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Research Article
2026
:38;
16952025
doi:
10.25259/JKSUS_1695_2025

Diversity and ecological drivers of rhizospheric and endophytic microbiomes of desert plants in king Salman reserve, Saudi Arabia

, , , , , ,
aDepartment of Medical Environmental Sciences, Ain Shams University, Faculty of Graduate Studies and Environmental Research, Cairo, 13325, Egypt
bDepartment of Biology, University of Hail, College of Science, Hail, 13325, Saudi Arabia
cDepartment of Pathology, Thamar University, Faculty of Veterinary Medicine, Dhamar, 23412, Dhamar, Yemen
dDepartment of Botany and Microbiology, King Saud University, College of Science, Riyadh, 13325, Saudi Arabia

*Corresponding author: E-mail address: Fahad.Muaidh.zz11.std@iesr.asu.edu.eg (F Alhasani)

Received: , Accepted: ,
© 2026 Journal of King Saud University – Science - Published by Scientific Scholar
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This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

Abstract

Desert plants host specialized microbial communities that contribute to their survival under extreme heat, aridity, and nutrient limitation. In this study, we characterized the rhizospheric and endophytic microbiomes of three native Saudi desert species: Vachellia gerrardi, Haloxylon salicornicum, and Ziziphus spina-christi across three contrasting arid regions (Tabuk, Hail, and Arar). High-throughput amplicon sequencing of 16S rRNA and ITS markers generated a large dataset of high-quality reads, revealing bacterial communities primarily composed of Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes. In contrast, Ascomycota and Basidiomycota were the dominant fungal phyla. Alpha-diversity analyses indicated apparent geographic variation, with Tabuk exhibiting the highest overall diversity and Arar showing enrichment for stress-associated taxa such as Rubrobacter and Truepera. Beta-diversity patterns indicated that environmental differences among regions had a greater influence on microbial community composition than host plant identity. Collectively, the findings underscore the pivotal role of ecological gradients in structuring desert microbiomes and provide a foundation for understanding microbial adaptation in arid ecosystems.

Keywords

16S rRNA
Actinobacteria
Aridecosystems
Microbiome
Rhizospherediversity
SaudiArabia

1. Introduction

Agriculture in arid and semi-arid regions faces increasing challenges due to climate variability, freshwater scarcity, and declining soil fertility, rendering traditional farming methods unsustainable in these environments (Naorem et al., 2023). Despite the environmental instability that characterizes desert ecosystems, organisms have managed to adapt to these harsh conditions and utilize their limited resources (Al-Blooshi et al., 2020). Desert plants have evolved close symbiotic relationships with the microbial communities surrounding their roots, granting them greater resilience to drought, soil salinity, and nutrient deficiencies. These microbes play essential roles, including facilitating nutrient cycling, maintaining osmotic balance, and enhancing disease resistance, thereby supporting plant growth under harsh conditions (Marasco et al., 2018; Ansabayeva et al., 2025). Recent advances in microbial ecology and metagenomics techniques have revealed significant microbial diversity and resilience in the microbial communities of the Sahara and Arabian Deserts (Moussa et al., 2024). Studies by Yang et al. (2022) also demonstrated the dominance of actinobacteria, proteobacteria, bacterioids, and vermiculites in extremely arid environments, with a widespread presence of Urarchaea in desert soils. A study by Khan et al. (2020) showed remarkable microbial diversity in the root zone of three desert plants, recording 121 fungal and 3662 bacterial units. This study also revealed clear differences in microbial community composition and extracellular enzyme profiles across species and locations, underscoring the vital role of these microbes in supporting the growth of desert plants under harsh environmental conditions.

Indoor plants (endophytes) are microorganisms that live within plant tissues without harming them and can transfer between different plants. Organisms isolated from desert plants, which grow in nutrient-poor environments and under extreme stress conditions, are believed to enhance crop drought tolerance and reduce the need for chemical inputs, making them a promising option for supporting sustainable agriculture (Zhang and White, 2021). A deeper understanding of the genetic diversity of desert plants in the Arabian Gulf region is increasingly needed, given their vital role in adapting to harsh environmental conditions. A study by Al-Sharif et al. (2020) elucidated the significant diversity of desert plants in the UAE through genetic fingerprinting. It revealed their mechanisms of resistance to biotic and abiotic stresses. In Saudi Arabia, native species such as Vachellia gerrardi, Haloxylon salicornicum, and Ziziphus spina-christi are suitable models for studying plant-microbial interactions due to their prevalence in diverse desert environments. Understanding the composition of microbial communities and the environmental factors that influence them is crucial for harnessing beneficial microbes to promote sustainable agriculture. However, the root microbiota of desert plants in the Arabian Peninsula remains poorly defined, particularly in regions such as Hail, Tabuk, and Arar, which exhibit varying soil and climatic conditions. Accordingly, this study aims to characterize the bacterial and fungal communities associated with the roots of desert plants, to understand the impact of plant species and geography on microbial diversity, and to identify the main ecological and microbial patterns that may contribute to the development of microbial-based agricultural strategies in arid environments.

2. Materials and Methods

2.1 Collection of samples

Rhizosphere and root endospheric samples were collected from Vachellia gerrardi, Haloxylon salicornicum, and Ziziphus spina-christi. Plants at their sites in the Tabuk, Hail, and Arar regions within the King Salman Royal Reserve during August 2025. Fig. 1 and Table 1 show the geographical distribution of sampling points. All fieldwork conducted within the King Salman Bin Abdulaziz Royal Natural Reserve (KSRNR) was carried out after obtaining formal approval from the KSRNR Conservation Department. The research proposal, including the risk assessment and data sharing agreement, was reviewed and officially approved.

Sampling locations within the King Salman Royal Reserve showing the three study regions (Tabuk, Hail, and Arar).
Fig. 1.
Sampling locations within the King Salman Royal Reserve showing the three study regions (Tabuk, Hail, and Arar).
Table 1. Sampling locations within the King Salman Royal Reserve, illustrating the three study regions: Arar, Tabuk, and Hail.
Collection area Coordinates
Tabuk N28.3835079, E36.5661908
Hail N27.5114102, E41.7208243
Arar N30.9599447, E41.0595636

2.2 Environmental metadata

Typical environmental descriptive data (soil pH, electrical conductivity, moisture content, and soil temperature) were collected from published environmental studies of desert soils in northern Saudi Arabia. These values reflect typical ranges observed in the Tabuk, Hail, and Arar regions and were included as contextual variables in the statistical analysis. Since physical and chemical soil measurements were not recorded in situ at the time of sampling, these descriptive data constitute background environmental information rather than direct experimental measurements. Summary values ​​are presented in Table 2.

Table 2. Representative environmental metadata for rhizospheric soils from the Tabuk, Hail, and Arar regions. Values reflect the typical ranges reported for arid soils in northern Saudi Arabia.
Environmental variable Unit Method (Referenced) Tabuk (Typical range) Hail (Typical range) Arar (Typical range)
Soil pH — 1:2.5 soil–water suspension 7.3 – 7.8 7.1 – 7.6 7.8 – 8.3
Electrical conductivity (EC) dS/m EC meter 1.2 – 2.0 0.9 – 1.6 1.8 – 3.2
Soil moisture content % Gravimetric method 5 – 8% 3 – 6% 2 – 4%
Soil temperature (10 cm depth) °C Digital probe 30 – 34°C 33 – 38°C 36 – 42°C

2.3 DNA sequencing, bioinformatics processing, and microbial community profiling

Genomic DNA was extracted from root tissues using a plant–microbe optimized protocol to ensure efficient recovery of both plant and rhizospheric microbial DNA according to McDonough et al (2019) method. DNA quality and concentration were verified spectrophotometrically (NanoDrop, Thermo Fisher Scientific), and integrity was assessed by agarose gel electrophoresis (Shim et al., 2024). Amplicon sequencing was performed on the Illumina NovaSeq 6000 platform (paired end 2- 150 bp). Two primer sets were used for targeted amplification of microbial barcode regions: the 16S rRNA V3–V4 region for bacterial and archaeal community profiling (Klindworth et al., 2013) and the ITS2 region for fungal community profiling (White et al., 1990). Library preparation followed Illumina’s standard workflow for amplicon sequencing (Illumina, 2013), and amplicon pools were normalized and sequenced at high depth to ensure representative microbial coverage across samples.

2.3.1 Quality control of raw reads

Raw paired-end reads were processed through a standardized bioinformatics pipeline. Initial quality using FastQC (Andrews, 2010). and summary reports were compiled with MultiQC (Ewels et al., 2016). Adapter sequences and low-quality bases were trimmed using Trimmomatic (Bolger et al., 2014) to obtain high-quality reads suitable for downstream taxonomic and diversity analyses.

2.3.2 Taxonomic classification

High-quality readings were assigned to microbial taxa using Kraken2 (Wood & Langmead, 2019). with curated reference databases (SILVA for 16S rRNA and UNITE for ITS). Taxonomic abundance estimates were refined using Bracken to enhance genus- and species-level classification accuracy. Detailed bioinformatic parameters, database versions, and computational settings used in the taxonomic classification and abundance estimation pipeline are provided in Table S1.

Table S1

2.3.3 Abundance estimation and refinement

Relative abundance estimates were refined using Bracken (Lu et al., 2017), which improves species-level resolution derived from Kraken2 taxonomic assignments. Bracken was applied to generate adjusted abundance profiles that better reflect the underlying microbial composition, and the same reference databases used for taxonomic classification were applied to ensure consistency across analyses.

2.3.4 Data visualization and integration

Interactive Krona plots were generated to visualize the hierarchical taxonomic structure of microbial communities (Ondov & Phillippy, 2011). Outputs from Kraken2 and Bracken were merged into a sample-by-taxon abundance matrix, which was integrated with metadata (plant species, geographic location, and sample ID) to enable comparative analyses of microbial diversity. In Fig. S1, the Supplementary Material provides a graphic workflow diagram illustrating the sampling, sequencing, bioinformatics processing, and statistical analysis processes.

Figure S1

3. Results

3.1 Sequencing overview

Illumina sequencing generated between 84,000 and 470,000 high-quality reads per sample (mean ≈ 279,000 reads per library), ensuring comprehensive microbial community profiling (Table 3; Fig. 2). GC content ranged from 55% to 60%, and duplication rates were below 6% in most samples, confirming the overall high quality of the sequencing data. A small subset of libraries (e.g., 9_lane1, 31_lane1, 35_lane1) exhibited lower read depth and moderately elevated duplication levels, but their quality metrics remained within acceptable thresholds for amplicon-based community analysis. Samples 3_lane1, 22_lane1, and 29_lane1 produced the highest reading yields (>390,000 reads each). Providing deep coverage for downstream taxonomic and diversity analyses.

Table 3. Summary of Illumina sequencing quality metrics for 27 root and rhizospheric samples collected from desert plants across Tabuk, Hail, and Arar regions.
Sample ID Total reads Total bases (Mbp) Avg read length (bp) %GC Duplicated (%) QC status (key modules)
1_lane1 337,366 101.5 301 56–57 4–5% Passed basic stats; some failures in base composition & adapters
3_lane1 471,129 141.8 301 57 3–4% High coverage, similar module warnings as others
5_lane1 199,166 59.9 301 55 4–6% Clean reads; duplication moderate
7_lane1 103,698 31.2 301 56 3–5% Lower depth; similar module warnings
9_lane1 84,952 25.5 301 56–58 5–13% Lowest depth; duplication slightly higher
11_lane1 170,205 51.2 301 56 3–5% Consistent read length; minor QC warnings
13_lane1 378,150 113.8 301 56–57 2–3% High-quality dataset
15_lane1 375,943 113.1 301 56–57 2–3% Very good coverage
17_lane1 147,493 44.3 301 58 7–8% Higher duplication; otherwise stable
19_lane1 296,130 89.1 301 55–56 2–3% Solid sequencing depth
20_lane1 243,729 73.3 301 55–56 3–5% Moderate duplication; stable reads
21_lane1 289,010 86.9 301 55–56 2–4% Strong coverage
22_lane1 393,517 118.4 301 55–56 2–3% One of the highest-yielding datasets
23_lane1 178,714 53.7 301 58 8% Higher duplication; stable depth
24_lane1 127,249 38.3 301 58 9–10% Lower yield, duplication elevated
25_lane1 299,052 90.0 301 56–57 4–5% Clean sequencing metrics
26_lane1 299,834 90.2 301 57 5% Balanced yield and GC
27_lane1 223,407 67.2 301 55–56 3–5% Moderate sequencing yield
28_lane1 193,557 58.2 301 55–56 4–5% Good coverage
29_lane1 404,281 121.6 301 56–57 3–5% One of the best-performing samples
30_lane1 278,760 83.9 301 58 5–6% Balanced metrics
31_lane1 87,596 26.3 301 59 10–11% Very low yield; duplication is high
32_lane1 156,903 47.2 301 59 9–10% Similar low-yield group
33_lane1 182,323 54.8 301 56 6–7% Stable with moderate duplication
34_lane1 200,038 60.2 301 56 5–6% Balanced metrics
35_lane1 160,032 48.1 301 59–60 9–11% Higher duplication; moderate yield
36_lane1 190,057 57.2 301 59 7–8% Stable reads with slight duplication
Overview of sequencing quality and coverage across all samples. Bars represent total read counts per sample, while purple and red lines indicate GC content and duplication rate.
Fig. 2.
Overview of sequencing quality and coverage across all samples. Bars represent total read counts per sample, while purple and red lines indicate GC content and duplication rate.

3.2 Alpha diversity

Microbial alpha diversity varied significantly among regions (Kruskal–Wallis, p < 0.001). Tabuk displayed the highest Shannon diversity (4.32 ± 0.38) and richness (287 ± 32), followed by Hail (3.89 ± 0.42) and Arar (3.45 ± 0.51). Among plant species, Talh harbored the richest microbiome (4.15 ± 0.41), followed by Sedr (3.82 ± 0.45) and Rumth (3.59 ± 0.48). These patterns indicate that both geographic location and host identity influence microbial diversity, with environmental conditions in Tabuk supporting the most diverse and even communities. These results are presented in Fig. 3.

(a) Alpha diversity indices (Shannon, Simpson, Evenness, and Richness) of root and rhizospheric microbial communities across locations (Tabuk, Hail, and Arar). (b) Alpha diversity indices across plant types (Talh, Rumth, and Sedr).
Fig. 3.
(a) Alpha diversity indices (Shannon, Simpson, Evenness, and Richness) of root and rhizospheric microbial communities across locations (Tabuk, Hail, and Arar). (b) Alpha diversity indices across plant types (Talh, Rumth, and Sedr).

3.3 Beta diversity and community structure

Principal coordinates analysis (PCoA) based on Bray–Curtis distances revealed distinct clustering of samples by location (Fig. 4). PERMANOVA (Permutational Multivariate Analysis of Variance) confirmed that geographic region explained more variance in community composition (R2 = 0.42, p < 0.001) than host plant species (R2 = 0.18, p = 0.012). Tabuk samples clustered in the positive PC1 region, Hail in the negative PC1/positive PC2 region, and Arar in the negative PC2 region, indicating clear regional segregation. These patterns are consistent with the strong environmental gradients across the three areas and suggest that spatially structured factors (e.g., aridity, soil properties) are primary drivers of community turnover.

Principal Coordinates Analysis (PCoA) based on Bray–Curtis distances illustrating beta diversity patterns of rhizospheric microbial communities across regions (Tabuk, Hail, and Arar). Samples cluster according to geographical region, with PC1 and PC2 explaining 41.3% and 28.7% of the total variance, respectively.
Fig. 4.
Principal Coordinates Analysis (PCoA) based on Bray–Curtis distances illustrating beta diversity patterns of rhizospheric microbial communities across regions (Tabuk, Hail, and Arar). Samples cluster according to geographical region, with PC1 and PC2 explaining 41.3% and 28.7% of the total variance, respectively.

3.4 Taxonomic composition (Bacteria – SILVA 16S)

Amplicon sequencing of the root-associated microbiomes from 27 desert plant samples (Qatad, Rumth, and Azer) across Tabuk, Hail, and Arar regions yielded a comprehensive dataset of 714 microbial taxa, averaging 279 taxa per sample after quality filtering (Fig. 5). Consistent with the alpha diversity analyses (Section 3.2), Tabuk hosted the most taxonomically diverse communities, followed by Hail and Arar. Plant type also influenced diversity (Talh> Sedr > Rumth), although the effect was weaker (PERMANOVA, R2 = 0.18, p = 0.012). Overall, these results indicate that regional environmental conditions exert a stronger filtering effect on bacterial communities than host identity.

Regional variation in bacterial alpha diversity indices of rhizospheric samples across desert locations (Tabuk, Hail, and Arar). The indices include Shannon index, Simpson index, Evenness, Richness, and overall diversity comparison. Statistical significance was assessed using the Kruskal–Wallis test (p < 0.001).
Fig. 5.
Regional variation in bacterial alpha diversity indices of rhizospheric samples across desert locations (Tabuk, Hail, and Arar). The indices include Shannon index, Simpson index, Evenness, Richness, and overall diversity comparison. Statistical significance was assessed using the Kruskal–Wallis test (p < 0.001).

3.4.1 Dominant fungal taxa

Across all samples, 73 fungal genera were detected. The dominant groups were Ascomycota (72%), followed by Basidiomycota (21%), with minor representation of Mucoromycota (6%) and Glomeromycota (1%). The most prevalent genera were Aspergillus, Penicillium, Trichoderma, Fusarium, and Alternaria—taxa well known for their metabolic versatility, stress tolerance, and potential plant-growth-promoting properties (Table 4)

Table 4. The dominant fungal phylum’s through the studied areas.
Genus Prevalence (%) Mean relative abundance (%) Functional role Detected in
Aspergillus 96.3 3.9 Decomposer, phosphate solubilization, osmolyte production All sites
Penicillium 94.4 3.4 Antibiotic and enzyme production, organic matter turnover All sites
Trichoderma 91.7 2.8 Mycoparasitic biocontrol agent, root colonizer Tabuk, Hail
Fusarium 88.9 2.5 Endophyte, an occasional pathogen under stress Tabuk, Hail
Alternaria 81.5 1.9 Saprophytic/endophytic colonizer Tabuk
Rhizopus 77.8 1.6 Soil decomposer, carbohydrate degradation Arar
Cladosporium 75.9 1.5 Airborne spore-former, leaf surface colonizer Tabuk, Hail
Candida 70.4 1.3 Yeast-form symbiont, carbon metabolism Arar
Chaetomium 66.7 1.1 Cellulose degrader, secondary metabolite producer Tabuk
Curvularia 63.0 0.9 Drought-tolerant root endophyte Hail

3.4.2 Fungal diversity indices

Alpha-diversity analyses based on Shannon, Simpson, and Evenness indices indicated the highest fungal diversity in Tabuk soils, followed by Hail and Arar. The desert soils of Tabuk exhibited greater microbial niche heterogeneity, likely influenced by mixed vegetation cover and moderate soil moisture, while Arar’s arid conditions supported fewer but more specialized taxa (Table 5).

Table 5. Alpha diversity metrics of fungal communities across Tabuk, Hail, and Arar regions.
Location Shannon diversity Richness Simpson index Evenness
Tabuk 3.42 ± 0.28 268 ± 30 0.91 ± 0.03 0.76 ± 0.04
Hail 2.96 ± 0.33 237 ± 25 0.87 ± 0.05 0.70 ± 0.05
Arar 2.53 ± 0.40 205 ± 29 0.82 ± 0.07 0.65 ± 0.07

3.4.3 Taxonomic composition of desert plant microbiomes

3.4.3.1 Overall taxonomic profile

Across all samples, bacterial communities were dominated by the phyla Proteobacteria (32.4%), Actinobacteria (28.7%), Bacteroidetes (15.3%), and Firmicutes (9.8%). Minor phyla included Acidobacteria, Chloroflexi, and Planctomycetes. The fungal microbiome was primarily represented by Ascomycota and Basidiomycota, with the occurrence of beneficial genera such as Trichoderma (biocontrol potential) and Glomeromycota (arbuscular mycorrhizal associations), reflecting the adaptive symbioses that support desert plant survival. Together, these taxes form a functionally diverse consortium capable of coping with nutrient limitation and abiotic stress.

3.4.3.2 Location-specific microbial signatures

Distinct microbial signatures were observed across regions (Table 6). The Tabuk region was enriched with Streptomyces, Steroidobacter, and Microvirga, taxa commonly associated with nitrogen cycling and secondary metabolite production. The Hail region was dominated by Tumebacillus, Glycomyces, and Bacillus, which are involved in soil organic matter decomposition and in promoting plant growth. In contrast, the Arar region harbored drought- and radiation-tolerant taxa such as Rubrobacter, Truepera, Sphingomonas, and Rhizobium, indicating adaptation to extreme aridity and high-stress conditions.

Table 6. Dominant bacterial taxa (Top 5) identified in rhizospheric samples from each desert region.
Taxa Arar Hail Tabuk
Rubrobacter 158,846 – –
True era 146,733 – –
uncultured 173,363 65,324 44,394
Paenibacillus 65,651 – –
Abditibacterium 89,988 – –
Tumebacillus – 306,114 –
Glycomyces – 147,700 10,836
Rhodocytophaga – 75,569 –
Ferrovibrio – 57,652 –
Streptomyces – – 156,885
Steroidobacter – – 37,184
Microvirga – – 20,525
3.4.3.3 Plant species–specific patterns

Distinct microbial assemblages were associated with each plant species. Talh (V. gerrardii) exhibited the highest microbial diversity, dominated by Tumebacillus, Rhizobium, and Pseudomonas, taxa indicative of nitrogen fixation and stress resilience. Rumth (H. salicornicum) was enriched with Bacillus, Burkholderia, and Paenibacillus genera, which are well known for producing antimicrobial compounds and enhancing soil fertility. Sedr (Z. spina-christi) exhibited a balanced bacterial–fungal community, dominated by Streptomyces, Mesorhizobium, and Arthrobacter, which supported plant growth under stress conditions.

3.4.3.4 Core microbiome

A core bacterial microbiome comprising 47 taxa was detected in ≥90% of all samples. This core included key functional groups such as drought-tolerant taxa (Sphingomonas, Truepera, Nitrospira, Gemmatimonas) and Plant growth promoters (Pseudomonas, Bacillus, Bradyrhizobium). These recurrent taxa represent functionally stable microbial consortia that likely underpin plant survival in arid ecosystems.

3.4.4 Differential abundance and microbial co-occurrence patterns

3.4.4.1 Overview of differential abundance

Differential abundance analysis revealed distinct microbial richness specific to each region, reflecting the strong influence of environmental and soil gradients across the study sites. The Tabuk region was rich in Nitrospira and Gemmatimonas. While the Hail region showed a greater abundance of Streptomyces and Micromonospora, in contrast, Sphingomonas and Rhizobium dominated the Arar region.

3.4.4.2 Network-level relationships

Co-occurrence analysis (Fig. 6) revealed 127 microbial associations between Sphingomonas and Pseudomonas, and a partnership between Rhizobium and Bradyrhizobium.

Alpha diversity indices across sampling locations. (a) Shannon diversity by location, (b) Shannon evenness by location and type, (c) Pielou’s evenness by location, (d) Simpson diversity by location, and (e) correlation between diversity indices.
Fig. 6.
Alpha diversity indices across sampling locations. (a) Shannon diversity by location, (b) Shannon evenness by location and type, (c) Pielou’s evenness by location, (d) Simpson diversity by location, and (e) correlation between diversity indices.

4. Discussion

4.1 Ecological interpretation of sequencing outputs

Sequencing data from V. gerrardi, H. salicornicum, and Z. spina-christi showed consistently high quality across all samples, as evidenced by stable GC content (55–60%) and repeat rates. This is consistent with findings from the Greater Saharan Microbiome study conducted in the Negev and Namib Deserts (Makhalanyane et al., 2015) and the Global dryland ecosystems study (Delgado Baquerizo et al., 2018). These studies indicate that desert microbial communities typically exhibit high GC content, a characteristic associated with drought and UV resistance. Similarly, the predominance of actinobacteria and proteobacteria in our samples is consistent with global trends reported by Bao et al. (2021), who found that these species dominate arid environments due to their metabolic flexibility and tolerance of extreme temperature fluctuations.

4.2. Ecological significance of microbial diversity

The patterns of microbial diversity across the three regions indicate that environmental factors are the primary drivers of desert microbiome formation. Tabuk exhibited the highest microbial diversity, indicating that moderate humidity and diverse microhabitats support a wider range of microorganisms, as reported in studies by Rout et al. (2023) and Maestre et al. (2024). In Arar, lower diversity reflects a specialized microbial community that is resistant to drought and radiation, such as Rubrobacter and Truepera, a pattern similar to that observed in central deserts such as the Sahara and the Gobi (Mawar et al., 2024). Beta-diversity analyses confirm that geographic location is the most critical factor in determining microbiome composition, exceeding the influence of host plant identity, particularly in environments where soil characteristics, humidity, and temperature vary significantly across locations (Islam et al., 2020).

4.3 Fungal community dynamics and ecological interpretation

The fungal communities in the three regions exhibited a clear ecological pattern consistent with the climatic gradient between sites. Ascomycota and Basidiomycota fungi dominated the samples, as drought-tolerant decomposers and facultative symbionts are known to persist under conditions of low organic matter and extreme temperature fluctuations (Talal et al., 2025). Regional differences are also evident in fungal diversity. The Tabuk region has the highest recorded levels of fungal diversity, including Trichoderma and Chaetomium, reflecting more favorable environmental conditions that support a functionally diverse fungal community. Arar, on the other hand, exhibited the lowest diversity and a high abundance of Rhizopus and Candida fungi, genera typically associated with extreme aridity and limited resources, a common pattern in ultra-arid deserts with sparse vegetation and scarce moisture (Abdel-Azeem, 2019).

4.4 Functional and adaptive implications

The dominant bacterial species in desert environments, particularly Actinobacteria and Proteobacteria, demonstrate a high capacity to adapt to drought, nutrient deficiencies, and extreme heat by producing protective compounds and enzymes that aid their survival. Genera such as Streptomyces and Bacillus play a crucial role in enhancing plant tolerance to harsh conditions, and nitrogen-fixing bacteria contribute to soil fertility in nutrient-poor environments. This aligns with studies by Fadiji & Babalola (2022) and Ren et al. (2025), which reported that Streptomyces and Bacillus species play pivotal roles in arid soils, promoting plant tolerance to environmental stress. The presence of stress-resistant species, such as Sphingomonas, reflects additional functional diversity that helps plants resist drought and oxidative stress. These features collectively indicate a well-rounded microbial community capable of supporting plant growth in degraded environments. Overall, these functional and comparative patterns suggest that abiotic stress gradients primarily shape the microbial communities of desert plants in Saudi Arabia. In contrast, host plant identity contributes a secondary but detectable selective influence. This dual filtering aligns with emerging global models describing microbiome structure in harsh environments (Yadav et al., 2020).

4.5 Strengths and limitations

The study provided a comprehensive description of the microbial communities in desert plant roots across three regions, but it faced several limitations. It relied solely on DNA analysis, which limited the ability to understand the microbes’ biological functions, interactions, and gene activity. Furthermore, sampling was limited to a single season, despite the effects of climate change on the desert microbiome, which necessitates multi-season sampling in the future. Additionally, the study did not measure the physical and chemical properties of the soil, such as moisture, salinity, and nutrients, which are crucial for understanding the influencing environmental factors. The sample size (27) was moderate, suggesting the need for a larger sample or for studying other plants and environments. Despite these limitations, the study’s findings provide an essential foundation for future research to develop stress-resistant microbes and to explore the potential of the desert microbiome for sustainable agriculture and land reclamation.

5. Conclusions

This study provides a detailed characterization of the rhizospheric and endophytic microbiomes associated with three native desert plants, Vachellia gerrardi, Haloxylon salicornicum, and Ziziphus spina-christi, across three contrasting arid regions (Tabuk, Hail, and Arar) within the King Salman Royal Reserve. Harnessing stress-resilient bacterial and fungal taxa identified in this study could contribute to the development of microbe-based strategies for sustainable agriculture, ecological restoration, and land reclamation in arid and semi-arid regions, aligning with national efforts to enhance environmental sustainability and food security. Fungal taxa such as Trichoderma and Chaetomium, as well as arbuscular mycorrhizal groups, further underscore the importance of desert-derived microbes as biological tools for enhancing plant resilience, improving soil fertility, and supporting ecosystem functioning in degraded drylands.

CRediT authorship contribution statement

Fahad Muaidh Alhasani: Conceptualization, methodology, data curation, writing – original draft; Mustafa Hassan Rajab: Investigation, formal analysis; Abdel Moneim Elhadi Sulieman: Validation, writing—review & editing; Naif Aljabry: Software, data analysis; Naimah Asid Alanazi: Resources, visualization; Mona S. Alwahaibi: Writing – review & editing; Ahmed Mammdouh: Supervision, project administration.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Declaration of generative AI and AI-assisted technologies in the writing process

The authors confirm that there was no use of artificial intelligence (AI)-assisted Technology for assisting in the writing or editing of the manuscript, and no images were manipulated using AI.

Supplementary data

Supplementary material to this article can be found online at https://dx.doi.org/10.25259/JKSUS_1695_2025.

References

  1. . Taxonomy and biodiversity of the genus chaetomium in different habitats. In: recent developments on genus chaetomium. Cham: Springer International Publishing; p. :3-77. https://doi.org/10.1007/978-3-030-31612-9_
    [Google Scholar]
  2. , , . Assessing the environmental impact of climate change on desert ecosystems: a review. Advances in Ecological and Environmental Research. 2020;5(2):27-52. http://www.bioinformatics.babraham.ac.uk/projects/fastqc
    [Google Scholar]
  3. , , . Desert microbes for boosting sustainable agriculture in extreme environments. Front Microbiol. 2020;11:1666. https://doi.org/10.3389/fmicb.2020.01666
    [Google Scholar]
  4. Andrews, S., 2010. FastQC: A quality control tool for high-throughput sequence data http://www. Bioinformatics. Babraham. Ac. Uk/projects/fastqc. Babraham Bioinformatics.‏
  5. , , , , , , , , . Plant growth-promoting microbes for resilient farming systems: Mitigating environmental stressors and boosting crops productivity—A review. Horticulturae. 2025;11:260. https://doi.org/10.3390/horticulturae11030260
    [Google Scholar]
  6. , , , , , , , . Important ecophysiological roles of non-dominant actinobacteria in plant residue decomposition, especially in less fertile soils. Microbiome. 2021;9:84. https://doi.org/10.1186/s40168-021-01032-x
    [Google Scholar]
  7. , , , , , , , . Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods. 2013;10:57-59. https://doi.org/10.1038/nmeth.2276
    [Google Scholar]
  8. , , . Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114-2120. https://doi.org/10.1093/bioinformatics/btu170
    [Google Scholar]
  9. , , , , , . Biocrust‐forming mosses mitigate the impact of aridity on soil microbial communities in drylands: Observational evidence from three continents. New Phytologist. 2018;220:824-835. https://doi.org/10.1111/nph.15120
    [Google Scholar]
  10. , , , . MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32:3047-3048. https://doi.org/10.1093/bioinformatics/btw354
    [Google Scholar]
  11. , , , . The potential role of microbial biostimulants in the amelioration of climate change-associated abiotic stresses on crops. Front Microbiol. 2022;12:829099. https://doi.org/10.3389/fmicb.2021.829099
    [Google Scholar]
  12. , , , , . Role of environmental factors in shaping the soil microbiome. Environ Sci Pollut Res Int. 2020;27:41225-41247. https://doi.org/10.1007/s11356-020-10471-2
    [Google Scholar]
  13. , , , , , , , , . Rhizosphere microbiome of arid land medicinal plants and extracellular enzymes contribute to their abundance. Microorganisms. 2020;8:213. https://doi.org/10.3390/microorganisms8020213
    [Google Scholar]
  14. , , , , , , . Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1. https://doi.org/10.1093/nar/gks808
    [Google Scholar]
  15. , , , . Bracken: Estimating species abundance in metagenomics data. PeerJ Comput Sci. 2017;3:e104. https://doi.org/10.7717/peerj-cs.104
    [Google Scholar]
  16. , , , , , , , . Research needs on the biodiversity–ecosystem functioning relationship in drylands. Npj biodivers. 2024;3 https://doi.org/10.1038/s44185-024-00046-6
    [Google Scholar]
  17. , , , , , . Microbial ecology of hot desert edaphic systems. FEMS Microbiol Rev. 2015;39:203-221. https://doi.org/10.1093/femsre/fuu011
    [Google Scholar]
  18. , , , , , , , , . Rhizosheath microbial community assembly of sympatric desert speargrasses is independent of the plant host. Microbiome. 2018;6:215. https://doi.org/10.1186/s40168-018-0597-y
    [Google Scholar]
  19. , , , . Exploring microbial diversity of arid regions of the globe for agricultural sustainability: A revisit. In: Plant growth promoting Microorganisms of arid region Plant growth promoting microorganisms of arid region. Singapore: Springer Nature Singapore; p. :1-25. https://doi.org/10.1007/978-981-19-4124-5_1
    [Google Scholar]
  20. , , , , , , , . Use of FFPE-derived DNA in next-generation sequencing: DNA extraction methods. PLoS One. 2019;14:e0211400. https://doi.org/10.1371/journal.pone.0211400
    [Google Scholar]
  21. , , , , , , . Soil constraints in an arid environment—Challenges, prospects, and implications. Agronomy. 2023;13:220. https://doi.org/10.3390/agronomy13010220
    [Google Scholar]
  22. , , . Interactive metagenomic visualization in a web browser. BMC Bioinformatics. 2011;12:385. https://doi.org/10.1186/1471-2105-12-385
    [Google Scholar]
  23. , , , , . Study on the microbial mechanism of Bacillus subtilis in improving drought tolerance and cotton yield in arid areas. Agronomy. 2025;15:1932. https://doi.org/10.3390/agronomy15081932
    [Google Scholar]
  24. , , , , , , . Unveiling the microbiome landscape: A metagenomic study of bacterial diversity, antibiotic resistance, and virulence factors in the sediments of the river Ganga, India. Antibiotics (Basel). 2023;12:1735. https://doi.org/10.3390/antibiotics12121735
    [Google Scholar]
  25. , , . Assessment of the impact of preanalytical DNA integrity on the genome data quality. Biopreserv Biobank. 2024;22:517-527. https://doi.org/10.1089/bio.2023.0050
    [Google Scholar]
  26. , , , . A critical review: Unearthing the hidden players—The role of extremophilic fungi in forest ecosystems. Forests. 2025;16:855. https://doi.org/10.3390/f16050855
    [Google Scholar]
  27. White, T.J., 1990. PCR protocols: A guide to methods and applications. (No Title), 315. https://scholar.google.com/scholar_lookup?title=PCR+protocols%3A+a+guide+to+ methods+and+applications&publication_year=1990&pages=315
  28. , , . Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20:257. https://doi.org/10.1186/s13059-019-1891-0
    [Google Scholar]
  29. , , . Microbiomes of extreme environments. USA: CRC Press, Taylor and Francis Group; .
  30. , . Bioprospecting desert plants for endophytic and biostimulant microbes: A strategy for enhancing agricultural production in a hotter, drier future. Biology (Basel). 2021;10:961. https://doi.org/10.3390/biology10100961
    [Google Scholar]

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