Aggregates play a key role in protecting soil organic carbon (SOC) from microbial decomposition. The objectives of this study were to investigate the influence of pore geometry on the organic carbon decomposition rate and bacterial diversity in both macro- (250–2000 μm) and micro-aggregates (53–250 μm) using field samples. Four sites of contrasting land use on Alfisols (i.e. native pasture, crop/pasture rotation, woodland) were investigated. 3D Pore geometry of the micro-aggregates and macro-aggregates were examined by X-ray computed tomography (μCT). The occluded particulate organic carbon (oPOC) of aggregates was measured by size and density fractionation methods. Micro-aggregates had 54% less μCT observed porosity but 64% more oPOC compared with macro-aggregates. In addition, the pore connectivity in micro-aggregates was lower than macro-aggregates. Despite both lower μCT observed porosity and pore connectivity in micro-aggregates, the organic carbon decomposition rate constant (Ksoc) was similar in both aggregate size ranges. Structural equation modelling showed a strong positive relationship of the concentration of oPOC with bacterial diversity in aggregates. We use these findings to propose a conceptual model that illustrates the dynamic links between substrate, bacterial diversity, and pore geometry that suggests a structural explanation for differences in bacterial diversity across aggregate sizes.

Subsurface soil organic carbon (SOC) is a large but still poorly understood component of the global carbon cycle. We investigated the depth distribution ofSOCin eastern Australia, testing the hypotheses thatSOCcontent near the surface is linked with water availability, whereas the distribution ofSOCwith depth is linked with land use, site factors and temperature. To do this, we measuredSOCconcentration to 1 m at 100 sites across eastern Australia, and fitted three parameter exponential depletion models to the results. Three machine learning algorithms were used to identify predictors important to the model parameters. Multiple regression models were then created based upon the machine learning results using bootstrapped stepwise regressions and the relative importance of the selected variables was assessed using proportional marginal variance decomposition. SurfaceSOCconcentration was influenced predominantly by climate variables, of which seasonal rainfall was by far the most important. At depth,SOCstorage was most influenced by site factors (mainly bulk density and soil type), and both land use and climate contributed similar amounts to model explained variance. The depth distribution ofSOCwas most influenced by land use, which accounted for ~60% of model explained variance, with site and climate factors being approximately equally important. These results support our hypotheses regarding the drivers ofSOCdepth distribution in eastern Australia and can be used to identify regions with the potential for additional subsurface soil carbon storage.