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Article

Bacterial Community Dynamics in Oil-Contaminated Soils in the Hyper-Arid Arava Valley

by
Varsik Martirosyan
1,
Ilan Stavi
2,3,*,
Tirza Doniger
4,
Itaii Applebaum
4,
Chen Sherman
4,
May Levi
4 and
Yosef Steinberger
4,*
1
C-Lab LLC Testing Laboratory, Artashisyan 105, Yerevan 0038, Armenia
2
Dead Sea and Arava Science Center, Yotvata 88820, Israel
3
Eilat Campus, Ben-Gurion University of the Negev, Eilat 88100, Israel
4
The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 5290002, Israel
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(5), 1198; https://doi.org/10.3390/agronomy15051198
Submission received: 10 April 2025 / Revised: 8 May 2025 / Accepted: 14 May 2025 / Published: 15 May 2025
(This article belongs to the Special Issue Innovations and Obstacles: Microbial Communities in the Journey of Soil Remediation)
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Abstract

:
Petroleum pollution has become a substantial challenge in soil ecology. The soil bacterial consortia play a major role in the biodegradation of petroleum hydrocarbons. The main objective of this study was to assess changes in bacterial composition and diversity in oil-contaminated dryland soils. The Illumina MiSeq high-throughput sequencing technique was used to study the bacterial diversity and structural change in hyper-arid oil-contaminated soil in the Arava Valley of Israel. The diversity and abundance of soil bacteria declined significantly following oil pollution. The dominant phyla in the petroleum-contaminated soils were Proteobacteria (~33% higher vs. control soil) and Patescibacteria (~2.5% higher vs. control soil), which are oil-associated and hydrocarbon-degrading bacteria. An opposite trend was found for the Actinobacteria (~8%), Chloroflexi (12%), Gemmatimonadetes (3%), and Planctomycetes (2%) phyla, with the lower abundances in contaminated soil vs. control soil. Investigation of long-term contaminated sites revealed significant genus-level taxonomic restructuring in soil bacterial communities. The most evident changes were observed in Mycobacterium, Alkanindiges, and uncultured bacterium-145, which showed marked abundance shifts between spill and control soils across decades. Particularly, hydrocarbon-degrading genera such as Pseudoxanthomonas demonstrated persistent dominance in contaminated sites. While some genera (e.g., Frigoribacterium, Leifsonia) declined over time, others—particularly Nocardioides and Streptomyces—exhibited substantial increases by 2014, suggesting potential ecological succession or adaptive selection. Minor but consistent changes were also detected in stress-tolerant genera like Blastococcus and Quadrisphaera. The effect of oil contamination on species diversity was greater at the 1975 site compared to the 2014 site. These patterns highlight the dynamic response of bacterial communities to chronic contamination, with implications for bioremediation and ecosystem recovery. The study results provide new insights into oil contamination-induced changes in soil bacterial community and may assist in designing appropriate biodegradation strategies to alleviate the impacts of oil contamination in drylands.

1. Introduction

Contamination of soil and water by crude oil, a worldwide environmental challenge [1], causes severe damage to ecosystems [2,3].
Such pollution degrades or destroys soils, leads to secondary pollution of air and groundwater, decreases soil–water permeability, and reduces or inhibits the growth of soil organisms [4,5]. Further, crude oil contains many mutagenic and carcinogenic compounds that may cause fatal effects at the genetic level, due to their toxicity [6]. The main components of crude oil are aromatic and saturated hydrocarbons, asphaltene, and colloids [7], which decompose slowly after entering the soil [8].
The reactive components of oil can interact with inorganic phosphorus and nitrogen, inhibiting both dephosphorization and nitrification processes, and reducing the availability of these essential nutrients in the soil [9]. Microorganisms require nutrients like nitrogen and phosphorus for their growth and metabolism activities during hydrocarbon degradation. Consequently, this nutrient limitation can reduce the absorption of water and essential nutrients by plant roots, ultimately inhibiting primary productivity [10]. Continued existence of oil also leads to the accumulation of heavy metals and inorganic salts, increasing the risk of groundwater contamination [11,12].
Crude oil has low water solubility, limiting its availability to microorganisms for degradation [13]. This challenge is further intensified in xeric environments, where water scarcity hampers microbial activity and contaminant transport. Additionally, xeric environments often experience extreme temperatures and high salinity due to evaporation, further stressing indigenous microorganisms, inhibiting enzyme activity, and reducing overall microbial diversity and functionality [14]. As a result, xeric ecosystems are more fragile than mesic ones, taking longer to recover from disturbances. Oil pollution in dryland environments can therefore cause severe ecological damage and significantly alter soil properties [15]. In these systems, where biotic activity is already low due to sharp fluctuations in water availability, radiation, and temperature, the biodegradation of hydrocarbon contaminants by native soil microorganisms is a slow and complex process [16].
Since microorganisms are effective in degrading petroleum products, it is important to understand changes that take place in the microbial composition following oil contamination [17]. The main microbial hydrocarbon decomposition pathways take place under aerobic conditions. In aerobic conditions, these microorganisms convert the dangerous organic pollutants from crude oil into compounds such as CO2 and H2O [18]. Nonetheless, bacteria can also utilize hydrocarbons anaerobically [19]; they convert the dangerous organic pollutants from crude oil into CH4. Interestingly, it has been demonstrated that bacteria can decompose aliphatic and aromatic compounds, whereas fungi are also able to degrade polycyclic aromatics. Moreover, bacteria utilize specific metabolic pathways to degrade hydrocarbons, including those involving dioxygenase and alkane monooxygenase. Additionally, some hydrocarbon-degrading microorganisms adapt by producing and secreting surfactant compounds that emulsify hydrocarbon and form micelles, which are subsequently taken up later through various mechanisms [7]. Such bacteria become more abundant in oil-polluted soils. Salinity plays a significant role in the distribution of microorganisms, particularly with petroleum pollution. It tends to reduce the abundance of non-salt-tolerant microorganisms while fostering an increase in salt-tolerant species. This shift reflects the adaptability of microbial communities to changing environmental conditions [20]. Yet, changes in the microbial composition can have serious deleterious effects on soil functions. Therefore, the modified abundance of microorganisms comprises an important indicator for restoring soil vitality [21,22].
Many studies have discovered that there are numerous hydrocarbon-degrading bacteria in oil-rich environments [14,23]. Their community composition was found to be related to the petroleum hydrocarbon types and the environmental conditions [24,25]. More than 79 genera of bacteria capable of degrading petroleum hydrocarbons were identified. These include Mycobacterium, Achromobacter, Marinobacter, Acinetobacter, Arthrobacter, Alkanindiges, Alteromonas, Burkholderia, Streptococcus, Enterobacter, Pseudomonas, Staphylococcus, Streptobacillus and Rhodococcus [18,26,27,28]. Interestingly, “conditionally rare taxa” in soil, such as Alkanindiges spp., were reported to undergo rare-to-dominant shifts which are greatly affected by environmental constraints, including diesel pollution [24]. In addition, some Obligate Hydrocarbon Clastic Bacteria, including Thallassolituus, Cycloclasticus, Alcanivorax, Marinobacter, Oleispira, and others, were scarce or undetectable before petroleum pollution and dominant after it [29].
Significant knowledge gaps remain in understanding the long-term ecological impacts of oil pollution and bioremediation in xeric ecosystems. The objective of the present study was to assess the effects of crude oil contamination on the soil microbial community composition and diversity. The study was conducted in the Evrona Nature Reserve, which is found in the hyper-arid (sandy loam soil) Arava Valley of southern Israel, where two petroleum hydrocarbon spills occurred. The first pollution event occurred 47 years ago (1975) and was never treated. This presents a unique time-point perspective in relation to the second, comparatively recent (2014) contamination event. Thus, soil samples were collected 47 years and 7 years after the spills, from the two contamination sites. The comparison of the two pollution sites enabled an investigation into the extent and progression of natural bioremediation over time. Molecular tools were used to analyze microbial diversity as well as to determine the effect of oil contamination on the soil bacterial community after 47 years [30]. We hypothesized that the bacterial community undergoes changes following oil spill contamination, and only bacterial species that adapt to the new, harsh, contaminated environment survive.

2. Materials and Methods

2.1. Study Site

The Evrona Nature Reserve (29.670349 N; 25.003633 E; 30 m.a.s.l.) is located in the southern Arava Valley of Israel. The valley’s lithology is highly varied, and mainly comprises marine calcareous rocks, such as chalk, limestone, and marl, alongside granite and magmatic rocks. The region’s soils are composed of coarse desert alluvium and sandy alluvium, with sandy loam texture being predominant. The climate is hyper-arid, with mild winters (mean daily temperature of 15 °C in January) and very hot summers (mean daily temperature of 32 °C in July). Annual precipitations are rather erratic, and average annual rainfall is 30 mm. Mean annual potential evapotranspiration is 2600 mm.

2.2. Soil Sampling

Topsoil samples (0–10 cm) were collected in October 2021. The samples were obtained from the 1975 and the 2014 contamination sites, 47 and 7 years after the spills, respectively. For each of the sites, soil samples were obtained from three oil-contaminated ephemeral stream channels and three nearby non-contaminated (control) ones. In each contaminated and control area, a 50 m2 plot was delineated. Soil was collected from five randomly selected locations across each plot and pooled into one sample (~1 kg) which represents the plot. In total, 12 samples representing the 12 plots were taken. The samples were placed in separate plastic bags and kept in a cooler box until they arrived at the laboratory. The samples were sieved with a 2 mm mesh and divided into two parts. One part was kept at −20 °C for genetic analysis, and the second was kept at 4 °C for abiotic analysis.

2.3. Soil Moisture (SM) Content and Electrical Conductivity (EC)

SM was determined gravimetrically by drying soil samples for 24 h at 105 °C (expressed as percentage of dry weight) [31].
EC was determined by an auto-ranging EC/temp meter (TH2400, EI-Hamma, Algeria) in the filtered supernatant (1:10 soil–double-distilled water ratio). This was followed by shaking for 30 min (160 rpm) and overnight incubation [32].

2.4. Organic Matter (OM) Content

Soil samples were placed in a muffle furnace at 400 °C for 6 h, to determine soil organic matter content.

2.5. Soil pH

Soil pH was determined using a pH electrode in a filtered supernatant of a mixture containing 20 g soil and 40 mL distilled water (1:2 soil–water ratio). This was followed by shaking for 10 min (160 rpm) and overnight incubation at room temperature.

2.6. Microbial Community Diversity Determination

DNA extraction: DNA was extracted from 0.5 g of soil, using an Exgene soil DNA minikit (GeneAll, Seoul, Republic of Korea); 550 µL buffer SL (extraction buffer), 900 µL buffer TB (tissue binding), 500 µL buffer NW (wash buffer N), 50 µL buffer RH, 300 µL buffer PD, and 50 µL elution buffer were used. Samples were centrifuged between each extraction step in a 5810R Eppendorf centrifuge.
Polymerase chain reaction (PCR): Partial sequences of the bacterial 16S ribosomal RNA (rRNA) gene region from the extracted DNA were amplified by PCR, using 1.0 μL extracted DNA, 1 μL CS1-515F (ACACTGACGACATGGTTCTACAGTGCCAGCMGCCGCGGT), and 1 μL CS2-806R (TACGGTAGCAGAGACTTGGTCTGGACTACHVGGGTWTCT) [33]. PCR was carried out in an Applied BiosystemsVeritiTM 96-Well Thermal Cycler (Thermo Fisher Scientific, Waltham, MA, USA). The 25 µL reaction mix used for the PCR runs consisted of 1.0 μL extracted DNA, 1 μL forward and reverse primers, 12.5 μL HS Taq Mix Red (PCR Biosystems, London, UK), and 9.5 μL ultrapure water. The PCR conditions used were 3 m at 98 °C, 20 cycles of 30 s at 98 °C, 30 s at 55 °C, and 60 s at 72 °C, followed by 5 m at 72 °C. The amplified DNA was stored at −20 °C until sequencing.
The final PCR products were detected by Hylabs Inc. (Rehovot, Israel), using the Fluidigm Access Array primers for Illumina to generate libraries compatible for sequencing on the Miseq. The samples were assessed for concentration by Qubit and for size by Tape Station. They were then sequenced on the Illumina Miseq using a Miseq V2 sequencing kit (500 cycles) to generate 2 × 250 paired end reads. Sequencing data have been deposited to NCBI under accession number PRJNA841836.

2.7. Data Analysis

The data were de-multiplexed using the Illumina Base Space cloud to generate two FASTQ files for each sample. The FASTQ files were imported into CLC-bio and analyzed: reads were trimmed for quality and adaptor sequences, merged, and subjected to OTU picking in order to generate abundance tables. Reads were processed in QIIME, version 1.7.0 [34]. The reads were first clustered into operational taxonomic units (OTUs) at the >97% similarity level using UCLUST, with the open reference protocol. Taxonomy was assigned to each OTU using the Greengenes [35] database. Downstream diversity analyses (including alpha and beta analyses) were run using Qiime. A 480 bp fragment was amplified and sequenced. Bacterial diversity was grouped at the class level. All bacterial classes with an abundance lower than 1% were eliminated.
Data were subjected to statistical analysis of variance using the SAS model (ANOVA, Duncan’s multiple range test, Pearson correlation coefficients) to assess differences in the studied properties. ANOVA was followed by Tukey’s HSD test for assessing the significance of differences, using the statistical package Statistica 4.3. Differences were considered significant at p < 0.05.

3. Results

3.1. Soil Physicochemical Properties

Results of abiotic analysis of soils are presented in Table 1. The mean soil moisture content (SM) was significantly higher (p < 0.05), and mean organic matter content (OM) was not significantly higher (p < 0.5) in the spill areas (1975 and 2014) than in the control soils (Table 1, Figure 1).
The mean SM value was lower in the 1975 control (p < 0.2) and spill soils (p < 0.2) than in the 2014 control and spill soils, respectively. Similar mean OM values were recorded for the control soils of the 1975 and 2014 spills, whereas OM for the 2014 spill samples was higher than that in the 1975 spill soil (Table 1, Figure 1). No significant differences between the control and contaminated soils were found for pH. At the same time, mean pH values of the 1975 control soil (8.12 ± 0.11) were higher than in the 1975 spill soil (7.95 ± 0.05). An opposite trend was recorded for the 2014 soil, in which the mean pH of control soil was lower (7.91 ± 0.05) than that in the spill soil (7.93 ± 0.14) (Table 1, Figure 2). Mean electric conductivity (EC) was significantly higher (p < 0.05) in the control and spill soils from 2014 than in the 1975 control and spill soils. In the 1975 site, the mean EC values of the control soil were higher than in the spill soil. An opposite trend was recorded for the 2014 spill soil, in which mean EC values were lower in the control soils than those in the spill soils (Table 1, Figure 2).

3.2. Miseq Sequencing Results and Bacterial Community Structures

Amplification of the 16S rRNA gene of bacteria enabled application of the Illumina high-throughput sequencing-by-synthesis approach [36]. This approach provides thorough identification of bacterial community structures, including those that cannot be cultured or detected using traditional approaches [37]. In total, 107,318 of the 16S rRNA gene operational taxonomic units (OTUs) were observed. They had an average length of 250 bp. The RDP classifier was used in hierarchical clustering analysis at a similarity threshold of 97%. The sequence information and bacterial diversity indices are presented in Table 2.
The number of observed OTUs was higher in the contaminated soils compared to both control soils (Table 2). Soil contaminated in 1975 hosted fewer species than soil contaminated in 2014, whereas an opposite trend was documented for the control samples; the 1975 control soil harbored fewer observed bacterial species than the 2014 control soil (Table 2).
The taxonomic diversity of the 16S rRNA bacterial genus detected in the soil samples, whose relative abundance was greater than 1%, is presented in Figure 3 (genus abundance greater than 1% in one soil sample but less in another was considered as greater than 1%). The dominating genus detected in the 1975 spill soil was Alkanindiges (14.45%, belonging to Proteobacteria phylum). This genus was absent in the 1975 control soil, very rare in the 2014 control soil (≤0.07%; Figure 3), and accounted for 4.61% in the 2014 spill soil (Figure 3). The second dominating genus for the 1975 spill soil was Mycobacterium (9.78%, belonging to Actinobacteria phylum), which was almost absent in the control soil (≤0.3%; Figure 3), but in 2014 spill soil, its abundance was 4.10%. The third dominating genus for the 1975 spill soil was Acinetobacter (9.23%; also belonging to Proteobacteria phylum); its value was double that of the 1975 control soil (4.53%). We also found Acinetobacter in the 2014 spill soil, but its trend was the opposite: abundance in the spill soil was lower than in the 2014 control soil (2.21% and 7.28%, respectively; Figure 3). The fourth dominating genus for the 1975 spill was Pseudoxanthomonas (8.43%; also belonging to Proteobacteria phylum), which was almost absent in the 1975 control and 2014 control soils (≤0.01%; Figure 3) and accounted for 6.57% in the 2014 spill soil (Figure 3). The fifth dominating genus for the 1975 spill was uncultured bacterium-145 (9.76%; also belonging to Proteobacteria phylum), which was almost absent in the 1975 control and 2014 control soils (≤0.04%; Figure 3), and was 31.92% higher in the 2014 spill soil (Figure 3).
Ambiguous taxa-025 genus (belonging to Actinobacteria phylum) was also found in 1975 spill soil (2.71%), 2014 spill soil (2.91%), and to a lesser extent in both control soils (≤0.3%; Figure 3). Frigoribacterium, Leifsonia, Microbacterium (belonging to Actinobacteria phylum), and uncultured bacterium-092 (belonging to Patescibacteria phylum) existed only in spill soils though in lesser percentages than the abovementioned genera, and were almost absent in the control soil. Blastococcus genus (belonging to Actinobacteria phylum) existed in all samples in similar percentages (1975 spill—4.42%; 1975 control—3.08%; 2014 spill—5.69%; 2014 control—4.30%) (Figure 3). Minor changes also were observed in the following bacterial genera: Quadrisphaera (1975 spill soil 1.93% and in control soil 0.36%; 2014 spill soil 1.08% and in control soil 0.50%), Nocardioides (1975 spill soil 1.11% and in control soil 0.81%; 2014 spill soil 5.56% and in control soil 0.96%), and Streptomyces (1975 spill soil 0.06% and in control soil 0.28%; 2014 spill soil 3.56% and in control soil 0.11%). Interestingly, the two most recent genera (Nocardioides, Streptomyces) showed a primary increase in the 2014 spill site.
The analysis of genomic DNA isolated from the 1975 and 2014 spill soils grouped the obtained sequences into 23 different phyla. These included two phyla that dominated the sum proportion of total phyla, accounting for ~92% in the spill soils and ~67% in the control soils. For the 1975 and 2014 control groups, dominating phyla included Proteobacteria and Actinobacteria (~67%), where the Proteobacteria were less abundant (~25%) than the Actinobacteria (~42%; Figure 4). For the 1975 and 2014 spill groups, dominant phyla also included Proteobacteria and Actinobacteria (~92%), but the Proteobacteria was found in higher percentages (~58%) than the Actinobacteria (~34%).

3.3. Alpha Diversity Within Samples and Rarefaction Curves

OTU-based alpha diversity or Shannon index was calculated using QIIME software (Table 2) in order to better present the mean species diversity in the two sites. The results confirmed that the sequencing depth [38] enabled us to accurately describe bacterial diversities in our database. The Shannon index and the information on the entropy of the observed OTU abundances accounting for evenness indicated that the bacterial community was more diverse in the control soils than in the spill soils (Table 2). The species diversity was as follows: 1975 control > 2014 control > 2014 spill > 1975 spill. Note that two metrics were used to calculate α-diversity in QIIME: (a) Chao1 metric estimated species richness, and (b) observed species metric (Figure 5).

3.4. Beta Diversity (Pairwise Sample Dissimilarity) Among Samples, and Principal Component Analysis

Analysis of β-diversity was performed to assess OTU distribution. QIIME was used to calculate β-diversity for each of the four bacterial communities. Principal coordinate analysis (PCoA) plots showed similarity between the spill soils (blue circles) and control soils (purple circles; Figure 6). For each site (i.e., 1975 and 2014), the distance between the spill and control clusters was relatively small (differences along the second principal component axis: PC2—11.42%). Greater differences between spill and control clusters for each site were observed along the first principal component axis (PC1—55.91%).

4. Discussion

This study demonstrates that oil pollution significantly reduced both the diversity and abundance of soil bacteria, while substantially altering the relative abundance of major bacterial populations. These findings are consistent with existing literature, which shows that long-term crude oil contamination leads to a decline in native microbial communities while promoting the survival or proliferation of those that have developed adaptive mechanisms [39,40,41].
The significant decrease in dominance in the oil-contaminated soils demonstrates the substantial impact of oil spills on the soil microbial diversity. The oil contamination’s impact on species diversity was more pronounced for the 1975 site than that for the 2014 site. This confirmed the gradual long-term deleterious effect of oil on soil microbial populations. The potential of natural bioremediation of soil contaminated by petroleum hydrocarbons is affected not only by biotic factors, but also by abiotic factors such as moisture, salinity, soil chemistry, and nutrient availability. These abiotic factors are influenced by hydrocarbon contamination and substantially affect microbial pollution catabolism.
The effects of oil on the physical and chemical properties of soil decline continuously. However, the reasons for this trend vary. Hydrocarbons, alkanes, aromatic, etc., comprise carbon sources for soil microorganisms, thus promoting their growth and metabolism. This results in the production of organic acids and CO2, and a decrease in the soil pH [42]. Yet, the buffering capacity of soil resists changes in pH. Therefore, the soil first experiences a change in its properties and later faces changes in its microbial community. Increased electrical conductivity in the contaminated soil indicates that the viscous hydrocarbons and soluble minerals found in oil not only increase soil salinity but also exacerbate soil slab formation and reduce water permeability [5,42].
The lack of water has a negative impact on soil microbial communities. It hampers their interactions with each other and with the environment, which may lead to a decrease in abundance and diversity [21]. Our results are in accordance with Truskewycz et al. [21], who reported that soil moisture content and organic matter parameters were lower in non-polluted samples than in spill samples, and the number of OTUs was also lower in non-polluted samples (Table 1 and Table 2). A direct correlation between the abiotic and biotic parameters was found, where lower soil moisture and organic matter values corresponded with fewer OTUs in the samples.
The soil pH is another crucial factor for the biodegradation of petroleum hydrocarbons and the biotransformation of toxic metals. Changes in pH can affect metal ions’ behavior and alter fungal and bacterial enzyme activities and community structure [43]. A strong relationship between soil pH and microbial communities is expected, where higher pH correlates with wider microbial diversity, especially in dryland environments [44]. Our data are in accordance with these previous studies, where high pH value led to an increased Shannon index for bacterial diversity (in the 1975 control sample). According to Samira et al. [45], after 18 years of oil contamination in Kuwait, the chemical composition of soil exhibited an increase in resins, a higher concentration of polycyclic aromatic hydrocarbons (PAHs), and a decrease in aromatic compounds. Actinobacteria, Proteobacteria, and Firmicutes are known oil-degrading microorganisms. These bacteria can use crude oil as their sole source of carbon and energy [46]. Huang et al. [22] identified eight core genera in an oil-contaminated soil. This is in partial accordance with our findings as presented in Figure 3.
Moreover, the dominance of Proteobacteria and the decrease in the diversity of microbial communities in pervaded arid soils indicate a specialized community adapted to hydrocarbon stress. While this shift increases degradation efficiency, it could also compromise wider ecosystem functions, such as those determining nutrient cycling and bioremediation capacity. The loss of Acidobacteria and Bacteroidetes—big players in carbon and nitrogen cycling—could shake up soil fertility. In Proteobacteria-based systems, hydrocarbon degradation may be favored over organic matter mineralization, thereby limiting nutrient availability to plants [47]. While hydrocarbon-degrading taxa (e.g., Pseudomonas, Rhodococcus) can flourish, their dominance may decrease functional redundancy and render the ecosystem susceptible to consequential additional perturbations [47].
Unlike non-arid ecosystems, arid ecosystems exhibit limited functional flexibility in response to oil pollution. After contamination, arid systems demonstrate greater loss of diversity than temperate soils [15], likely resulting from an inherently lower baseline resilience. In humid areas, Actinobacteria and fungi (e.g., Phanerochaete) are the primary degraders [15], while Proteobacteria and halotolerant genera (Marinobacter) are dominant in arid environments. Non-arid ecosystems are still able to retain higher functional diversity despite pollution [48], which in turn provides buffering for nutrient cycling [48].
Overall, insights of this study suggest that oil pollution changes the soil microbial community dramatically and poses a threat to soil health. Identifying petroleum hydrocarbon-degrading bacteria in contaminated soils can comprise an effective strategy for alleviating oil pollution. These bacterial strains can comprise the basic microbial agent for natural bioremediation. Also, future studies might investigate changes in the composition and diversity of Archaea, which are of similar appearance and numerous similarities in oil-contaminated dryland soils, as Archaea play a crucial role in the transformation of petroleum compounds.

5. Conclusions

This study examined the long-term changes in the soil microbial populations following oil spill contamination in the Evrona Nature Reserve in the Arava Valley, Israel. Over a span of 47 years, microbial biodiversity substantially decreased in the oil-contaminated soils. Most of the original bacterial species disappeared, replaced by species capable of utilizing oil as their sole source of carbon and energy. This shift occurred gradually, as evidenced by comparisons between soils contaminated by oil in 1975 and 2014. Focusing future research on identifying microbial genera well-suited for petroleum degradation may improve the effective degradation of oil in contaminated environments.

Author Contributions

Conceptualization, Y.S.; Methodology, V.M., I.S., T.D., I.A., C.S. and M.L.; Software, T.D.; Validation, V.M., T.D., I.A., C.S. and Y.S.; Investigation, V.M., I.S., I.A., C.S. and M.L.; Resources, I.S.; Data curation, Y.S.; Writing—original draft, V.M., T.D., I.A., M.L. and Y.S.; Writing—review & editing, I.S. and Y.S.; Project administration, Y.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data generated or analyzed during this study are included in this article. Raw data may be available upon proper request to the corresponding author.

Acknowledgments

The authors gratefully acknowledge Michelle Finzi for proofreading the manuscript.

Conflicts of Interest

The authors declare that they have no competing interests.

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Figure 1. Soil moisture (SM) and organic matter (OM) content of soils from spill and control samples.
Figure 1. Soil moisture (SM) and organic matter (OM) content of soils from spill and control samples.
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Figure 2. Measured pH and electric conductivity (EC) parameters of soils from spill and control samples.
Figure 2. Measured pH and electric conductivity (EC) parameters of soils from spill and control samples.
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Figure 3. The relative abundance of the 16S rRNA genus detected in soils from spill and control samples.
Figure 3. The relative abundance of the 16S rRNA genus detected in soils from spill and control samples.
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Figure 4. Heatmap of the 16S rRNA gene phyla detected in soil from spill and control samples, by percentage.
Figure 4. Heatmap of the 16S rRNA gene phyla detected in soil from spill and control samples, by percentage.
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Figure 5. Soil bacterial α-diversity parameters: (A)—diversity as a function of sampling—Chao1 matrix; (B)—the Shannon–Wiener index; (C)—species richness.
Figure 5. Soil bacterial α-diversity parameters: (A)—diversity as a function of sampling—Chao1 matrix; (B)—the Shannon–Wiener index; (C)—species richness.
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Figure 6. Principal components analysis (PCA) of 16S rRNA gene sequencing of soil microbes obtained from spill and control samples. PC1 and PC2 account for 55.91% and 11.42% of the variation, respectively.
Figure 6. Principal components analysis (PCA) of 16S rRNA gene sequencing of soil microbes obtained from spill and control samples. PC1 and PC2 account for 55.91% and 11.42% of the variation, respectively.
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Table 1. Mean (±SD; n = 3) values of physicochemical properties of soil samples obtained from the 1975 and 2014 sites.
Table 1. Mean (±SD; n = 3) values of physicochemical properties of soil samples obtained from the 1975 and 2014 sites.
Soil Moisture (%)Organic Matter (%)pHElectrical Conductivity (µS cm−1)
1975 Control0.28 ± 0.040.13 ± 0.018.12 ± 0.11111.60 ± 16.57
1975 Spill0.41 ± 0.060.28 ± 0.047.95 ± 0.0587.80 ± 23.90
2014 Control0.49 ± 0.030.13 ± 0.037.91 ± 0.05272.67 ± 67.20
2014 Spill0.75 ± 0.210.59 ± 0.097.93 ± 0.14348.67 ± 158.60
Table 2. Mean (±SD; n = 3) sequence information and bacterial diversity indices of soil samples obtained from the 1975 and 2014 sites.
Table 2. Mean (±SD; n = 3) sequence information and bacterial diversity indices of soil samples obtained from the 1975 and 2014 sites.
Mean No. of
OTUs ± SD		EvennessObserved SpeciesChao1Shannon–Weaver
1975 control7373.3 ± 1090.50.6 ± 0.03931.7 ± 75.11285.4 ± 55.75.8 ± 0.06
1975 spill11,234.7 ± 2585.20.8 ± 0.01338.7 ± 119.6472.2 ± 171.83.5 ± 0.31
2014 control8334.0 ± 524.70.6 ± 0.05806.0 ± 28.31010.72 ± 59.15.2 ± 0.13
2014 spill11,608.7 ± 981.60.8 ± 0.02360.7 ± 57.6477.4 ± 47.93.6 ± 0.39
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Martirosyan, V.; Stavi, I.; Doniger, T.; Applebaum, I.; Sherman, C.; Levi, M.; Steinberger, Y. Bacterial Community Dynamics in Oil-Contaminated Soils in the Hyper-Arid Arava Valley. Agronomy 2025, 15, 1198. https://doi.org/10.3390/agronomy15051198

AMA Style

Martirosyan V, Stavi I, Doniger T, Applebaum I, Sherman C, Levi M, Steinberger Y. Bacterial Community Dynamics in Oil-Contaminated Soils in the Hyper-Arid Arava Valley. Agronomy. 2025; 15(5):1198. https://doi.org/10.3390/agronomy15051198

Chicago/Turabian Style

Martirosyan, Varsik, Ilan Stavi, Tirza Doniger, Itaii Applebaum, Chen Sherman, May Levi, and Yosef Steinberger. 2025. "Bacterial Community Dynamics in Oil-Contaminated Soils in the Hyper-Arid Arava Valley" Agronomy 15, no. 5: 1198. https://doi.org/10.3390/agronomy15051198

APA Style

Martirosyan, V., Stavi, I., Doniger, T., Applebaum, I., Sherman, C., Levi, M., & Steinberger, Y. (2025). Bacterial Community Dynamics in Oil-Contaminated Soils in the Hyper-Arid Arava Valley. Agronomy, 15(5), 1198. https://doi.org/10.3390/agronomy15051198

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