DOI: 10.5061/dryad.w9ghx3g21
title: Grazing-N addition interactions drive soil carbon priming and balance via bacterial assimilation in a meadow steppe

description: Grassland carbon storage depends on microbial-mediated interactions
 between grazing and nitrogen (N) addition, which regulates the
 balance between soil organic carbon (SOC) retention and priming effects.
 However, uncertainties regarding these interactive mechanisms constrain
 projections of SOC vulnerability under global change. We conducted a
 factorial field experiment involving grazing and N addition in a Leymus
 chinensis meadow in northeastern China. In the fifth year of the
 experiment, we collected soil to conduct a 70-day soil incubation
 combined with labile carbon (glucose) addition to examine the
 effects of the grazing and N addition treatments soil
 carbon priming and carbon retention. Grazing
 consistently increased priming effects regardless of N addition. In
 contrast, N addition strongly reduced priming by 41.0% in ungrazed plots
 but had minimal increase effects (3.2%) under grazing. Mechanistically,
 bacterial glucose assimilation capacity primarily mediated
 grazing-dependent N effects on priming, explaining 65.0% of the variation
 and correlating positively with priming intensity. Grazing notably
 decreased the net SOC balance (35.7 mg kg-1 soil) and
 diminished the beneficial effect of N addition on SOC (+79.6% in ungrazed
 vs. +12.3% in grazed plots). Priming effects and bacterial glucose
 assimilation were dominant drivers of SOC responses under grazing,
 exhibiting negative correlations with net SOC balance. Synthesis and
 applications: Our results show that grazing-induced bacterial
 dominance in carbon assimilation alters priming effects and net soil
 carbon balance under N addition, offsetting potential carbon sequestration
 benefits by accelerating native organic matter decomposition. Thus,
 microbial carbon assimilation capacity, particularly bacterial substrate
 assimilation, may serve as an indicator of SOC vulnerability under global
 change.
description: # Grazing-N addition interactions drive soil carbon priming and balance
 via bacterial assimilation in a meadow steppe Dataset DOI:
 [10.5061/dryad.w9ghx3g21](https://doi.org/10.5061/dryad.w9ghx3g21) ##
 Description of the data and file structure This dataset focuses on
 the soil priming effect and associated microbial dynamics under different
 grazing and nitrogen (N) addition treatments. It includes raw measurement
 data, derived ecological indices, and R code for data analysis, enabling
 the exploration of how grazing disturbance and N input regulate soil
 organic carbon (SOC) cycling and microbial activity. ### Files and
 variables #### File: Wangcl_et_al_data.csv **Description:** Derived
 summary data of soil properties, microbial traits, and ecosystem C
 balance. Integrates key indices from the raw data (e.g., priming effect
 percentage, microbial carbon use efficiency) and additional measurements
 (e.g., soil enzyme activity, biomass, pH). ##### Variables * Summarizes
 treatment-level averages of soil and ecosystem traits: * Soil organic
 carbon (mg kg⁻¹ soil): Total SOC content. * The contributions of microbial
 necromass carbon to soil organic (%): Percentage of SOC derived from
 microbial necromass carbon. * Net SOC balance (mg kg⁻¹ soil): Net change
 in SOC storage (sequestration minus loss), reflecting ecosystem C
 sink/source status. * Root:shoot ratio: Ratio of root biomass to
 aboveground biomass, indicating plant allocation to belowground C. ####
 File: Priming_effect_data.csv **Description:** Raw time-series data of
 soil microbial respiration and carbon (C) isotope dynamics under glucose
 addition (to induce priming effect) and no-glucose control. Includes
 measurements across 70 days of incubation, covering multiple treatment
 combinations (grazed/ungrazed, N addition/no N addition). ##### Variables
 * All variables include units of measure in their headers. Key variables
 are highlighted below: * ID: Unique identifier for each sample replicate
 (e.g., CK1, G1, N1). * Treatments of grazing: Grazing status of the sample
 (Ungrazed = no livestock grazing; Grazed = livestock grazing). * Nitrogen
 Addition Treatments: N input status (No N addition = no external N; N
 addition = external N added). * ti: sampling time after add Glucose（Day）:
 Time point (in days) after glucose addition (0, 1, 2, 3, 4, 5, 6, 7, 14,
 28, 42, 70). * δ¹³CO₂ at ti: Carbon isotope ratio of respired CO₂ (per
 mil, ‰), used to distinguish C from glucose vs. native SOC. * Microbial
 respiration rate at ti: Multiple units (e.g., μg CO₂ h⁻¹, μg C g⁻¹ soil
 h⁻¹) to reflect respiration intensity per unit volume, soil mass, or C. *
 Priming effect from 0 to ti (%): The priming effect accumulated from the
 start of incubation (0 days) to time ti. Priming_effect_data.xlsx is also
 provided to preserve heading formatting.  #### File: Calculation code in
 R.docx **Description:** R code script for processing, analyzing, and
 visualizing the dataset. Includes steps for descriptive statistics, ANOVA,
 correlation analysis, and linear regression modeling to quantify
 relationships between variables. ##### Variables * No variables
 (code-only). Key functions include:​ * read_csv(): Import CSV files and
 handle missing values.​ - aov(): Conduct analysis of variance (ANOVA) to
 test treatment effects.​ * cor()/corrplot(): Calculate and visualize
 correlations between variables.​ - lm(): Build linear regression models to
 quantify drivers of priming effect and SOC balance. **Missing Value
 Codes** n/a: Indicates not applicable. These occur in: * Priming effect
 data.csv: Rows where cumulative values (e.g., Total amount of released
 soil carbon during ti to ti+1) cannot be calculated (e.g., at ti = 0 days,
 no prior time point exists to compute a "ti to ti+1" interval).
 * Columns for 70-day cumulative data (e.g., The cumulative of released
 soil carbon from 0 to ti) where ti = 70 days (no subsequent time point to
 finalize the cumulative sum). **Rationale for Missing Data** Missing
 values are not due to measurement errors but to logical constraints or
 mathematical limitations. All missing values are consistent across
 treatment groups and do not bias the dataset.