A disturbance in the aggregates: wildfire reorganizes seasonal microbial substrate use in volcanic soils of native and managed forests

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We hypothesized that wildfires would reduce cumulative substrate-induced respiration by contracting pore connectivity, with stronger and more persistent effects in native forest microaggregates and plantation macroaggregates. We quantified cumulative substrate-induced CO2 production with the MicroResp whole-soil multiple substrate-induced respiration (MSIR) assay in topsoil (0–5 cm) macroaggregates (2000–250 µm) and microaggregates (250–53 µm) from paired burned and unburned Nothofagus forest and Pinus radiata plantation sites sampled in February 2024 (dry/warm) and August 2024 (wet/cool). Burning lowered total C from 21.8 to 15.2% in the native forest and from 17.5 to 14.3% in the plantation, and reduced meso- and macroporosity. Across campaigns, amino acid cumulative CO2-C in native forest microaggregates decreased from 475 to 181 mg kg−1 in the dry/warm campaign and from 535 to 342 mg kg−1 in the wet/cool campaign, whereas carbohydrate responses in plantation macroaggregates decreased from 372 to 119 mg kg−1 and from 419 to 234 mg kg−1, respectively. Although several contrasts weakened during the wet/cool campaign, fire sensitivity remained concentrated in native-forest microaggregates and plantation macroaggregates. These results support the hypothesis that wildfires reorganize microbial substrate use within aggregates in ways that depend on ecosystem type and aggregate microhabitat structure in volcanic soils. wildfire volcanic soils soil aggregates MicroResp pore architecture substrate-induced respiration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Highlights Fire reduced meso- and macroporosity, particularly in burned pine soils. Burning lowered the total C and shifted inorganic N and exchangeable Al. MicroResp/MSIR detected persistent fire-associated declines from February to August 2024. Native microaggregates and pine macroaggregates were the most fire-sensitive compartments. Amino acid, carboxylic acid, and carbohydrate responses explained ecosystem-specific fire sensitivity. 1. Introduction Wildfire seasons are lengthening in many regions, increasing the need to quantify post-fire controls on soil carbon cycling (Jolly et al., 2015). Fire restructures the physical and chemical properties of soil through heating and the introduction of ash, thereby redefining microbial habitats (Certini, 2005; Roshan & Biswas, 2023). The relationship between soil aggregation and microbial community function became clearly mechanistic in fire-affected environments because aggregates define diffusion, protection, and resource gradients (Tisdall & Oades, 1982; Six et al., 2004). Heating depolymerizes organic binding agents and weakens aggregate stability, increasing susceptibility to post-fire slaking (Arcenegui et al., 2008; Mataix-Solera et al., 2011). Such soil structure disruption redistributes pore space, nutrients, and water retention, with effects extending into the recovery period (Roshan & Biswas, 2023; Johnson et al., 2024). Soil microbial communities regulate the decomposition and stabilization of soil organic matter (SOM) through enzyme production, interactions between microbial necromass and mineral particles (Condron et al., 2010), and aggregate formation (Sharifi et al., 2018). Fire reduces microbial biomass and shifts community composition, with responses depending on fire severity and vegetation type (Aponte et al., 2022; Pérez-Valera et al., 2019). Heat pulses remove sensitive taxa, whereas post-fire nutrient and pH pulses enhance fast-growing opportunistic taxa (Barreiro & Díaz-Raviña, 2021; Chebykina et al., 2024). Consequently, the community reorders substrate use and respiration dynamics over the years, and severity sets the pace of recovery (Fox et al., 2022; Merino et al., 2026). Soil aggregates serve as microhabitats, providing spatial structures that constrain gas and water diffusion and protect particulate organic matter, thereby shaping microbial physiology (Six et al., 2004; Totsche et al., 2018), and exhibit distinct gradients of oxygen, moisture, and nutrients, supporting communities with contrasting metabolic strategies (Naveed et al., 2016; Li et al., 2024). Volcanic ash soils contain reactive short-range-order oxides that stabilize large soil organic carbon pools. Here, the limited number of macroaggregates indicates that small macro- and microaggregates regulate microbial access to carbon under these mineral controls (Asano & Wagai, 2014; Torn et al., 1997; Matus et al., 2014). The effects of wildfires on aggregate-scale interactions in volcanic soils remain poorly quantified. This study quantified MicroResp-based substrate-induced CO 2 production in the macro- and microaggregate fractions of burned and unburned soils from a temperate Nothofagus spp. forest and a managed Pinus radiata plantation. The same sampling points were resampled in a dry/warm campaign (February 2024) and a wet/cool campaign (August 2024) to test whether aggregate-scale fire effects persisted across contrasting climatic windows. We hypothesized that wildfire would contract pore connectivity and reduce substrate-induced respiration, with stronger and more persistent effects in native-forest microaggregates and in high-activity plantation macroaggregates. 2. Materials and methods 2.1 Study design and wildfire/site selection High-severity wildfire scars from the 2021 fire season were identified in native forest and pine plantation stands using Landsat surface reflectance imagery processed in Google Earth Engine. Burn severity in the study area was characterized in our previous work using NDVI, NBR, and dNBR, and a one-time fire-occurrence filter was applied with the LandTrendr algorithm (Nájera et al., 2024). Four paired site conditions were selected: unburned native forest (NUB), burned native forest (NB), unburned pine plantation (PUB), and burned pine plantation (PB) (Fig. 1). The same paired plots were sampled twice in 2024: in February (dry/warm) and in August (wet/cool). The same five georeferenced sampling points within each condition were revisited in the second campaign so that temporal variation could be evaluated with repeated measurements. 2.2 Soil sampling Soils were collected from the 0-5 cm mineral layer after removing surface litter and loose ash. Five georeferenced auger points were established within each condition during the February 2024 campaign and the same points were resampled in August 2024. Each core was retained as an independent biological replicate for aggregate fractionation and MicroResp/MSIR assays (n = 5 per condition per campaign). Equal-mass composite samples prepared from the five cores of the dry/warm campaign were used for soil chemistry, selective dissolution, WDXRF, and micro-CT descriptors, which are interpreted here as contextual variables. Bulk density was measured at three points per condition using undisturbed cores of known volume dried at 105 °C to constant mass. Fresh soil for MicroResp/MSIR and aggregate fractionation was stored at 4 °C and processed within 48 h of collection. Additional material from the dry/warm campaign was air-dried, gently disaggregated, passed through a 2 mm sieve, and stored for chemical, mineralogical, and structural analyses. All five field cores were retained for the repeated-measures analysis. 2.3 Soil physicochemical and structural analyses 2.3.1 Soil chemical analyses Total C and inorganic C were measured with a TOC-SSM-5000A-VCSH analyzer (Shimadzu, Kyoto, Japan). Total C was determined by dry combustion at 900 °C. Inorganic C was measured after HCl addition and combustion at 200 °C. Soil organic C was calculated as total C minus inorganic C. Total N was determined by Kjeldahl distillation. Inorganic N (NH 4 + and NO 3 - ) was quantified after extraction by steam distillation for mineral N forms (Bremner & Tabatabai, 1972). Soil pH (1:2.5 water:soil), electrical conductivity, cation exchange capacity, Olsen-P, and available boron were analyzed as described by Sadzawka et al. (2006). 2.3.2 Selective dissolution Selective dissolution methods were applied to evaluate Al, Fe, Mn, and Si associated with organic complexes and poorly crystalline mineral phases (Parada et al., 2024). Acid ammonium oxalate extraction was used to quantify poorly crystalline phases (Al o and Fe o ) (McKeague & Day, 1966). Dithionite-citrate-bicarbonate extraction was used to quantify pedogenic free Fe oxides (Fe d ) (Mehra & Jackson, 1958). Sodium pyrophosphate extraction was used to quantify organically complexed Al and Fe (Al p and Fe p , respectively) (Bascomb, 1968). Selective dissolution procedures followed Parfitt and Wilson (1985, as cited by Mizota and van Reeuwijk, 1989) and the workflow described for volcanic soils by Parada et al. (2024). 2.3.3 Soil water repellency A wettability test was conducted to assess soil hydrophobicity after fire. Soil water repellency was evaluated using the water drop penetration time (WDPT) test and classified on a five-class scale from 1 (wettable) to 5 (extremely hydrophobic) following Doerr et al. (2000). 2.3.4 Soil description At each site, a soil morphological description was performed in situ , including texture, color, structure, and root abundance, using soil taxonomy methodology (Soil Survey Staff, 1999). 2.3.5 Wavelength-dispersive X-ray fluorescence (WDXRF) Dry soil was milled and pelletized to quantify major and minor element composition with a wavelength-dispersive X-ray fluorescence spectrometer (Rigaku ZSX Primus IV WDXRF) equipped with a 3 kW X-ray generator (60 kV, 100 mA), a 48-position automatic sample changer, and three crystals (LiF(200) for Ti-U, PET for Al-Ti, and RX-26 for O-Mg). Pressed pellets (40 mm diameter) were prepared from oven-dried, homogenized soil powder to standardize matrix effects across samples. 2.3.6 Micro-CT analyses Aggregates dry-sieved to 250-500 µm were used for micro-CT imaging. This fraction represents small macroaggregates and overlaps the upper limit of the microaggregate fraction used for the MicroResp/MSIR assays. Aggregates were packed gently in the sample holder and scanned with a SKYSCAN 1273 system (Bruker) at a voxel size of 5.7 µm. Pore-space analyses were performed in triplicate per sample by selecting three homogeneous regions of interest (1 mm diameter) that avoided aggregate boundaries (Peng et al., 2023). Image stacks of 500-600 slices were reconstructed into 3D TIFF volumes and processed with Fiji/ImageJ 2.16/1.54p and the SoilJ plugin (Koestel, 2018). Pores were classified as 100 µm (Peng et al., 2023; Cao et al., 2024). A second classification contrasted large pores (30-150 µm) with smaller pores near the resolution limit, acknowledging that pores below the voxel size cannot be quantified robustly (Li et al., 2024). 2.4 MicroResp whole-soil MSIR 2.4.1 Aggregate-size fractionation Fresh soil was sieved gently under field-moist conditions to obtain macroaggregates (2000-250 µm) and microaggregates (250-53 µm). Moist sieving minimized aggregate disruption and microbial stress during fractionation (Cambardella & Elliott, 1993). 2.4.2 MicroResp assay and substrate additions The MicroResp system was used as a whole-soil multiple substrate-induced respiration (MSIR) assay, not as a growth-based or culturable CLPP method (Campbell et al., 2003; Jones et al., 2018). Defined C substrates were added directly to intact soil or aggregate fractions, and short-term CO 2 evolution was quantified colorimetrically. Water-holding capacity (WHC) was determined gravimetrically for each sample using a saturated subsample that was allowed to drain for 2 h and then oven-dried at 105 °C; WHC was expressed as g H 2 O g −1 dry soil. Samples were adjusted to 50% WHC with deionized water and pre-incubated for 4 d at 25 °C to stabilize basal respiration. For each sample, 0.40 g dry-mass equivalent of bulk soil or aggregate fraction was dispensed into deep-well plates and amended with 25 µL of one of 15 carbon sources at 30 mg g −1 soil H 2 O: D-glucose, D-fructose, galactose, γ-aminobutyric acid, N-acetyl-glucosamine, L-alanine, L-arabinose, arginine, L-lysine-HCl, L-cysteine-HCl, α-ketoglutarate, citric acid, oxalic acid, protocatechuic acid, and L-malic acid. Water-amended control wells received 25 µL deionized water and were included in technical triplicate on each plate. Technical triplicates were averaged before statistical analysis, and substrate-induced responses were expressed relative to the corresponding water control for each sample. Indicator plates were incubated at 25 °C and read after 6, 12, 24, 36, 48, 60, and 72 h. Absorbance at 570 nm was measured with a microplate reader (Fendt-A300, Allsheng, China) before and after each interval. During plate reading, deep-well plates were ventilated for 15 min and resealed with a fresh indicator plate. CO 2 production was calculated from absorbance change using the MicroResp calibration procedure and expressed as interval respiration (mg C kg −1 h −1 ) and cumulative CO 2 -C over 6-72 h (mg kg −1 ) (Campbell et al., 2003; Renault et al., 2013). Each plate contained an equal representation of site conditions to minimize positional bias. 2.5 Statistical analyses All analyses were performed on biological replicates (field cores; n = 5 per condition per campaign) after averaging technical triplicates within each sample × substrate combination. Because the same five georeferenced points were resampled in February and August 2024, cumulative CO 2 -C (6-72 h) was analyzed with linear mixed-effects models that included ecosystem, fire status, sampling campaign, aggregate fraction, and their interactions as fixed effects, with sampling point as a random intercept. Separate models were fitted for substrate groups and for individual substrates when required. Residual normality and homoscedasticity were checked from Q-Q plots and residual-versus-fitted plots; responses were log10-transformed when needed to improve variance homogeneity. Estimated marginal means were compared with Tukey adjustment at α = 0.05. Substrate groups were defined by chemistry (carbohydrates, amino acids, amino sugars, phenolic acids, and carboxylic acids) using the mean cumulative response within each group for each core and aggregate fraction. A fire sensitivity index (FSI) was calculated within each campaign as FSI = (Burned - Unburned)micro - (Burned - Unburned)macro. FSI 0 indicates a larger decline in macroaggregates. Soil chemistry, WDXRF, and micro-CT variables were interpreted descriptively because they were measured on dry/warm campaign composite samples. All analyses were conducted in R version 4.1.2 using lme4, lmerTest, emmeans, and ggplot2, and no field core was excluded from the analysis. 3. Results 3.1 Soil chemical and physical properties Table 1 summarizes composite topsoil chemistry and bulk density from the dry/warm campaign (February 2024) and is presented as descriptive context because these analyses were conducted on composite material. Burning reduced total C from 21.8 % to 15.2 % in the native forest and from 17.5 % to 14.3 % in the pine plantation. Burning reduced total N from 0.95 % to 0.78 % in the native forest and from 0.94 % to 0.87 % in the pine plantation. The C:N ratio declined from 23.0 to 19.5 in the native forest and from 18.6 to 16.5 in the pine plantation. Inorganic N increased from 32 to 60 mg kg −1 in the native forest and from 55 to 174 mg kg −1 in the pine plantation. The exchangeable base cations declined after burning in both ecosystems. The exchangeable Ca declined from 11.0 to 1.89 cmolc kg -1 in the native forest and from 2.90 to 1.48 cmolc kg -1 in the pine plantation. The exchangeable Al increased from 0.12 to 0.44 cmolc kg -1 in the native forest and shifted from 0.45 to 0.39 cmolc kg -1 in the plantation. The effective CEC declined from 15.3 to 3.06 cmolc kg -1 in the native forest and from 4.37 to 2.42 cmolc kg -1 in the plantation. The bulk density increased from 0.36 to 0.65 g cm-3 in the native forest and remained at 0.60 g cm -3 in the plantation (Table 1). Oxalate-extractable Fe increased from 0.57 % to 1.83 % in the native forest and from 1.31 % to 2.22 % in the pine plantation. Oxalate-extractable Si increased from 0.63 % to 1.09 % in the native forest and from 1.46 % to 2.02 % in the plantation. Oxalate-extractable Al increased from 3.17 % to 9.35 % in the native forest and declined from 7.10 % to 4.51 % in the plantation. Dithionite-extractable Fe declined from 1.43 % to 1.24 % in the native forest and from 1.44 % to 1.23 % in the plantation (Table 1). Table 1. Soil chemical and physical properties of composite topsoil samples (0-5 cm) from native forest unburned (NUB), native forest burned (NB), pine plantation unburned (PUB), and pine plantation burned (PB) sampled in February 2024 (dry/warm). Condition Units NUB NB PUB PB Latitude (°) WGS84 -38.55872 -38.56020 -38.55872 -38.56024 Longitude (°) -72.38150 -72.38729 -72.38150 -72.38571 Total N % 0.95 0.78 0.94 0.87 Total C % 21.8 15.2 17.5 14.3 C:N 23.0 19.5 18.6 16.5 Inorganic N mg kg -1 32 60 55 174 Available P 9.5 14 15.2 11.3 Available K 231 74 82 70 Available B 0.59 0.33 0.36 0.30 pH H 2 O 4.95 4.83 4.82 4.89 Exc K cmolc kg -1 0.59 0.19 0.21 0.18 Exc Na 0.09 0.06 0.07 0.06 Exc Ca 11.0 1.89 2.90 1.48 Exc Mg 3.5 0.48 0.74 0.31 Exc Al 0.12 0.44 0.45 0.39 Al Sat (%) % 0.78 14.4 10.3 16.1 ECEC cmolc kg -1 15.3 3.06 4.37 2.42 Sum of bases 15.2 2.62 3.92 2.03 EC ds m -1 0.83 0.14 0.20 0.11 Hydrophobicity EXH STH SLH SLH Fe o % 0.57 1.83 1.31 2.22 Mn o 0.13 0.08 0.28 0.23 Si o 0.63 1.09 1.46 2.02 Al o 3.17 9.35 7.10 4.51 Fe d 1.43 1.24 1.44 1.23 Mn d 0.14 0.16 0.18 0.14 Si d 0.09 0.13 0.07 0.02 Al d 0.93 0.71 0.88 0.74 Fe p 0.50 0.46 0.45 0.51 Mn p 0.04 0.03 0.03 0.04 Si p 0.08 0.02 0.03 0.03 Al p 2.19 2.01 2.09 2.76 Bulk density g cm -3 0.36 0.65 0.60 0.60 ECEC: cation exchange capacity; EC: Electrical conductivity; Hydrophobicity: EXH: Extremely hydrophobic; STH: highly hydrophobic; SLH: Slightly hydrophobic. 3.2 Major oxide composition Major oxides were assessed in dry/warm campaign composite samples and were dominated by SiO 2 (36.0-44.2 %), Al 2 O 3 (14.7-19.8 %), and Fe 2 O 3 (8.42-11.1 %) across the four site conditions (Fig. 2; Table S1). The unburned pine plantation had higher CaO (2.63 %) and P 2 O 5 (0.88 %) contents than the other conditions (Table S1). The close relation between the soil spectra and the proportional distribution of oxides supports a shared volcanic parent material across the sites. 3.3 Aggregate pore-size distribution in small macroaggregates Unburned native forest aggregates had the highest frequencies of mesopores (30–100 µm) and macropores (>100 µm) (Fig. 3). Plantation and burned aggregates had lower pore frequencies in these classes, with pore-size distributions shifting toward smaller diameters. The reconstructed volumes supported these contrasts (Fig. 4). 3.4 Substrate-group responses across aggregate fractions and campaigns Across campaigns, amino-acid responses were lower in burned soils than in the corresponding unburned soils in both ecosystems and aggregate fractions (Fig. 5). In native forest, amino-acid cumulative CO 2 -C decreased from 401 to 297 mg kg −1 in macroaggregates and from 475 to 181 mg kg −1 in microaggregates during the dry/warm campaign; the same direction was maintained in the wet/cool campaign (460 vs 438 mg kg −1 in macroaggregates and 535 vs 342 mg kg −1 in microaggregates). Carbohydrate responses showed smaller fire contrasts in native forest than in pine plantation. In pine soils, carbohydrate cumulative CO 2 -C remained highest in unburned macroaggregates (372 and 419 mg kg −1 in the dry/warm and wet/cool campaigns, respectively) and lowest in burned macroaggregates during the dry/warm campaign (119 mg kg −1 ). Carboxylic-acid responses remained more separated by fire in native-forest microaggregates and in pine-plantation macroaggregates. 3.5 Substrate-level fire effects across campaigns and aggregate fractions The heatmap of Δ cumulative CO 2 -C (Burned - Unburned) showed that the strongest negative fire effects were concentrated in pine-plantation macroaggregates and native-forest microaggregates (Fig. 6). During the dry/warm campaign, the most negative contrasts occurred for GABA and L-cysteine-HCl in pine macroaggregates (-531 and -398 mg kg −1 , respectively) and for L-cysteine-HCl and citric acid in native microaggregates (-548 and -306 mg kg −1 , respectively). In contrast, several carbohydrates in native macroaggregates showed small positive Δ values. During the wet/cool campaign, the same compartments remained the most responsive, although the magnitude of several negative contrasts decreased. For example, the fire effect for GABA in pine macroaggregates remained negative (-385 mg kg −1 ), and the effect for L-cysteine-HCl in native microaggregates was -352 mg kg −1 . Positive contrasts in native macroaggregates were again restricted mainly to carbohydrates. 3.6 Fire sensitivity across aggregate fractions and campaigns The fire sensitivity index (FSI) separated the two ecosystems clearly (Fig. 7). In native forest, group mean FSI values were negative for amino acids, carbohydrates, and carboxylic acids in both campaigns, ranging from -190 to -103 mg kg −1 during the dry/warm campaign and from -171 to -86 mg kg −1 during the wet/cool campaign. In pine plantation, group mean FSI values were positive in both campaigns, ranging from 16 to 109 mg kg −1 in the dry/warm campaign and from 21 to 113 mg kg −1 in the wet/cool campaign. The separation between ecosystems was strongest for amino acids and carbohydrates, whereas carboxylic acids in pine plantation remained closer to zero than the other substrate groups. 4. Discussion 4.1 Fire legacy depends on the pre-fire ecosystem and the post-fire chemical template Wildfire severity and land use impose chemical constraints on post-fire microbial activity in surface volcanic soils. Total C declined from 21.8 to 15.2 % in the native forest and from 17.5 to 14.3 % in the pine plantation, indicating a sustained reduction in organic inputs and thermally altered organic matter in the 0-5 cm layer (Certini, 2005; Johnson et al., 2024). Increased inorganic N is consistent with the pulse of mineral N commonly reported after high-severity fires and with a more substantial legacy in managed stands (Certini, 2005; Roshan & Biswas, 2023). The exchangeable base cations followed the same trend in both ecosystems, with Ca declining in the native forest and the plantation after fire, thereby increasing Al saturation and shifting the soil solution toward metal stress and lower nutrient supply under both conditions (Certini, 2005; Roshan & Biswas, 2023). These coupled constraints support a mechanism in which post-fire microbial metabolism becomes more dependent on short-lived, labile inputs and less supported by sustained nutrient cycling, with stronger limitations expected in diffusion-controlled microaggregate microhabitats (Barreiro & Díaz-Raviña, 2021; Johnson et al., 2024). 4.2 Pore-network contraction links fire to microhabitat constraints in aggregates The pore-size distribution in small macroaggregates indicates a contraction of meso- and macroporosity after fire and after the land-use shift from native forest to pine plantation. Bulk density increased from 0.36 to 0.65 g cm -3 in the native forest, which is consistent with structural collapse and pore occlusion after intense heating and aggregate disruption (Certini, 2005; Mataix-Solera et al., 2011). Mesopores and macropores provide interconnected pathways that sustain oxygen renewal, microbial dispersal, and fungal exploration, whereas smaller pores support localized activity under diffusion-limited conditions (Young & Crawford, 2004; Menon et al., 2020). The observed reduction in pore frequencies implies lower habitat connectivity and a narrower range of redox and moisture niches that can sustain functionally diverse decomposer assemblages (Naveed et al., 2016; Yudina et al., 2022). Recent pore-scale evidence has shown that microbial composition and metabolism differ sharply between small pores (4–10 μm) and large pores (30–150 μm), with substrate processing and metabolic pathways depending on pore size and connectivity (Li et al., 2024). This mechanism aligns with the post-fire shift toward lower substrate-induced CO 2 production in several substrates and a weaker differentiation between aggregate fractions after disturbance, indicating that pore architecture can constrain functional heterogeneity even when substrates are supplied experimentally (Totsche et al., 2018; Li et al., 2024). 4.3 Aggregate-specific substrate responses reveal fire sensitivity Across both 2024 campaigns, the strongest fire-associated functional losses remained concentrated in native-forest microaggregates and pine-plantation macroaggregates. The native forest FSI remained negative for all three substrate groups, whereas plantation FSI remained neutral to positive, indicating that the aggregate fraction most sensitive to fire depended on ecosystem context. The persistence of this contrast from the dry/warm to the wet/cool campaign suggests that the distribution of microbial function among aggregate microhabitats was not a transient moisture effect alone. At the same time, the weaker negative contrasts during the wet/cool campaign indicate partial seasonal relaxation rather than full functional recovery. These patterns support the working hypothesis that fire contracts pore connectivity and reorganizes substrate access, but they refine it by showing that the direction of greatest sensitivity differs between ecosystems. 4.4 Reactive Al-Fe phases may buffer C stabilization, yet functional homogenization can persist Volcanic soils often stabilize organic matter through short-range-order minerals and organo-metal complexes, which can moderate C loss after disturbance. Feo increased from 0.57 to 1.83 % in the native forest and from 1.31 to 2.22 % in the plantation, suggesting a larger pool of reactive phases able to bind organic compounds after heating and post-fire re-aggregation (Fukumasu et al., 2021; Parada et al., 2024). The combination of high organic inputs and reactive Al–Fe pools is a known driver of C retention in volcanic soils, providing a mechanistic basis for the partial resistance of mineral-associated carbon despite large shifts in pore structure and nutrient availability (Parada et al., 2024; Ichinose et al., 2025). Functional data indicate that mineral buffering does not necessarily restore pre-fire heterogeneity in substrate use across aggregate fractions because pore connectivity and access limitations can keep communities functionally constrained, even when C sources are supplied (Li et al., 2024; Totsche et al., 2018). This coupling between reactive mineral phases and altered pore networks supports a scenario in which post-fire soils maintain some capacity for carbon stabilization while showing a reduced capacity to express diverse metabolic niches across micro- and macroaggregates, which can influence C persistence under increasing fire recurrence (Johnson et al., 2024; Roshan & Biswas, 2023). The design targeted aggregates from the topmost soil layer (0-5 cm), which is most affected by a surface fire (Santín & Doerr, 2016), but inference is therefore limited to surface processes. MicroResp/MSIR quantifies substrate-induced respiration under standardized conditions and does not resolve community composition or dormancy states. The micro-CT voxel size (5.7 µm) limits the quantification of pores below that threshold. Because chemistry and structural descriptors were measured on dry/warm campaign composites and repeated inference was based on the same paired sites and resampled points, conclusions should remain restricted to the paired sites and the two climatic windows studied rather than generalized to full post-fire recovery trajectories or all fire-severity gradients. 5. Conclusion Across two contrasting campaigns in 2024, wildfire altered the chemical template and aggregate habitat structure of volcanic ash soils in both a native Nothofagus forest and a Pinus radiata plantation. Fire reduced soil C and shifted exchange chemistry, while MicroResp/MSIR responses showed persistent reorganization of substrate use across aggregate fractions. Native-forest microaggregates and plantation macroaggregates remained the compartments most sensitive to fire across campaigns, although several contrasts weakened under wet/cool conditions. This aggregate-scale framework links pore architecture to microbial substrate use and provides a practical basis for diagnosing post-fire constraints on soil recovery and carbon retention in volcanic soils. Declarations Conflict of interest: The authors declare no conflicts of interest. Authors’ contributions: FNF led the conceptualization, data analysis, figure preparation, and initial drafting. CM and IJ contributed to study design, supervision, interpretation, and manuscript revision. FM and FA contributed to methodological development, interpretation, and editing. JK and CR contributed to laboratory workflows, data curation, and manuscript review. All authors read and approved the final manuscript. Acknowledgments: This research was supported by ANID Postdoctoral Project 3230428, FONDECYT Regular 1220116, ANILLO ACT192006 (FIRING), Fondequip EQM 220061 for the micro-CT instrumentation, and the BIOREN scientific nucleus. 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Clays Clay Miner 7:317-327. https://doi.org/10.1346/CCMN.1958.0070122 Menon M, Mawodza T, Rabbani A et al. (2020) Pore system characteristics of soil aggregates and their relevance to aggregate stability. Geoderma 366:114259. https://doi.org/10.1016/j.geoderma.2020.114259 Merino C, Jofré I, Stock S et al. (2026) Return of soil function: texture and thermal load drive post-fire microbial reactivation. Appl Soil Ecol 219:106819. https://doi.org/10.1016/j.apsoil.2026.106819 Mizota C, van Reeuwijk LP (1989) Clay mineralogy and chemistry of soils formed in volcanic material in diverse climatic regions. International Soil Reference and Information Centre (ISRIC), Wageningen, The Netherlands Nájera F, Duarte E, Smith-Ramirez C et al. (2024) Multi-temporal assessment of a wildfire chronosequence by remote sensing. MethodsX 13:103011. https://doi.org/10.1016/j.mex.2024.103011 Naveed M, Herath L, Moldrup P et al. (2016) Spatial variability of microbial richness and diversity and relationships with soil organic carbon, texture and structure across an agricultural field. Appl Soil Ecol 103:44-55. https://doi.org/10.1016/j.apsoil.2016.03.004 Parada J, Neaman A, Zamorano D et al. (2024) Management and liming-induced changes in organo-Al/Fe complexes and amorphous mineral-associated organic carbon: implications for carbon sequestration in volcanic soils. Soil Till Res 242:106133. https://doi.org/10.1016/j.still.2024.106133 Peng J, Yang QS, Zhang CY et al. (2023) Aggregate pore structure, stability characteristics, and biochemical properties induced by different cultivation durations in the Mollisol region of Northeast China. Soil Till Res 233:105797. https://doi.org/10.1016/j.still.2023.105797 Pérez-Valera E, Goberna M, Verdú M (2019) Fire modulates ecosystem functioning through the phylogenetic structure of soil bacterial communities. Soil Biol Biochem 129:80-89. https://doi.org/10.1016/j.soilbio.2018.11.007 Renault P, Ben-Sassi M, Bérard A (2013) Improving the MicroResp substrate-induced respiration method by a more complete description of CO2 behavior in closed incubation wells. Geoderma 207-208:82-91. https://doi.org/10.1016/j.geoderma.2013.05.010 Roshan A, Biswas A (2023) Fire-induced geochemical changes in soil: implication for the element cycling. Sci Total Environ 868:161714. https://doi.org/10.1016/j.scitotenv.2023.161714 Sadzawka A, Carrasco MA, Grez R, Mora ML, Flores H, Neaman A (2006) Métodos de análisis recomendados para los suelos de Chile. Revisión 2006. Instituto de Investigaciones Agropecuarias, Serie Actas INIA No 34, Santiago, Chile Santín C, Doerr SH (2016) Fire effects on soils: the human dimension. Philos Trans R Soc Lond B Biol Sci 371:20150171. https://doi.org/10.1098/rstb.2015.0171 Sharifi Z, Azadi N, Rahimi S, Certini G (2018) The response of glomalin-related soil proteins to fire or tillage. Geoderma 329:65-72. https://doi.org/10.1016/j.geoderma.2018.05.008 Six J, Bossuyt H, Degryze S, Denef K (2004) A history of research on the link between (micro)aggregates, soil biota, and soil organic matter dynamics. Soil Till Res 79:7-31. https://doi.org/10.1016/j.still.2004.03.008 Soil Survey Staff (1999) Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Surveys, 2nd edn. USDA-NRCS, Agriculture Handbook 436. U.S. Government Printing Office, Washington, DC Tisdall JM, Oades JM (1982) Organic matter and water-stable aggregates in soils. J Soil Sci 33:141-163. https://doi.org/10.1111/j.1365-2389.1982.tb01755.x Torn MS, Trumbore SE, Chadwick OA, Vitousek PM, Hendricks DM (1997) Mineral control of soil organic carbon storage and turnover. Nature 389:170-173. https://doi.org/10.1038/38260 Totsche KU, Amelung W, Gerzabek MH et al. (2018) Microaggregates in soils. J Plant Nutr Soil Sci 181:104-136. https://doi.org/10.1002/jpln.201600451 Young IM, Crawford JW (2004) Interactions and self-organization in the soil-microbe complex. Science 304:1634-1637. https://doi.org/10.1126/science.1097394 Yudina AV, Klyueva VV, Romanenko KA, Fomin DS (2022) Micro-within macro: how micro-aggregation shapes the soil pore space and water-stability. Geoderma 415:115771. https://doi.org/10.1016/j.geoderma.2022.115771 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Graphicalabstract.png Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9395928","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623574538,"identity":"6283ab0a-641b-4157-b518-c0ebfe84b4e1","order_by":0,"name":"Francisco Nájera Ferrari","email":"","orcid":"","institution":"Universidad de La Frontera","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"Nájera","lastName":"Ferrari","suffix":""},{"id":623574540,"identity":"3a6deabe-7799-4e1d-942c-2652ec99dd50","order_by":1,"name":"Ignacio Jofré-Fernández","email":"","orcid":"","institution":"Universidad de La Frontera","correspondingAuthor":false,"prefix":"","firstName":"Ignacio","middleName":"","lastName":"Jofré-Fernández","suffix":""},{"id":623574541,"identity":"7fa30ad9-6ac4-42a4-baac-632e0702b9cc","order_by":2,"name":"Francisco Matus","email":"","orcid":"","institution":"Universidad de La Frontera","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"","lastName":"Matus","suffix":""},{"id":623574542,"identity":"745f676e-c765-4f32-b3a5-866e26368c4f","order_by":3,"name":"Felipe Aburto","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Felipe","middleName":"","lastName":"Aburto","suffix":""},{"id":623574543,"identity":"5e3f6e3c-f765-4fa4-9530-4cafa3c56783","order_by":4,"name":"Jonathan Kerman","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Kerman","suffix":""},{"id":623574544,"identity":"3e1b72ea-ab32-4044-b226-9a37cd14c16e","order_by":5,"name":"Claudia Rojas","email":"","orcid":"","institution":"University of O'Higgins","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Rojas","suffix":""},{"id":623574545,"identity":"516e5446-ca97-4a5e-91c5-d5502dbb462d","order_by":6,"name":"Carolina Merino-Guzmán","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIie2PMUvEMBiGEwJxqXaTQuD6F76SSaj6V1IK6eJx+g8CQh1vrf+io2MgEJdzdxBsKXQ+cFEQ9GqP48D0XB3yTC/J+/B9H0Iez3+lgW0QCM0Q0bsPrB3tsbmncETFmAamlb2cqb+U8O6pbcT1S4xOjV23D2lR2+O2u3lPF6EijUuJVgUHAX2imMyrbCXntT3ivBLyrNIUXAogSSMBBit2xVFWmnn9WlIWCANIB87FIOx/lEvFFm+DUoClg/IF8ZQSjVOyzRQyKGKraIAJJXruyeYWk5dMDovJ5N5SwgOZQ2Lct4RLiZv1pzlfsrzDH2Uan1iKuyC9gNnjbds4lB309xM51Pd4PB7PIb4Bp8lfr+ljbVEAAAAASUVORK5CYII=","orcid":"","institution":"Universidad de La Frontera","correspondingAuthor":true,"prefix":"","firstName":"Carolina","middleName":"","lastName":"Merino-Guzmán","suffix":""}],"badges":[],"createdAt":"2026-04-12 17:08:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9395928/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9395928/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107348927,"identity":"332f67e2-3039-448a-8887-8f32f72ee47d","added_by":"auto","created_at":"2026-04-20 15:41:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":669058,"visible":true,"origin":"","legend":"\u003cp\u003eStudy and sampling area in the La Araucanía Region, south-central Chile. Thermal image from NASA FIRMS maps (\u003ca href=\"https://firms.modaps.eosdis.nasa.gov/map/#d:24hrs;@0.0,0.0,3.0z\"\u003ehttps://firms.modaps.eosdis.nasa.gov/map/#d:24hrs;@0.0,0.0,3.0z\u003c/a\u003e). Burned and unburned sites conditions included native unburned forest (NUB), native burned forest (NB), unburned pine plantation (PUB), and burned pine plantation (PB)\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9395928/v1/e5c6638cb6734fb69405dd90.png"},{"id":107348921,"identity":"4024cd18-3b63-4228-bf96-906623fdbdf6","added_by":"auto","created_at":"2026-04-20 15:41:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":174073,"visible":true,"origin":"","legend":"\u003cp\u003eSpectrum derived from WDXRF oxide composition (%) for native forest unburned (NUB), native forest burned (NB), pine plantation unburned (PUB), and pine plantation burned (PB). The peaks are placed at the characteristic Kα/Lα energies for each element, and the peak heights are scaled to the oxide percentages. The y-axis represents arbitrary units that do not correspond to the instrument counts.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9395928/v1/34e39e5051855340534996c3.png"},{"id":107348906,"identity":"fa423ff5-6897-42f8-ae0a-077d5c156523","added_by":"auto","created_at":"2026-04-20 15:41:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":199923,"visible":true,"origin":"","legend":"\u003cp\u003ePore-size frequency distribution in small macroaggregates measured by micro-CT (voxel size 5.7 µm). The solid and dashed lines represent the unburned and burned soils, respectively. The curves correspond to native forest and pine plantation soils. The pore diameter classes were \u0026lt;30, 30–75, 75–100, and \u0026gt;100 µm.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9395928/v1/4e3189e68304547362f23cae.png"},{"id":107348932,"identity":"37ffbe70-7143-4dde-86fe-ac1be1311f42","added_by":"auto","created_at":"2026-04-20 15:41:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1033271,"visible":true,"origin":"","legend":"\u003cp\u003eAggregates CT scan from: A) Native unburned (NUB), B) Native Burned (NB), C) Pine Unburned (PUB), and D) Pine Burned (PB) soils.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9395928/v1/31cfbe9abdff8467ff9516e3.png"},{"id":107348897,"identity":"17ebe8b8-1490-427f-8eed-81c600edce53","added_by":"auto","created_at":"2026-04-20 15:41:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":357474,"visible":true,"origin":"","legend":"\u003cp\u003eTwo-campaign MicroResp/MSIR responses summarized by substrate group. Points show mean CO\u003csub\u003e2\u003c/sub\u003e-C corresponding to cumulative substrate-induced CO\u003csub\u003e2\u003c/sub\u003e-C over 6-72 h for carbohydrates, amino acids, and carboxylic acids in macroaggregates and microaggregates from native forest and pine plantation soils sampled in February 2024 (dry/warm) and August 2024 (wet/cool). Error bars denote SE (n = 5 resampled field cores).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9395928/v1/46e45fc9461089499a31304b.png"},{"id":107488313,"identity":"a21c7c52-faf0-43ae-9ed8-59fd5b7ee94e","added_by":"auto","created_at":"2026-04-22 02:44:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":126966,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of fire-associated change in cumulative substrate-induced CO\u003csub\u003e2\u003c/sub\u003e-C. Cells show Δ cumulative CO\u003csub\u003e2\u003c/sub\u003e-C (Burned - Unburned, 6-72 h; mg kg\u003csup\u003e−1\u003c/sup\u003e) for individual substrates in macroaggregates and microaggregates from native forest and pine plantation soils sampled in February 2024 (dry/warm) and August 2024 (wet/cool). Negative values indicate lower respiration in burned than in unburned soils.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-9395928/v1/18c2890fa8eea1c321e76b4c.png"},{"id":107486092,"identity":"82435fbd-b719-4bcf-bee5-e13e5939c7fe","added_by":"auto","created_at":"2026-04-22 02:37:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":103873,"visible":true,"origin":"","legend":"\u003cp\u003eFire sensitivity index (FSI) across aggregate fractions and sampling windows. FSI was calculated from cumulative CO\u003csub\u003e2\u003c/sub\u003e-C as FSI = (Burned - Unburned)micro - (Burned - Unburned)macro for each substrate group within each ecosystem and campaign. Small points represent individual substrates; large points and error bars represent group mean ± SE. FSI \u0026lt; 0 indicates larger fire-associated declines in microaggregates, whereas FSI \u0026gt; 0 indicates larger declines in macroaggregates. Campaigns correspond to February 2024 (dry/warm) and August 2024 (wet/cool).\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-9395928/v1/270762df120df81bdea252d9.png"},{"id":107489483,"identity":"9f376600-3bf9-4c56-86fe-87a934819ba1","added_by":"auto","created_at":"2026-04-22 02:47:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3533994,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9395928/v1/122cc0df-4f55-4549-8f6b-702ba994820d.pdf"},{"id":107348918,"identity":"0a7cf4f1-402b-4440-9293-f0a58b6577d4","added_by":"auto","created_at":"2026-04-20 15:41:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23675,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9395928/v1/f78c8fbc96fb84b332d47cfc.docx"},{"id":107348896,"identity":"ac8e5bc9-55bb-49ac-8e87-2ef8cf333176","added_by":"auto","created_at":"2026-04-20 15:41:31","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10926510,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.png","url":"https://assets-eu.researchsquare.com/files/rs-9395928/v1/66497fb43527031519f57e1e.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"A disturbance in the aggregates: wildfire reorganizes seasonal microbial substrate use in volcanic soils of native and managed forests","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eFire reduced meso- and macroporosity, particularly in burned pine soils.\u003c/li\u003e\n \u003cli\u003eBurning lowered the total C and shifted inorganic N and exchangeable Al.\u003c/li\u003e\n \u003cli\u003eMicroResp/MSIR detected persistent fire-associated declines from February to August 2024.\u003c/li\u003e\n \u003cli\u003eNative microaggregates and pine macroaggregates were the most fire-sensitive compartments.\u003c/li\u003e\n \u003cli\u003eAmino acid, carboxylic acid, and carbohydrate responses explained ecosystem-specific fire sensitivity.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eWildfire seasons are lengthening in many regions, increasing the need to quantify post-fire controls on soil carbon cycling (Jolly et al., 2015). Fire restructures the physical and chemical properties of soil through heating and the introduction of ash, thereby redefining microbial habitats (Certini, 2005; Roshan \u0026amp; Biswas, 2023). The relationship between soil aggregation and microbial community function became clearly mechanistic in fire-affected environments because aggregates define diffusion, protection, and resource gradients (Tisdall \u0026amp; Oades, 1982; Six et al., 2004). Heating depolymerizes organic binding agents and weakens aggregate stability, increasing susceptibility to post-fire slaking (Arcenegui et al., 2008; Mataix-Solera et al., 2011). Such soil structure disruption redistributes pore space, nutrients, and water retention, with effects extending into the recovery period (Roshan \u0026amp; Biswas, 2023; Johnson et al., 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSoil microbial communities regulate the decomposition and stabilization of soil organic matter (SOM) through enzyme production, interactions between microbial necromass and mineral particles (Condron et al., 2010), and aggregate formation (Sharifi et al., 2018). Fire reduces microbial biomass and shifts community composition, with responses depending on fire severity and vegetation type (Aponte et al., 2022; P\u0026eacute;rez-Valera et al., 2019). Heat pulses remove sensitive taxa, whereas post-fire nutrient and pH pulses enhance fast-growing opportunistic taxa (Barreiro \u0026amp; D\u0026iacute;az-Ravi\u0026ntilde;a, 2021; Chebykina et al., 2024). Consequently, the community reorders substrate use and respiration dynamics over the years, and severity sets the pace of recovery (Fox et al., 2022; Merino et al., 2026).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSoil aggregates serve as microhabitats, providing spatial structures that constrain gas and water diffusion and protect particulate organic matter, thereby shaping microbial physiology (Six et al., 2004; Totsche et al., 2018), and exhibit distinct gradients of oxygen, moisture, and nutrients, supporting communities with contrasting metabolic strategies (Naveed et al., 2016; Li et al., 2024).\u003c/p\u003e\n\u003cp\u003eVolcanic ash soils contain reactive short-range-order oxides that stabilize large soil organic carbon pools. Here, the limited number of macroaggregates indicates that small macro- and microaggregates regulate microbial access to carbon under these mineral controls (Asano \u0026amp; Wagai, 2014; Torn et al., 1997; Matus et al., 2014). The effects of wildfires on aggregate-scale interactions in volcanic soils remain poorly quantified.\u003c/p\u003e\n\u003cp\u003eThis study quantified MicroResp-based substrate-induced CO\u003csub\u003e2\u003c/sub\u003e production in the macro- and microaggregate fractions of burned and unburned soils from a temperate Nothofagus spp. forest and a managed Pinus radiata plantation. The same sampling points were resampled in a dry/warm campaign (February 2024) and a wet/cool campaign (August 2024) to test whether aggregate-scale fire effects persisted across contrasting climatic windows. We hypothesized that wildfire would contract pore connectivity and reduce substrate-induced respiration, with stronger and more persistent effects in native-forest microaggregates and in high-activity plantation macroaggregates.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study design and wildfire/site selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-severity wildfire scars from the 2021 fire season were identified in native forest and pine plantation stands using Landsat surface reflectance imagery processed in Google Earth Engine. Burn severity in the study area was characterized in our previous work using NDVI, NBR, and dNBR, and a one-time fire-occurrence filter was applied with the LandTrendr algorithm (N\u0026aacute;jera et al., 2024). Four paired site conditions were selected: unburned native forest (NUB), burned native forest (NB), unburned pine plantation (PUB), and burned pine plantation (PB) (Fig. 1). The same paired plots were sampled twice in 2024: in February (dry/warm) and in August (wet/cool). The same five georeferenced sampling points within each condition were revisited in the second campaign so that temporal variation could be evaluated with repeated measurements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Soil sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoils were collected from the 0-5 cm mineral layer after removing surface litter and loose ash. Five georeferenced auger points were established within each condition during the February 2024 campaign and the same points were resampled in August 2024. Each core was retained as an independent biological replicate for aggregate fractionation and MicroResp/MSIR assays (n = 5 per condition per campaign). Equal-mass composite samples prepared from the five cores of the dry/warm campaign were used for soil chemistry, selective dissolution, WDXRF, and micro-CT descriptors, which are interpreted here as contextual variables. Bulk density was measured at three points per condition using undisturbed cores of known volume dried at 105 \u0026deg;C to constant mass.\u003c/p\u003e\n\u003cp\u003eFresh soil for MicroResp/MSIR and aggregate fractionation was stored at 4 \u0026deg;C and processed within 48 h of collection. Additional material from the dry/warm campaign was air-dried, gently disaggregated, passed through a 2 mm sieve, and stored for chemical, mineralogical, and structural analyses. All five field cores were retained for the repeated-measures analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Soil physicochemical and structural analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.1 Soil chemical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal C and inorganic C were measured with a TOC-SSM-5000A-VCSH analyzer (Shimadzu, Kyoto, Japan). Total C was determined by dry combustion at 900 \u0026deg;C. Inorganic C was measured after HCl addition and combustion at 200 \u0026deg;C. Soil organic C was calculated as total C minus inorganic C.\u003c/p\u003e\n\u003cp\u003eTotal N was determined by Kjeldahl distillation. Inorganic N (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e) was quantified after extraction by steam distillation for mineral N forms (Bremner \u0026amp; Tabatabai, 1972).\u003c/p\u003e\n\u003cp\u003eSoil pH (1:2.5 water:soil), electrical conductivity, cation exchange capacity, Olsen-P, and available boron were analyzed as described by Sadzawka et al. (2006).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.2 Selective dissolution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSelective dissolution methods were applied to evaluate Al, Fe, Mn, and Si associated with organic complexes and poorly crystalline mineral phases (Parada et al., 2024). Acid ammonium oxalate extraction was used to quantify poorly crystalline phases (Al\u003csub\u003eo\u003c/sub\u003e and Fe\u003csub\u003eo\u003c/sub\u003e) (McKeague \u0026amp; Day, 1966). Dithionite-citrate-bicarbonate extraction was used to quantify pedogenic free Fe oxides (Fe\u003csub\u003ed\u003c/sub\u003e) (Mehra \u0026amp; Jackson, 1958). Sodium pyrophosphate extraction was used to quantify organically complexed Al and Fe (Al\u003csub\u003ep\u003c/sub\u003e and Fe\u003csub\u003ep\u003c/sub\u003e, respectively) (Bascomb, 1968). Selective dissolution procedures followed Parfitt and Wilson (1985, as cited by Mizota and van Reeuwijk, 1989) and the workflow described for volcanic soils by Parada et al. (2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.3 Soil water repellency\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA wettability test was conducted to assess soil hydrophobicity after fire. Soil water repellency was evaluated using the water drop penetration time (WDPT) test and classified on a five-class scale from 1 (wettable) to 5 (extremely hydrophobic) following Doerr et al. (2000).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.4 Soil description\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt each site, a soil morphological description was performed \u003cem\u003ein situ\u003c/em\u003e, including texture, color, structure, and root abundance, using soil taxonomy methodology (Soil Survey Staff, 1999).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.5 Wavelength-dispersive X-ray fluorescence (WDXRF)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDry soil was milled and pelletized to quantify major and minor element composition with a wavelength-dispersive X-ray fluorescence spectrometer (Rigaku ZSX Primus IV WDXRF) equipped with a 3 kW X-ray generator (60 kV, 100 mA), a 48-position automatic sample changer, and three crystals (LiF(200) for Ti-U, PET for Al-Ti, and RX-26 for O-Mg). Pressed pellets (40 mm diameter) were prepared from oven-dried, homogenized soil powder to standardize matrix effects across samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.6 Micro-CT analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAggregates dry-sieved to 250-500 \u0026micro;m were used for micro-CT imaging. This fraction represents small macroaggregates and overlaps the upper limit of the microaggregate fraction used for the MicroResp/MSIR assays. Aggregates were packed gently in the sample holder and scanned with a SKYSCAN 1273 system (Bruker) at a voxel size of 5.7 \u0026micro;m.\u003c/p\u003e\n\u003cp\u003ePore-space analyses were performed in triplicate per sample by selecting three homogeneous regions of interest (1 mm diameter) that avoided aggregate boundaries (Peng et al., 2023). Image stacks of 500-600 slices were reconstructed into 3D TIFF volumes and processed with Fiji/ImageJ 2.16/1.54p and the SoilJ plugin (Koestel, 2018). Pores were classified as \u0026lt;30, 30-75, 75-100, and \u0026gt;100 \u0026micro;m (Peng et al., 2023; Cao et al., 2024). A second classification contrasted large pores (30-150 \u0026micro;m) with smaller pores near the resolution limit, acknowledging that pores below the voxel size cannot be quantified robustly (Li et al., 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 MicroResp whole-soil MSIR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.1 Aggregate-size fractionation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFresh soil was sieved gently under field-moist conditions to obtain macroaggregates (2000-250 \u0026micro;m) and microaggregates (250-53 \u0026micro;m). Moist sieving minimized aggregate disruption and microbial stress during fractionation (Cambardella \u0026amp; Elliott, 1993).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.2 MicroResp assay and substrate additions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MicroResp system was used as a whole-soil multiple substrate-induced respiration (MSIR) assay, not as a growth-based or culturable CLPP method (Campbell et al., 2003; Jones et al., 2018). Defined C substrates were added directly to intact soil or aggregate fractions, and short-term CO\u003csub\u003e2\u003c/sub\u003e evolution was quantified colorimetrically. Water-holding capacity (WHC) was determined gravimetrically for each sample using a saturated subsample that was allowed to drain for 2 h and then oven-dried at 105 \u0026deg;C; WHC was expressed as g H\u003csub\u003e2\u003c/sub\u003eO g\u003csup\u003e\u0026minus;1\u003c/sup\u003e dry soil. Samples were adjusted to 50% WHC with deionized water and pre-incubated for 4 d at 25 \u0026deg;C to stabilize basal respiration. For each sample, 0.40 g dry-mass equivalent of bulk soil or aggregate fraction was dispensed into deep-well plates and amended with 25 \u0026micro;L of one of 15 carbon sources at 30 mg g\u003csup\u003e\u0026minus;1\u003c/sup\u003e soil H\u003csub\u003e2\u003c/sub\u003eO: D-glucose, D-fructose, galactose, \u0026gamma;-aminobutyric acid, N-acetyl-glucosamine, L-alanine, L-arabinose, arginine, L-lysine-HCl, L-cysteine-HCl, \u0026alpha;-ketoglutarate, citric acid, oxalic acid, protocatechuic acid, and L-malic acid. Water-amended control wells received 25 \u0026micro;L deionized water and were included in technical triplicate on each plate. Technical triplicates were averaged before statistical analysis, and substrate-induced responses were expressed relative to the corresponding water control for each sample. Indicator plates were incubated at 25 \u0026deg;C and read after 6, 12, 24, 36, 48, 60, and 72 h. Absorbance at 570 nm was measured with a microplate reader (Fendt-A300, Allsheng, China) before and after each interval. During plate reading, deep-well plates were ventilated for 15 min and resealed with a fresh indicator plate. CO\u003csub\u003e2\u003c/sub\u003e production was calculated from absorbance change using the MicroResp calibration procedure and expressed as interval respiration (mg C kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e h\u003csup\u003e\u0026minus;1\u003c/sup\u003e) and cumulative CO\u003csub\u003e2\u003c/sub\u003e-C over 6-72 h (mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e) (Campbell et al., 2003; Renault et al., 2013). Each plate contained an equal representation of site conditions to minimize positional bias.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Statistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were performed on biological replicates (field cores; n = 5 per condition per campaign) after averaging technical triplicates within each sample \u0026times; substrate combination. Because the same five georeferenced points were resampled in February and August 2024, cumulative CO\u003csub\u003e2\u003c/sub\u003e-C (6-72 h) was analyzed with linear mixed-effects models that included ecosystem, fire status, sampling campaign, aggregate fraction, and their interactions as fixed effects, with sampling point as a random intercept. Separate models were fitted for substrate groups and for individual substrates when required. Residual normality and homoscedasticity were checked from Q-Q plots and residual-versus-fitted plots; responses were log10-transformed when needed to improve variance homogeneity.\u003c/p\u003e\n\u003cp\u003eEstimated marginal means were compared with Tukey adjustment at \u0026alpha; = 0.05. Substrate groups were defined by chemistry (carbohydrates, amino acids, amino sugars, phenolic acids, and carboxylic acids) using the mean cumulative response within each group for each core and aggregate fraction. A fire sensitivity index (FSI) was calculated within each campaign as FSI = (Burned - Unburned)micro - (Burned - Unburned)macro. FSI \u0026lt; 0 indicates a larger fire-associated decline in microaggregates, whereas FSI \u0026gt; 0 indicates a larger decline in macroaggregates. Soil chemistry, WDXRF, and micro-CT variables were interpreted descriptively because they were measured on dry/warm campaign composite samples. All analyses were conducted in R version 4.1.2 using lme4, lmerTest, emmeans, and ggplot2, and no field core was excluded from the analysis.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Soil chemical and physical properties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 summarizes composite topsoil chemistry and bulk density from the dry/warm campaign (February 2024) and is presented as descriptive context because these analyses were conducted on composite material. Burning reduced total C from 21.8 % to 15.2 % in the native forest and from 17.5 % to 14.3 % in the pine plantation. Burning reduced total N from 0.95 % to 0.78 % in the native forest and from 0.94 % to 0.87 % in the pine plantation. The C:N ratio declined from 23.0 to 19.5 in the native forest and from 18.6 to 16.5 in the pine plantation. Inorganic N increased from 32 to 60 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e in the native forest and from 55 to 174 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e in the pine plantation.\u003c/p\u003e\n\u003cp\u003eThe exchangeable base cations declined after burning in both ecosystems. The exchangeable Ca declined from 11.0 to 1.89 cmolc kg\u003csup\u003e-1\u003c/sup\u003e in the native forest and from 2.90 to 1.48 cmolc kg\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003ein the pine plantation. The exchangeable Al increased from 0.12 to 0.44 cmolc kg\u003csup\u003e-1\u003c/sup\u003e in the native forest and shifted from 0.45 to 0.39 cmolc kg\u003csup\u003e-1\u003c/sup\u003e in the plantation. The effective CEC declined from 15.3 to 3.06 cmolc kg\u003csup\u003e-1\u003c/sup\u003e in the native forest and from 4.37 to 2.42 cmolc kg\u003csup\u003e-1\u003c/sup\u003e in the plantation. The bulk density increased from 0.36 to 0.65 g cm-3 in the native forest and remained at 0.60 g cm\u003csup\u003e-3\u003c/sup\u003e in the plantation (Table 1).\u003c/p\u003e\n\u003cp\u003eOxalate-extractable Fe increased from 0.57 % to 1.83 % in the native forest and from 1.31 % to 2.22 % in the pine plantation. Oxalate-extractable Si increased from 0.63 % to 1.09 % in the native forest and from 1.46 % to 2.02 % in the plantation. Oxalate-extractable Al increased from 3.17 % to 9.35 % in the native forest and declined from 7.10 % to 4.51 % in the plantation. Dithionite-extractable Fe declined from 1.43 % to 1.24 % in the native forest and from 1.44 % to 1.23 % in the plantation (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Soil chemical and physical properties of composite topsoil samples (0-5 cm) from native forest unburned (NUB), native forest burned (NB), pine plantation unburned (PUB), and pine plantation burned (PB) sampled in February 2024 (dry/warm).\u003c/p\u003e\n\u003ctable style=\"width: 92%;border: none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCondition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eUnits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNUB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePUB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eLatitude (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eWGS84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e-38.55872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e-38.56020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e-38.55872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e-38.56024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eLongitude (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e-72.38150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e-72.38729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e-72.38150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e-72.38571\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTotal N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTotal C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eC:N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eInorganic N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"4\"\u003e\n \u003cp\u003emg kg\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAvailable P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAvailable K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAvailable B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eExc K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"5\"\u003e\n \u003cp\u003ecmolc kg\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eExc Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eExc Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eExc Mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eExc Al\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAl Sat (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eECEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003ecmolc kg\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSum of bases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eds m\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHydrophobicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eEXH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSLH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSLH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eFe\u003csub\u003eo\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"12\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMn\u003csub\u003eo\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSi\u003csub\u003eo\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAl\u003csub\u003eo\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eFe\u003csub\u003ed\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMn\u003csub\u003ed\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSi\u003csub\u003ed\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAl\u003csub\u003ed\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eFe\u003csub\u003ep\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMn\u003csub\u003ep\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSi\u003csub\u003ep\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAl\u003csub\u003ep\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eBulk density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eg cm\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eECEC: cation exchange capacity; EC: Electrical conductivity; Hydrophobicity: EXH: Extremely hydrophobic; STH: highly hydrophobic; SLH: Slightly hydrophobic.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Major oxide composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMajor oxides were assessed in dry/warm campaign composite samples and were dominated by SiO\u003csub\u003e2\u003c/sub\u003e (36.0-44.2 %), Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e (14.7-19.8 %), and Fe\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e (8.42-11.1 %) across the four site conditions (Fig. 2; Table S1). The unburned pine plantation had higher CaO (2.63 %) and P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e (0.88 %) contents than the other conditions (Table S1). The close relation between the soil spectra and the proportional distribution of oxides supports a shared volcanic parent material across the sites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Aggregate pore-size distribution in small macroaggregates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnburned native forest aggregates had the highest frequencies of mesopores (30\u0026ndash;100 \u0026micro;m) and macropores (\u0026gt;100 \u0026micro;m) (Fig. 3). Plantation and burned aggregates had lower pore frequencies in these classes, with pore-size distributions shifting toward smaller diameters. The reconstructed volumes supported these contrasts (Fig. 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Substrate-group responses across aggregate fractions and campaigns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross campaigns, amino-acid responses were lower in burned soils than in the corresponding unburned soils in both ecosystems and aggregate fractions (Fig. 5). In native forest, amino-acid cumulative CO\u003csub\u003e2\u003c/sub\u003e-C decreased from 401 to 297 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e in macroaggregates and from 475 to 181 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e in microaggregates during the dry/warm campaign; the same direction was maintained in the wet/cool campaign (460 vs 438 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e in macroaggregates and 535 vs 342 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e in microaggregates). Carbohydrate responses showed smaller fire contrasts in native forest than in pine plantation. In pine soils, carbohydrate cumulative CO\u003csub\u003e2\u003c/sub\u003e-C remained highest in unburned macroaggregates (372 and 419 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e in the dry/warm and wet/cool campaigns, respectively) and lowest in burned macroaggregates during the dry/warm campaign (119 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e). Carboxylic-acid responses remained more separated by fire in native-forest microaggregates and in pine-plantation macroaggregates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Substrate-level fire effects across campaigns and aggregate fractions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe heatmap of \u0026Delta; cumulative CO\u003csub\u003e2\u003c/sub\u003e-C (Burned - Unburned) showed that the strongest negative fire effects were concentrated in pine-plantation macroaggregates and native-forest microaggregates (Fig. 6). During the dry/warm campaign, the most negative contrasts occurred for GABA and L-cysteine-HCl in pine macroaggregates (-531 and -398 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e, respectively) and for L-cysteine-HCl and citric acid in native microaggregates (-548 and -306 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e, respectively). In contrast, several carbohydrates in native macroaggregates showed small positive \u0026Delta; values.\u003c/p\u003e\n\u003cp\u003eDuring the wet/cool campaign, the same compartments remained the most responsive, although the magnitude of several negative contrasts decreased. For example, the fire effect for GABA in pine macroaggregates remained negative (-385 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e), and the effect for L-cysteine-HCl in native microaggregates was -352 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e. Positive contrasts in native macroaggregates were again restricted mainly to carbohydrates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Fire sensitivity across aggregate fractions and campaigns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe fire sensitivity index (FSI) separated the two ecosystems clearly (Fig. 7). In native forest, group mean FSI values were negative for amino acids, carbohydrates, and carboxylic acids in both campaigns, ranging from -190 to -103 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e during the dry/warm campaign and from -171 to -86 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e during the wet/cool campaign. In pine plantation, group mean FSI values were positive in both campaigns, ranging from 16 to 109 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e in the dry/warm campaign and from 21 to 113 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e in the wet/cool campaign. The separation between ecosystems was strongest for amino acids and carbohydrates, whereas carboxylic acids in pine plantation remained closer to zero than the other substrate groups.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1 Fire legacy depends on the pre-fire ecosystem and the post-fire chemical template\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWildfire severity and land use impose chemical constraints on post-fire microbial activity in surface volcanic soils. Total C declined from 21.8 to 15.2 % in the native forest and from 17.5 to 14.3 % in the pine plantation, indicating a sustained reduction in organic inputs and thermally altered organic matter in the 0-5 cm layer (Certini, 2005; Johnson et al., 2024). Increased inorganic N is consistent with the pulse of mineral N commonly reported after high-severity fires and with a more substantial legacy in managed stands (Certini, 2005; Roshan \u0026amp; Biswas, 2023).\u003c/p\u003e\n\u003cp\u003eThe exchangeable base cations followed the same trend in both ecosystems, with Ca declining in the native forest and the plantation after fire, thereby increasing Al saturation and shifting the soil solution toward metal stress and lower nutrient supply under both conditions (Certini, 2005; Roshan \u0026amp; Biswas, 2023). These coupled constraints support a mechanism in which post-fire microbial metabolism becomes more dependent on short-lived, labile inputs and less supported by sustained nutrient cycling, with stronger limitations expected in diffusion-controlled microaggregate microhabitats (Barreiro \u0026amp; Díaz-Raviña, 2021; Johnson et al., 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Pore-network contraction links fire to microhabitat constraints in aggregates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pore-size distribution in small macroaggregates indicates a contraction of meso- and macroporosity after fire and after the land-use shift from native forest to pine plantation. Bulk density increased from 0.36 to 0.65 g cm\u003csup\u003e-3\u003c/sup\u003e in the native forest, which is consistent with structural collapse and pore occlusion after intense heating and aggregate disruption (Certini, 2005; Mataix-Solera et al., 2011). Mesopores and macropores provide interconnected pathways that sustain oxygen renewal, microbial dispersal, and fungal exploration, whereas smaller pores support localized activity under diffusion-limited conditions (Young \u0026amp; Crawford, 2004; Menon et al., 2020). The observed reduction in pore frequencies implies lower habitat connectivity and a narrower range of redox and moisture niches that can sustain functionally diverse decomposer assemblages (Naveed et al., 2016; Yudina et al., 2022). Recent pore-scale evidence has shown that microbial composition and metabolism differ sharply between small pores (4–10 μm) and large pores (30–150 μm), with substrate processing and metabolic pathways depending on pore size and connectivity (Li et al., 2024). This mechanism aligns with the post-fire shift toward lower substrate-induced CO\u003csub\u003e2\u003c/sub\u003e production in several substrates and a weaker differentiation between aggregate fractions after disturbance, indicating that pore architecture can constrain functional heterogeneity even when substrates are supplied experimentally (Totsche et al., 2018; Li et al., 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Aggregate-specific substrate responses reveal fire sensitivity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross both 2024 campaigns, the strongest fire-associated functional losses remained concentrated in native-forest microaggregates and pine-plantation macroaggregates. The native forest FSI remained negative for all three substrate groups, whereas plantation FSI remained neutral to positive, indicating that the aggregate fraction most sensitive to fire depended on ecosystem context. The persistence of this contrast from the dry/warm to the wet/cool campaign suggests that the distribution of microbial function among aggregate microhabitats was not a transient moisture effect alone. At the same time, the weaker negative contrasts during the wet/cool campaign indicate partial seasonal relaxation rather than full functional recovery. These patterns support the working hypothesis that fire contracts pore connectivity and reorganizes substrate access, but they refine it by showing that the direction of greatest sensitivity differs between ecosystems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Reactive Al-Fe phases may buffer C stabilization, yet functional homogenization can persist\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVolcanic soils often stabilize organic matter through short-range-order minerals and organo-metal complexes, which can moderate C loss after disturbance. Feo increased from 0.57 to 1.83 % in the native forest and from 1.31 to 2.22 % in the plantation, suggesting a larger pool of reactive phases able to bind organic compounds after heating and post-fire re-aggregation (Fukumasu et al., 2021; Parada et al., 2024). The combination of high organic inputs and reactive Al–Fe pools is a known driver of C retention in volcanic soils, providing a mechanistic basis for the partial resistance of mineral-associated carbon despite large shifts in pore structure and nutrient availability (Parada et al., 2024; Ichinose et al., 2025). Functional data indicate that mineral buffering does not necessarily restore pre-fire heterogeneity in substrate use across aggregate fractions because pore connectivity and access limitations can keep communities functionally constrained, even when C sources are supplied (Li et al., 2024; Totsche et al., 2018). This coupling between reactive mineral phases and altered pore networks supports a scenario in which post-fire soils maintain some capacity for carbon stabilization while showing a reduced capacity to express diverse metabolic niches across micro- and macroaggregates, which can influence C persistence under increasing fire recurrence (Johnson et al., 2024; Roshan \u0026amp; Biswas, 2023).\u003c/p\u003e\n\u003cp\u003eThe design targeted aggregates from the topmost soil layer (0-5 cm), which is most affected by a surface fire (Santín \u0026amp; Doerr, 2016), but inference is therefore limited to surface processes. MicroResp/MSIR quantifies substrate-induced respiration under standardized conditions and does not resolve community composition or dormancy states. The micro-CT voxel size (5.7 µm) limits the quantification of pores below that threshold. Because chemistry and structural descriptors were measured on dry/warm campaign composites and repeated inference was based on the same paired sites and resampled points, conclusions should remain restricted to the paired sites and the two climatic windows studied rather than generalized to full post-fire recovery trajectories or all fire-severity gradients.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eAcross two contrasting campaigns in 2024, wildfire altered the chemical template and aggregate habitat structure of volcanic ash soils in both a native Nothofagus forest and a Pinus radiata plantation. Fire reduced soil C and shifted exchange chemistry, while MicroResp/MSIR responses showed persistent reorganization of substrate use across aggregate fractions. Native-forest microaggregates and plantation macroaggregates remained the compartments most sensitive to fire across campaigns, although several contrasts weakened under wet/cool conditions. This aggregate-scale framework links pore architecture to microbial substrate use and provides a practical basis for diagnosing post-fire constraints on soil recovery and carbon retention in volcanic soils.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFNF led the conceptualization, data analysis, figure preparation, and initial drafting. CM and IJ contributed to study design, supervision, interpretation, and manuscript revision. FM and FA contributed to methodological development, interpretation, and editing. JK and CR contributed to laboratory workflows, data curation, and manuscript review. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by ANID Postdoctoral Project 3230428, FONDECYT Regular 1220116, ANILLO ACT192006 (FIRING), Fondequip EQM 220061 for the micro-CT instrumentation, and the BIOREN scientific nucleus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and code availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study, together with the R scripts used for statistical analysis and figure preparation, are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAponte H, Galindo-Casta\u0026ntilde;eda T, Y\u0026aacute;\u0026ntilde;ez C et al. 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Geoderma 415:115771. https://doi.org/10.1016/j.geoderma.2022.115771\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"wildfire, volcanic soils, soil aggregates, MicroResp, pore architecture, substrate-induced respiration","lastPublishedDoi":"10.21203/rs.3.rs-9395928/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9395928/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Wildfires alter aggregate microhabitats that regulate microbial access to carbon in volcanic ash soils, but the persistence of these effects across contrasting climatic windows is poorly understood. We hypothesized that wildfires would reduce cumulative substrate-induced respiration by contracting pore connectivity, with stronger and more persistent effects in native forest microaggregates and plantation macroaggregates. We quantified cumulative substrate-induced CO2 production with the MicroResp whole-soil multiple substrate-induced respiration (MSIR) assay in topsoil (0–5 cm) macroaggregates (2000–250 µm) and microaggregates (250–53 µm) from paired burned and unburned Nothofagus forest and Pinus radiata plantation sites sampled in February 2024 (dry/warm) and August 2024 (wet/cool). Burning lowered total C from 21.8 to 15.2% in the native forest and from 17.5 to 14.3% in the plantation, and reduced meso- and macroporosity. Across campaigns, amino acid cumulative CO2-C in native forest microaggregates decreased from 475 to 181 mg kg−1 in the dry/warm campaign and from 535 to 342 mg kg−1 in the wet/cool campaign, whereas carbohydrate responses in plantation macroaggregates decreased from 372 to 119 mg kg−1 and from 419 to 234 mg kg−1, respectively. Although several contrasts weakened during the wet/cool campaign, fire sensitivity remained concentrated in native-forest microaggregates and plantation macroaggregates. These results support the hypothesis that wildfires reorganize microbial substrate use within aggregates in ways that depend on ecosystem type and aggregate microhabitat structure in volcanic soils.","manuscriptTitle":"A disturbance in the aggregates: wildfire reorganizes seasonal microbial substrate use in volcanic soils of native and managed forests","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 15:41:05","doi":"10.21203/rs.3.rs-9395928/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41e08f6e-3693-4395-b9d6-bd8bc953ea3e","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-15T13:22:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T09:54:33+00:00","index":11,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T06:55:26+00:00","index":10,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T13:38:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 15:41:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9395928","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9395928","identity":"rs-9395928","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}