Comprehensive Investigation of Qatar Soil Bacterial Diversity and Its Correlation with Soil Nutrients

Abstract

Arid and semi-arid regions show distinctive bacterial groups important for the sustainability of ecosystems and soil health. This study aims to investigate how environmental factors across five Qatari soils influence the taxonomic composition of bacterial communities and their predicted functional roles using 16S rRNA amplicon sequencing and soil chemical analysis. Soil samples from five different locations in Qatar (three coastal and two inland) identified 26 bacterial phyla, which were dominated by Actinomycetota (35–43%), Pseudomonadota (12–16%), and Acidobacteriota (4–13%). Species-level analysis discovered taxa such as Rubrobacter tropicus, Longimicrobium terrae, Gaiella occulta, Kallotenue papyrolyticum, and Sphingomonas jaspsi, suggesting the presence of possible novel microbial families. The functional predictions showed development in pathways related to amino acid metabolism, carbohydrate metabolism, and stress tolerance. In addition, heavy-metal-related taxa, which are known to harbor genes for metal resistance mechanisms including efflux pumps, metal chelation, and oxidative stress tolerance. The presence of Streptomyces, Pseudomonas, and Bacillus highlights their roles in stress tolerance, biodegradation, and metabolite production. These findings improve the understanding of microbial roles in dry soils, especially in nutrient cycling and ecosystem resilience. They highlight the importance of local bacteria for sustaining desert soil functions. Further research is needed to validate these relationships, using metabolomic approaches while monitoring microbial-community-changing aspects under fluctuating environmental conditions.

1. Introduction

The sustainability of ecosystems, soil health, and climate regulation depends on soil microorganisms [1]. These diverse microscopic communities are the foundation of fundamental functions. Soil microbes are key to ecosystem sustainability. They help decompose organic matter, recycle nutrients, and build soil structure. Microbes also fix nitrogen, solubilize phosphorus, and suppress pathogens, supporting plant growth. In arid soils, they adapt through dormancy, osmotic control, and exopolysaccharide production. Microbial diversity and function are vital markers of soil health, especially under climate change [2]. This includes carbon sequestration, decomposition of organic matter, and nutrient cycling. All of these are required for maintaining soil fertility and agricultural output [3,4,5]. Soil microbial flora also plays a key role in creating persistent carbon compounds by the breakdown of organic matter [6]. An estimated 108 to 1011 prokaryotic cells are present per gram of soil, with a phylogenetic diversity consisting of hundreds of microbial groups [7,8]. These microbial communities are considered very important for dry regions like Qatar. The limited water resources, lower organic matter availability, extreme heat, and intense weather conditions make the microbes very selective [9,10]. Currently, there is limited knowledge regarding the taxonomic and functional composition of microbial communities in the hyper-arid soils of Qatar. Current research in the region has predominantly applied low-resolution methods like culture-based techniques that infrequently address functional potential or rare taxa. Even though they are considered to be very important, little is known about the soil microbial composition of such regions. The microbes of such extreme areas also affect the emissions of greenhouse gases like carbon dioxide, methane, and oxides of nitrogen. Thus, microbial activities in such environments reinforce the connection between soil processes and atmospheric composition [11]. Healthy soils have specific microbial communities with distinct functions that support nutrient turnover. They maintain basic structural and moisture-holding capacity despite extreme conditions. The major functions are nitrogen fixation, decomposition, and soil aggregate production [12]. Soil health is influenced by its capacity to retain water, cycle nutrients, and support stable microbial activity [13]. On the other hand, a decrease in microbial diversity and activity may indicate that the health of the soil is failing. This failure might result in lower crop yields, greater susceptibility to pests and illnesses, and a decreased ability to sequester carbon [14]. Therefore, the development of soil health, boosting ecosystem resistance, and lowering the effects of climate change all depend on the soil microbiome.

Regarding microbial activity and plant growth, soil chemistry is also a major factor in determining fertility and soil health [15]. Nutrients like nitrogen, which is necessary for proteins and nucleic acids, are frequently cycled among ammonium and nitrate forms. Another key nutrient is carbon, which forms the basis of organic matter and helps in soil structure [16,17]. Sulfur, mostly as sulfate, is essential for amino acids and vitamins, where hydrogen, which is found in water and organic substances, affects soil pH and redox processes [18]. Since phosphorus normally interacts with soil particles, it frequently becomes limited. Phosphorus is essential for energy transmission and is normally present as inorganic phosphate [19]. Even though heavy metals are found in lesser amounts, like iron, lead, cadmium, chromium, manganese, and zinc, they are essential for plant nutrition and enzymatic activity [20]. Other significant characteristics include soil pH, which affects nutrient availability, and cation exchange capacity (CEC). These traits measure the soil’s capability to hold and exchange vital nutrients [21]. Water retention, nutrient availability, and overall soil fertility are all significantly influenced by the texture and organic matter of the soil [22]. In order to preserve productive ecosystems and sustainable land use, it is important to comprehend and manage these chemical characteristics.

Even though the precise number of single celled species on earth is still unknown. The significance of unusual microbes to the planet’s ecology cannot be embellished [23]. Only a tiny portion of these species have been successfully cultured [24]. However, because of improvements in efficiency and cost-effectiveness, DNA sequencing technology has now made it feasible to investigate even the most complex microbial communities [25]. With the help of cutting-edge bioinformatics, these methods may reconstruct entire, almost full, or partial genomes from small DNA fragments [26,27]. The study of soil microbiomes has been transformed by recent developments in metagenomics. Primarily high-throughput sequencing methods allow scientists to profile microbial communities at a previously unknown depth and resolution [28,29]. Recent years have witnessed significant breakthroughs in the field of metagenomics that allow for the direct DNA sequencing of genomes from environmental samples [30].

In soil metagenomics, many methods are used, like amplicon sequencing, which focuses on genetic markers. For instance, ITS regions are used for fungi, and the 16S rRNA coding DNAs are used for bacteria. On the other hand, shotgun metagenomic sequencing reads all the DNA in a sample [31,32,33]. Metagenomics enables the identification of microbial taxa and genes related to various functions without culturing [34].

Metagenomics helps identify adaptive traits in microbes from harsh environments. These include osmoprotection, membrane transport, and stress response mechanisms [35]. Studies from deserts like Atacama, Negev, and the Arabian Peninsula show unique microbial traits. It is unclear if Qatar’s saline, hot, and nutrient-poor soils follow similar patterns. The role of geochemical gradients on microbes in coastal and inland Qatar remains poorly understood.

Because of the advances in metagenomics, our understanding of the diversity and usefulness of microbes has increased significantly. It is also providing us with new tools for addressing issues related to climate change, ecosystem restoration, and agriculture.

This study investigates the bacterial communities present in five soil samples (three coastal and two inland) collected from the north and northwest of Qatar, which are zones known by their extreme environments. The landscape of Qatar is marked by extreme aridity, a bit of natural vegetation, and challenging soil. This study also combines metagenomics with soil chemical analysis to explore the microbial diversity and related functional potentials in these regions. We specifically aim to (i) identify taxonomic and functional profiles of soil bacterial communities, (ii) evaluate how soil physicochemical properties influence microbial composition, and (iii) highlight stress-related traits that may facilitate microbial survival in Qatari desert conditions. The results of this study will help advance our understanding of microbial ecology in harsh or extreme environments. In this context, identifying core and unique bacterial taxa can help distinguish shared microbial signatures from site-specific adaptations shaped by local soil conditions. It will also have consequences for soil management and conservation efforts in Qatar and comparable locations across the world.

2. Materials and Methods

2.1. Soil Sampling

Soil sampling shows significant challenges due to harsh weather, desertification, and limited rainfall. The study examines desert soil bacterial diversity. The locations were selected based on soil chemical characterization. Soil samples (200 g) were collected from each of the five different locations in the northeastern and northwestern parts of Qatar (Q1, Q3, and Q4 represent coastal areas, whereas Q2 and Q5 represent inland areas), along with the green areas (Figure S1). Three coastal and two inland sites were chosen to capture Qatar’s environmental gradients, including salinity, organic matter, and moisture. Coastal soils had higher salinity from seawater and evaporation, while inland soils showed lower salinity and different minerals. This design helps assess how soil conditions shape microbial communities. For each site, three sub-samples were randomly collected from a 0–10 cm soil depth and mixed to form a single composite sample for each location. To prevent contamination, care was taken to avoid touching the soil directly with the hands. Tools were properly cleaned with water and ethanol-sterilized after each use, and nitrile gloves were worn during collection. All soil samples were placed in sterile plastic bags and immediately brought to the laboratory. Once in the lab, soil samples for chemical analysis were homogenized by passing them through a 2 mm sieve. One subsample from each was stored at −20 °C for eDNA extraction and subsequent metagenomic analysis.

2.2. Chemical Characterization of the Soil

Soil physicochemical properties like pH, electrical conductivity (EC), total carbon (TC), total inorganic carbon (TIC), total nitrogen (TN), and soil ionic concentrations were analyzed. Soil pH was measured using a portable digital pH meter (Mettler Toledo, FE20 ATC, Schwerzenbach, Switzerland). EC (dS m⁻¹) was measured with an inductive electromagnetic device (Mettler Toledo, S230 Seven Compact, Schwerzenbach, Switzerland). TC and TN were calculated using a CHNS/O analyzer (Perkin Elmer, Series II 2400 CHNS/O Elemental Analyzer, Boston, MA, USA). All measurements followed the standard methods recommended by the instrument manufacturers. Soil ionic constituents, including cations and anions, were extracted in water and analyzed using ion chromatography (Metrohm, MagIC Net 3.3, Herisau, Switzerland). For extraction, 0.5 g of soil was mixed with 50 mL of deionized water and agitated for 24 h. The resulting solution was filtered, and 2 mL of it was transferred to fresh tubes. Then 10 mL of deionized water was added and the diluted samples were transferred to fresh tubes and injected into the ion chromatograph for analyte quantification. All analyses were performed in triplicate, and mean values were reported.

2.3. Soil eDNA Extraction and 16S rRNA Detection

Environmental DNA (eDNA) was isolated from soil samples using PureLink™ Microbiome DNA Purification Kit (Invitrogen, Waltham, MA, USA) by following the manufacturer protocol with one modification of using customized glass beads. All soil samples were well-mixed before aliquoting to ensure consistency, and 0.3 g from each sample was used for the extraction step. The quality and quantity of extracted eDNA were checked with 1% agarose gel electrophoresis and a Nanodrop^TM (Thermo Scientific, Waltham, MA, USA), respectively. The samples were then stored for further examination at −20 °C. Polymerase chain reaction (PCR) was performed using 30 ng of high-quality DNA template and 16S rRNA coding DNA-specific fusion primers (Rib73: 5′-AGAGTTTGATCCTGGTCAG-3′ and Rib74: 5′-AAGGAGGTGATCCAGCCGCA-3′). The amplification process was carried out to ensure efficient and specific amplification of the target region. The purified DNA was sent to BGI Genomics China for library preparation and sequencing. The raw sequencing reads have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1306804, with BioSample accession numbers SAMN50648467–SAMN50648471.

2.4. Library Preparation

For library construction, the 30 ng of purified DNA was amplified using 16S rRNA fusion primers. PCR products were purified using Agencourt AMPure XP beads and eluted in buffer. The size and concentration of the constructed libraries were assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), ensuring compliance with quality control standards. Libraries passing quality checks were sequenced based on their respective insert sizes using a high-throughput sequencing platform. Specifically, sequencing was performed on the Illumina MiSeq platform using 2 × 300 bp paired-end reads.

2.5. Bioinformatic Analysis Workflow

Raw sequencing data were quality-filtered to remove low-quality reads, adapters, and contaminants. Paired-end reads were merged into tags based on overlap. Tags were then clustered into operational taxonomic units (OTUs) at 97% similarity using the UPARSE algorithm in USEARCH (v7.0.1090). Chimeras were filtered using UCHIME (v4.2.40). OTU representative sequences were used for taxonomic annotation and functional prediction. Alpha diversity (observed species, Chao1, ACE, Shannon, Simpson, Good’s coverage) was computed using Mothur v1.31.2. Beta diversity (Bray–Curtis, UniFrac, PCoA) was analyzed with QIIME v1.8.0 and the vegan package in R v3.5.1.

2.6. Taxonomy Annotation

OTU representative sequences were aligned to reference databases for taxonomic annotation using the RDP Classifier (v2.2) with a sequence identity threshold 0.6. The databases used included Greengenes (v202210) and RDP (Release 19, 20 July 2023) for 16S rDNA (bacteria and archaea) sequences. Annotation results were filtered to exclude OTUs that lacked annotation or were taxonomically inconsistent with the research focus. For example, in 16S bacterial samples, OTUs annotated as archaea were removed, ensuring only relevant taxa were included for further analysis.

2.7. Functional Analysis

The microbial functional profile was predicted utilizing PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States, v2.3.0-b), which estimates the functional potential of microbial communities based on 16S rRNA gene sequences [36]. The analysis focused on the abundance of KEGG Orthologs (KOs) and metabolic pathways from the KEGG and MetaCyc databases. Functional predictions were inferred by placing OTUs into a reference phylogenetic tree and using hidden-state prediction to estimate gene family abundances. Results were summarized at multiple pathway levels, highlighting key metabolic and stress-related functions across samples.

2.8. Statistical Analysis

All statistical analyses were performed using suitable computational tools like Excel and Python (3.12.4). Pearson correlation analysis was conducted to measure the relationships between nutrient concentrations and microbial abundance. Heatmaps were generated to show positive and negative associations. The raw sequencing data were used for the determination of alpha diversity. It was measured using the Observed Species Index (Sobs), Chao Index, ACE Index, Shannon Index, Simpson Index, and Coverage Index.

3. Results

3.1. Chemical Analysis of the Soil

Chemical analysis of the soil samples showed considerable variation among sampling locations (

Table 1. Examination of the soil chemical composition across five Qatar locations. Mean values with standard error are indicated. The five locations are Q1, Q2, Q3, Q4, and Q5, as indicated in the Material and Methods section.

Soils Chemical Composition	Q1	Q2	Q3	Q4	Q5
N (%w)	0.20 ± 0.02	0.17 ± 0.01	0.01 ± 0.00	0.16 ± 0.02	0.11 ± 0.00
C (%w)	10.05 ± 0.28	7.15 ± 0.44	5.35 ± 0.30	5.70 ± 0.31	4.11 ± 0.10
TOC (%w)	0.13 ± 0.00	0.05 ± 0.00	0.00 ± 0.00	0.10 ± 0.00	0.05 ± 0.00
TIC (%w)	9.92 ± 0.28	7.10 ± 0.44	5.35 ± 0.30	5.60 ± 0.31	4.06 ± 0.10
pH	7.60 ± 0.00	7.06 ± 0.00	7.35 ± 0.00	8.12 ± 0.00	7.58 ± 0.00
EC (ms/cm)	9.20 ± 0.00	22.70 ± 0.00	34.30 ± 0.00	36.10 ± 0.00	0.90 ± 0.00
Na (ppm)	0.88 ± 0.14	26.01 ± 0.71	13.81 ± 0.41	31.51 ± 0.97	0.82 ± 0.03
K (ppm)	0.27 ± 0.00	0.45 ± 0.02	0.70 ± 0.03	1.64 ± 0.06	0.27 ± 0.00
Ca (ppm)	17.65 ± 1.36	15.34 ± 0.30	20.08 ± 0.83	97.12 ± 3.52	13.90 ± 0.30
Mg (ppm)	0.00 ± 0.00	0.91 ± 0.02	1.19 ± 0.04	1.56 ± 0.01	0.00 ± 0.00
Cl (ppm)	0.63 ± 0.00	40.96 ± 1.08	27.43 ± 0.91	48.60 ± 1.62	0.00 ± 0.00
SO4 (ppm)	15.79 ± 1.38	10.05 ± 0.53	18.94 ± 2.41	247.15 ± 10.75	8.03 ± 0.41
Ba (ppm)	74.36 ± 0.26	95.50 ± 0.22	47.92 ± 0.34	69.63 ± 0.31	198.93 ± 0.24
Be (ppm)	0.08 ± 0.04	0.26 ± 0.03	0.26 ± 0.05	0.30 ± 0.04	1.07 ± 0.01
Co (ppm)	1.63 ± 0.41	5.06 ± 0.84	5.49 ± 0.36	6.35 ± 0.83	21.31 ± 0.28
Cr (ppm)	14.54 ± 0.19	32.24 ± 0.16	32.57 ± 0.04	29.17 ± 0.89	107.83 ± 0.56
Cu (ppm)	12.33 ± 1.06	23.33 ± 0.40	21.30 ± 1.00	20.75 ± 0.51	45.63 ± 0.47
Fe (ppm)	3655.07 ± 11.90	8057.77 ± 10.88	8335.04 ± 22.32	7034.93 ± 10.61	15,680.08 ± 13.23
Mn (ppm)	122.14 ± 0.53	267.88 ± 0.39	228.49 ± 0.68	187.89 ± 0.23	577.49 ± 1.75
Mo (ppm)	0.00 ± 0.00	0.00 ± 0.00	2.34 ± 0.99	1.21 ± 1.21	1.83 ± 0.26
Ni (ppm)	12.99 ± 0.59	32.96 ± 0.53	34.73 ± 0.88	25.68 ± 2.75	86.66 ± 0.56
Pb (ppm)	139.48 ± 4.28	362.80 ± 22.09	345.23 ± 40.53	359.54 ± 20.66	944.79 ± 39.37
Zn (ppm)	19.20 ± 0.14	25.67 ± 0.26	32.51 ± 0.29	25.48 ± 0.20	59.50 ± 0.28
Al (ppm)	4653.73 ± 14.89	9417.05 ± 12.10	10,040.53 ± 17.03	9107.30 ± 32.00	31,464.11 ± 52.62

). Soil pH ranged from slightly neutral to moderately alkaline (7.06–8.12), with the highest pH observed in sample Q4. Electrical conductivity (EC) varied significantly from 0.90 mS/m in Q1 to 36.10 mS/m in Q4, showing higher salinity at sites Q3 and Q4. Total nitrogen content (%N) was low across all samples, ranging from 0.01% (Q3) to 0.20% (Q1), indicating limited nitrogen availability in the samples. Total organic carbon (TOC) levels were consistently low (0.00–0.13%), while total inorganic carbon (TIC) showed comparatively higher values (4.06–9.92%). This suggests a dominance of inorganic carbon forms, such as carbonates. Major cation and anion concentrations varied widely among samples. Sodium (Na) concentrations were markedly higher in samples Q2 (26.01 ppm) and Q4 (31.51 ppm), corresponding with elevated EC values. Sulfate (SO₄) levels showed significant variation, and were particularly high in Q4 (247.15 ppm). This indicates possible sulfate accumulation or gypsum presence. Trace element analysis discovered prominent variability, with raised concentrations of heavy metals such as iron (Fe), aluminum (Al), manganese (Mn), lead (Pb), and chromium (Cr) in sample Q1. This is possibly influencing microbial community structure through metal induced stress or adaptation mechanisms. The highest concentrations of Pb (944.79 ppm) and Al (31,464.11 ppm) were also observed in Q1, highlighting significant geochemical heterogeneity. Together, all these results indicate substantial chemical composition differences within the studied locations, which are likely to influence the composition and functionality of the soil microbial communities. Analysis of the soil samples showed considerable variation among sampling locations (Table 1).

3.2. Assessment of Microbial Community Diversity Through e-DNA Sequencing

To understand the microbial diversity and richness in different Qatari soil ecosystems, sequencing depth and OTU distribution were first evaluated. The microbial community analysis was carried out on a dataset of five samples, with the 16S rRNA V3–V4 area as the target region. A total of 50,000 sequencing reads were produced, and taxonomy categorization was conducted using the RDP database. The sequencing data were clustered into operational taxonomic units (OTUs) based on 97% sequence similarity. The number of sequencing tags (Q1—28,589, Q2—30,376, Q3—26,100, Q4—29,222, and Q5—27,042) and OTUs (Q1—1471, Q2—1264, Q3—1459, Q4—1309, and Q5—1528) varied across the five samples. The total number of sequencing tags for V3–V4 regions ranged from 26,100 in sample Q3 to 30,376 in sample Q2. Similarly, the OTU count ranged from 1264 in sample Q2 to 1528 in sample Q5. The distribution of OTUs across samples revealed variations in microbial diversity, with Q5 exhibiting the highest OTU richness and Q2 showing the lowest. Soil pH ranged from slightly neutral to moderately alkaline (7.06–8.12), with the highest pH observed in sample Q4. Electrical conductivity (EC) varied significantly from 0.90 ms/cm in Q1 to 36.10 ms/cm in Q4.

3.3. Identification and Distribution of the Soil Bacterial Communities

Analyses of taxonomic annotation showed diverse bacterial communities at various taxonomic ranks, highlighting distinct microbial taxa adapted to Qatar’s extreme soil environments. Taxonomic annotation of the detected operational taxonomic units (OTUs) indicated a diversified microbial community structure across all samples. The annotated OTUs were divided into 26 different phyla, including Chloroflexota, Bacteroidota, Actinomycetota, Acidobacteriota, and Pseudomonadota. Among them, significant taxa were Kallotenue, Segitibacter, Thermoleophilum, Longimicrobium, Azospirillum, and Roseomonas, demonstrating the microbial communities’ functional and ecological diversity. The quantity of OTUs varied greatly, with some, such as OTU1324 (given to Roseomonas), having the maximum abundance (72 reads), while others, such as OTU1219 (Kallotenue papyrolyticum), had the lowest representation (8 reads). Certain species, such as those from Thermomonosporaceae and Bacteroidota, were found in several samples, indicating their ubiquity and possible ecological relevance. Furthermore, unclassified taxa, such as Acidobacteria_Gp6, suggest the inclusion of possibly unique microbial lineages in the collection.

The relative abundance of the bacterial classes, families, genera, and orders is presented in Figures S3–S6, and their percentage abundances are presented in the Supplementary Data Tables (Tables S1–S6).

There is a clear variation among the five soil samples, which shows the diversity in community structure. The relative abundance of microbial communities at the phylum level across the five samples (Q1–Q5) is presented in Figure 1. The microbial composition was dominated by Actinomycetota (35–43%), Pseudomonadota (12–16%), and Acidobacteriota (4–13%), which collectively accounted for a significant proportion of the overall community in each sample. Other phyla, such as Cyanobacteriota, Chloroflexota, and Abditibacteriota, were consistently present but in lower relative abundances. Notable variation in relative abundance was observed among the samples. For instance, Actinomycetota was most abundant in Q1 and Q5 (43% and 40%, respectively), while Pseudomonadota showed consistent dominance across all samples. Gemmatiomonadota abundance was observed to be higher (10%) in Q2 and Q4, whereas Chloroflexota is higher (5%) in Q2 and Q5, with slight variations in other samples. The category “Other” includes low-abundance phyla that contributed to the overall microbial diversity.

Due to the limitations of short-read sequencing in resolving microbial taxonomy at the species level, taxonomic summaries were reported at the genus level. This approach provides a more reliable classification and reduces potential misannotations inherent in short-read metagenomic assignments. The genus-level profiles offer sufficient resolution to assess community structure and ecological trends while maintaining classification confidence. The taxonomic composition of the identified bacterial genera (Figure 2) showed that all the microbial composition was dominated by taxa classified as other, which accounted for the majority of relative abundance (52–57%) in all samples Q1–Q5. Among the identified bacterial genera, Rubrobacter (4–13.3%), Sphingomonas, longimicrobium (1.1–4.9%), Gaiella (1.3–5.6%), Acidobacteria_Gp6 (0.5–6%), Solirubrobacter (1.4–2.9%), Kallenue (0.5–3.7%), and Trujillonella (0.7–2.1%) showed relatively high abundance across all samples. This suggests a relatively stable microbial community structure with slight shifts in specific taxa between samples. The details of the percentages of all the species in different samples (Q1, Q2, Q3, Q4, and Q5) are available in Supplementary Table S1.

3.4. Relationships Among the Bacterial Species

Correlation analyses were conducted to identify ecological relationships among microbial species. A correlation heatmap was constructed to explore relationships among microbial species in the sampled soil (Figure 3). The color gradient represents Pearson correlation coefficients, with positive correlations (brown) and negative correlations (blue) indicating potential co-occurrence or exclusion patterns, respectively. Strong positive correlations were observed between kallotenue papyrolyticum, sphignomonas jaspsi, Burkholderia cepacia, and Longimicrobium terrae, as well as between Blastococcus deserti and Neobacillus niacin, suggesting potential synergistic interactions or shared ecological niches. Conversely, negative correlations, such as between Rubrobacter tropicus and Kallotenue papyrolyticum, indicate possible competitive interactions or distinct ecological preferences. The clustering of species with similar correlation patterns further underscores the presence of functional or ecological groupings within the microbial communities.

3.5. Evolutionary Relationships of the Soil Bacteria

The phylogenetic analysis further clarified the relationships and taxonomic diversity among microbial genera and species identified in the soil samples. The phylogenetic relationships among microbial taxa at the genus level were visualized using a circular phylogenetic tree (Figure S2). Taxa were grouped into distinct phyla, including Actinomycetota, Bacteroidota, Pseudomonadota, and Acidobacteriota, reflecting their evolutionary divergence. Prominent genera, such as those within Actinomycetota and Bacteroidota, were highly branched, highlighting their diversity and ecological relevance. Important clustering of closely related genera and species was observed, particularly within Pseudomonadota, suggesting functional or ecological relatedness. Less abundant but taxonomically distinct groups, such as Fibrobacterota and Thermomicrobiota, were positioned on unique branches, indicating their evolutionary uniqueness within the community.

The GraPhlAn map (Figure 4) shows the phylogenetic structure and taxonomic composition of bacterial phyla across the five locations in this study. The hierarchical layout centers on the phylum Acidobacteria, with radiating branches demonstrating minor taxonomic levels. Key phyla, such as Actinobacteria, Bacillota (Firmicutes), Bacteroidota (Bacteroidetes), and Pseudomonadota (Proteobacteria), dominate the outer rings, showing their dominance across samples. The family Rubrobacteraceae, node A, and its genus Rubrobacter, node, B are especially prominent within the Actinobacteria phylum. This suggests their important contribution to the community structure at different locations. Similarly, Longimicrobiaceae, node C, and its genus Longimicrobium, node D, are highlighted, possibly showing specific roles in the five locations.

Actinobacteria appears as a highly varied phylum. This includes classes like Actinomycetia, Thermoleophilia, Acidimicrobiia, Rubrobacteria, Nitriliruptoria, and Kineosporiia. Remarkably, this phylum contributes to the outermost ring, with metabolically useful groups like Cyanobacteriota and Chlorolekota. These two groups are often associated with photosynthetic or oxidative metabolic functions. The repeated clustering of Bacillota and Bacteroidota adjacent to Actinobacteria suggests potential ecological interactions or co-occurrence patterns in the studied ecosystem.

3.6. Identification of Core and Unique Bacterial Taxa

Core microbiome analysis was performed to identify microbial taxa consistently present across all sampling sites (Q1–Q5), distinguishing them from location-specific taxa. The distribution of bacterial operational taxonomic units (OTUs) across the five samples (Q1–Q5) was visualized using a Venn diagram (Figure 5). A total of 443 OTUs were shared among all samples, representing the core microbiota. Unique OTUs were identified in each sample, with Q3 exhibiting the highest number (202), followed by Q5 (181), Q2 (130), Q1 (86), and Q4 (82). The Venn diagram also highlighted overlaps between subsets of samples, indicating varying degrees of microbial community similarity.

3.7. Microbial Community Dominance Patterns

To better visualize the distribution and dominance patterns within microbial communities, OTU rank–abundance analysis was conducted. The rank–abundance distribution of OTUs across the five samples (Q1–Q5) was visualized using a rank curve (Figure 6). The curves show a typical long-tailed distribution, where a small number of highly abundant OTUs dominated the microbial community, while the majority of OTUs were present at lower relative abundances. In contrast, Q4 and Q2 showed a narrower range, indicating relatively lower diversity. This trend was consistent across all samples, indicating a similar community structure with core taxa contributing significantly to overall abundance and rare taxa representing the broader microbial diversity.

3.8. Alpha Diversity Analysis

Alpha diversity indices were calculated to quantify and compare microbial richness and evenness within each soil sample. Alpha diversity metrics, including observed species (sobs), richness estimators (Chao1 and ACE), and diversity indices (Shannon and Simpson), were calculated for each sample to evaluate microbial diversity within the dataset (

Table 2. Alpha diversity of the bacterial communities.

16S rRNA V3-V4
Sample Name	Sobs	Chao	Ace	Shannon	Simpson	Coverage
Q1	1471	1632.751	1638.77	5.884531	0.007399	0.990801
Q2	1264	1407.52	1397.33	5.918906	0.005366	0.993152
Q3	1459	1642.683	1620.237	5.948955	0.006864	0.989885
Q4	1309	1505.994	1487.976	5.85778	0.006929	0.991445
Q5	1528	1680.675	1669.841	6.087614	0.006976	0.991051

). The number of observed OTUs (sobs) ranged from 1264 in Q2 to 1528 in Q5, with corresponding Chao1 estimates suggesting that the sequencing effort captured the majority of the microbial diversity (coverage > 0.98 for all samples). Shannon diversity indices ranged from 5.857 (Q4) to 6.088 (Q5), indicating a high level of microbial diversity across all samples. Similarly, Simpson indices, which ranged from 0.005 (Q2) to 0.007 (Q1), confirmed the evenness of species distribution within each sample.

3.9. Predicted Functional Roles of the Soil Microbial Communities

PICRUSt2 predictions showed dominant pathways for amino acid, carbohydrate, and energy metabolism across all samples (Q1–Q5), suggesting functional convergence under arid stress. Moderately abundant pathways included lipid metabolism, DNA repair, and signal transduction, supporting membrane stability and oxidative stress resistance. Low-abundance pathways like environmental adaptation and xenobiotic degradation suggest strategies for osmotic adjustment and nutrient uptake. Signal transduction and transport systems, including ABC transporters and two-component systems, aid survival under salinity and desiccation. Disease-related pathways were rare, likely due to background noise. Q2 and Q3 clustered closely, implying similar environmental pressures shape their functional profiles.

The functional analyses were conducted to predict the ecological roles and metabolic capabilities of microbial communities based on the KEGG and MetaCyc pathway databases. The functional potential of microbial communities across the five samples (Q1–Q5) was predicted and visualized through a heatmap of relative abundances for KEGG level 2 functional pathways (Figure 7). Pathways related to amino acid metabolism, carbohydrate metabolism, and energy metabolism were the most abundant across all samples, highlighting their central role in microbial community function. Other highly represented pathways included lipid metabolism, replication and repair, and signal transduction, reflecting essential metabolic and regulatory processes. Lower abundance pathways, such as those associated with infectious diseases (bacterial and parasitic) and environmental adaptation, exhibited sample-specific variation. Clustering analysis indicated that samples Q2 and Q3 shared greater similarity in functional profiles compared to other samples, suggesting potential ecological or environmental factors driving these differences.

The functional potential of microbial communities across the five samples (Q1–Q5) was also assessed based on the relative abundance of MetaCyc pathways (Figure S7). Core metabolic processes such as carbohydrate biosynthesis, amino acid biosynthesis, and fatty acid and lipid biosynthesis were consistently abundant across all samples, highlighting their fundamental roles in microbial metabolism. Other pathways, including glycan biosynthesis, antibiotic resistance, and secondary metabolite biosynthesis, showed moderate to low abundances, reflecting their specific functional contributions.

Diversity indices were calculated to quantify and compare microbial richness and evenness within each soil sample. Alpha diversity metrics, including observed species (sobs) and richness estimators, were calculated.

3.10. Network Analysis

The co-occurrence network showed the interactions among bacterial species based on correlation coefficients > 0.2 (Figure 8). Each node represents a bacterial species with node size corresponding to its average relative abundance. Pink edges represent positive correlations while blue edges represent negative correlations. The thickness of the edge reflects the strength of the association. Network analysis was conducted to infer potential ecological relationships and identify hub taxa: microorganisms that may play central roles in structuring microbial communities. Important taxa, such as Rubrobacter tropicus, Trujillonella endophytica, and Pseudarthrobacter phenanthrenivorans, showed high connectivity, suggesting possible keystone or hub status. These taxa resist desiccation, radiation, and nutrient scarcity, which are key traits for survival in arid Qatar. Their central roles suggest importance in network stability and nutrient exchange. The network shows that microbial interactions, not just abundance, drive resilience and function under extreme conditions.

3.11. Correlation Heatmap of the Soil Chemical Analysis and Microbial (Bacterial) Abundance

The correlation heatmap conducted between the soil chemical composition and the bacterial abundance (Figure 9) illustrates significant correlations between geochemical variables and microbial taxa, offering insights into the environmental determinants influencing microbial community composition. Significant beneficial correlations were noted between various geochemical factors, including sulfate, chloride, heavy metals (e.g., barium, beryllium, cobalt, chromium, copper, iron, manganese, nickel, lead, zinc, and aluminum), and particular microbial taxa. In contrast, negative correlations were observed between nutrient levels (e.g., nitrogen, total organic carbon, and inorganic carbon with sodium, potassium, and calcium) and many microbial species linked to high-salinity or nutrient-deficient environments. Neutral or weak correlations among certain variables and taxa suggest minimal impact or an absence of discernible association. The correlation heatmap highlights distinct relationships between anions, cations, and microbial abundances, highlighting the influence of salinity, sulfate, and nutrient availability on community composition. Candidatus Saccharibacteria (r = 0.9), Bacteriodota (r = 0.9), and Gemmatimonadota (r = 0.8) showed strong positive correlations with salinity-related variables (EC, sodium, chloride) and sulfate, reflecting their halotolerance and roles in sulfur cycling. Fibrobacteriota (r = 0.3), Deinococcota (r = 0.3), and Pseudomonadota showed a weak positive relationship. Conversely, Acidobacteriota, Mycoplasmatota, Abditibacteriota, Poribacteria, and Chloroflexota exhibited negative correlations with these factors. Spirochaetota, Cyanobacteriota, and Thermomicrobiota are positively correlated with TOC (%w) and TIC (%w), indicating their preference for nutrient-rich, low-salinity environments. Additionally, potassium and calcium were positively associated with Bacteroidota, BRC1, and Gemmatimonadata, highlighting nutrient-specific preferences. These results demonstrate the ecological specialization of microbial taxa in response to geochemical gradients, with salinity and sulfate as key drivers of community structure.

Similarly, for trace elements, strong positive correlations were observed with Mycoplasmotata, Campylobacterota, Poribacteria, Verrucomicrobiota, Nitrospirota, and Armatimonadota, indicating their tolerance or potential involvement in metal cycling. Similarly, Acidobacteriota, Abditibacteriota, and Chloroflexota showed weak positive correlations with heavy metals, suggesting low adaptations to metal-rich environments. Conversely, Acidobacteriota and Actinomycetota displayed negative correlations with most trace elements, highlighting their sensitivity to heavy metal stress. Thermomicrobiota, Deinococcota, BRC1, Fibrobacterota, and Spirochaetota showed strong negatiove relationships with trace elements. Similarly, phyla like Actinomycetota, Pseudomonadota, Gemmatimonadota, Bacillota, Cyanobacteriota, and Rhodothermota showed weak negative correlation with trace elements. These results emphasize the role of trace elements as drivers of microbial distribution and ecological specialization, particularly in metal-rich environments. In the same way, bacterial phyla also showed strong positive and negative correlation with each other. More details about the correlation of cation, anion, and microbial groups with values are available in the Supplementary Figures (Figures S8–S11).

4. Discussion

The results obtained in the present work demonstrate that the microbial community composition in the analyzed Qatar soil samples is remarkably affected by geochemical parameters. A clear pattern was observed in relation to variations in salinity, sulfate concentration, organic carbon, and heavy metal content. This is consistent with earlier research emphasizing the significant influence of environmental variables on microbial diversity and function [37,38,39]. Metal resistant taxa like Armatimonadota, Candidatus Saccharibacteria, Bacteriodota, and Actiomycetota showed a positive correlation with sodium, potassium, chlorine and sulfate concentrations. This suggests their adaptation to extreme conditions and nutrient-deficient conditions [40,41,42]. Similarly Pseudomonadata, Spirochaetota, Fibrobacterota, BRC1, and Acidobacteriota were more common in regions characterized by high carbon and nitrogen matter and low sodium and chloride [43]. The concentration of heavy metals in the soil samples affects the richness of metal-sensitive phyla like Gemmatimonadota, Thermomicrobiota, Bacteroidota, Rhodothermota, BRC1, Fibrobacterota, Spirochaetota, and Bacteroidota. This variation in the abundance indicates the ecological roles of metal tolerant and metal sensitive phyla in biogeochemical cycling. The results explained the complex relationship between the microbial taxa and environmental factors. Geochemical elements like carbon, nitrogen, the pH of the soil, trace elements, and anion and cation levels affect the microbial abundance and interactions with surrounding ecosystem. This can happen by following processes (for example, nutrient cycling, exopolysaccharide production, and organic matter degradation) and thereby affect community structure and resilience.

Alpha diversity analysis revealed moderate to high microbial richness and evenness across all samples. Observed OTUs ranged from 1264 to 1528, and Shannon indices from 5.86 to 6.09, which is consistent with findings from other arid ecosystems [44]. Coastal samples, particularly Q5, showed the highest diversity, while Q2, an inland site, had the lowest. Physicochemical parameters such as electrical conductivity (EC) and organic matter were key factors shaping these patterns. EC was negatively correlated with diversity, likely due to osmotic stress, while higher organic content was positively associated with richness and evenness. These results suggest that resource availability and salinity gradients jointly structure bacterial diversity in Qatar’s soils.

The findings of our study across five sites in Qatar highlight the soil connection with microflora [45]. In addition to showing the biological health of the soil, microbial diversity also shows the effect of many natural and anthropogenic factors. The microbe’s composition was consistent throughout five locations at the phylum level with a slight change in relative abundance. The top 10 bacterial phyla identified in this study are common in most arid soils across the world [46,47] and are more dominant in desert soils. The prevalence of Actinomycetota, Pseudomonadota, and Acidobacteriota across all five locations aligned with other studies [48,49]. Actinomycetota exhibits significant metabolic versatility, which enables their survival in extreme environmental conditions. Their occurrence in the five locations was 35–43%, which indicates a foundational microbial community. This community structure is resilient to environmental changes and other edaphic factors like trace metals [50,51,52]. The consistent stable abundance of these phyla aligned with regions characterized by moderate organic carbon levels and neutral to slightly alkaline pH [53,54]. Furthermore, the ability to synthesize siderophores help its tolerance to high levels of iron and aluminum [55,56]. Al-Thani and Yasseen [57] previously noted an abundance of Actinobacteria in Qatari soils through cultivation techniques. Actinobacteria dominate arid environments due to their capacity for sporulation. In addition, large metabolic and degradative abilities, repair mechanisms, and antimicrobial compound production make them important extremophiles [58,59,60].

Pseudomonadota make up 13–17% of the total taxa, which demonstrates its adaptability to survive in varied nutrients levels. The increased presence across Q1, Q4, and Q3 corresponds with high EC and sulfate concentrations, which indicate halotolerance [61,62]. Members of this phylum are known for their ability to produce biofilms and exopolysaccharides. These features helps in harsh conditions of anion, cation, and metal toxicity [63,64]. Pseudomonadota is a phylum of copiotrophic bacteria [65,66]. Acid activation by root exudates increases nutrient availability in the rhizosphere, which stimulates growth of bacterial communities [67,68,69].

Bacillota, which was previously known as Firmicutes, is a phylum of Gram-positive bacteria. This phylum is distinguished by their capacity to produce endospores, which help in survival in adverse environmental conditions including desiccation, nutrient scarcity, and extreme temperatures [70,71]. Members of Bacillota, comprising 1–7% and peaking in sample Q3, are essential in biogeochemical cycles like carbon and nitrogen cycling [72]. Bacillota play a major role in soil ecosystems by decomposing organic matter. They do this by the production of extracellular enzymes like cellulases and proteases, which break down complex polymers into simpler compounds [73,74,75]. Its ability to synthesize and store carbon as polyhydroxyalkanoates in nutrient-poor conditions is also important. These polyhydroxyalkanoates can act as an energy source under unfavorable conditions [76,77]. Moreover, Bacillota showed resistance to environmental stresses like high salinity and heavy metals. In agriculture members of this phylum enhance plant health through the production of growth-promoting hormones such as indole-3-acetic acid [78]. They also inhibit pathogens via the synthesis of antimicrobial compounds, including bacitracin and fengycin [79,80,81]. Bacillota are often linked to environmental restoration, as specific species possess the ability to bioremediate pollutants [82,83,84]. Shifts in their abundance are indicative of environmental changes, as shown by an increase in their presence under saline or nutrient-poor conditions. This trend highlights their adaptability, while also signaling ecosystem stress.

Acidobacteriota are appropriate to acidic soils, although they can also survive in neutral to slightly alkaline environments [85,86]. They break down complex organic materials like cellulose and lignin and help in cycling of carbon and nitrogen [87,88,89]. Acidobacteriota exhibit slow growth, yet will still grow in low-nutrient environments. They show sensitivity to variations in pH, moisture, and salinity, placing them as significant indicators of environmental changes [90,91].

Cyanobacteriota varied from 2 to 5%, reaching a peak in Q1 and Q2 under conditions of higher sulfate and slight alkalinity. Their contribution likely indicates their phototrophic abilities and involvement in nitrogen fixation, thereby stabilizing ecosystems in high-stress environments [92]. Thermomicrobiota (1–6%) and Chloroflexota (1–5%) are phyla comprising thermophilic and moderately halophilic bacteria that contribute to organic matter degradation and sulfur cycling, flourishing in elevated temperature and saline conditions [93,94,95].

A tiny but important phylum of bacteria, Gemmatimonadota, high in Q2 and Q4, is found in soil and water, especially in nutrient-poor and dry conditions [96,97]. These organisms are recognized for their resilience to desiccation and osmotic stress, playing a significant role in phosphorus cycling via polyphosphate accumulation, and they flourish in arid soils characterized by low organic carbon content [98,99]. Gemmatimonadota, as aerobic heterotrophs, utilize a variety of organic substrates, with certain species exhibiting light-harvesting carotenoids that suggest possible phototrophic abilities [100,101]. Their specialized roles in stabilizing fragile ecosystems render them significant contributors to microbial diversity [27].

Planctomycetota, a bacterial phylum higher in Q5, is prevalent in soil, aquatic, and marine habitats, has compartmentalized cells and no peptidoglycan in its cell walls [102,103]. These bacteria help cycle carbon and nitrogen, especially by anaerobically oxidizing ammonium in aquatic settings [104]. Planctomycetota degrade polysaccharides and other complex organic substances, contributing to organic matter turnover [76,105]. Their nutrient cycling and flexibility make them ecologically important in terrestrial and marine settings [106,107]. Rare taxa in the other category play a significant role in enhancing diversity and ecosystem resilience in changing conditions.

To sum up, the bacteria that live in dry soil are closely connected to the natural conditions there. Understanding how hardy bacterial species affect the health of the soil can help with conservation efforts and encourage long-term land use in dry areas.

This study has a few limitations. First, 16S rRNA gene amplicon sequencing was conducted on a limited number of samples, restricting geographic representation. Second, short-read data limit taxonomic resolution at the species level and may overlook rare or novel taxa. Third, functional predictions using PICRUSt2 are inference-based and do not capture real-time gene expression. Fourth, the bioinformatics workflow, while based on validated pipelines, it relies on tools such as QIIME 1.8 and USEARCH, which are now considered outdated. Future analyses will adopt more current platforms such as QIIME 2 and DADA2 to improve reproducibility and resolution. Future work should expand spatial and seasonal sampling, utilize long-read sequencing, and integrate multi-omics approaches.

5. Conclusions

In conclusion, the study demonstrated a significant effect of geochemical factors like C, N, Na, K, Cl, sulfate, and heavy metals on the existing microbial community composition in Qatar’s soil. Salinity and sulfate are negatively correlated with Pseudomonadota, Thermomicrobiota, Cyanobacteriota, Deinococcota, BRC1, Fibrobacterota, and Spirochaetota. This indicates their halotolerance and involvement in sulfur cycling. On the other hand, Acidobacteriota, Spirochaetota, Fibrobacterota, Rhodothermota, and Actinomycetota preferred organic-rich, low-salinity environments, indicating their ecological role in nutrient cycling. Trace metals like Al, Fe, and Zn positively affected Planctomycetota and Nitrospirota, while others like Spirochaetota and Deinococcota were metal-sensitive. The positive correlations among TIC, TOC, and nitrogen with various bacterial taxa highlight the significance of nutrient availability, showing the complex relationship between geochemical factors and bacterial ecology. These findings demonstrated the relationship between soil chemistry and microbial ecology and highlighted the need to explore microbial metabolic pathways under extreme geochemical stress. Long-term monitoring and integration of metagenomic and metabolomic approaches will be important for validating these connections. This is important for predicting how well microbial ecosystems can resist changes and for developing sustainable environmental solutions.