Physical soil architectural traits are functionally linked to carbon decomposition and bacterial diversity

Abstract

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.

Soil aggregates as biogeochemical reactors and implications for soil–atmosphere exchange of greenhouse gases—A concept

“…Another area of technique development could exploit isotope pool dilution to measure gross fluxes of GHGs, followed by separation of soil aggregates to determine correlations between aggregate size distributions, physiochemical properties, and the gross gas fluxes [e.g., CH 4 by von Fischer and Hedin () and N 2 O by Yang, Teh, and Silver (2011)]. These methods could be combined with technique advancements in computer‐aided tomography (CT) and electron microscopy (e.g., SEM and TEM; Williams & Carter, ) that provide soil structural information in terms of aggregate reactor size and distribution (e.g., Young & Crawford, ; Rabbi et al, 2016).…”

“…Third, microbial community composition and structure are influenced by aggregate size (e.g., Van Gestel, Merckx, & Vlassak, 1996;Mummey, Holben, Six, & Stahl, 2006;Kravchenko et al, 2014;Rabbi et al, 2016;Ebrahimi & Or, 2016). For instance, Mummey et al, 2006 found that micro-aggregates select for specific microbial lineages across disparate soils.…”

Soil aggregation and associated microbial communities modify the impact of agricultural management on carbon content

“…In micro-aggregates, significantly higher variations in the amount of soil C and enzymatic activities remained un-explained as compared with larger sized aggregates, suggesting different control mechanisms regulating soil C turnover. It has been shown pore geometry and connectivity; oxygen diffusion rates and bacterial community structure have more pronounced effects on the mechanics of soil C dynamics in micro-aggregates as compared with larger sized aggregates (Six et al, 2004;Vos et al, 2013;Rabbi et al, 2016). Conceptually, microaggregate conditions are understood to be responsible for the protection of soil C, however quantitative knowledge on the specific factors driving SOC dynamics in microaggregates is still lacking (Kravchenko et al, 2015).…”

Characterising and linking X-ray CT derived macroporosity parameters to infiltration in soils with contrasting structures

“…In fact, a number of reviews have been published on the successful application and the potential of applying X-ray CT in soil science (Cnudde et al, 2006;Helliwell et al, 2013;Schlüter et al, 2014;Wildenschild et al, 2002). One reason for applying X-ray CT in soil science is to link the geometrical features analyzed, applying X-ray CT to soil functions such as water flow, gas exchange, and solute transport (Deurer et al, 2009;Katuwal et al, 2015;Larsbo et al, 2014;Paradelo et al, 2016;Rabbi et al, 2016). The outcome of such a linkage could be that the prediction of properties and processes that cannot be easily measured and observed might be improved and facilitated with structural parameters (Vogel et al, 2010).…”