DOI: 10.5061/dryad.j9kd51ct1
title: Data from: Necromass carbon but not microbes constrain soil carbon release in restoration of degraded alpine grassland

description: Alpine grassland restoration, a critical strategy for enhancing soil
 organic carbon (SOC) sequestration in high-altitude ecosystems, profoundly
 influences plant‒soil‒microbe interactions that govern the magnitude of
 carbon (C)-climate feedback. However, mechanisms driving plant and
 microbial regulation of SOC mineralization (i.e., soil CO2-C release)
 during degraded alpine grassland restoration remain unresolved, limiting
 predictions of SOC cycling in these vulnerable ecosystems. Here, by
 integrating passive and active restoration experiments with aerobic
 incubation, high-throughput sequencing, and biomarker analyses, we
 disentangled how restoration-induced shifts in SOC composition (plant- and
 microbial-derived C) and microbial activity and diversity regulate soil
 CO2-C release in degraded alpine grassland on the Qinghai-Tibetan Plateau.
 Our results showed that soil CO2-C release increased significantly with
 restoration progression under both passive and active approaches. Alpine
 grassland restoration markedly enhanced plant-derived C accumulation and
 its SOC contribution, while microbial-derived C remained unchanged due to
 reduced necromass accumulation coefficients. Notably, although active
 restoration accelerated plant-derived C accumulation, its oxidation
 decomposition degree was lower compared to passive restoration and even to
 unrestored heavily degraded grasslands, increasing the SOC pool lability.
 Fungal community restructuring, particularly in the saprophytic fungal
 community, emerged as a hallmark of restoration. More importantly, we
 found that elevated soil CO2-C release during degraded alpine grassland
 restoration was not primarily mediated by microbial activity and diversity
 shifts but strongly linked to divergent plant- and microbial-derived C
 accumulation patterns, especially the dynamics of plant-derived C. These
 insights underscore the critical roles of plant- and microbial-derived C
 redistribution in grassland restoration and suggest new mechanisms for
 restoration-induced soil C dynamics.
description: # Data from: Necromass carbon but not microbes constrain soil carbon
 release in restoration of degraded alpine grassland Dataset DOI:
 [10.5061/dryad.j9kd51ct1](https://doi.org/10.5061/dryad.j9kd51ct1) ##
 Description of the data and file structure The data collected in this
 study aims to investigate the limiting factors of soil carbon release
 during the restoration of degraded alpine grasslands, with a particular
 focus on the relative roles of plant-derived carbon and microbial-derived
 carbon (necromass carbon). The experiment encompasses grassland sites with
 varying degrees of degradation (from non-degraded to heavily degraded) and
 restoration approaches (active and passive restoration). Multiple sets of
 indicators were measured, including soil physicochemical properties,
 plant- and microbial-derived carbon compounds, microbial community biomass
 and diversity, and soil carbon mineralization rates, in order to decipher
 the key biogeochemical factors driving soil carbon dynamics along the
 restoration gradient. ### Files and variables #### File:
 AlpineGrassland_NecromassC_Data.xlsx **Description:**  This file contains
 the metadata and data dictionary for the entire dataset. The table
 provides a comprehensive list of all variables (attributes), their
 descriptions, and their units of measurement. ##### Variables | Attribute
 (Abbreviation) | Description | Category/Units | | :-----------------------
 | :------------------------------------------------ | :---------------- |
 | PRC | Passive restoration | – | | ARC | Active restoration | – | | HD |
 Heavy degradation | – | | MD | Moderate degradation | – | | LD | Light
 degradation | – | | ND | Non-degradation | – | | A3 | Active restoration
 (3 years) | – | | A7 | Active restoration (7 years) | – | | SOC | Soil
 organic carbon content | mg/g soil | | TN | Total nitrogen content | mg/g
 soil | | C:N | Soil organic carbon-to-total nitrogen ratio | – | | Clay |
 Clay content | % | | Silt | Silt content | % | | Sand | Sand content | % |
 | pH | Soil pH | – | | SWC | Soil water content | % | | Tlp | Total lignin
 phenols | mg/kg soil | | S | Syringyl monomer | mg/kg soil | | V |
 Vanillyl monomer | mg/kg soil | | C | Cinnamyl monomer | mg/kg soil | |
 plant-derived C | Plant-derived carbon | % | | (Ad/Al)v | Acid-to-aldehyde
 ratio (vanillyl) | – | | (Ad/Al)s | Acid-to-aldehyde ratio (syringyl) | –
 | | GlcN | Glucosamine | mg/g soil | | GalN | Galactosamine | mg/g soil |
 | ManN | Mannosamine | mg/g soil | | MurA | Muramic acid | mg/g soil | |
 TAS | Total amino sugars | mg/g soil | | FNC | Fungal necromass carbon |
 mg/g soil | | BNC | Bacterial necromass carbon | mg/g soil | | MNC |
 Microbial necromass carbon | mg/g soil | | MNC/SOC | Contribution of
 microbial necromass carbon to SOC | % | | NAC | Necromass accumulation
 coefficient (MNC/MBC) | – | | MBC | Microbial biomass carbon | mg/g soil |
 | MBN | Microbial biomass nitrogen | mg/g soil | | F~S~ | Fungal Shannon
 index | – | | F~A~ | Fungal ACE index | – | | B~S~ | Bacterial Shannon
 index | – | | B~A~ | Bacterial ACE index | – | | CO~2~-C | Soil CO₂–C
 release | mg CO₂–C g⁻¹ soil | ### **Code/software** All statistical
 analyses were performed using R version 4.2.2 (R Core Team, 2023). The
 data files (e.g., .xlsx，.csv) can be viewed with any standard spreadsheet
 software or text editor. **Key R Packages for Analysis** The following R
 packages, along with their primary use in this study, were essential for
 the statistical workflow: * piecewiseSEM: Used to construct and evaluate
 the piecewise structural equation model (SEM) to assess direct and
 indirect effects of biotic and abiotic factors on soil CO2-C release. -
 randomForest: Employed to run the random forest model for evaluating and
 ranking the relative importance of various soil variables on CO2-C
 release. * rfPermute: Used in conjunction with randomForest to determine
 the statistical significance of variable importance via permutation tests.
 - vegan: Utilized for conducting Principal Component Analysis (PCA) to
 create comprehensive metrics (PC1) from correlated variable groups for
 input into the SEM. * stats (base R): Used for checking assumptions of
 homogeneity of variance and normality, and for performing one-way analysis
 of variance (ANOVA) to test for differences among treatment groups.
 **Analytical Workflow** The statistical workflow proceeded in the
 following logical sequence, with each step dependent on the output of the
 previous one: * Data Preparation & Assumption Checking: Data were
 imported and cleaned. Assumptions of homogeneity of variance and normality
 were checked prior to parametric tests. - Group Comparisons (ANOVA):
 One-way ANOVA was used to test for significant differences in soil biotic
 and abiotic factors across restoration treatments. * Dimensionality
 Reduction (PCA): PCA was performed separately on highly correlated
 variable groups (plant-derived C, microbial-derived C, microbial
 biomass/diversity, edaphic factors) to extract the first principal
 component (PC1) for each group, which was used as a composite metric in
 subsequent modeling. - Path Analysis (SEM): A piecewise SEM was built
 using the piecewiseSEM package to evaluate the network of direct and
 indirect influences on soil CO2-C release. Model fit was assessed using
 Fisher’s C statistic, Akaike information criterion (AIC), and associated
 p-values. * Variable Importance (Random Forest): The randomForest and
 rfPermute packages were used to train a model predicting CO2-C release
 from all measured soil variables. The analysis ranked predictor importance
 based on the percentage increase in mean squared error (%IncMSE) and
 tested the significance of each variable set. ## Access information Other
 publicly accessible locations of the data: * None. This dataset is
 submitted here for the first time and is not currently stored in any other
 public repository or platform. Data was derived from the following
 sources: * This dataset consists of original data specifically collected
 and generated by the authors for this study. It is not derived from any
 pre-existing datasets. All data were obtained through the field sampling
 and laboratory analyses conducted for this research. - **Associated
 Publication:** This dataset supports the accepted manuscript titled
 "Necromass carbon but not microbes constrain soil carbon release in
 restoration of degraded alpine grassland" (Manuscript ID: EAP25-0349)
 in the journal *Ecological Applications*.