The study site was located in Chaouat, Manouba, Tunisia (36°51′51″N, 9°58′39″E), placed in the basin of the low valley of Medjerda river, an alluvial plain occupied by quaternary sandy-clay to sandy-clay-silt sediments. The climate is semiarid Mediterranean with annual mean temperature of 19°C, annual mean precipitation of 450 mm, and annual mean potential evapotranspiration of 1,370 mm. Three commercial apple orchards (Malus domestica Borkh var. Gala) were selected in the area. The orchards had an extension of 3–5 ha, with trees planted in 2016 at a spacing of 3 m between rows and 1.5 m between trees within the same row. The Medjerda river is used to convey water released from the Sidi Salem and El Aroussia dams to irrigate croplands. The average analytical data of the river water used for irrigation is shown in Supplementary Table S1. Irrigation was performed by pumping water from the Medjerda river, transporting it through pipes and distributing it to the field through a drip irrigation system. The frequency of irrigation varied according to the evaporative demand, which was once every month in winter, once every 2 weeks in autumn and spring, and two times per week in summer. Soil type is a Halpic Calcisol (IUSS Working Group WRB, 2022), with silt loam texture, soil organic matter (SOM) content of 1.2%, pH of 7.9 and mean electrical conductivity of 1.2 mS cm⁻¹ in the Ap horizon (0–30 cm). Standard cultural practices (e.g., fertilization, pruning, fruit thinning and banding) were carried out by the technical team of the commercial orchard. Weed control was managed manually. Fertilizers, such as cow manure and crop residues, were applied two times per year, as provider of nutrients for crop growth.

One soil sampling campaign was conducted in August 2021 to assess differences in soil depth after the management with irrigation water with high salinity since 2016 (5 years period). We collected four random composite samples in each orchard across the entire surface, at two soil depths: topsoil (0–10 cm) and subsoil (30–50 cm), to ensure differences in soil bacterial and fungal communities in top- and subsoil layers. Although we also collected soil at 10–30 cm depth, owing to budget constraints, we decided to use only those. This is because topsoil is considered between 0 and 30 cm (Poeplau et al., 2020; Szatmári et al., 2019), but it has been previously reported that microbial activity and abundance are generally higher within the 0–10 cm range (Zhao et al., 2022). The 30–50 cm depth is already widely considered as subsoil (Poeplau et al., 2020; Szatmári et al., 2019). Sampling was performed in the tree row area, in the middle of two trees (at 70–80 cm distance from the tree). Each composite sample derived from 5 random subsamples collected in the same spot for topsoil and subsoil. Soil samples were divided in the field into two aliquots, one maintained at ambient temperature for physicochemical analyses, while the other was kept in a cool box with ice for biological analyses. The samples were immediately taken to the laboratory. The soil was air-dried for 1 week and sieved at <2 mm for analyses. Soil intended for biological analyses was sieved at <2 mm upon arrival at the lab and stored at −80°C.

Particle size distribution was determined using the Mastersizer 2000LF laser diffraction analyzer (Malvern Instruments) following the oxidation of organic matter and dispersion of clays. Soil organic carbon (SOC), inorganic carbon (IC) and total nitrogen (N) were measured using an elemental analyzer (CNHS-O, Leco Soil Standard 502-697). The CaCO₃ content was calculated from the analysis of IC. The pH and electrical conductivity (EC) were measured in deionized water (1:2.5 and 1:5 m/v, respectively) in a pH-meter Crison GLP-21 and a conductivity meter Crison GLP-31. Actual available cations (Ca²⁺, Mg²⁺, Na⁺ and K⁺) and anions (Cl⁻, NO₃ ⁻, SO₄ ^2- and NO₂ ⁻) were extracted with deionized water (1:5 m/v) and quantified using ion chromatography (Metrohm 861). Soil CO₂ and N₂O emission rates were measured by the quantifying the gases released from 2 g of soil in 25 mL glass vials closed with crimp caps after 24 h incubation at 25°C and 60% of water holding capacity, using a gas chromatograph (AGILENT 6890N).

Soil DNA was extracted from 0.25 g of soil using the DNeasy PowerSoil Pro Kit (Qiagen, Hilden, Germany). All procedures were done following the instruction manual, except for the bead-beating step, which was conducted using the environmental sample setting on an MP Fastprep-24 instrument (MP-bio, Solon, OH, USA). Purification of the obtained DNA was performed using NucleoMag^® NGS Clean-up and Size Select (Macherey-Nagel, Düren, Germany). Qubit^® 2.0 Fluorometer combined with Qubit dsDNA HS Assay Kit (Life Technologies, Carlsbad, California, US) and NanoPhotometer N60 (Implen GmbH, Munich, Germany) were employed for subsequent DNA quantification and purity assessment, respectively. Extracted DNA was stored at −20°C until further analysis (Barrena-González et al., 2025).

For bacterial library preparation, a ∼290 bp fragment of the V4 region of the bacterial 16S rRNA gene was amplified using the primers 515F and 806R (modified from Parada et al., 2016, and from Apprill et al., 2015; respectively). Fungal library preparation was constructed targeting a ∼300 bp fragment of the internal transcribed spacer (ITS) region 1 employing the primers ITS1f and ITS2 (White et al., 1990). Both primers sets included the Illumina primer adapters attached to their 5′ ends to facilitate downstream sequencing. PCR amplifications were performed in a final volume of 12.5 μL, containing 1.25 μL of template DNA, 0.3 μM of each primer, 3.25 μL of Supreme NZYTaq 2x Green Master Mix (NZYTech, Lisbon, Portugal), and ultrapure nuclease-free water up to 12.5 μL. The thermal cycling conditions for bacterial amplification consisted of an initial denaturation step at 95°C for 5 min, and 25 cycles of 95°C for 30 s, 46°C for 45 s, 72°C for 45 s, followed by a final extension step at 72°C for 7 min (Ollio et al., 2025). For fungal library preparation, PCR cycling conditions included the same initial denaturation step and final extension step as for bacteria, but cycling conditions consisted of 30 cycles of 95°C for 30 s, 49°C for 45 s, 72°C for 45 s. A second PCR was conducted to incorporate dual indices and Illumina sequencing adapters, using the same reaction composition. This indexing PCR was performed under the same cycling conditions but limited to 5 cycles and used a 60°C annealing temperature. Negative controls containing no DNA template (BPCR) were included in every PCR round to assess potential contamination. The libraries were visualized on 2% agarose gels stained with GreenSafe (NZYTech, Lisbon, Portugal), and examined under UV light to confirm the expected library size. Libraries were then purified using the Mag-Bind RXNPure Plus magnetic beads (Omega Biotek, Norcross, GA, USA) and pooled in equimolar amounts based on quantification data obtained by the Qubit dsDNA HS Assay (Thermo Fisher Scientific, Waltham, MA, USA) (Ollio et al., 2025). This pool, which included a testimonial amount (1 μL) of the PCR negative controls, was sequenced on an Illumina NovaSeq 6000 platform (PE250 configuration), generating approximately 25 GB of data (Barrena-González et al., 2025). Amplicon sequencing was carried out by AllGenetics & Biology SL (www.allgenetics.eu). Raw sequencing data are publicly available at: https://github.com/MohamedMdaini8/Mohamed-Mdaini-16S-and-ITS-raw-data.

Illumina paired-end raw reads were sorted by library and their quality scores, indices and sequencing primers were trimmed during the demultiplexing step. FastQC (Andrews, 2010) was employed to check the quality of the FASTQ files while MultiQC (Ewels et al., 2016) was employed to aggregate their content. Bioinformatic analysis was performed with QIIME 2 2021.8 (Bolyen et al., 2019), being raw sequence data quality filtered, denoised, chimera-checked, merged, and clustered into amplicon sequence variants (ASVs) (Callahan et al., 2017) with DADA2 (Callahan et al., 2016) (via q2-dada2). Bacterial ASVs were aligned with mafft (Katoh et al., 2002) (via q2-alignment) and used to construct a phylogeny with fasttree2 (Price et al., 2010) (via q2-phylogeny), employed for the construction of alpha rarefaction curves and phylogenetic alpha and beta diversity indices. Alpha and beta diversity metrics and Principal Coordinate Analysis (PCoA) were estimated using q2-diversity, and one underperformed library was removed from the analysis in the case of fungal sequences. PCoA is an ordination method, which was based on a Bray–Curtis distance matrix in this study, used to visualize the differences in microbial community composition between samples. Each point on the plot represents one soil sample, and the distance between points reflects how dissimilar the microbial communities are. Clustering or separation of points indicates differences in community structure related to soil depth (Seuradge et al., 2017). Taxonomy was assigned to bacterial and fungal ASVs via the q2-feature-classifier plugin (Bokulich et al., 2018) using a pre-trained classifier (Bokulich et al., 2018) of the SILVA reference database (Quast et al., 2013, release 138.1 August 2020), for bacteria, and a pre-trained classifier of the UNITE reference database UNITE (Abarenkov et al., 2021, release May 2021) for fungi.

All data used in this study has been uploaded to the repository Zenodo (https://doi.org/10.5281/zenodo.14517703). The Shapiro test was used to assess whether the data followed a normal distribution. The Student’s t-test was performed to assess significant differences for soil physicochemical properties between topsoil and subsoil. A Kruskal-Wallis test was used to assess differences between soil depth of alpha diversity estimates (observed number of ASVs, Chao1, Shannon, Faith and Pielou’s evenness). A non-parametric permutational multivariate analysis of variance (PERMANOVA) (Anderson, 2001), with 999 random permutations was employed on Bray-Curtis distance matrices to evaluate the variance in the community composition across soil depth (beta diversity). Bivariate correlations were performed to assess the relationships between the relative abundance of fungal and bacterial taxa and physicochemical properties using Pearson analysis in the ggcorrplot package in R software. A redundancy analysis (RDA) was conducted on Hellinger-transformed data (Legendre and Gallagher, 2001) using the vegan and ggplot packages in R to evaluate the extent to which the variation in bacterial and fungal communities (at the genus level) can be attributed to differences in physicochemical properties. Samples with similar bacterial and fungal profiles have similar scores and therefore appear closer to each other in the plot. Soil physicochemical properties are depicted as vectors; those with longer lengths and smaller angles relative to an axis are more strongly associated with that axis (Özbolat et al., 2023).

Most of the studied properties showed significant differences between topsoil and subsoil (Table 1). Soil pH was significantly lower in the topsoil (7.73) compared to the subsoil (8.18), although CaCO₃ content was not significantly different between depths (∼39.5%). EC showed significantly higher values in topsoil (1.71 mS cm⁻¹) compared to subsoil (0.56 mS cm⁻¹), and there was a strong negative correlation between pH and EC (R = −0.94; P < 0.001; Supplementary Figure S1), and between EC and CaCO₃ (R = −0.68; P < 0.001). SOC was significantly higher in topsoil (1.61%) than in subsoil (0.79%), although N showed no significant differences with depth. SOC was positively correlated with EC (R = 0.61; P < 0.001), and negatively correlated with pH (R = −0.82; P < 0.001). The concentrations of SO₄ ^2-, Cl⁻, Na⁺ and Ca²⁺ were the dominant ions in the topsoil, accounting for 97%, 81%, 74% and 82%, respectively, of the total ionic content (Table 1). These ions, however, accounted for 3%, 19%, 26% and 18% in the subsoil. The concentration of cations and anions was significantly higher in the topsoil than in the subsoil, except for SO₄ ²⁻, NO₂ ⁻ and NH₄ ⁺, that showed no significant differences with depth. Soil CO₂ emission rates were significantly higher in topsoil (879 μg kg⁻¹ h⁻¹) compared to subsoil (644 μg kg^-1 h^-1), while N₂O emission rates were significantly higher in subsoil (4.94 ηg kg⁻¹ h⁻¹) than in topsoil (2.81 ηg kg⁻¹ h⁻¹). EC showed strong positive correlations with all cations and anions, and a strong negative correlation with N₂O (Supplementary Figure S1). Contrarily, soil pH showed strong negative correlations with all the cations and anions, and a strong positive correlation with N₂O. N₂O was also negatively correlated with SOC, NO₂ ⁻ and NO₃ ⁻.

After the filtering steps, the 16S rRNA gene sequences ranged from 99,387 to 167,345 sequences per sample (mean: 128,646.47) and 8,830 ASVs were included in the ASV table. For ITS1 region fungal reads ranged from 34,991 to 249,389 sequences per sample (mean: 143,659.2) and a total of 1,648 ASVs were included in the table.

With regards to the alpha diversity indices, there were no significant differences for Chao1, Pielou’s evenness, observed number of ASVs, and Shannon index across the soil depth gradient for bacteria (Supplementary Table S2). For fungi, Pielou’s evenness and Shannon index showed significant differences between depths, with higher values in the subsoil for Pielou’s evenness, and higher values in the topsoil for Shannon index (Supplementary Table S2). The beta diversity analysis, based on the Bray-Curtis dissimilarity matrix, revealed significant differences in the bacterial (F = 3.52; P < 0.001), and fungal (F = 1.70; P < 0.05) communities between the top- and subsoil. The composition of both bacterial and fungal communities was strongly clustered by soil depth, suggesting that the microbiome is shaped by soil properties and characteristics unique to that depth (Figure 1).

Soil fungal community composition was more affected by soil depth than bacterial community composition, with most phyla and genera showing significant differences between depths (Figures 2, 3). The ASVs that could not be identified below family level are summarized as “Unidentified”, and the genera represented by less than 1% (bacteria) and less than 0.5% (fungi) of total reads were collapsed in “Others”. For the soil bacterial community, the most abundant phyla in both topsoil and subsoil, were Actinobacteriota (36%–40%) and Proteobacteria (20%–16%), followed by Firmicutes (5%–6%), Chloroflexi (5%–7%), Crenarchaeota (5%–3%) and Acidobacteriota (4%–6%) (Figure 2A). Topsoil exhibited higher relative abundances of Proteobacteria and Bacteroidota, while subsoil showed higher relative abundances of Actinobacteriota and Chloroflexi. The most abundant classified bacterial genera for both soil depths were 67–14 (8%–9%), Rubrobacter (9%–6%), Nitrososphaeraceae (4%–5%), and Bacillus (4%–7%) (Figure 2B). The genera Rhizobium, Rubrobacter, Skermanella, and Microvirga showed higher relative abundances in topsoil, while MB-A2-108, bacteriap25, Streptomyces and Gaiella showed higher relative abundances in subsoil. For the fungal community, Ascomycota (89%–86%), Mortierellomycota (3%–9%) and Basidiomycota (1%–2%) were the most abundant fungal phyla (Figure 3A). Topsoil showed higher relative abundances of Ascomycota, Basidiomycota and Glomeromycota, while subsoil showed higher relative abundances of Mortierellomycota, Rozellomycota, Olpidiomycota and Basidiobolomycota. The genera Aspergillus (17%), Gibellulopsis (8%), Alternaria (5%), Preussia (4%), Mycosphaerella (3%), Monocillium (2%), Gibberella (2%), Cordyceps (2%), and Saroclaudium (1%) were more abundant in the topsoil, while Fusarium (6%), Emericellopsis (5%), Neocosmospora (7%), Mortierella (6%), Chaetomium (3%) and Acremonium (2%) were more abundant in the subsoil (Figure 3B).

The RDA showed that the first two axes explained 34.2% of the variance of the bacterial community (Figure 4). The first axis explained 21.5% of the variation, and clearly separated topsoil (negative scores) from subsoil (positive scores). The second axis explained 12.7% of the variation and was mostly associated with soil carbonate content. Soil pH, CO₂ emission rates, SOC and EC were the properties that mostly contributed to explaining the variability in the composition of bacterial communities. The RDA performed with the fungal genera showed that the first and second axes explained 27.6% of the total variation, accounting for 15.5% and 12.1%, respectively (Figure 5). Topsoil and subsoil samples were clearly separated by axis 2. As for bacteria, soil pH, CO₂ emission rates, SOC and EC were the properties that mostly contributed to explain the variability in the fungal community composition.

Pearson correlation analysis between the relative abundance of the most abundant classified bacterial genera and soil physicochemical properties is shown in Figure 6. The relative abundances of Skermanella, Ensifer, Geodermatohphilus, Microvirga and Blastoccocus showed significant positive correlations with SOC, while were negatively correlated with pH and N. Soil pH was strongly associated with the relative abundances of Bacteriap25, Gaiella, MB.A2.108 and Gitt-GS-136. CO₂ emissions had positive correlations with the relative abundances of Agromyces, Ensifer, Steroidobacter and Rhizobium and negative correlations with JG30-KF-CM45 and bacteriap25. EC showed positive correlations with Microvirga, Ensifer, Blastococcus, and Geodermatophilus, and a negative correlation with Gaiella. Soil N₂O emissions showed positive correlation with the abundance of JG30-KF-CM45 and X67-14. For the fungal community (Figure 7), soil pH exhibited significant positive correlations with the relative abundances of Chaetomium, Acremonium, Emericellopsis, Neocosmosphora, Solicoccozyma, Mortierella and Penicillium, while showing a negative correlation with Aspergillus. EC showed significant positive correlations with Aspergillus, while negatively correlated with Mortierella, Chaetomium, Acremonium, Emericellopsis, and Chrysosporium. The relative abundances of Alternaria, Stemphyllium and Mycosphaerella had a significant positive correlation with CO₂ emissions. Chlamydocillium, Alternaria, Gibellulopsis, and Gibberella were positively correlated with SOC, while Chaetomium showed a negative correlation with SOC. Gibellulopsis showed positive correlations with all the cations and anions. N₂O emissions showed positive correlations with Emerecillopsis, Chaetomium, Trichoderma, Cephaliophora and Chrysosporium, and negative correlations with Alternaria, Monocillium, and Gibellulopsis. N was positively correlated Emerecillopsis and Chaetomium.

Soil pH and EC showed a significant negative relationship since pH decreased with an increasing salinity in the topsoil. This is contrary to most research, since the increase in the concentration of bases is linked to increases in soil pH (Ge et al., 2023; Hou et al., 2021; Luo et al., 2024). However, several studies reported that soil pH was higher in low salinity levels (Yang et al., 2021; Yang and Sun, 2020). An increase in salinity is typically caused by the input of Na⁺ and Cl⁻ from irrigation water drawn from rivers (Arikan, 2022), and also SO₄ ^2- ions (Srivastava et al., 2019). The high concentrations of SO₄ ^2- within the saline irrigation water proceeded from the river in the study area (Ferchichi et al., 2018), and this is also confirmed by the results of SO₄ ^2- in the soil samples analyzed in this study. The Na⁺ ions compete with other cations from attachment sites on soil colloids, releasing to the soil solution previously retained bases such as Ca²⁺, Mg²⁺ or K⁺ (Violante, 2013). This can explain the significant increases in the concentration of these bases in the topsoil compared with subsoil, besides the application of manure that could slightly be contributing to the increase of salinity on the topsoil (Awad et al., 2015). However, soil pH is a key property that influences almost all soil characteristics, and it is impacted by the soil buffering capacity (Hong et al., 2019). Hence, soil carbonates function as a pH buffer and maintain a stable pH (Li A. et al., 2023), given the high content of carbonates across the soil surface. Nevertheless, owing to the high quantity of carbonates and soluble salts on the topsoil, it is more likely that soil pH decreased in topsoil owing to the highest SOC content. Soil pH tends to decrease in the topsoil as a result of SOM accumulation, because the decomposition of organic compounds generates additional organic acids, resulting in the reduction of pH in the topsoil (Macias-Benitez et al., 2020). Thus, the presence of organic acids in the soil can help maintain a lower pH by counteracting the effects of other pH-increasing factors such as bases (Ca, Mg, Na, K). In fact, Rukshana et al. (2014) reported that the addition of organic acids into the soil, such as citrate/citric acid, were successful to decrease soil pH in basic/alkaline soils with problems of nutrient availability. Hossain et al. (2023) explored the effect of organic farming practices in an agricultural soil on soil pH and EC, and exhibited that the application of manure as an organic practice led to the decline in soil pH and the increase of soil salinity. Xue et al. (2020) investigated how increased soil salinity influences the topsoil SOC (0–30 cm) in a salt marsh field using both field and manipulative experiments. They reported a significant positive relationship between SOC and soil salinity in the topsoil, indicating that soil salinity could be a key factor controlling SOC concentrations.

Our study revealed higher N₂O emissions in the subsoil than in the topsoil, strongly associated with higher pH values. These findings suggest that denitrification rates may be higher in the subsoil compared to the topsoil, likely due to lower aeration and higher pH (Fudjoe et al., 2022). Zhang B. et al. (2023) investigated the interactive effects of soil pH and NO₃ ⁻ concentration on soil denitrification through a mesocosm experiment. They reported that soil NO₃ ⁻ concentration significantly impacted the denitrification-derived N₂O emissions, and soil pH influenced N₂O emissions from denitrification. Qiu et al. (2024) conducted several field experiments with N fertilizers to identify the relationship between soil pH and N₂O emissions, and reported positive correlations between soil pH and N₂O emissions, and negative correlation between SOC and N₂O, as in our study. Moreover, manure addition to the soil, with higher availability of available N and labile organic carbon sources, that can leach through the profile, could significantly enhance N₂O emissions (Shakoor et al., 2021). The genus JG30-KF-CM45, that was associated with N₂O emissions, can be affected by the use of organic fertilizers, and has been found to be playing a crucial role in soil functioning, including N cycling and organic decomposition (Guo et al., 2024). With regards to the N content, we observed a high relationship between N and the genus Streptomyces. Dahal et al. (2017) investigated and characterized free-living diazotrophs in arid soils of South Dakota (USA) and concluded that Streptomyces have the ability to fix N. Several species of Streptomyces have been identified with the ability to fix N (Vurukonda et al., 2018). Furthermore, Streptomyces are renowned for their capability to produce antimicrobial compounds (Aislabie and Deslippe, 2013). Within the fungal community, the genera Emericellopsis and Chrysosporium, with higher abundance in the subsoil, were associated with higher soil pH and N₂O emissions. While both Ascomycota and Basidiomycota are the predominant fungal phyla, Ascomycota appears to be a more functionally important phylum for N₂O production based on their greater relative abundance and stronger positive correlations with N₂O fluxes in the soil (Muneer et al., 2021; Peng and Valentine, 2021; Xun et al., 2020). Overall, fungi play a complex and important role in N₂O emissions from saline and semiarid soils, with factors like oxygen availability and SOM being crucial for their activity (Li X. et al., 2023; Lin et al., 2024). Emericellopsis and Chaetomium were also significantly correlated with N in our study, as previously reported by Ji et al. (2023), suggesting that they may play an important role in N cycling processes (Yang et al., 2022). Emericellopsis species are capable of producing various of bioactive metabolites with antimicrobial properties effective against plant and human pathogens, as well as anticancer activity (Kuvarina et al., 2021, 2022).

This study showed that some of the most dominant phyla, such as Actinobacteria, Proteobacteria and Chloroflexi, have already been identified in other studies as the main bacterial taxa in other saline soils (Ding et al., 2023; Li J. et al., 2023; Lin L. et al., 2023; Xia et al., 2023; Yang et al., 2023). Ascomycota and Basidiomycota, on the other hand, have been identified as the dominant fungal phyla (Zhang G. et al., 2023b; Zhang Z. Y. et al., 2023). Consistently with our hypothesis, the composition of the soil microbial communities across soil depth, was mostly impacted by soil pH, followed by SOC and soil salinity, with a significant association with CO₂ emissions, highly dependent on SOC content. Some studies have also reported that pH, salinity and SOC were the main drivers modifying the composition and structure of the microbial communities across a soil depth gradient (O’Brien et al., 2019; Xu et al., 2021; Zhao et al., 2021).

The alpha diversity metrics were higher in the topsoil for bacterial communities. Numerous studies also reported this decrease of bacterial diversity across a soil depth gradient (Aguado-Norese et al., 2023; Will et al., 2010; Zhang W. et al., 2019), suggesting that this variation could be related to variations in the soil properties, such as soil pH, between top- and subsoil (He et al., 2023). Soil pH plays a key role in shaping the bacterial composition across soil depth (Yun et al., 2016). Several genera from the Proteobacteria phylum, such as Microvirga, were negatively correlated with soil pH. The relative abundance of Microvirga was higher in the topsoil, suggesting that this genus thrives in nutrient-rich environments with high SOC content (Araujo et al., 2023). Additionally, the optimal growth of many Microvirga strains occurs at a pH around 7 (Msaddak et al., 2017). Many studies have reported a negative relationship between soil pH and Proteobacteria, as in our study (Muneer et al., 2022; Wang et al., 2019). Soil pH and the genus MB-A2-108 (Actinobacteria) were positively correlated (Cheng et al., 2023), being soil pH and the abundance of MB-A2-108 higher in subsoil, suggesting that MB-A2-108 has the potential to thrive in low nutrient environments (Megyes et al., 2021). The highest SOC content in the topsoil contributed to higher CO₂ emissions by microbial mineralization (Sosulski et al., 2023; Xin et al., 2023). CO₂ emissions were strongly correlated with several bacterial genera, such as Rhizobium, Ensifer and Steroidobacter, belonging to the phylum Proteobacteria, which showed a higher relative abundance in the topsoil compared to the subsoil. Proteobacteria are a prevalent group in soil that play a crucial role in soil metabolism and nutrient cycling (Magadlela et al., 2023), making them essential for the global C cycling in soils (Spain et al., 2009). Proteobacteria play a crucial role as the primary bacteria responsible for the breakdown and transformation of organic matter (Zhang Z. Y. et al., 2023), and a key role in nutrient cycling under climate warming (Zhou et al., 2023). The relative abundance of Actinobacteria was slightly higher in the subsoil, likely due to the relatively high N content in the subsoil, as Actinobacteria are involved in nitrogen-fixing processes and nutrient cycling (Ravi Kumar et al., 2023). On the contrary, the Rubrobacter genus (Actinobacteria) was more abundant in the topsoil, and it has been reported to be a significant part of microbial communities in harsh soil environments, playing a vital role in processes such as nutrient cycling, which are essential for ecosystem functioning in these difficult habitats (Lupwayi et al., 2021; Miralles et al., 2023; Raimi et al., 2023).

Salt-associated microorganisms are promising both as indicators and as tools for improving soil health in saline agricultural systems (Mishra et al., 2023). Their abundance and diversity respond rapidly to changes in soil salinity (Hou et al., 2021), making them valuable for monitoring soil conditions and guiding management practices (Kumawat et al., 2022). The genera Microvirga and Ensifer, which are members of the Alphaproteobacteria (Rahimlou et al., 2021), correlated positively with salinity. A meta-analysis study reported that Alphaproteobacteria showed higher abundance under higher salinity conditions (Chen et al., 2022). In environments under salt stress, certain Actinobacteria genera, such as Solirubrobacter, tend to maintain a stable population. These bacteria can endure osmotic stress and have the ability to promote plant growth (Jiang et al., 2023), and have genes that help them adapt to harsh environments, playing a key role in nutrient cycling and contributing to the stability of soil EF (Ezeokoli et al., 2020; Jiang et al., 2023; Xun et al., 2021). The genus Geodermatophilus (Actinobacteria) was associated with EC and SOC, hence their abundances were higher in the topsoil, suggesting their involvement in the decomposition of SOC, owing to a higher organic matter content in the topsoil. The genera Microvirga, Solirubrobacter and Geodermatophilus are known to flourish in nutrient-rich conditions, and their increased abundance may serve as an indicator of elevated SOM levels after compost application (Araujo et al., 2023). The positive correlation between Skermanella genus (Proteobacteria) and SOC can be attributed to its involvement in nutrient cycling and the decomposition of organic matter (Liu et al., 2023). Certain Proteobacteria have been reported to utilize the reductive tricarboxylic acid cycle for autotrophic CO₂ fixation (Hügler et al., 2005). CO₂ emissions were strongly associated with Rhizobium (Proteobacteria). Sugawara and Sadowsky (2013) studied the transcriptional responses of Bradyrhizobium japonicum cells inhabiting in the rhizoplane of soybean plants exposed to increased atmospheric CO₂ levels. They found that several B. japonicum genes related to carbon cycling were upregulated in the soybean rhizoplane under elevated CO₂.

Previous studies reported that soil salinity, soil pH and SOC content were the driving factors in shaping and affecting fungal diversity and structure across soil depth (Estrada et al., 2024; Ma W. et al., 2024). In this study, the fungal phylum Ascomycota was more abundant in topsoil, where soil salinity was higher, and the relative abundance of Mortierellomycota was higher in subsoil. These fungal phyla play a crucial role in the C cycle and the SOM decomposition (Muneer et al., 2021). In this study, these two phyla were the most abundant fungi, alongside Basidiomycota, as previously reported in agricultural soils (Guo et al., 2020). Ascomycota hold a significant role in the saline soils and play a crucial role in the stability of fungal communities (Ji et al., 2023). Basidiomycota are considered to be the second largest fungal group in soil (Ko et al., 2017), and are known as an important element in the saprotrophic functional group, and play a key factor in decomposing organic matter (Curlevski et al., 2010). Aspergillus was the genus with the higher relative abundance among all Ascomycota genera. It has been reported that this genus has developed an increased salt tolerance and could serve as a bioindicator for salt-tolerant communities (Cao et al., 2021). The genera Chaetomium (Ascomycota) and Mortierella (Mortierellomycota) were linked to higher pH and lower salinity in the subsoil. Guo et al. (2020) explored the effects of irrigation water salinity on fungal communities and reported a negative relationship between salinity and Mortierella, suggesting that the abundance of Mortierellomycota would be reduced with high salinity. This genus, which was more abundant in the subsoil, has the potential to decompose organic compounds and improve soil health (Li et al., 2018), and it is also known as plant growth-promoting fungi (Ozimek and Hanaka, 2021). Adding organic amendments along with Ascomycota-rich inoculants (Chaetomium and Aspergillus strains) can enhance root development and nutrient absorption in crops cultivated on saline soils (Ma Y. N. et al., 2024).

Alpha diversity Pielou’s evenness index was significantly higher in the subsoil, suggesting that fungal communities tend to become more evenly distributed with soil depth, although drivers should be still elucidated with further research. One of the drivers of the evenness in fungal distribution may be soil pH, identified as the most important factor linked to variations in fungal community composition (Huang et al., 2023; Kang et al., 2021). Aspergillus and pH were negatively correlated, suggesting that Aspergillus may not prefer to grow under basic conditions (Murphy et al., 2011). Contrarily, Acremonium (Ascomycota) was positively associated with pH, suggesting that this genus would prefer basic soils. Pereira et al. (2013) investigated the yields and characteristics of lipase enzyme from Acremonium alcalophilium, and reported that the enzyme has properties that could be useful for several industrial sectors. Several fungal genera belonging to Ascomycota phylum were strongly related with CO₂ emissions and SOC, such as Alternaria and Mycosphaerella, with the highest relative abundances in the topsoil. These genera seemed not to be negatively affected by the high content of salts in the soil (Ji et al., 2023). In fact, previous studies have shown that Alternaria and Mycosphaerella are resilient to projected climate change scenarios (Wahdan et al., 2020), and they are also important decomposers that help break down complex organic matter, contributing to soil CO₂ emissions (Campbell et al., 2022; García-Díaz et al., 2018). Despite Alternaria is considered an important fungal pathogen, this genus has some species that could be used in the industry, such as Alternaria citri, which is a fungi enzyme producer (e.g., Pectinase) and it is known for its potential application in the pre-treatment of pectin rich wastewater from vegetable food processing (Mostafa & Abd El Aty, 2013; Ma et al., 2019).

The findings of this study highlight the importance of adopting soil management strategies that not only mitigate soil salinity but also preserve microbial diversity. However, effective strategies are essential to sustain productivity and mitigate soil salinity, such as crop selection emphasizing salinity-resistant species (Tedeschi et al., 2023), and applying organic amendments (e.g., biochar) to enhance soil quality under saline water conditions (Chen et al., 2024). Maintaining microbial diversity involves different practices such as the introduction of beneficial microbes like PGPR into the rhizosphere, which can reduce plant susceptibility to salinity stress (Khan et al., 2022), and the incorporation of cover crops which can help counteract the negative effects of salinity on soil microbial communities and promote sustainable productivity in saline agroecosystems (Dasgupta et al., 2023; Souza et al., 2025).

As a final remark, it is important to highlight that climate change is supposed to increase soil salinity due to the accumulation of salts in the topsoil owing to higher evaporation and lower quality of irrigation water (Akça et al., 2020). Thus, in this study, we provide insights about several bacterial and fungal taxonomic groups associated with soil salinity that could be enhanced in the future to counteract the effects of climate change. This will be also associated to changes in soil functionality associated to these microbial groups, such as Proteobacteria and Actinobacteria in the bacterial communities, and Ascomycota, Basidiomycota and Mortierellomycota in the fungal communities. Moreover, although the 10–30 cm layer was not included due to budget constraints, the selected depth (0-10 and 30–50 cm) represent ecologically distinct zones, a biologically active topsoil and a less altered subsoil. These contrasting layers are widely used to assess vertical patterns and are potentially sufficient to capture meaningful depth-related differences in microbial communities and soil properties (Poeplau et al., 2020; Szatmári et al., 2019; Zhao et al., 2022). Additionally, we have to comment that our experimental approach of a single sampling date prevents understanding the temporal variability of microbial communities with depth. It has been previously reported that seasonality is important at shaping soil bacterial and fungal community composition (Lin Y. et al., 2023; Zhang G. et al., 2023a). Thus, seasonality is a factor that should be included in further research to properly understand the links between soil salinity, soil depth and microbial community structure (Kramer et al., 2025; Wang et al., 2025).