DOI: 10.5061/dryad.ksn02v7g8
title: Hierarchical drivers shaping the global patterns of soil organic carbon

description: 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.
description: # Hierarchical drivers shaping the global patterns of soil organic carbon
 [https://doi.org/10.5061/dryad.ksn02v7g8](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](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