Abstract

Soil organic carbon (SOC) is heterogeneous, consisting of fractions with differing turnover rates. Climate, vegetation, and soil properties can all affect the characteristics of these different soil carbon fractions. However, there has been little investigation into the interactive effects of biotic and abiotic drivers on a large spatial scale. In this study, we utilized data from the international soil radiocarbon database (ISRaD) to investigate the radiocarbon abundance (an indicator of carbon persistence) in soil fractions from several different biomes. Bulk SOC was categorized into three fractions according to the density fractionation method: a free light fraction (fLF), an occluded light fraction (oLF) and a heavy fraction (HF). In addition to the impacts of significant factors such as depth and climate, interactive effects between soil fractions and environmental factors on radiocarbon abundance were prevalent. Specifically, there were significant interactions between climate, vegetation types, soil properties, and soil fractions affecting Δ14C levels. The difference in Δ14C of the shallow depth fractions was significant in the temperate forest, and was not significant in the boreal and tropical forests. The interactive effect between mean annual temperature (MAT) and mean annual precipitation (MAP) on Δ14C was significant in the shallower depth (i.e., 0–30 cm and 30–60 cm) of the oLF and in the deeper soils (i.e., 30–60 cm and 60–100 cm) of the HF. Soil properties also interact with soil fractions in determining Δ14C. After accounting for depth effect, oxalate-extractable aluminum (Alo) accounted for 63.5% of the remaining Δ14C variation in the fLF and accounted for 35.9% of the remaining Δ14C variation in the oLF. Rather than Alo, cation exchange capacity (CEC) accounted for 46.1% of the remaining Δ14C variation in the HF. These findings suggest that the way the interactions between climate, vegetation, and soil properties affect soil carbon persistence at various fractional depths is critical for the accurate prediction of soil carbon dynamics.

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