# How is the carbon use efficiency of microbial communities distributed within the soil pore network

Maëlle Maestrali, Xavier Raynaud, Haotian Wu, Steffen A Schweizer, Ines Guillot, Thomas Lerch, Stéphane Paolillo, Naoise Nunan

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Maëlle Maestrali, Xavier Raynaud, Haotian Wu, Steffen A Schweizer, Ines Guillot, et al. How is the carbon use efficiency of microbial communities distributed within the soil pore network. Applied Soil Ecology, 2026, 219, pp.106771. 10.1016/j.apsoil.2025.106771. hal-05480279

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# How is the carbon use efficiency of microbial communities distributed within the soil pore network

Maëlle Maestrali<sup>a,*</sup>, Xavier Raynaud<sup>a</sup>, Haotian Wu<sup>b</sup>, Steffen A. Schweizer<sup>b</sup>, Ines Guillot<sup>c</sup>, Thomas Lerch<sup>c</sup>, Stéphane Paolillo<sup>c</sup>, Naoise Nunan<sup>a</sup>

## ARTICLE INFO

### Keywords:

Carbon use efficiency

Microbes

Porosity

## ABSTRACT

Soils play a key role in mitigating global warming due to their capacity to sequester CO₂, a process likely impacted by the efficiency with which microbial communities transform organic matter into biomass. The activity of microbes is affected by the conditions they find themselves in (moisture, oxygen, etc.). Soils are characterized by a network of pores of different sizes, which create a variety of microenvironments that can influence resource availability, microbial activity and soil C dynamics. However, the influence of conditions at the pore-scale on microbial carbon use efficiency remains poorly quantified. In this study, we conducted short-term incubations using a mixture of ¹³C-labelled compounds to assess the carbon use efficiency of microbial communities residing in pores with maximum neck diameters between 15 and 30 µm and between 75 and 200 µm. The study was carried out in an agricultural soil (Eutric Cambisol) under three different management practices and a grassland (Sandy Cambisol) in order to have a range of pore structures, C contents, pH and different microbial communities. Our findings show that microbial mineralization of added substrate was higher in the large pores but that the carbon utilization efficiency was lower. The differences across pore sizes were likely due to the different constraints at the microenvironment scale (moisture, predation, available space, substrate abundance, etc.). These results suggest that microbes adopt distinct carbon processing strategies and functional roles, depending on pore size.

## 1. Introduction

Soil organic carbon is the largest terrestrial carbon pool in dynamic interaction with the atmosphere and may therefore either contribute to or mitigate global warming, through the release or sequestration of CO₂, respectively. The efficiency with which microbial communities process organic carbon (C) inputs to produce biomass is considered to be a major determinant of C sequestration in soils. This consideration is derived from the fact that a large portion of soil organic matter (SOM) is composed of microbial necromass (Camenzind et al., 2023) and that the production of necromass requires the prior production of biomass. The concept of microbial carbon use efficiency (CUE) has therefore emerged as a central indicator of C dynamics in soils, with a number of studies suggesting that the microbial CUE is a significant regulator of soil SOM accumulation (Tao et al., 2023; Wieder et al., 2013).

The CUE represents the balance between the C that microorganisms allocate to growth and the C they oxidize for energy production, which results in the release of CO₂ (Manzoni et al., 2012; Schimel et al., 2022). The CUE is known to be affected by both biotic factors, such as competition and predation, and several abiotic factors, including nutrient availability, pH, moisture or oxygen levels (Domeignoz-Horta et al., 2020; He et al., 2024), all of which can vary within the pore network. However, current measurements of CUE overlook the pronounced heterogeneity in microenvironmental conditions that microorganisms experience within soils, despite the likely variations in CUE these may induce.

The microenvironment of soil microbial communities is shaped by the physical arrangement of solid phases, which defines the size of pores in which microbial cells develop. The different sizes of soil pores create highly heterogeneous environments for microbes. In the smallest pores,

<footnote><sup>a</sup> Institute of Ecology and Environmental Sciences, iEES Paris, Sorbonne Université, CNRS, IRD, INRA, UPEC, Univ Paris Diderot, 4 Place Jussieu, 75005, Paris, France</footnote>

<footnote><sup>b</sup> Technical University of Munich, TUM, School of Life Science, Chair of Soil Science, Emil-Ramann Strasse 2, 85354, Freising, Germany</footnote>

<footnote><sup>c</sup> Centre for Experimental and Predictive Ecology Research, CEREEP, CNRS, ENS UMS 3194, Ecotron Ile de France, Rue du Château, 77140, St-Pierre-les-Nemours, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: maelle.maestrali@sorbonne-universite.fr (M. Maestrali).</footnote>
connectivity is restricted, which can limit the supply of oxygen, carbon and nutrients (Chen et al., 2020; Li et al., 2024a; Strong et al., 1999). Conversely, large pores are spatially well connected, with prevailing oxic conditions and carbon sources derived from root exudates and rhizodeposits (Li et al., 2024b). With the combined application of ¹³C-labelling and X-ray tomography, Li et al. (2024b) delineated three broad classes of micro-habitats: (i) large-pore habitats, which are spatially well connected and characterized by oxic conditions and C sources of root origin; (ii) substrate-rich small-pore habitats, with somewhat restricted hydraulic connectivity and oxygen availability, as well as an abundant supply of carbon and nutrients, and (iii) substrate-poor small-pore habitats, which have restricted connectivity and are deficient in oxygen, nutrients and carbon. Microbial communities are not randomly distributed in these micro-habitats; their spatial organization depends on factors such as the availability of organic matter and oxygen (Kravchenko et al., 2014; Ruamps et al., 2011). Large pores are likely dominated by aerobic bacteria and fungi, while micropores can contain both aerobic and anaerobic microbes (in the anoxic interior of aggregates), including denitrifiers and methanogens (Tiedje et al., 1984; Ranjard and Richaume, 2001; Ebrahimi and Or, 2015; Gupta and Germida, 2015). Overall, prokaryotes are more abundant and diverse in smaller pores, partly because fungal hyphae and protists are physically excluded by pore size and therefore cannot prey upon or compete with bacteria and archaea (Tecon and Or, 2017). Such spatial organization is likely to lead to different functional profiles within microbial communities depending on pore size and underlines the need to study microbial processes at micro-scale resolutions.

The structural heterogeneity of soil has profound implications for microbial activity and respiration, as studies that have used ¹³C labelling to measure microbial growth, CUE or activity in different parts of the pore network have shown (e.g. Killham et al., 1993; Li et al., 2024b; Chenu et al., 2025). However, all of these studies used incubations lasting days to weeks, which necessarily includes a degree of recycling of microbial products and necromass and therefore does not reflect the intrinsic CUE of microbial communities but also their capacity to recycle products. This has been defined as the ecosystem CUE (CUEE) by Geyer et al. (2016). No study has measured the microbial CUE at the community scale, termed CUEC, in different parts of the pore network. The CUEC reflects the gross production of primary biomass per unit substrate taken up over short time scales, without recycling of microbial necromass (Geyer et al., 2016).

The majority of studies that have used the ¹³C-labelling approach to measure the microbial CUE have been based on the addition of single substrates (e.g. Kilham et al., 1993; Li et al., 2024b; Sánchez-Rodríguez et al., 2024; Hu et al., 2025) However, it has been shown that the activity and CUEC of microbial communities are influenced by the nature of the available substrates (Frey et al., 2013). Therefore, measurements of CUEC using a mixture of substrates is likely to be more representative of the actual CUEC in soils, where a wide range of substrates are available to microbial communities.

The aim of the study was to compare microbial CUEC between different pore size classes when microbial communities were exposed to a substrate mixture containing 6 simple organic substrates. We set up short-term incubations to focus on the intrinsic CUE of the microbial communities residing in the different pore size classes. By examining CUE at the community scale, we sought to link microbial functioning to carbon cycle dynamics and bridge the gap between system-level assessments and community-scale microbial processes in different soil microenvironments. The incubations were carried out using four different soils: a Luvisol subjected to three different management practices and a sandy Cambisol. The soils and management practices were chosen to ensure a range of pore size distributions, organic matter contents as well as different microbial community compositions.

# 2. Material and methods

## 2.1. Soils and sampling

Two soil types were sampled from the Paris region. Three Luvisols were taken from a long-term experimental field-trial ("La Cage", INRAE, Versailles; 48°48'N,2°08'E) in which the environmental impacts of agricultural management systems are being tested. The three cropping systems sampled were a conventional cropping system (CONV), a no-till agriculture system (NT) and an organic management system (ORG). Samples were also taken from a semi-natural grassland Sandy Cambisol (GRASS) at the CEREEP-Ecotron Ile de France (48°17' N, 2°41' E, Saint-Pierre-les-Nemours). The soils were chosen to present a relative variety of physical structures, organic matter contents and nutrient statuses, as these are all likely to affect microbial processing of organic substrates. Basic properties of the different soils can be found in Supplementary Table S1. For each soil, 27 undisturbed soil cores (5 cm internal diameter, 5 cm deep) were taken from the surface. Samples were stored at -20 °C until use. Two weeks before use, the soils were thawed at room temperature.

In the following, the term "soil environments" will be used to refer to the 4 soils used, which includes both the Eutric Cambisol and Sandy Cambisol soil types and the CONV, NT, ORG and GRASS soil management practices.

## 2.2. Soil water retention curves and pore size distribution

Soil water retention curves were established in triplicate for each soil using pressure plates (Supplementary Fig. 1). Soil wetting curves were also established to quantify soil hysteresis. Pore diameters corresponding to these potentials were then calculated using Jurin's law:

$$D = \frac{(4 \times \sigma \times \cos\lambda)}{(|h| \times g \times \rho)}$$

where D is soil pore diameter (in centimeters), σ is water surface tension (=72,86 dynes/cm), λ is the contact angle between water and pore wall (=0), or wetting angle, h is soil water potential (in centimeters), g is acceleration due to gravity (=980,7 cm/s²), ρ is water density (=0,998 g/cm³).

## 2.3. Incubation experiments

The matric potential of the soil cores was adjusted to target different parts of the pore networks. For each field treatment, the samples were divided into two batches, hereafter called 'large pores' and 'small pores', and their matric potential adjusted to -4 or -20 KPa (75 or 15 µm), respectively. The soils were then stored at 20 °C in the dark for 6 days. This pre-incubation was carried out to allow the soil microbial communities to settle after the freezing.

Subsequently, 1 mL of the ¹³C-labelled substrate mixture δ¹³C = 1000 ‰ (relative to VPDB) or water was carefully added drop by drop to the top of each soil core in order to minimize disturbance. The drops were distributed as evenly as possible across the core surface and allowed to infiltrate by capillarity, thus ensuring a homogeneous distribution throughout the core. Two substrate mixture concentrations were used, one equivalent to 1 µg substrate C mg⁻¹ soil C (low C), the other to 10 µg mg⁻¹ soil C (high C). The rates of substrate C addition were adjusted to the soil organic carbon content of the soils to ensure that the microbial communities received a relatively constant amount of substrate C. The addition rates were relatively low in order to avoid inducing exponential microbial growth and to better reflect substrate inputs under natural, non-perturbed soil conditions (Manzoni et al., 2012). The addition of the solutions brought the matric potential from -4 KPa to -1.5 KPa and from -20 KPa to -10 KPa. Thus, the pore sizes targeted in each treatment were 75 to 200 µm (large pores) and 15 to 30
µm (small pores), respectively. The substrate mixture consisted of two sugars (fructose and glucose), two amino acids (glutamic acid and alanine), and two organic acids (pyruvate and acetate), each contributing equally to the total carbon added. The incubation took place in the dark at 20 °C and lasted for 24 h.

## 2.4. Respiration measurements

The CO₂ concentrations in the microcosm headspaces were measured by gas chromatography (Agilent 3000A), immediately after the addition of the substrate solution, and then 2, 4, 6 and 24 h later. The ¹³C-CO₂ in the headspace was determined by cavity ring-down spectroscopy (G2201-i Analyzer for Isotopic CO₂/CH₄; Picarro, Inc., Santa Clara, CA, USA). It was assumed that the CO₂ in the atmosphere of the microcosms at any time t originated from one of three pools: (i) the CO₂ initially present in the microcosm atmosphere, C₀, (ii) the CO₂ derived from soil C, Cₐoil, and (iii) the CO₂ originating from the substrate mixture, Cₐmixture. The mineralization of substrate C was calculated using Eq. (3), which was derived from Eqs. (1) and (2).

$$C_{\text{measured}} \times \delta^{13}C_{\text{measured}} = C_{\text{soil}} \times \delta^{13}C_{\text{soil}} + C_{0} \times \delta^{13}C_{0} + C_{\text{mixture}} \times \delta^{13}C_{\text{mixture}} \quad (1)$$

$$C_{\text{measured}} = C_{\text{soil}} + C_{0} + C_{\text{mixture}} \quad (2)$$

$$C_{\text{mixture}} = \frac{C_{\text{measured}}(\delta^{13}C_{\text{measured}} - \delta^{13}C_{\text{soil}}) + C_{0}(\delta^{13}C_{\text{soil}} - \delta^{13}C_{0})}{\delta^{13}C_{\text{mixture}} - \delta^{13}C_{\text{soil}}} \quad (3)$$

where Cₐmeasured is the total amount of C-CO₂ measured, δ¹³Cₐmeasured is the isotopic signature of the C-CO₂, Cₐsoil is the amount of C-CO₂ of soil organic matter origin, δ¹³Cₐsoil is the isotopic signature of the soil organic C, C₀ is the amount of C-CO₂ in the microcosm atmosphere at the beginning of the incubation and δ¹³C₀ the isotopic signature of this C-CO₂, Cₐmixture is the amount of C-CO₂ resulting from the mineralization of the ¹³C-labelled substrate mixtures and δ¹³Cₐmixture is the isotopic signature of the C added to the samples. C₀ and δ¹³C₀ were obtained from the measurements carried out immediately after the addition of the substrate mixture, and Cₐmeasured and δ¹³Cₐmeasured were determined with the respiration measurement after 24 h of incubation.

## 2.5. Microbial biomass carbon analyses

At the end of the incubations, microbial biomass carbon was obtained by fumigation-extraction (Vance et al., 1987). Extracts were freeze-dried, and the C and ¹³C contents determined using a Picarro isotopic analyzer (G2131-i, Picarro, Inc., Santa Clara, CA, USA) coupled to a Combustion Module CM-CRDS (A0201, Picarro, Inc., Santa Clara, CA, USA). An extraction efficiency correction factor of kC = 2.22 was applied (Wu et al., 1990).

## 2.6. Carbon use efficiency calculation

The CUE was calculated as in Eq. (4) (Manzoni et al., 2012; Schimel et al., 2022).

$$CUE = \frac{C_{\text{biomass}}}{C_{\text{biomass}} + C_{\text{respired}}} \quad (4)$$

where Cₐbiomass is equivalent to the ¹³C-labelled biomass measured after 24 h and Cₐrespired is the amount of ¹³CO₂ respired during the incubation.

## 2.7. Catabolic profiling

In order to determine whether there were differences in the microbial communities' abilities to use the compounds in the substrate mixture, the microtiter plate method developed by Campbell et al. (2003) was used to measure substrate-induced respiration. Sieved soil

(<2 mm) was added to 96 deep well plates and the soil moisture adjusted to a matric potential of -316 hPa (pF = 2.5). Samples were pre-incubated for two weeks at 25 °C in the dark at a constant humidity. Subsequently, the different substrates were added (one substrate per well at 60 mg C/mL), bringing the matric potential to -10 hPa (pF = 1.0). There were 6 replicates per substrate. The samples were incubated for 3 h at 25 °C in the dark. The samples were incubated for 3 h at 25 °C in the dark. The evenness of substrate utilization was calculated using Pielou's evenness index (J' = H'/lnS), where H' is the Shannon diversity index of substrate respiration, H' = - ΣS ∑i=1 pᵢ ln(pᵢ), and S is the number of substrates.

## 2.8. Statistical analysis

For each soil, 27 cores were initially collected (three of which were reserved for soil water retention curve analyses). The remaining 24 samples were divided into two pore treatments (large, small) crossed with three concentration treatments (none, low, high), resulting in four replicates per treatment. The number of replicates per treatment was 3 in some cases due to experimental problems. Data management, data analysis and figures were all performed using R version 4.2 (R Core Team, 2024). Mean comparisons for microbial respiration, fraction respired, and CUE was performed using linear models (lm) with pore size, substrate concentration, and soil type as fixed factors. Model simplification was based on the examination of AIC values, but all main effects and relevant interactions were initially included. ANOVAs were then performed on the linear models, and post hoc pairwise comparisons were conducted using Tukey's HSD test (TukeyHSD) for multiple comparisons. Evenness of substrate utilization profiles was analyzed similarly using ANOVA. Between-Class Analysis (BCA) was used to determine whether the C use profiles were different among soils. The significance of the separation between groups was tested by permutation (n = 99,999) using the randtest function (package Ade4, Dray and Dufour, 2007).

# 3. Results

## 3.1. Microbial respiration

The addition of C, even at a low concentration (1 µg substrate C per mg soil C), significantly increased (F = 74.71; P < 0.001) total respiration in the microcosms compared to microcosms without the addition of C (Fig. 1). After 24 h, CO₂ emissions from microcosms that received the low C addition were 26 % higher than in the control samples. The microcosms that received the high C mixture showed total respiration rates that were 220 % higher than in the controls. CO₂ emissions were also significantly higher (37 %) in the large pore treatment than in small pore treatments (F = F = 11.91; P < 0.001). The respiration was significantly different (F = 9.45; P < 0.001) among soil environments (soil type X management practice), with higher values found in the conventional cropping and organic agriculture treatments than in the no-till treatment or the grassland. There were significant interaction effects between soil environment and pore size treatment (F = 3.35; P = 0.023), between substrate concentration and soil environment (F = 3.09; P < 0.001) but not between substrate concentration, soil environment and pore size treatment (F = 1.55; P = 0.17). In general, respiration in the large pore treatment was higher than in small pore treatment, but this was not always the case: no significant effects were observed in the controls and respiration was larger in the small pores in the grassland with low C input and in the organically managed samples with high C input. Furthermore, the high C mixture did not stimulate respiration in the grassland as much as it did in the other soils. All (12) pairwise comparisons between large and small pores were performed for each combination of soil type and substrate concentration. All differences but 3 were not statistically significant (Fig. 1).
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** C respired in microgrammes of C per g of soil after 24 h of incubation. NT refers to no-till, ORG to organic farming, CONV to conventional farming and GRASS to grassland. Control refers to water supply in the soil. Low and high concentrations refer to the C concentrations applied to the samples at the start of the incubations, respectively 1 µg and 10 µg substrate C per mg soil C. The ‘large’ and ‘small’ pores indicate the pore sizes targeted by the addition of C. (*) Indicate a *p*-value<0.05 between the two pore groups (Tuckey HSD).</figcaption></figure>

In addition to total respiration, we partitioned CO₂ emissions into substrate-derived (¹³C) and SOM-derived (¹²C) fractions using the δ¹³C of the respired CO₂. For the high C addition, the fraction of substrate-derived CO₂ was on average 3.75 ± 1.51 %, whereas for the low C

addition it was 3.67 ± 1.19 %. This indicates that, even at low substrate concentrations, the majority of respired CO₂ originated from native soil organic matter, while substrate decomposition contributed a small but measurable proportion of total respiration. The addition of exogenous

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Proportion C respired from the carbon mixture in the different soil environments over time. NT refers to no-till, ORG to organic farming, CONV to conventional farming and GRASS to grassland. Low and high concentrations refer to the C concentrations applied to the samples at the start of the incubations, respectively 1 µg and 10 µg substrate C per mg soil C. The ‘large’ and ‘small’ pores indicate the pore sizes targeted by the addition of C.</figcaption></figure>
carbon induced a marked priming effect in both treatments. In samples that received a low concentration of C, the average priming effect reached 36 ± 55 %, while it rose to 227 ± 120 % in those that received a higher concentration. These values indicate that a significant proportion of the CO₂ emitted during incubation comes from the degradation of unlabeled SOM. The priming effect did not differ significantly between NT, ORG, and CONV soils, but remained lower in GRASS soils (p < 0.01). No significant differences were detected between soil fractions differentiated by pore size (p > 0.1).

## 3.2. Pore size effects on substrate mineralization

Pore size had a significant effect on the fraction of the substrate mixture that was mineralized after 24 h, but only in the case of high substrate concentration (F = 7.07, P = 0.014), with a higher fraction respired in large pores (4.24 % ± 1.14) than in small pores (3.27 % ± 1.71), as is shown in Fig. 2. The soil environment had a significant effect (F = 20.89; P < 0.001), with a lower fraction mineralized in the grassland (2.08 % ± 1.05) compared to the other soils (4.33 % ± 1.09).

## 3.3. Microbial biomass

At the end of incubations, total biomass was significantly different between substrate concentration treatments (F = 1304.08, P < 0.01; supplementary Fig. 2), with higher values in the high C addition compared to low C addition. In contrast, there were no significant differences in labelled biomass (F = 0.20, P = 0.65). The fraction of microbial biomass that was 13C-labelled was very low (supplementary Fig. 3), averaging to 0.22 ± 0.28 % in the low C addition treatment and 0.087 ± 0.047 % in the high C addition treatment. No significant differences were observed between the large and small pore treatments (P > 0.1) or among soil types (P > 0.1) for either total or labelled biomass, indicating that the bulk microbial community size remained largely similar across soils and pore treatments.

## 3.4. Carbon use efficiency

Overall, regardless of the amount of C added, CUE was significantly (F = 8.85; P < 0.01) higher in the small pore size class (0.50 ± 0.20) than in the large pore size class (0.38 ± 0.22), with the exception of the grassland samples receiving the low substrate concentration (Fig. 3). The differences between pore size classes were more prominent in samples receiving the high substrate concentration.

The CUE was significantly affected by the concentration of the substrate mixtures (F = 22.89; P < 0.001). The CUE in samples that received low substrate mixture concentrations (0.55 ± 0.19) was significantly higher than in those that received higher concentration of C (0.34 ± 0.19). The effect of soil environments on CUE was also significant (F = 4.10; P = 0.011): the grassland had the highest values (0.52 ± 0.24), while no-till samples had the lowest CUE (0.32 ± 0.18). All the soil environments were significantly different (P < 0.05), except the organic and conventional tillage treatments.

## 3.5. Catabolic profiling

MicroResp© plates were used to assess the uniformity (evenness) of carbon substrate utilization across the different soil environments. The mean evenness values were 0.28 ± 0.02 for the no-till samples, 0.25 ± 0.05 for the organic agriculture samples, 0.32 ± 0.05 for the conventional agriculture samples, and 0.25 ± 0.05 for the grassland soils. An analysis of variance did not reveal any significant differences between soil environments (F = 1.68, P = 0.254), suggesting that the overall evenness of substrate use was comparable among management systems. However, as illustrated in Supplementary Fig. 5, the grassland soil had a somewhat distinct distribution of substrate use, with a higher respiration of certain compounds. These differences were further supported by a Between-Class Analysis (BCA), which revealed a significant separation among soil groups in the multivariate space (P = 0.0487; Supplementary Fig. 6). Together, these results indicate that while the general level of

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Carbon use efficiency in large and small pore size classes across soil. NT refers to no-till, ORG to organic farming, CONV to conventional farming and GRASS to grassland. Low and high concentration refer to the C concentrations applied to the samples at the start of the incubations, respectively 1 µg and 10 µg substrate C per mg soil C. The ‘large’ and ‘small’ pores indicate the pore sizes targeted by the addition of C.</figcaption></figure>
functional evenness was similar across soils, the detailed composition of substrate use profiles, particularly in the grassland soil, differed from those of cultivated soils.

## 4. Discussion

We investigated how microbial community-level carbon use effi- ciency (CUEC) varies across different pore size classes when a mixture of simple substrates is available in order to better understand microbial carbon processing in distinct soil microenvironments. The microbial CUEC values measured in this study fall within the range reported in the literature from 0.28 to 0.78 (He et al., 2024; Jones et al., 2018; Schnecker et al., 2023). Furthermore, He et al. (2024) has suggested that the CUEC tends to be higher in grasslands than in croplands, as was found here. Several studies have already examined the effect of tillage on CUEC (Kallenbach et al., 2019; Liu et al., 2025; Mo et al., 2021; Yang et al., 2020). Some studies have reported that no-till increases CUEC compared with tillage (Kallenbach et al., 2019; Mo et al., 2021; Sau- vadet et al., 2018), but others have found no effect (van Groenigen et al., 2013). Here, the CUEC in the no-till treatment was not significantly higher, suggesting that other factors affected microbial community processing of C. Zhang et al. (2022) suggested that one possible expla- nation for these different results is that nitrogen availability affects CUE by modulating microbial growth and respiration (Kallenbach et al., 2019; Mo et al., 2021; van Groenigen et al., 2013).

While the CUEC values were similar to what has been reported in the literature, the amounts of substrate C consumed by the microbial com- munities were comparatively low (e.g. Li et al., 2024a). This can be attributed to the short incubation time (24 h) and the use of a substrate mixture rather than individual substrates. As the microresp data shows, the substrates (fructose, glucose and pyruvate) that have been used in other studies (e.g. Ruamps et al., 2011; Li et al., 2024a; Chenu et al., 2025) induced a bigger response from the microbial communities. The respiration curves (Fig. 2) show that the peak substrate utilization had already occurred within the first hours of incubation, suggesting that microbial uptake was initially rapid but quickly limited by constraints such as substrate accessibility or microbial physiology. A substantial proportion of the added C thus remained unutilized after 24 h. This unconsumed fraction likely represents compounds that were less bioavailable, probably adsorbed by minerals, or temporarily immobi- lized in extracellular polymeric substances that would only be progres- sively mineralized over longer incubation periods (Manzoni and Cotrufo, 2024; Zhang et al., 2024). This low utilization of the added substrate is directly reflected in the magnitude of the priming effect observed, indicating that the stimulation of SOM mineralization stems mainly from activated microbial processes despite the persistence of a significant fraction of unconsumed C. The priming effect observed in the high C concentration treatment (an increase of 227 %) was high, but similar to the values reported elsewhere after the addition of easily biodegradable C (Tian et al., 2023; Yan et al., 2023; Zhang et al., 2025). The differences observed between management systems show that GRASS soil was less sensitive to this stimulation, while NT, ORG, and CONV soils showed comparable responses. Finally, the absence of any effect of pore size on the intensity of the priming effect suggests that, under our experimental conditions, the contrasts we observed between large and small pores were not the consequence of differences in priming intensity.

The measured CUEC values were sensitive to substrate availability. Microbial communities receiving high substrate concentrations dis- played lower CUEC values, a pattern often interpreted as a decline in metabolic efficiency due to overflow respiration (Manzoni et al., 2012; Russell and Cook, 1995), where excess carbon is released as CO₂ via fermentation-like pathways rather than efficiently incorporated into biomass. However, this is unlikely to be the explanation for the differ- ences observed here as the utilization of the added substrates was low. Other mechanisms, as discussed by Geyer et al. (2016), may also explain

this phenomenon, such as incomplete oxidation of glucose to CO₂, which produces fermentation products, or the simultaneous use of more reduced substrates such as native soil organic matter (SOM). Evidence for incomplete oxidation came from calorimetric data showing that the cumulative heat yield per mole of C was far lower than expected for complete oxidation (Geyer et al., 2019). This suggests that high glucose availability may promote metabolic pathways that conserve energy differently, such as fermentation or increased exudation, rather than direct biomass production. Moreover, high glucose inputs can trigger changes in microbial allocation, with a larger fraction of assimilated C diverted to non-growth processes (e.g., extracellular enzyme production or cell turnover) (Sawada et al., 2008), which would reduce CUE esti- mates derived from isotope- or oxygen-based methods, but not neces- sarily indicate a drop in biochemical processing efficiency as captured by metabolic flux analysis. Thus, lower CUEC under high substrate availability should be interpreted cautiously, as it may reflect shifts in carbon fate rather than an intrinsic loss of microbial efficiency. Conversely, under low substrate availability, the carbon input may have been insufficient to stimulate growth across the whole microbial com- munity. In such conditions, only a subset of opportunistic taxa may respond, while most of the community remains metabolically inactive or relies primarily on native SOM. This would result in lower levels of labelled biomass and respiration despite potentially high efficiency in the active fraction, leading to an underestimation of community-level CUEC. Consequently, substrate availability can skew apparent CUEC values by limiting microbial growth.

To specifically capture short-term microbial allocation patterns while minimizing recycling effects, we used a short 24-hour incubation period. The low substrate concentrations were chosen to better simulate realistic field conditions. This design allowed us to focus on the intrinsic microbial processing of the added carbon rather than on secondary SOM decomposition dynamics.

We observed greater respiration in large pores. Large pores, due to their size, can accommodate not only larger bacterial colonies but also more fungal hyphae, which cannot develop in smaller pores (Chenu et al., 2001; Gupta and Germida, 1988). Moreover, large pores tend to be more aerated, facilitating microbial activity, whereas oxygen diffusion is more restricted in smaller pores, thereby limiting microbial processes. Large pores are highly influenced by soil wetting and desiccation pro- cesses caused by fluctuations in humidity and temperature (Ceriotti et al., 2022; Kravchenko et al., 2020). These processes are particularly pronounced in these microenvironments, which often receive greater carbon inputs and function cyclically between periods of rewetting and desiccation. In large pores, the periodic rewetting after drought events may trigger microbial responses such as the synthesis of trehalose, a sugar produced primarily to protect microbes from stress. Upon rewet- ting, trehalose is hydrolyzed into glucose, enabling microbes to rapidly utilize available carbon sources over short time frames (Li et al., 2024a). It has also been shown that soluble organic matter—considered to be the most bioavailable to microbial communities—varies according to pore size class. The soluble organic matter found in large pores is less chemically complex than that in smaller pores (Bailey et al., 2017). This suggests that the microbial communities in larger pores may be more adapted to using simple substrates, such as those added in this experi- ment, and were therefore able to use them in larger quantities. In contrast, small soil pores experience more stable moisture conditions, and their microbial communities, dominated by slow-growing oligo- trophic microbes, are adapted to a more conservative use of C, favoring recycling over mineralization (Li et al., 2024a, p. 20). The C present in small pores is often integrated into microbial biomass or remains un- processed (Li et al., 2024a; Xia et al., 2022), leading to a lower observed mineralization fraction compared to large pores.

Furthermore, bacterial communities are more exposed to predation in large pores, particularly by protozoa and nematodes (Erktan et al., 2020; Wright et al., 1995). Wright et al. (1995) showed that bacteria introduced into intermediate-sized pores (6–30 µm) experienced a
greater reduction in density than those in small pores (< 6 µm). In large pores, more intense predation can accelerate the turnover of bacterial communities and promote faster C cycling. Conversely, bacteria inhabiting smaller pores are less exposed to predators (Erktan et al., 2020), which is likely to slow microbial turnover and reduce C turnover rates. Because predation stimulates microbial growth and renewal, it increases the proportion of assimilated C that is respired rather than retained in biomass, thereby lowering CUE. Large pores thus tend to favor higher metabolic activity, faster microbial turnover, and lower CUE compared to smaller pores, due to the combined effects of predation and the more variable access to resources.

The differences in CUE observed between pore classes may have important implications for soil carbon storage. The higher CUE in small pores suggests more efficient microbial biomass production, which could result in increased accumulation of microbial necromass, a major component of SOM (Cамenzind et al., 2023; Liang, 2020; Liang et al., 2019; Whalen et al., 2022; Zhang and Amelung, 1996). Thus, microbiological functioning in smaller pores could play a decisive role in the formation of persistent organic carbon. These results support the idea that the physical structure of the soil, by influencing both metabolic efficiency and microbial survival, contributes indirectly to carbon stabilization. There is evidence to suggest that a significant portion of the mineral associated organic matter is of microbial origin (Kallenbach et al., 2016; Xuan et al., 2024) and therefore, it is possible that the conditions and microbial activity in the larger pores may result in the formation of less organo-mineral associations due to the lower biomass, and subsequent necromass formation.

As already mentioned, we observed significant differences in CUE between soils. In agricultural soils, CUE was higher in the two tilled treatments (CONV and ORG) soils than in no-till (NT) soils. This pattern suggests that modifications of soil structure induced by tillage, rather than the fertiliser regime, shaped microbial CUE. Reduced or no-tillage systems tend to increase total porosity and the proportion of large pores, while reducing the proportion of small pores (Xiao et al., 2024), which has the effect of lowering CUE. In contrast, conventional tillage produces a more heterogeneous but less connected pore system (Xiao et al., 2024). Similar trends were reported by Rösinger et al. (2025), who found higher CUE in standard agricultural systems than in pioneer systems such as organic farming with reduced tillage. The grassland soil showed the highest CUE overall, which may be linked to differences in microbial substrate use (Xuan et al., 2024), as supported by the distinct catabolic profile observed in the MicroResp experiment (Fig. S6). Substrate utilization profiles were otherwise broadly similar among the agricultural soils, indicating that microbial communities metabolised the supplied compounds in a comparable way. Substrate-induced respiration is known to be sensitive to land management practices and local soil properties, especially pH and organic C content (Cremer et al., 2016). The profile of the grassland soil, which had a pH of 5.22 was distinct from the pH (pH 7.08) of the agricultural soils. Since pH strongly shapes bacterial community structure and can act as a stressor (Adingo et al., 2021), it is also likely to influence CUE. For example, Sinsabaugh et al. (2016) found a minimum CUE at pH 5.4 in a global meta-analysis, which they attributed to shifts in the bacterial/fungal ratio. In our case, the high CUE observed in grassland could be linked to a greater contribution of fungi, which preferentially colonize acidic soils and are more efficient at using C than bacteria. In addition to this, differences in porosity in terms of both pore size and connectivity may also explain the more contrasting differences in CUE between agricultural soils and grassland soils. It should also be kept in mind that, in our experiment, we added the same amount of substrate-C per gram of bulk soil across soil samples and pore size classes. However, differences in pore size distribution, particularly the proportion of large versus small pores, directly affect the local concentration of the added solution and thus the micro-scale substrate environment experienced by microbes. As a result, microhabitat-scale substrate availability may have varied among soils despite applying the same bulk-soil dose, which could in turn influence

microbial activity, carbon uptake efficiency, and the CUE values obtained. We could also have used an approach that took into account the relative pore volumes. However, such approach has also some drawbacks and is more complex to implement. Our approach represents a practical compromise that ensures feasibility and comparability across treatments. We nonetheless acknowledge that this design likely introduced variability related to heterogeneous substrate availability at the micro-scale. These limitations should be considered when interpreting soil-specific differences in microbial responses and CUE. Future work would benefit from approaches that explicitly account for pore-volume differences or better approximate microhabitat-level substrate concentrations.

Overall, this study highlights the central role of soil porosity in microbial activity and carbon turnover. By demonstrating, for the first time, that microbial CUE<sub>C</sub> varies as a function of pore size, it advances our understanding of how micro-scale heterogeneity influences the carbon cycle (Fig. 4). Future work should focus on disentangling the respective roles of microenvironmental conditions and microbial community composition in the emergence of these differences and assessing their implications for carbon storage in distinct pore domains. In this context, one possible avenue for interpretation is that microbial communities occupying different pore size classes may adopt distinct life strategies. For example, small pores could favor survival-oriented, stress-tolerant organisms, while larger pores may provide conditions that allow for faster growth and opportunistic resource use (cf. r/K strategies; Pfeiffer et al., 2001). While speculative, this perspective could help generate hypotheses linking pore-scale heterogeneity, microbial traits, and carbon use efficiency in soils.

## 5. Conclusion

The results of this study highlight the role of soil porosity in controlling the carbon use efficiency of microbial communities (CUE<sub>C</sub>) and therefore in carbon storage dynamics. This work gives rise to several practical recommendations:

1. For agricultural managers and decision-makers:

* Take soil pore structure into account when evaluating management practices, particularly tillage. Promoting agricultural systems that maintain a balanced proportion of small and large pores could optimise both productivity and carbon sequestration.

* Reconsider the effects of no-till farming: although it is beneficial for limiting erosion and improving biodiversity, it can increase the proportion of macropores and thus reduce CUE<sub>C</sub>. Approaches combining conservation practices and maintaining fine porosity are therefore worth exploring.

2. For researchers:

* Study in greater detail the physiological and ecological mechanisms explaining the differences in CUE<sub>C</sub> between pore classes, in particular by distinguishing the effect of micro-environmental conditions (oxygenation, humidity, substrate availability) from that of microbial composition.

* Better characterise the respective contributions of bacteria, fungi and microbial predators (protozoa, nematodes) to the differentiated functioning of pores, in order to establish solid links between microbial traits and carbon storage.

3. For climate and environmental policies:

* Integrate soil porosity into carbon storage prediction models in order to better anticipate soil responses to land use changes and global warming.
<figure><img src="image_5.png" /><figcaption>Fig. 4. Diagram showing the multiple effects of pore geometry on the characteristics of soil micro-habitats, microbial strategies and the fate of organic matter.</figcaption></figure>

* Consider CUE<sub>C</sub> as a relevant functional indicator for assessing the potential for carbon sequestration in soils and guiding the implementation of agricultural practices that promote the sustainability of agroecosystems.

### CRediT authorship contribution statement

**Maëlle Maestrali:** Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization.
**Xavier Raynaud:** Supervision, Funding acquisition, Conceptualization.
**Haotian Wu:** Methodology, Conceptualization.
**Steffen A. Schweizer:** Supervision, Methodology, Conceptualization.
**Ines Guillot:** Formal analysis, Data curation.
**Thomas Lerch:** Methodology.
**Stéphane Paolillo:** Formal analysis, Data curation.
**Naoise Nunan:** Supervision, Methodology, Investigation, Funding acquisition, Conceptualization.

### Declaration of competing interest

The authors declare no conflicts of interest related to this work.

### Acknowledgements

This research was supported by the Deutsche Forschungsgemeinschaft (DFG) and the Agence Nationale de la Recherche (ANR) as part of the SoilPACMAN Project (DFG project no. 505667051). We gratefully acknowledge their funding and support.

### Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.apsoil.2025.106771.

### Data availability

Data will be made available on request.

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