Hierarchical drivers shaping the global patterns of soil organic carbon
Data files
Nov 03, 2025 version files 66.85 KB
-
data_NPP.xlsx
64.64 KB
-
README.md
2.22 KB
Abstract
Recent advancements in observational data and experimental research have significantly enhanced our understanding of the mechanisms governing the magnitude, distribution, and dynamics of soil organic carbon (SOC). However, few studies have systematically explored the hierarchical drivers of SOC. This study addresses this gap by integrating multiple independent datasets—covering productivity, carbon allocation, carbon turnover, and carbon fractions—to construct a comprehensive framework for the hierarchical drivers shaping global SOC patterns. Using this framework, we examine the impact pathways and contributions of primary drivers. Our findings show that climate, as the fundamental primary driver, mainly influences SOC through carbon input pathways, while soil properties, as a secondary driver, predominantly affect SOC via carbon output pathways. Further analysis reveals that carbon input plays a key role in shaping topsoil SOC distribution, while carbon output is more influential in regulating subsoil SOC. In both cases, these drivers exert their effects primarily through stable organic carbon fractions, particularly mineral-associated organic carbon (MAOC), whose influence on particulate organic carbon (POC) is the opposite. This study provides the first quantification of the impact pathways and relative strengths of the hierarchical drivers shaping global SOC. It also underscores the need to consider the hierarchical structure of these drivers when assessing the magnitude, distribution, and dynamics of SOC, particularly in relation to its fractions.
https://doi.org/10.5061/dryad.ksn02v7g8
Description of the data and file structure
The NPP data (data_NPP.xlsx) were mainly sourced from a peer-reviewed meta-analysis (Xiao et al., 2023), which synthesized NPP data from published papers and the ORNL DAAC (https://daac.ornl.gov/cgi-bin/dataset_lister.pl?p=13). Besides, we filtered the NPP database for grassland (Sun, Chang, & Fang, 2023) using following criteria: (1) Sites must include measurements of NPP, BNPP, and ANPP, enabling the calculation of the fraction of net primary production (NPP) allocated belowground (fBNPP); (2) Sites should not have been subject to experimental treatments; and (3) BNPP measurements were obtained using "soil core" or "ingrowth core" methods, as both approaches yielded comparable fBNPP values and provided the largest sample size. Finally, our analysis includes 747 observations of fBNPP across the globe.
- Data_Source: The repository or platform from which the data were obtained.
- Source_Original: The original publication or dataset from which the data were extracted.
- Location: The name of the study site.
- Latitude: The latitude of the study site in decimal degrees (WGS84).
- Longitude: The longitude of the study site in decimal degrees (WGS84).
- TNPP: Total Net Primary Productivity, calculated as TNPP = ANPP + BNPP.
- ANPP: Aboveground Net Primary Productivity.
- BNPP: Belowground Net Primary Productivity.
- fBNPP: The fraction of TNPP contributed by BNPP, calculated as fBNPP = BNPP / TNPP.
- Units: The measurement units for TNPP, ANPP, and BNPP (kg/m2/yr).
References:
Xiao, L. J., Wang, G. C., Chang, J. F., Chen, Y. Y., Guo, X. W., Mao, X. L., . . . Luo, Z. K. (2023). Global depth distribution of belowground net primary productivity and its drivers. Global Ecology and Biogeography, 32(8), 1435-1451. doi:10.1111/geb.13705
Sun, Y. F., Chang, J. F., & Fang, J. Y. (2023). Above- and belowground net-primary productivity: A field-based global database of grasslands. Ecology, 104(2). doi:10.1002/ecy.3904