Metagenomic Insights Into Microbial Controls of Carbon Cycling in Alpine Soils

Alpine riparian zones span topographic gradients from wet soils on the plain near streams10 to drier soils on adjacent slopes. These differences in soil moisture are generally associated with shifts11 in soil redox state from anoxic on the plain to oxic on the slope. In anoxic plain soils, soil organic12 carbon (SOC) may accumulate due to thermodynamic constraints on microbial activity. Here, we13 used shotgun metagenomics to examine how microbial diversity and functional potential varies across14 differing redox conditions on plain and slope soils in two catchments in the Swiss Alps. We com-15 plemented these analyses with soil physicochemical characteristics and information on the chemical16 composition of organic matter. Plain soils had higher SOC stocks and higher relative abundance of17 phenol compounds relative to slope soils, consistent with SOC preservation and preferential miner-18 alisation of easily degradable organic compounds under anoxic conditions. Microbial communities in19 plain soils further exhibited greater taxonomic and functional diversity, including an increased poten-20 tial for anaerobic respiration pathways. Genes for nitrate, iron, and sulfate reduction were linked to21 Chloroflexota, Acidobacteria, and Desulfobacterota phyla, respectively. Based on NMDS correlations,22 electron accepting capacity, calcium content, and pH shaped microbial community composition. Slope23 soils, by contrast, supported less diverse microbial communities, determined mainly by electron do-24 nating capacity and clay content. Our work demonstrates how soil redox conditions and microbial25 functional potential shape carbon cycling across landscape positions in alpine riparean zones. This26 mechanistic understanding is critical to anticipate changes in carbon cycling in alpine ecosystems in27 a changing climate.28 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint

Soil organic carbon, soil redox dynamics, microbial community composition,29 microbial metabolism, Alpine riparian soils, shotgun metagenomics30 Introduction31 In subalpine and alpine ecosystems, more than 90% of ecosystem carbon are stored in soils as a32 consequence of short plant growing seasons and limitations on the degradation of soil organic matter33 by microorganisms under harsh climatic conditions (Davidson and Janssens, 2006; K¨ orner, 2021).34 The fate of organic carbon in the soil is determined by microorganisms that can mineralize organic35 matter to greenhouse gases or stabilize it within soils (Duan et al., 2023; Gunina and Kuzyakov, 2022).36 It remains unclear how SOC stocks are linked to microbial community composition and functional37 potential in (sub)alpine ecosystems.38 Alpine riparian zones express strong differences in hydrology and soil biogeochemistry between soils on39 low-lying plains near streams to those on adjacent slopes (Berhe and Kleber, 2013; Pacific et al., 2011).40 Plain soils, influenced by shallow groundwater and seasonal water inputs, are periodically saturated,41 producing oxygen-limited redox conditions where microbial respiration depends on alternative terminal42 electron acceptors (TEAs). These less energy-efficient pathways slow organic matter decomposition43 and promote SOC accumulation (Boye et al., 2017; Keiluweit et al., 2016; Schimel and Schaeffer,44 2012; Zhang and Furman, 2021). In contrast, slope soils are well drained and maintain more oxidized45 conditions that support aerobic microbial activity and greater SOC mineralization, resulting in smaller46 SOC stocks relative to plains (Philben et al., 2020).47 Soil redox conditions, along with other edaphic factors such as pH and nutrient availability, are48 linked to microbial community composition and metabolic diversity (Philippot et al., 2023). Microbial49 characteristics can be assessed using metagenomics, which has proven particularly valuable in extreme50 environments such as thawing permafrost, where genomic analyses have revealed microbial adaptations51 to redox-stratified conditions and geochemical gradients (Romanowicz et al., 2023; Waldrop et al.,52 2023; Woodcroft et al., 2018). Romanowicz et al., 2023, for instance, showed that imicrobial iron53 reduction strongly influences microbial carbon degradation in thawing permafrost. Similarly, Waldrop54 et al., 2023 demonstrated that permafrost microbial communities and functional genes are structured55 2 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint by latitudinal gradients and soil geochemistry. In alpine plains, fluctuating water tables and variable56 oxygen conditions likely necessitate a wide microbial metabolic repertoire, enabling microorganisms57 to adapt their respiration strategies to the availability of TEAs (Fu et al., 2023; Keiluweit et al., 2016;58 Ruan et al., 2024). Alpine systems therefore express similar redox variability and potentially microbial59 adaptation strategies as thawing permafrost, yet integrated metagenomic assessments remain rare in60 alpine environments.61 Here, we investigate the relationships between SOC stocks, microbial community structure and func-62 tional potential, and environmental conditions across plain and slope areas in two alpine headwater63 catchments. Although environmental conditions differ slightly between the two catchments, both share64 similar geomorphic structures and hydrological regimes and can therefore be treated as landscape-level65 replicates. We hypothesise that:66 1. Plain soils exhibit anoxic conditions that are associated with higher SOC contents and higher67 levels of poorly degradable SOC, such as phenols and aromatics.68 2. Microbial communities exhibit greater metabolic diversity in plain than slope soils due to larger69 temporal variability in soil redox conditions.70 To test our hypotheses, we combine the analysis of soil physicochemical characteristics with analy-71 ses of soil redox state by mediated electrochemistry, soil organic matter chemistry by pyrolysis gas72 chromatography-mass spectrometry, and microbial functional diversity and metabolic capabilities by73 shotgun metagenomics. We compared SOC content and composition across landscape positions and74 soil depths, correlated taxonomic lineages with functional gene potentials, and incorporated environ-75 mental vectors into a non-metric multidimensional scaling (NMDS) ordination analysis to assess the76 relationships between microbial communities and environmental factors.77 3 .CC-BY-NC-ND 4.0 International licenseperpetuity. 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and Methods78 Site Description and Sample Collection79 Soils were collected from the riparean areas of two natural headwater catchments in the Swiss Alps:80 Blatt in the Binntal valley (46°22’N/8°16’E) and Ar du Tsan (46°12’N/7°30’E) in the Vallon de R´ echy.81 Both catchments feature a mixed bedrock mainly comprised of gneiss and carbonated rock, and82 diverse vegetation made up of siliceous alpine grasslands and moorlands (Fr´ elechoux and Gallandat,83 1995; Matteodo et al., 2018; Richard et al., 1993; swisstopo, 2024). The sampling sites within the84 two catchments were strategically chosen to encompass both slope and plain areas. At R´ echy, the85 elevation of sampling locations varied from 2154 to 2243 meters above sea level (m a.s.l.), while at86 Binntal, the range was between 1984 and 2105 m a.s.l. Soil sampling was carried out in late July 2023.87 Average July temperatures at l’Ar du Tsan and Binntal are 12.9◦C and 8.7 ◦C, with precipitation levels88 of 76 mm and 97 mm, respectively (Fick and Hijmans, 2017). At 13 sampling locations (Figure 1),89 soil from three soil depths (0-10 cm, 10-30 cm, and 30-50 cm, when available - see Table S1) was90 collected with an auger. Precise coordinates, slope, and specific landscape position were recorded for91 each location. Once gathered, the samples were placed in zip-lock bags; bags for water-logged soils92 contained oxygen scrubbers. Samples were kept cool with ice packs, and transported to the laboratory.93 Samples for soil physicochemical characterization were immediately processed; sub-samples for soil94 redox characterization were stored at -20 °C until analysis. Samples for DNA extraction were placed in95 a sterile manner into Whirl-Pack®bags and homogenised directly by kneading. Post-homogenisation,96 the soil was split into triplicate subsamples, put into cryotubes snd shock-frozen using liquid nitrogen.97 After transport to the laboratory, they were stored at -80 °C until analysis.98 4 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint Figure 1: Spatial distribution of mean soil organic carbon (SOC) stocks, calculated from three soil depth-specific samples within the top 50 cm of soil, at a R´ echy andb Binntal. Slope soils are represented in shaded terrain, plain soils in unshaded areas. Background: SWISSIMAGE orthophoto and hillshade from swissALTI3D. Soil Physicochemical Characterisation99 Sample preparation, pH measurements, and soil texture analyses: Soil samples were air-dried100 and subsequently oven-dried at 105 °C, homogenised, sieved through a 2 mm mesh to remove coarse101 particles (e.g., plant roots and stones), and ground using a ball mill (Pulverisette 7, Fritsch) to achieve102 a fine, uniform powder. Dry weight was determined from changes in soil mass upon drying. Soil pH103 was measured SevenDirect SD50 pH meter, Mettler Toledo) in a 1:5 soil-to-deionised water suspension104 after 30 minutes of agitation at 200 RPM, followed by 30 minutes of settling (Table S2). Soil texture105 was analysed using laser diffraction (LS 13 320, Beckman Coulter) with a grain size analyser, on 0.5 g106 of air-dried, sieved bulk soil following organic matter digestion with hydrogen peroxide over two weeks107 (Table S2).108 Elemental composition: Total carbon was measured by chromatography after combustion at 900109 °C on a CHNS element analyser (Flash EA 1112, Thermo Finnigan). As the soils lacked carbonates,110 the total SOC content (expressed as weight % of dry soil) was considered equivalent to the measured111 total carbon. Total content of Fe, Mn, and S were determined on 5 g of dried, sieved, and powdered112 soil using X-ray fluorescence spectroscopy (SPECTRO XEPOS).113 SOC composition: The relative abundance of major compound classes were determined using py-114 rolysis gas chromatography–mass spectrometry (Py-GC-MS, Tolu et al., 2015). Soil samples were115 5 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint placed in clean, fire-polished quartz tubes and pyrolysed at 600 °C for 20 seconds under a helium flow.116 The released pyrolysis moieties were transferred via a heated transfer line into an Agilent 7980A GC117 equipped with a Zebron ZB-5MS column (Phenomenex, Woerden, the Netherlands; 30 m × 250 µm118 × 0.25 µm) coupled to an Agilent 5975C MSD single quadrupole mass spectrometer operating in119 electron ionisation mode (scanning m/z 50 to 650 at 2.7 scans per second; ionisation energy: 70 eV),120 using helium as the carrier gas and introduced in split mode (70: 1 split ratio; constant flow of 2 ml121 per min, with gas saver mode active). The pyrolysis transfer line and rotor oven temperature were122 maintained at 325°C, the heated GC interface at 280 °C, the electron ionisation source at 230 °C, and123 the quadrupole at 150°C. The GC oven was programmed from 40°C (held for 5 minutes) to 300 °C at124 5°C per minute, where it was held for 3 minutes, giving a total run time of 60 minutes. Approximately125 106 of the most abundant pyrolysis moieties were identified, identified by comparing their retention126 times and spectra to entries in the NIST Mass Spectral Library and grouped into categories based on127 their origin and chemical characteristics: lipids, lignins, polysaccharides, phenols, nitrogen containing128 compounds, and aromatics (Figure S1 and Table S1). Given the complexity of the pyrograms, it was129 not possible to integrate individual moiety in total ion current mode due to significant overlap between130 ion peaks. Instead, single ion filtering was used to measure the peak area of each compound. The131 major ions of each compound were filtered and integrated (Table S3). The relative abundance of each132 identified compound was calculated as a percentage of the total identified compounds.133 Electron accepting & donating capacities: Electron accepting and electron donating capacities134 (EAC & EDC), were determined through mediated electrochemical analyses using an 8-channel po-135 tentiostat (CH Instruments, Inc.) in an anoxic environment inside a glovebox workstation (Labmaster136 pro MBraun), as previously described (Aeppli et al., 2018, 2022). Experiments were conducted using137 a pH-buffered solution at pH 5.5 (0.4 M sodium acetate-acetic acid) with 10 mM sodium chloride138 as a background electrolyte. Mediated electrochemical reduction (MER) potentials were set versus139 standard hydrogen electrodes at -0.51 V vs. SHE (E H, MER ) and mediated electrochemical oxidation140 (MEO) potentials at +0.82 V vs. SHE ( EH, MEO ). For EAC measurements, ethyl viologen (Michaelis141 and Hill, 1933) was used as an electron transfer mediator; for EDC, ABTS (Thomas et al., 2004) was142 used. To prepare the samples, 1 g of frozen soil was transferred into 10 mL of Milli-Q water under143 anaerobic conditions to create a slurry. For each measurement, 30 µL of the slurry was used for EAC144 determination, and 20 µL for EDC determination. An additional 1 mL aliquot was taken from each145 6 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint slurry in triplicate to determine soil dry weight for normalisation. During EAC determination, elec-146 trons are delivered from the electrode via the mediator to the sample’s redox reactive constituents,147 thereby reducing them. Conversely, during EDC measurement, electrons are withdrawn from these148 constituents through the mediator and transferred to the electrode. EAC and EDC values were deter-149 mined from current responses measured upon the addition of the sample to the electrochemical cells at150 EH, MER and EH, MEO , respectively. Capacities (mol e− g−1 dried soil) were determined by integrating151 the baseline-corrected current, i(t), over time from t0 until the current returned to baseline at tend152 following equations 1 and 2.153 EAC = 1 F × msample Z tend t0 i(t) dt (1) EDC = − 1 F × msample Z tend t0 i(t) dt (2) where F ≈ 96485 C mol−1 is the Faraday constant and msample is the dry mass of the soil.154 DNA Extraction, Sequencing, and Analysis155 DNA was extracted from 0.5 g of soil using the DNeasy PowerSoil Pro Kit (Qiagen®, Germany) follow-156 ing the manufacturer’s protocol. DNA content and purity were assessed using microspectrophotometry157 (NanoDrop One; Thermo Fisher Scientific Inc., USA). Library preparation and shotgun metagenomic158 sequencing were performed by Novogene (UK) with Illumina NovaSeq 6000 platform to generate159 paired-end (150 bp) reads.160 Initial quality checks of raw sequencing reads were conducted using FastQC to ensure data integrity161 (Andrews, 2010). Reads were subjected to quality filtering using fastp (Chen et al., 2018), followed by162 a second round of quality checks with FastQC to verify improvements in read quality. De novo assem-163 bly of high-quality reads was performed with MEGAHIT, generating contigs suitable for downstream164 analyses (Li et al., 2015). Assembly statistics were evaluated using QUAST to ensure completeness165 and accuracy (Gurevich et al., 2013). High-quality reads were mapped to assembled contigs using166 Strobealign to generate coverage profiles (Sahlin, 2022). Metagenome-assembled genomes (MAGs)167 7 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint were reconstructed using MetaBAT2 (Kang et al., 2019), and bin quality was assessed using CheckM2168 to ensure completeness and contamination metrics were within acceptable thresholds (Chklovski et169 al., 2023). Taxonomic classification of MAGs was assigned using the Genome Taxonomy Database170 Toolkit (GTDB-Tk, Chaumeil et al., 2020). Functional annotation of MAGs was conducted using171 METABOLIC (Zhou et al., 2021), allowing for the prediction of key metabolic pathways and bio-172 geochemical functions. Dereplication of MAGs was performed using dRep to consolidate redundant173 genomes and generate a representative set (Olm et al., 2017). The relative abundance of dereplicated174 MAGs was calculated using CoverM (Wood and Salzberg, 2024).175 Statistical Analysis176 SOC content and composition were analysed using Wilcoxon rank-sum tests for comparison of plain177 and slope soils and one-way ANOVA with Tukey’s HSD post-hoc tests for soil depth effects. Spearman178 correlations were used to examine associations between functional gene categories and taxonomic lin-179 eages identified in the metagenomic data. Non-metric multidimensional scaling (NMDS) analysis was180 used to to visualise microbial community dissimilarities based on Bray-Curtis distances and identify ef-181 fects of environmental factors on microbial community composition. This analysis was complemented182 by a PERMANOVA test to assess the influence of location, landscape position, and soil depth on183 microbial community structures. Figures and statistical analyses were generated in R using the vegan184 package to explore microbial community composition and diversity metrics (Oksanen et al., 2015).185 8 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint Results186 Plain Soils Store More Organic Carbon Than Slope Soils187 Plain soils had higher SOC contents than slope soils at all soil depths (Figure 2): SOC ranged was188 24.3 ± 8.3% in the 0 - 10 cm layer and 22 .8 ± 20.5% at 30 - 50 cm. with highest SOC values observed189 in the mid-soil depth layer (10 - 30 cm: 29.6 ± 16.3%) for plain soils. In contrast, slope soils exhibited190 a clear soil depth-dependent trend in SOC concentrations, with SOC decreasing from 6 .49 ± 4.92% in191 the surface layer (0 - 10 cm) to 2.48 ± 0.66% at 10 - 30 cm, and 1.80 ± 0.20% at 30 - 50 cm. SOC192 content in plain soils was significantly higher than that of the slope across all soil depths, with mean193 SOC at 0 - 10 cm soil depth in plain soils being approximately 3.7 times higher than in slope soils. The194 spatial distribution of SOC stocks is shown in Figure 1. In both the R´ echy and Binntal catchments,195 plain soils in unshaded terrain showed higher SOC (R´ echy: 10 - 30%, Binntal: 20 - 42%), while shaded196 slope soils were lower (2 - 10% for both).197 Plain Slope Soil organic carbon (%) Figure 2: Soil organic carbon content (percentage of soil dry weight) at three soil depths in plain and slope soils (n = 20). Asterisks indicate levels of significance: * for p < 0.05, ** for p < 0.01, and *** for p < 0.001. 9 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint Soil Organic Carbon Composition Differs by Landscape Position and Soil Depth198 The composition of organic matter varied by landscape position and soil depth (Figure 3). Slope199 soils were enriched in polysaccharides (31.9 %), whereas plain soils contained higher proportions of200 phenols (20.9 %). The proportions of aromatics, lipids, nitrogen-containing compounds, and lignin was201 comparable between both types of soils. The relative contribution of aromatic compounds increased202 with soil depth from 23.3 % at 0 - 10 cm to 36.4 % at 30 - 50 cm, while the relative contriubtion of203 lignins decreased from 8.2 % at 0 - 10 cm to 2.1 % at 30 - 50 cm.204 a) b) Figure 3: Composition of soil organic carbon (SOC) in plain ( a) and slope soils (b). Relative abun- dances for each compound class are provided in Table S1. Plain Soils Exhibit soil depth-Dependent Redox Zonation205 Soil redox state was described the EAC and EDC values, which represent the contribution of the206 soils’ pools of redox-active oxidised and reduced geochemical species, respectively. In plain soils,207 EAC and EDC values exhibited an inverse relationship, with EDC increasing from 0 .12 ± 0.05 to208 0.33 ± 0.07 mmol g −1 soil and EAC decreasing from 0.41 ± 0.10 to 0.16 ± 0.05 mmol g −1 soil from209 0-10 cm to 30-50 cm soil depth, consistent with increasingly reducing conditions (Figure 4a). In210 contrast, slope soils showed low EDC values (e.g., 0 .02 ± 0.01 at 10 cm) and constant EAC values211 (e.g., 0.48 ±0.12 at 10 cm) across all soil depths, indicating oxic conditions (Figure 4b). We compared212 total electron exchanging capacity (sum of EAC and EDC) to elemental composition to attribute the213 EAC and EDC responses to geochemical phases (Figure S2). In plain soils, iron explained most of the214 10 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint redox reactivity, followed by sulfur. In slope soils, iron was the dominant redox-active phase, with a215 small contribution from redox-active organic matter.216 a) b) Figure 4: Average electron donating (EDC, green) and accepting capacities (EAC, orange) of plain (a) and slope soils (b). Shaded areas represent the standard error of the mean. Each data point reflects the average of five soil samples. Microbial Community Composition and Functional Potential Are Linked to Soil Redox217 Conditions218 Microbial community composition differed between plain and slope soils (Figure 5): plain soils had219 higher relative abundances of Chloroflexota, Acidobacteriota, and Desulfobacterota, whereas slope soils220 contained greater proportions of Verrucomicrobiota, Thermoproteota, and Dormibacterota. The heat221 map of functional genes (Figure 6) shows that plain soils exhibited higher relative abundances of genes222 assigned to nitrate, metal (Fe/Mn), and sulfate reduction across all soil depths, while slope soils had223 lower relative abundances of these genes. Potential methanogenesis genes were not detected, whereas224 genes attributed to methane oxidation (Methylomirabilota ) were present in both landscape positions225 at low abundance.226 Taxon–function links were identified between specific microbial lineages and key reductive processes227 (Table 1). Chloroflexota lineages (e.g., class Anaerolineae; class Dehalococcoidia lineages DSTF029228 and SM23-31) correlated with nitrate-reduction genes. Acidobacteriota classes Thermoanaerobaculia,229 Acidobacteriae, and Blastocatellia correlated with Fe/Mn-reduction genes. Orders within Desulfobac-230 11 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint terota (Geobacterales, BSN033, and Desulfatiales) correlated with sulfate-reduction genes.231 Plain Slope Figure 5: Microbial community composition. The relative abundance of key prokaryotic phyla is shown for plain and slope soils at three soil depths. Circle size represents the relative abundance of each taxon. 12 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint Plain Slope Figure 6: Heatmap of potential anaerobic microbial respiration pathways in plain and slope soils across three soil depths. Genes associated with nitrate, iron/manganese, and sulfate reduction, as well as methane production, are shown as relative abundances. 13 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint Table 1: Spearman correlations between functional gene categories and taxonomic lineages identified in metagenomic data. Lineages are grouped by taxonomic rank: p = phylum, c = class, o = order. Only statistically significant associations (p < 0.05) are shown. T axon Lineage Potential F unction Correlation Estimate P-V alue p Chloroflexota c Anaerolineae o Anaerolineales Nitrate reduction 0.598 0.00535 p Chloroflexota c Dehalococcoidia o DSTF029 Nitrate reduction 0.560 0.01025 p Chloroflexota c Dehalococcoidia o SM23-31 Nitrate reduction 0.372 0.01802 p Acidobacteriota c Thermoanaerobaculia o Thermoanaerobaculales Fe/Mn reduction 0.786 9.83e-14 p Acidobacteriota c Acidobacteriae o Acidobacterales Fe/Mn reduction 0.738 0.000202 p Acidobacteriota c Blastocatellia o Pyrinomonadales Fe/Mn reduction 0.734 0.000228 p Desulfobacterota c Desulfuromonadia o Geobacterales Sulfate reduction 0.400 0.01051 p Desulfobacterota c BSN033 o BSN033 Sulfate reduction 0.466 0.03830 p Desulfobacterota c DSM-4660 o Desulfatiales Sulfate reduction 0.457 0.04254 14 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint Microbiomes Exhibit Enhanced Metabolic Versatility and Greater Potential for232 Anaerobic Carbon Turnover in Plain Soils233 We assessed the core set of metabolic functions relative to carbon turnover following the flowgram234 pipeline by Zhou et al., 2021. Distribution of these functions, showing the proportion of metagenome-235 assembled genomes (MAGs) that encode the genes required for each transformation, indicate that236 SOC oxidation genes were present in 24.67 % of plain soil genomes (444 MAGs) versus 18.94 % in237 slope soil genomes (Figure 7). Fermentation potential was likewise greater in plain soil communities238 (12.85 %; 234 MAGs) than in slope soil communities (8.10 %). Hydrogen generation genes occurred239 in 11.61 % of plain soil genomes (231 MAGs) compared with 7.51 % of slope soil genomes, whereas240 hydrogen oxidation genes were found in 4.47 % and 1.30 % of genomes, respectively (74 MAGs). In241 contrast, acetate-oxidation genes showed slightly higher representation in slope soil communities (1.78242 %; 28 MAGs) than in plain soil communities (1.30 %) while genes for ethanol oxidation and carbon243 fixation were detected at low levels in both settings (ethanol oxidation: 10.35 % plain, 9.55 % slope;244 carbon fixation: 1.09 % plain, 0.40 % slope). Methanotrophy genes were rare but detectable (0.66245 % plain, 0.76 % slope; 21 MAGs), whereas methanogenesis genes were absent from all assemblies.246 Overall, the higher prevalence of fermentation, hydrogen metabolism, and SOC-oxidation genes in247 plain soil MAGs indicates a larger genomic investment in anaerobic carbon turnover than in slope248 soils.249 15 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint Figure 7: Soil organic carbon (SOC) transformations mediated by microbial communities in plain and slope soils. The flowgram illustrates SOC-related metabolic steps reconstructed from metagenomic data using a modified script from METABOLIC (Zhou et al., 2021). Each arrow represents a distinct transformation step, with boxes denoting key compounds involved. Arrow labels indicate the step number and transformation type, the number of genomes encoding the necessary genes (in brackets), and the relative abundance of those genomes in plain (purple) and slope soil communities (teal), expressed as a percentage of total community composition. Community-level genome abundance and function were inferred from metagenome-assembled genomes. 16 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint Microbial Community Composition Correlates with Soil Physicochemical Properties250 Across Landscape Positions and Catchments251 Similarities of microbial community composition across catchments and landscape positions were as-252 sessed using an NMDS plot (Figure 8, supplementary environmental values in Table S2). Plain soil253 communities cluster on the bottom left, while slope soil communities cluster on the top right with254 minimal overlap between groups. Vectors for Ca content, soil pH, EAC, and phenols have the largest255 percentage value and point toward the plain soil cluster, indicating strong positive correlations with256 those communities. Conversely, vectors for polysaccharides, nitrogen-containing compounds, EDC,257 and clay content project toward the slope soil cluster. Sand and silt vectors plot between the two258 groups with intermediate vector lengths. Thus, variation in Ca content, pH, EAC, EDC, and specific259 SOC fractions (phenols, nitrogen compounds, and polysaccharides) aligns with the primary ordination260 axis that separates plain and slope microbial assemblages.261 17 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint Figure 8: NMDS plot illustrating the microbial community composition of soils from two alpine head- water catchments, Binntal and R´ echy, based on Bray-Curtis dissimilarity. Environmental vectors overlaid on the ordination indicate the direction and strength of correlations between environmental variables and microbial community composition. Vector length is scaled by the square root of the r² value, reflecting the strength of these correlations. The vectors represent the top ten environmen- tal variables, selected based on descending r ² values from envfit analysis. These variables include polysaccharides (PS), nitrogen compounds (N compounds), silt, clay, sand, soil pH, electron accept- ing capacity (EAC), calcium (Ca), phenols, and electron donating capacity (EDC). PERMANOVA attributes 23.3 % of the Bray–Curtis variation to landscape position ( R2 = 0.2325, p = 0.001) and 10.0 % to catchment identity (R 2 = 0.0999, p = 0.030); soil depth effects are negligible. 18 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint Discussion262 Soil Redox State Is Linked to Soil Organic Carbon Quantity and Chemistry263 Plain soils had substantially higher SOC content than slope soils, with nearly four times more carbon264 per g dry soil in the surface layer (0 - 10 cm; Figure 2). These differences mirrored differences in265 soil redox state (Figure 4). In plain soils, conditions became increasingly reducing with soil depth.266 The observed EDC values likely reflect the accumulation of reduced organic compounds, ferrous iron,267 and sulfide, produced vial microbial respiration under past anoxic conditions. Iron was the major268 contributor to electron exchanging capacity and was therefore a key TEA in plain soils. In slope soils,269 no EDC was detected, suggesting that these soils were fully oxic. Most of the EAC response was270 explained by iron with some contribution from organic matter. The observed patterns in SOC content271 and soil redox state are therefore in agreement with our first hypothesis stating that soils on the plain272 exhibit anoxic conditions that are associated with higher SOC contents.273 Differences in SOC composition between plain and slope soils are likely due to variations in organic274 matter inputs and preservation mechanisms. Plain soils were enriched in phenolic compounds, whereas275 slope soils contained higher proportions of polysaccharides (Figure 3). This pattern was linked to con-276 trasting vegetation types and moisture regimes. On the plain, grasses and sedges produce litter rich in277 phenol-containing structural polymers, which are selectively preserved under periodically anoxic con-278 ditions because the degradation of phenolic compounds depends on extracellular oxidative enzymes,279 such as phenol oxidase and peroxidase (Fenner and Freeman, 2011; Freeman et al., 2001). In con-280 trast, slope soils dominated by dwarf shrubs and upland herbs receive litter rich in easily degradable281 carbohydrates, which likely causes the higher abundance of polysaccharides. Across both landscape282 positions, we observed a decline in lignin-derived compounds and an increase in aromatic compound283 contributions with soil depth. The relative higher lignin content in surface soils likely reflects recent284 plant inputs from vascular tissue. Aromatic compounds are chemically more stable and persist un-285 der oxygen-limited conditions (Fenner and Freeman, 2011; Freeman et al., 2001; Sinsabaugh, 2010).286 Combined, these findings indicate that as soil depth increases, lignin is progressively broken down or287 transformed, while less bioavailable aromatic structures accumulate.288 The observed trends in SOC content and composition, and soil redox state align with the expected289 19 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint sequential microbial use of substrates based on reaction thermodynamics. Compared to polysac-290 charides, phenolic and aromatic compounds are chemically more reduced on average and therefore291 require higher energy input to be oxidized. Under anoxic conditions, this required energy input may292 outweigh the energy released upon redeuction of alternative TEAs, resulting in the accumulation of293 these compounds (Boye et al., 2017; Gunina and Kuzyakov, 2022).294 Microbial Community Composition and Potential Functions are Linked to Landscape295 Position and Soil Physicochemical Characteristics296 Microbial community composition differed between plain and slope soils (Figure 5), with plain soils297 having higher relative abundances of potential microbial respiratory pathways, in agreement with our298 second hypothesis. These higher abundances are likely driven by seasonal moisture fluctuations and299 nutrient-rich conditions. Recurrent anoxic windows likely support a rich assemblage of anaerobic mi-300 crobial metabolisms, including the potential reduction of nitrate, iron, manganese and sulfate, thereby301 sustaining SOC turnover in the riparian corridor (Keiluweit et al., 2016). We found higher abundance302 of taxa commonly associated with anaerobic respiration at soil depth in plain soils, including members303 of the Chloroflexota or Desulfobacterota phyla (Eilers et al., 2012), in line with the redox stratification304 inferred from EDC–EAC profiles. Conversely, slope soils were dominated by phyla such as Verrucomi-305 crobiota, Thermoproteota and Dormibacterota. These soils were well-drained and oxygen-rich which, in306 concert with lateral inputs of carbohydrate-rich litter, promotes high-energy yielding aerobic microbial307 respiration pathways. Methanogenesis genes were detected neither in plain nor slope soils; however,308 low-abundance methanotrophs (anaerobic Methylomirabilota) were present in both soils, implying that309 any methane produced in deeper plain soils or in anoxic microsites in slope soil was oxidised. The310 absence of canonical mcrA-bearing MAGs does not necessarily preclude methanogenesis; these genes311 are often confined to low-abundance taxa inhabiting deeper, more water-logged soil horizons and can312 evade detection when reads assemble into short or unbinned contigs (Woodcroft et al., 2018). Differen-313 tial expression analyses further highlighted up-regulation of Group 3 and Group 1 NiFe hydrogenases314 in plain soils (Figure S3), enzymes that confer redox plasticity by allowing microbial communities to315 alternate between fermentative and respiratory metabolic strategies as oxygen availability fluctuates316 (Pich´ e-Choquette and Constant, 2019).317 20 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint Microbial community composition aligned with the observed differences in SOC composition across318 landscape positions (Figure 5). Microbial community assemblages in plain soils likely need to invest319 more in enzymatic machinery for the partial degradation and preservation of phenol-rich litter derived320 from hydrophilic grasses and sedges (Pich´ e-Choquette and Constant, 2019; Yang et al., 2021). The321 selective preservation of phenolic compounds under anoxic conditions likely reflects slow decomposition322 of soil organic matter, and these compounds may also contribute to the elevated EDC observed in323 plain soils by providing redox-active moieties that function as extracellular electron shuttles (Fenner324 and Freeman, 2020; Kappler et al., 2004). In contrast, microbial communities in slope soils were325 dominated by fast-growing copiotrophs and likely preferred polysaccharide-rich substrates, congruent326 with the higher proportion of depolymerisable carbohydrates we observed in these soils. Building on327 the previous discussion, the abundance of aromatic compounds increased with soil depth, whereas328 lignin-derived phenols decreased. Surface soil horizons may therefore support microbial communities329 that favour easily oxidisable polysaccharides and secrete ligninolytic oxidases, driving rapid lignin330 turnover (Baldrian, 2017; Dao et al., 2022).331 Several environmental variables influenced microbial community structure in plain and slope soils (Fig-332 ure 8). EAC, Ca content, pH and phenolic content explained most variance in microbial community333 composition in plain soils. Calcium has previously been shown to stabilise SOC through cation bridg-334 ing with negatively charged organic surfaces, potentially restricting microbial access to SOC (Rowley335 et al., 2018). Given the pH-dependence of these interactions and the propensity of alpine plain sys-336 tems to experience seasonal water saturation (Ma et al., 2021), it is plausible that associated redox337 and pH fluctuations influenced microbial niche differentiation (Blagodatskaya and Kuzyakov, 2008).338 The observed association between phenolic content and microbial community composition may also339 reflect the presence of redox-active substrates that select for microbial groups capable of utilising these340 substrates either as carbon sources or as electron shuttles under oxygen-limited conditions. In slope341 soils, microbial assemblages were more closely aligned with polysaccharide content, EDC, and clay342 content. While mean EDC values were relatively low, the spatial variation across samples may point343 to micro-heterogeneity in the distribution of redox-active substrates, which could influence microbial344 organisation even under well-drained conditions. Polysaccharides, derived from rapid cycling of plant345 litter, may provide readily accessible energy, selecting for copiotrophic lineages (notably several pro-346 teobacterial MAGs that our metagenomic analysis showed to be enriched in slope soils). Clay content347 21 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint may also play a role through its known capacity to stabilise organic matter via adsorption, thereby348 modifying substrate accessibility (Sollins et al., 1996). Our results suggest that substrate quality,349 rather than the amount of SOC only, shape microbial assemblages under well-aerated conditions.350 Conclusions351 Our work shows how microbial community composition varies across landscape positions from wet352 plain soils to drier slope soils in alpine riparian zones. Plain soils contained three to four times more353 SOC than adjacent slope soils, were enriched in phenolic compounds, had higher EDC values, and har-354 bored microbial communities with genes for nitrate, iron, manganese, and sulfate reduction—features355 consistent with periodic anoxia and the accumulation of SOC due to thermodynamic limitations on356 microbial activity. In contrast, slope soils had lower SOC contents, were not reduced, had a higher357 proportion of labile polysaccharides, and microbial communities dominated by aerobic taxa. Together,358 these patterns demonstrate how moisture-driven redox regimes shape microbial potential and SOC359 composition, influencing the balance between SOC preservation and mineralization across the land-360 scape. By comparing analogous landscape positions in two independent alpine catchments, our work361 provides a case study of how topographically driven redox gradients govern microbial ecology.362 Several open questions remain regarding the role of microbial metabolism in carbon cycling in alpine363 riparian soils, particularly during seasonal transitions. Microbial communities may remain active364 beneath the winter snowpack, but their response to the spring melt pulse of dissolved organic carbon365 is not well understood. Future studies applying metatranscriptomics, extracellular enzyme assays, and366 stable isotope probing could capture these dynamics, revealing how microbial activity under shifting367 redox and substrate conditions influences SOC stability. Such insights would clarify how seasonal and368 topographic variability regulates organic carbon turnover and ultimately the net carbon balance of369 alpine catchments.370 Acknowledgements371 The authors thank Lorenz Schwab, Antoine Wallart, and Eric Pizem for their support with soil372 sampling, and Gordanna Pistoletti for technical assistance. We also thank the Swiss National Science373 22 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 22, 2025. ; https://doi.org/10.1101/2025.09.22.677713doi: bioRxiv preprint Foundation for financial support (Grant No. 212056).374 References375 Aeppli, M., Thompson, A., Dewey, C., & Fendorf, S. (2022). 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