool_Code.R).
(3) Supplementary: This folder contains all the figures and tables in supplementary information generated by running the R code (WarmCool_Code.R).
Code/software
The R codes for generating results presented in the Figures and Tables of the main text and supplementary information in our analysis are in the Software related works section.