DOI: 10.5061/dryad.vx0k6dk3v
title: Data from: Responses of subsoil organic carbon to climate warming and cooling is determined by microbial community rather than its molecular composition

description: 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.
description: # Responses of subsoil organic carbon to climate warming and cooling is
 determined by microbial community rather than its molecular composition
 Dataset DOI: [10.5061/dryad.vx0k6dk3v](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](mailto: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.