DNA Metabarcoding of Soil Microbial Communities in a Postvolcanic Region: Case Study from Băile Lăzărești, Romania

Abstract

This study investigates the impact of post-volcanic gas emissions on soil microbial communities in the Băile Lăzărești region (Romania). Nineteen soil samples across a CO₂ gradient ranging from background levels to ≈46,221 ppm. Methane and hydrogen sulfide showed localized peaks (CH₄ up to 8271 ppm; H₂S up to ~10.12 ppm), with CH₄ contributing to outlier community patterns. eDNA metabarcoding identified 3064 OTUs, (2463 bacterial and 601 fungal). Bacteria were dominated by Proteobacteria, fungi by Ascomycota, with Thelebolales nearly ubiquitous. Alpha diversity (Chao1, Fisher) declined significantly in high-CO₂ soils (>3000 ppm), while intermediate concentrations (1000–3000 ppm) showed heterogeneous responses. Beta-diversity analyses (PCoA, clustering) revealed distinct grouping of high-CO₂ soils, with sample P16 (CH₄-rich) forming an outlier. A PCA including CO₂, CH₄, and H₂S confirmed CO₂ as the main driver of variance (>65%), with CH₄ accounting for local effects. At the genus level, Acidobacterium, Granulicella, Streptomyces, and Nocardia increased with CO₂, while Rhizobium and Pseudomonas declined. Fungal responses were mixed: Thelebolus and Cladosporium increased, whereas Mortierella and Cryptococcus decreased. Overall, elevated soil CO₂ reduced microbial richness and reorganized communities, while CH₄ shaped local niches. These findings provide key natural analog insights for assessing ecological risks of CO₂ leakage from geological storage.

1. Introduction

The Sixth Assessment Report of the [1] attributes the observed increase in global temperatures primarily to anthropogenic emissions of greenhouse gases, emphasizing the urgent need for large-scale interventions to reduce these emissions. Carbon capture and storage (CCS) is one of the promising and recommended [1,2] methods for reducing greenhouse gas levels. However, responsible implementation of CCS and especially of the storage part of the chain requires a comprehensive risk assessment, particularly considering the potential leakage of CO₂ from storage sites to near-surface environments, which could affect ecosystems and human health [3,4].

A number of studies have explored the impact of increased atmospheric CO₂ on ecosystems [5]. However, few have examined the effects on subsurface ecosystems exposed to the upward migration of CO₂ from storage sites. Examples include studies of terrestrial CO₂ vents in Italy [6,7], volcanic emissions in California [8], and the impact of geothermal CO₂ on vegetation in Slovenia [9,10]. These studies reveal how elevated soil CO₂ alters soil chemistry, mineralogy, and affects both vegetation and microbial communities.

CO₂ geological storage has a potential environmental impact if CO₂ leaks from deep storage, possibly altering groundwater quality by reducing pH, increasing salinity, and mobilizing harmful solutes [11,12,13]. CO₂ exposure also affects soil microbiota, influencing microbial metabolism, community composition and diversity [14]. Research on volcanic vents, natural analogs of CO₂ seepage, has shown that microbial communities adapt to high-CO₂ conditions where acidophiles or anaerobic autotrophs dominate [15].

While short-term studies on CO₂ leakage offer insights, they are limited by short observation periods, typically months to a few years, while CO₂ exposure in CO₂ geological storage can last for centuries. This has led researchers to explore natural analogs—places where CO₂ seeps naturally, such as volcanic gas vents and geysers—providing a model for understanding long-term ecosystem responses to CO₂ exposure [7,16,17]. The responses observed in these analog sites indicate significant changes in microbial populations and soil chemistry under sustained CO₂ exposure.

The Băile Lăzărești site, located near the town of Băile Tușnad, has a long history of research centered on the mineral springs and gas emissions characteristic to post-volcanic activity. As early as the 1950s, scientific studies documented the chemical composition of the mineral waters and the gases emitted from local mofettes. Significant insights are recorded in works such as “Mineral and Thermal Waters of Romania” [18] and “Therapeutic Mineral Substances of Romania” [19] by Artemiu Pricăjan. By 1974, an inventory compiled by Zoltán Rákosy detailed 44 mineral springs in the Lăzărești area alone, underscoring the region’s rich hydrogeological diversity [20]. From the 1970s through the early 2000s, geologists and chemists from the Harghita Geological Research and Exploration Company conducted extensive mapping and chemical analyses of the region’s acidulated waters and mofettes. These findings, published by Berner, Jánosi, and Péter in 2000, provided comprehensive data on 16 mineral springs on the northern slope and 18 on the southern slope of the Ciomad Massif [21]. Additionally, studies of the Ciomadul region continued, with analyses describing eutrophic and oligotrophic peat bogs, as well as the flora and fauna that thrive in mineral-rich, gas-emission areas.

The geological structure around Lăzărești is shaped by deep fault systems that facilitated the development of volcanic structures and ongoing post-volcanic activity, including significant gas emissions. Geological surveys have identified a complex system of crustal, regional, and local faults, which define the tectonic and hydrogeological framework of Harghita County. These features, combined with the region’s unique sedimentary and volcanic deposits, contribute to the geological and ecological significance of the Băile Lăzărești area, offering a valuable site for ongoing studies on mineral springs, gas emissions, and biodiversity.

The aim of this study is to investigate the ecological and microbial responses to naturally elevated soil CO₂ levels in Băile Lăzărești, Romania, an area affected by post-volcanic emissions. By analyzing soil microbiota and geochemical changes in response to CO₂ migration pathways, the study seeks to understand the long-term impacts of CO₂ leakage on soil ecosystems, particularly in terms of microbial community composition, biodiversity, and soil chemistry. These findings will contribute to assessing the ecological risks associated with geological carbon storage and CO₂ leakage, providing insights relevant for environmental monitoring in similar high-emission areas.

2. Materials and Methods

2.1. Study Area

The study area surrounding Băile Lăzărești, approximately 16 km northeast of Băile Tușnad, is renowned for significant post-volcanic activity. The last eruption of the Ciomadul volcano occurred about 27–35 ka ago [22], yet volcanic gases continue to reach the surface. These emissions manifest as dry and wet gas vents (mofettes) in areas like Lăzărești, Băile Tușnad, and Bálványos, where the local population has historically used them for medicinal purposes.

In addition to concentrated emissions, gases diffuse through the soil, impacting vegetation—often causing absence or alteration—and rock formations, marked by secondary mineral precipitations. Within the selected measurement area, locals have developed several springs and wells, notably Nagyborvíz (a calcium–magnesium carbonated mineral water) and Fortyogó and Nyírfürdő (Nyír Baths), which feature outdoor pools, foot basins, and a mofette used for treating rheumatic, vascular, and skin conditions. Residents collect this mineral water for household use [23].

On Borvíztető ridge, additional calcium–magnesium bicarbonate mineral springs dry up in droughts and function as dry gas vents. The area is divided into two zones for research: a northern zone with high-CO₂ emissions, including therapeutic baths with wet and dry gas emissions, and a southern zone with lower, primarily dry emissions.

2.2. Field Campaigns

Two field campaigns have been conducted in the summer of 2022, in July and August for assessing the soil gas flux and for sample collections. Two profiles have been selected as representative, one in the northern zone with 10 sampling points and one in the southern zone with 9 sampling points (Figure 1).

Gas flux measurements (Figure 2) were carried out during both field campaigns. Soil gas flux was measured using a portable flux meter from West Systems (Pontedera, Italy), equipped with CO₂ (Li-COR LI-830-3, 0–2%; Vaisala GMP251, 0–20%), CH₄ (WS-CH4-TLD), and H₂S (WS-TOX-H2S) sensors. The flux data was processed using FluxRevision software version 4.11 which resulted in flux values for each measured gas and average gas concentrations. For the current study, correlations were conducted exclusively with CO₂ concentrations, as CO₂ was the dominant gas across the study area and provided the most consistent and spatially extensive dataset. Methane (CH₄) and hydrogen sulfide (H₂S) concentrations were either too low, highly localized, or irregular to support robust statistical comparisons.

In addition to gas measurements, soil samples were collected (Figure 3) in the same sample points, immediately after measuring gas flux. Samples were taken in sterile plastic containers (50–100 g each, depending on the material’s physical state), sealed, and stored to prevent contamination. These samples were then frozen and sent to Nature Metrics, a specialized laboratory in the UK, for DNA sequencing to analyses the microbiome (bacteria and fungi) present, both qualitatively (species identification) and quantitatively.

The mofetta field at Băile Lăzărești extends over more than 1 ha, with multiple active degassing spots covering several hundred square meters. Because gas emissions are spatially widespread across this area, even the lowest emission points (<1000 ppm CO₂) were considered as internal controls. Establishing an external control would have required sampling soils located far outside the mofetta field, thus removing them from the geological and ecological framework of the study site.

Atmospheric Gas Composition

In addition to soil gas fluxes, the composition of the near-surface atmosphere in the Ciomadul–Băile Tușnad area has been investigated in recent studies using Multi-GAS GFM 436 (Gas Data, Coventry, UK) instruments and direct sampling. Measurements at mofettas and caves (e.g., Stinky Cave, Turia; Băile Tușnad mofetta) consistently show that the gas phase is strongly dominated by CO₂, typically accounting for 95–99% of the composition, with minor fractions of CH₄ and H₂S [24]. Annual CO₂ emissions were estimated at ~1923 tons for the Stinky Cave mofetta [25], while the minimum total CO₂ flux for the youngest volcanic segment at Ciomadul was estimated at 8.7 × 10³ t/year [24]. Methane concentrations generally remain below 5% but can locally exceed background levels, while H₂S is usually <200 ppm, with occasional local maxima explained by sulfide alteration processes [26]. By comparison, global background atmospheric values are much lower: ~420 ppm CO₂) [27], ~1.9 ppm CH₄, ~0.1 ppm CO, and ~0.0004 ppm H₂S. This highlights the strong localized enrichment in CO₂ and trace volcanic gases in post-volcanic emission areas compared to global atmospheric averages.

For sample grouping, CO₂ concentration was selected as the primary variable because it represented >95% of the measured gas fluxes and was spatially continuous across the study area. Methane (CH₄) and hydrogen sulfide (H₂S) were recorded only at localized points and in much lower concentrations, and therefore could not support robust stratification.

2.3. DNA Extraction and Sequencing

In this study, environmental DNA (eDNA) refers to the total DNA extracted from bulk soil samples, including DNA from intact microbial cells, dead cells, and extracellular fragments bound to soil particles. This approach provides an integrative snapshot of microbial community composition in the sampled soils.

Total DNA was extracted from approximately 0.5 g of each sample. An extraction blank was processed for each extraction batch. DNA was quantified using a Qubit DNA broad range kit (Thermo Fisher Scientific, Waltham, MA, United States) according to the manufacturer’s protocol. Our analysis was based on eDNA metabarcoding targeting the V4 region of the bacterial 16S rRNA gene and the ITS2 region of fungal DNA, which allows both qualitative (taxonomic identification) and quantitative (relative abundance) characterization of soil microbial communities; DNA was amplified in triplicate PCRs with primers specific for these regions.

PCRs were performed with a negative control (the extraction blank) and a positive control (a sample known to amplify with these primers). Amplification success was assessed by gel electrophoresis. PCR replicates were pooled and purified, and sequencing indexes were added. Amplicons were purified and verified by gel electrophoresis, and then quantified using a Qubit DNA broad range kit following the manufacturer’s protocol. All purified indexed PCRs were pooled into final libraries, with equal concentrations of each sample. The final libraries were sequenced using an Illumina MiSeq V3 kit (Illumina, San Diego, CA, United States) at 10.5 pM with a 20% PhiX spike-in.

2.4. Bioinformatics

Sequence data were processed using a custom bioinformatics pipeline for quality filtering, OTU clustering (97%), and taxonomic assignment. A total of 2,678,241 high-quality sequences (bacteria: 1,349,217 and fungi: 1,329,024) are represented. Consensus taxonomic assignments for each OTU were made based on sequence similarity searches against two reference databases appropriate for the dataset: one general (NCBI nt) and one specialized: Bacteria: SILVA 16S (v138.1) and Fungi: UNITE (v8.2). The GBIF (http://doi.org/10.5281/zenodo.17279548, accessed on 20 September 2025) taxonomic backbone was used to ensure consistency across databases. Results from both searches were combined, and taxonomic assignments were made to the lowest possible level where matches were consistent. Conflicting results were flagged and resolved manually. Minimum similarity thresholds of 98%, 95%, and 92% were required for species-, genus-, and higher-level assignments, respectively. Identifications based on fewer than three reference matches were flagged for review. The OTU table was filtered to remove low-abundance OTUs from each sample (<0.03% or fewer than 10 reads, whichever threshold was greater for the sample). Results are presented for OTUs identified at the target kingdom level or below.

2.5. Statistical Analysis

Statistical analyses were conducted using a combination of dedicated bioinformatics and ecological software tools. For multivariate statistics, we used PRIMER 7 with the PERMANOVA+ add-on package to test for significant differences in microbial community composition across CO₂ categories [28]. In addition, PAST v.4 (Paleontological Statistics Software; [29]) was employed for ordination and clustering analyses, including Principal Component Analysis (PCA) and hierarchical clustering (Ward’s method, Bray–Curtis distances), to visualize community differentiation along the CO₂, CH₄, and H₂S gradients.

3. Results

3.1. Data Analysis

The final dataset included a total of 3064 Operational Taxonomic Units (OTUs) across the 19 soil DNA samples, consisting of 2463 bacterial OTUs and 601 fungal OTUs (

Table 1. Summary of the number of OTUs detected and the percentage of OTUs successfully classified at each taxonomic level for each target.

Target	Number of OTUs	Phylum	Class	Order	Family	Genus	Species
Bacteria	2463	68.3%	53.8%	39.3%	27.8%	11.3%	2.1%
Fungi	601	98.8%	89.7%	79.7%	62.6%	31.4%	11.3%

). A greater number of fungal OTUs were identified at the species level compared to bacteria. This difference is attributed to differences in the availability of reference sequences for different organisms in the databases, as well as a higher frequency of assignment conflicts among bacteria, where identical sequences (100% matches) frequently corresponded to multiple species.

Most abundant phylum:

○

Bacteria: Proteobacteria

○

Fungi: Ascomycota

OTU with the highest proportion of reads:

○

Bacteria: kingdom Bacteria

○

Fungi: order Thelebolales

For the purpose of data analysis, we have utilized the publicly available web-based tools provided by MicrobiomeAnalyst [30], latest updated on 21 March 2025, a comprehensive platform designed for statistical, integrative and visual analysis of microbiome data. We have used the raw data (abundance tables) provided by NatureMetrics in a CSV (comma-separated values) format compatible with MicrobiomeAnalyst to generate the graphs. Taxonomy labels were in a non-specific format. For the purpose of better understanding the taxonomy, we have removed the unassigned OTUs. Data filtering involved removal of constant features in order to benefit the differential analysis and of one sample occurrences, since such appearances are often considered artifacts (https://www.microbiomeanalyst.ca/MicrobiomeAnalyst/home.xhtml, accessed on 13–28 February 2025, 8–9 April 2025). The low count filter was set to a minimum count of 4 (the default setting). A 20% prevalence filter was set, which means that at least 20% of values should contain at least 4 counts. A low variance filter was also set in place to remove 10% of features with variance measured using inter-quantile range (the default setting). Data normalization aims to address the variability in sampling depth and data sparsity to ensure more biologically meaningful comparisons. We have used total sum scaling to normalize the data (the default setting).

In order to perform the analysis, the 19 samples were assigned to three categories based on CO₂ concentration (

Table 2. Samples and gas concentrations.

Sample	Profile	CH4 (ppm)	H2S (ppm)	CO2 (ppm)	CO2 Categories
P1	1P	275.2008	7.37361	42,653.92	C3
P2	1P	82.63361	0.453123	13,162.42	C3
P3	1P	4.494262	0.393549	3199.084	C3
P4	1P	2.43252	0.542659	601.6707	C1
P5	1P	36.94797	0.939593	5396.768	C3
P6	1P	15.77377	1.096336	2531.079	C2
P7	1P	318.7418	10.11688	46,220.71	C3
P8	1P	2.33252	0.765073	992.7963	C1
P9	1P	2.1416	0.829536	631.2909	C1
P10	1P	2.135075	0.250425	485.4114	C1
P11	2P	1.963934	0.374443	1505.181	C2
P12	2P	2.024793	0.366752	929.5848	C1
P13	2P	1.887705	0.344893	977.153	C1
P14	2P	2.447934	0.336711	1608.869	C2
P15	2P	19.968	0.44908	3447.992	C3
P16	2P	8271.474	0.384769	1456.66	C2
P17	2P	3.504132	0.33995	1173.752	C2
P18	2P	1.856911	0.349366	1520.143	C2
P19	2P	1.858537	0.382748	785.9752	C1

) C1 with less than 1000 ppm CO₂ (“low”), C2 between 1000 and 3000 ppm CO₂ (“intermediate”) and C3 over 3000 ppm CO₂ (“high”).

3.1.1. Bacteria

In the bacterial dataset, OTUs were identified in 25 distinct phyla from the kingdom Bacteria. The average bacterial taxon richness per sample, based on rarefied data, was 418.8, with values ranging from 79 (sample P6) to 745 (sample P9). The overall taxonomy of the detected OTUs is illustrated in Figure 4, indicating that the phylum Proteobacteria exhibited the highest OTU richness. The bacterial OTU with the highest number of reads was detected in 6 of the 18 samples analyzed, while a single bacterial OTU was consistently identified in each sample. In addition, 975 OTUs (39.6%) were identified exclusively in individual samples.

A PCA including CO₂, CH₄, and H₂S concentrations (Figure 4) confirmed that CO₂ is the dominant factor explaining gas variability among samples. PC1 (65.7% of total variance) was strongly correlated with CO₂ and separated low-, intermediate-, and high-CO₂ soils. PC2 (33.2%) was associated mainly with CH₄, distinguishing sample P16 with the highest methane concentration. H₂S contributed negligibly, consistent with its low concentrations (<10 ppm).

The taxon richness (number of OTUs) for bacterial communities within each CO₂ concentration group is shown in Figure 5. The composition and variability of soil bacterial communities between samples are shown in Figure 6. Samples with similar community compositions tended to form distinct clusters. Considerable variability in both bacterial abundance and community composition was observed between samples. In particular, sample P16, characterized by the highest methane concentration (8271.5 ppm), represented an outlier with a significantly increased relative abundance of the bacterial classes Actinomycetia and Planctomycetes compared to other samples.

Analyzing the distribution of bacterial classes in relation to CO₂ concentration groups revealed specific trends. The classes Acidobacteriae, Actinomycetia and Bacteroidia increased in relative abundance with increasing CO₂ concentrations, while the classes Gammaproteobacteria, Alphaproteobacteria and Verrucomicrobiae showed a decreasing trend under the same conditions. However, substantial variability within the group was observed. Given this variability and the limited sample size, these trends should be interpreted with caution and further research with additional samples is needed to confirm the observed patterns. The unique microbial composition of sample P16 highlights the potential influence of elevated methane levels on bacterial community structure (Figure 7 and Figure 8).

Alpha diversity profiling was performed using data filtered at the taxonomy level at the trait level, with the three CO₂ concentration groups defined as experimental factors. Chao1 and Fisher diversity indices were used, with statistical comparisons performed using Welch’s T/ANOVA test and pairwise post hoc tests. Specifically, samples with CO₂ concentrations below 1000 ppm generally showed higher bacterial diversity, while samples exceeding 3000 ppm showed significantly lower diversity. Intermediate CO₂ concentrations (1000–3000 ppm) showed a wide range of diversity values, indicating that this CO₂ range may correspond to diverse ecological niches that support varying levels of bacterial diversity (Figure 8, Figure 9 and Figure 10).

Beta diversity analyses further supported the influence of gas composition on bacterial communities. A Principal Coordinates Analysis (PCoA) based on Bray–Curtis dissimilarities (Figure 11) showed that high-CO₂ soils (>3000 ppm) tended to cluster separately from low-CO₂ soils (<1000 ppm), while intermediate soils (1000–3000 ppm) displayed heterogeneous positions, consistent with variable ecological niches. Hierarchical clustering analysis (Ward’s method, Bray–Curtis distance) confirmed these patterns (Figure 12), with most high-CO₂ samples forming a distinct cluster, while sample P16 (characterized by the highest CH₄ concentration) appeared as an outlier. These results indicate that CO₂ is the main driver of microbial community structure, with additional local effects of CH₄ contributing to outlier behavior.

Statistical testing confirmed the influence of CO₂ on microbial diversity and community composition. As shown in

Table 3. Summary of statistical tests (Welch’s ANOVA for alpha diversity; PERMANOVA for beta diversity) with corresponding p-values across CO₂ categories.

Analysis	Test	Result	p-Value
Alpha diversity (Bacteria, Chao1)	Welch’s ANOVA + post hoc	Significant	0.017
Alpha diversity (Bacteria, Fisher)	Welch’s ANOVA + post hoc	Significant	0.023
Alpha diversity (Fungi, Chao1)	Welch’s ANOVA	Not significant	0.11
Alpha diversity (Fungi, Fisher)	Welch’s ANOVA	Not significant	0.09
Beta diversity (Bacteria)	PERMANOVA (Bray–Curtis, 999 permutations)	Significant	0.001
Beta diversity (Fungi)	PERMANOVA (Bray–Curtis, 999 permutations)	Significant (weak)	0.045

, Welch’s ANOVA revealed significant differences in bacterial alpha diversity between low- and high-CO₂ groups (Chao1: p = 0.017; Fisher: p = 0.023). In contrast, fungal alpha diversity differences were not statistically significant (Chao1: p = 0.11; Fisher: p = 0.09). PERMANOVA analysis further supported these results, showing a strong effect of CO₂ category on bacterial community composition (p = 0.001) and a weaker but significant effect for fungi (p = 0.045).

A preliminary correlation analysis suggests that bacterial diversity decreases under high-CO₂ concentrations (>3000 ppm), while the highest richness values occur in soils with <1000 ppm CO₂. Intermediate CO₂ concentrations (1000–3000 ppm) show large variability in community composition, consistent with the existence of heterogeneous ecological niches. Moreover, sample P16, characterized by the highest CH₄ concentration (~8271 ppm), displayed a distinct community structure with increased relative abundances of Actinomycetia and Planctomycetes, suggesting a potential role of CH₄ in shaping microbial assemblages. Although these results indicate that gas composition (CO₂, CH₄) can influence bacterial diversity and community structure, the limited number of samples does not yet allow for robust statistical correlation, and further work with expanded datasets is needed to confirm these trends.

3.1.2. Fungi

In the fungal dataset, OTUs were detected in six phyla of the kingdom Fungi. The average fungal taxa richness per sample, calculated using rarefied data, was 81, ranging from a low of 3 OTUs (sample LAZ-22p-2p-05) to a high of 208 OTUs (sample LAZ-22p-2p-08). Ascomycota was the dominant fungal phylum in terms of OTU richness, while the fungal OTU with the highest number of reads belonged to the order Thelebolales, detected in 18 out of 19 samples. No single fungal OTU was universally present in all samples, and 268 OTUs (44.6%) occurred in only one sample each (Figure 13).

At the class level, Leotiomycetes and Sordariomycetes were the most abundant classes and did not show a clear association with CO₂ concentrations. However, other abundant classes, such as Dothideomycetes (Ascomycota), Mortierellomycetes (Zygomycota), and Tremellomycetes (Basidiomycota), displayed variable responses to CO₂ levels. Dothideomycetes generally increased in relative abundance with higher CO₂, although their distribution was inconsistent between samples. Mortierellomycetes and Tremellomycetes showed a trend of decreasing abundance as CO₂ levels increased, but exhibited significant variability between samples. Thus, none of these classes demonstrated sufficient consistency in abundance between samples to serve as reliable indicators of CO₂ levels (Figure 14, Figure 15 and Figure 16).

Alpha diversity indices (Chao1 and Fisher) for fungi indicated that samples from high-CO₂ environments (>3000 ppm) generally exhibited lower diversity compared to those with CO₂ levels below 3000 ppm. Samples with lower CO₂ displayed a wide range of fungal diversity values, highlighting that fungal communities can vary significantly even under similar CO₂ conditions (Figure 17 and Figure 18).

For fungi, Welch’s ANOVA showed a negative trend in diversity with increasing CO₂, though differences between groups were not statistically significant (Chao1: p = 0.11; Fisher: p = 0.09). However, PERMANOVA detected a weak but significant effect of CO₂ category on fungal community composition (pseudo-F = 1.69, p = 0.045).

Beta diversity analyses revealed that fungal communities also varied along the CO₂ gradient. PCoA based on Bray–Curtis dissimilarities (Figure 19) showed that high-CO₂ soils (>3000 ppm) tended to cluster apart from low-CO₂ soils (<1000 ppm), while intermediate soils (1000–3000 ppm) were more scattered. Hierarchical clustering (Figure 20) confirmed this pattern, with most high-CO₂ samples forming a distinct group. However, the separation was less pronounced than for bacteria, consistent with the higher variability observed in fungal responses.

4. Discussion

4.1. Quantitative Synthesis of the eDNA Set

It should be noted that our analysis is based on environmental DNA (eDNA), which captures both living and non-living DNA traces in the soil. While this provides a comprehensive view of community composition, it does not directly distinguish between metabolically active and inactive organisms.

Soil DNA analysis identified a total of 3064 OTUs in 19 samples: 2463 bacterial OTUs and 601 fungal OTUs. Species-level identifications were more frequent in fungi than in bacteria, a difference attributed to uneven database coverage and higher frequency of ambiguities in bacteria. Among bacteria, Proteobacteria had the highest OTU richness, while Ascomycota dominated among fungi [31,32]. One bacterial OTU was consistently detected in all samples, suggesting ubiquity, while fungal OTUs from the order Thelebolales were present in almost all samples. The mean bacterial richness was 418.8 OTUs/sample (range: 79 OTUs at P6—745 OTUs at P9).

The beta diversity analyses indicate that fungal communities also shift along the CO₂ gradient, although the separation between groups was less pronounced than for bacteria. High-CO₂ soils (>3000 ppm) tended to cluster together, while intermediate soils (1000–3000 ppm) displayed high variability, suggesting that CO₂-enriched environments support diverse fungal responses depending on local conditions. This is consistent with observations from natural analog sites, where fungi show both tolerance and sensitivity to CO₂ exposure. For example, Thelebolus species are frequently reported as dominant in high-CO₂ mofettas, reflecting adaptation to acidic, CO₂-rich soils [33,34], whereas genera such as Mortierella tend to decline in abundance under elevated CO₂ [31]. The weaker clustering observed for fungi compared to bacteria therefore highlights their greater ecological plasticity, with some taxa persisting under stressful conditions while others are excluded.

4.2. Geochemical Context

Field measurements show that CO₂ is the dominant gas at the site scale, while CH₄ and H₂S appear mainly as local, spatially heterogeneous peaks. According to Table 2, the maxima in the set of 19 samples are: CO₂ = 46,220.71 ppm at P7 (~4.62%), CH₄ = 8271.47 ppm at P16 (~0.83%), H₂S = 10.12 ppm at P7.

Methane concentrations at Băile Lăzărești were generally lower, but local peaks were recorded (e.g., up to ~0.83% in sample P16), a pattern compatible with the influence of local ecological conditions (e.g., peaty/marshy microhabitats), also reported in studies from areas with natural CO₂ degassing, such as those investigated by [31,35]).

Hydrogen sulfide. H₂S levels were relatively low throughout the site (maximum ~10.12 ppm at P7), consistent with observations from other analogous degassing sites where H₂S occurs at much lower concentrations than CO₂ [35,36]. Variability in gas composition is plausible to contribute to differential effects on the structure and diversity of microbial communities.

4.3. Distribution of Gases by CO₂ Category (C1/C2/C3)

The samples were classified into three categories based on CO₂ concentration: C1 < 1000 ppm, C2 = 1000–3000 ppm, C3 > 3000 ppm. Based on the CH₄–H₂S–CO₂ sums in Table 2, the aggregate composition by category is:

• C1 (n = 7): CO₂ ≈ 99.66%, CH₄ ≈ 0.27%, H₂S ≈ 0.06%;
• C2 (n = 6): CO₂ ≈ 54.13%, CH₄ ≈ 45.85%, H₂S ≈ 0.02%;
• C3 (n = 6): CO₂ ≈ 99.34%, CH₄ ≈ 0.64%, H₂S ≈ 0.02%.

This shows that the extremes of the gradient (C1 and C3) are clearly dominated by CO₂, while the intermediate zone (C2) includes CH₄-rich micro-niches, strongly influenced by P16.

The choice of three CO₂ concentration categories was guided by ecological relevance and sample distribution. Concentrations below 1000 ppm (C1) represent near-background conditions, where microbial diversity is expected to be least affected. The 1000–3000 ppm interval (C2) captures intermediate degassing environments, often characterized by high spatial variability and the coexistence of micro-niches. Concentrations above 3000 ppm (C3) correspond to strongly degassing mofetta zones, where gas exposure is sufficiently high to induce stress responses in soil biota. These thresholds also provided a balanced number of samples per category, enabling comparative analyses of microbial community structure along the CO₂ gradient.

4.4. Gas–Bacteria Relationship

At the bacterial level, Proteobacteria remains the most abundant phylum in terms of OTU richness, but community structure reconfigures along the CO₂ gradient: classes such as Acidobacteriae, Actinomycetia and Bacteroidia increase their relative abundance with increasing CO₂, while Gammaproteobacteria, Alphaproteobacteria and Verrucomicrobiae tend to decrease. P16 represents a special case: high CH₄ concentration is associated with a relative increase in Actinomycetia and Planctomycetes taxa, suggesting that electron availability from methane may regulate community composition in anoxic or poorly oxygenated micro-niches.

At the genus level, several taxa showed consistent responses to the CO₂ gradient. Acidobacterium and Granulicella increased under high CO₂, reflecting their preference for acidic, low-nutrient soils [37,38]. Streptomyces and Nocardia (Actinomycetia) were enriched, consistent with their resilience and ability to decompose complex organic matter under stress [39]). Flavobacterium and Chitinophaga (Bacteroidetes) were more abundant in CO₂-rich soils, suggesting adaptation to altered carbon cycling and reduced oxygen availability [40]. In contrast, Rhizobium and Pseudomonas (Proteobacteria) declined at high CO₂, in line with their sensitivity to low pH and oxygen depletion [33,35].

Alpha diversity profiling (Chao1, Fisher) confirms the decrease in bacterial richness towards high CO₂ values (>3000 ppm), with pronounced variability in the intermediate range (1000–3000 ppm). This variability is plausible in a mosaic of geochemical conditions (diffuse fluxes vs. point emanations, moisture, pH, organic carbon availability), which can support contrasting ecological niches within the same CO₂ class.

Overall, the variability of bacterial composition is accentuated, with P16 (CH₄ peak) as an outlier, where Actinomycetia and Planctomycetes increase relatively. Along the CO₂ gradient, the same general trends are observed: Acidobacteriae, Actinomycetia and Bacteroidia increase with CO₂, while Gammaproteobacteria, Alphaproteobacteria and Verrucomicrobiae decrease (in the context of high intra-group variability and limited sampling). Alpha bacterial diversity is, on average, higher below 1000 ppm CO₂ and lower above 3000 ppm, and the range 1000–3000 ppm shows a wide range of responses (diverse micro-niches).

The observed decrease in bacterial diversity with increasing CO₂ and the distinct taxonomic profile in the CH₄-rich sample (P16) support the idea that gas composition exerts a selective pressure on microbial communities. Elevated CO₂ appears to reduce overall diversity, whereas localized CH₄ enrichment may create ecological niches favoring taxa such as Actinomycetia and Planctomycetes. These findings align with previous studies linking gas flux heterogeneity to shifts in microbial composition in volcanic and post-volcanic environments. While correlations in our dataset remain preliminary, they highlight the importance of integrating geochemical and microbial data to better understand ecosystem responses to gas seepage.

Correlation analysis (Spearman’s rank) confirmed a significant negative relationship between CO₂ concentration and bacterial alpha diversity indices (Chao1: ρ = −0.58, p < 0.05; Fisher: ρ = −0.62, p < 0.05). This quantitative evidence supports the observed decrease in bacterial richness with increasing CO₂ levels.

4.5. Gas–Fungi Relationship

In the fungal set, Ascomycota dominates at the phylum level, and Thelebolales occurs in most samples (almost ubiquitously), suggesting a broad tolerance to acidic and CO₂-rich conditions. At the class level, Leotiomycetes and Sordariomycetes are not clearly related to CO₂ levels, while Dothideomycetes tend to increase with CO₂, and Mortierellomycetes and Tremellomycetes tend to decrease; however, these patterns are not consistent enough to serve as robust bioindicators of CO₂ levels.

Fungal responses to CO₂ enrichment were heterogeneous at the genus level. Thelebolus (Thelebolales) was nearly ubiquitous, consistent with its adaptation to acidic, CO₂-rich environments [33,34]. Mortierella decreased in high-CO₂ soils, reflecting its sensitivity to pH and oxygen limitation [31]. Cladosporium showed tolerance and even enrichment under elevated CO₂, likely due to its ecological plasticity [32]. In contrast, yeast genera such as Cryptococcus and Vishniacozyma declined, reflecting their dependence on aerobic niches [41].

The dominance of Ascomycota and the prevalence of the order Thelebolales, consistent with other volcanic regions [33] (wet skunk), [34] (Hartoušov, Eger Rift)), indicate that these taxa are specifically adapted to high-CO₂ environments. However, fungal diversity was considerably lower than that reported by Zhao et al. [31] in similar areas with high CO₂ flux. This discrepancy may result from local variations in environmental conditions, such as moisture content, temperature regimes, and spatial heterogeneity of gas emissions, all of which influence fungal diversity.

Similarly to bacteria, fungal alpha diversity decreases in the high-CO₂ zone (>3000 ppm), while at low CO₂ a wide range of values is observed, highlighting the sensitivity of fungal communities to fine-spatial environmental heterogeneity. Overall, the mean fungal richness is 81 OTU/sample (range 3–208 OTU).

The CO₂ concentrations recorded at Băile Lăzărești, reaching ~4.62% v/v (i.e., 46,220.71 ppm in sample P7) near the wet mofettas, significantly exceed levels typically reported under similar volcanic conditions [42,43,44,45,46]. Such high concentrations substantially influence microbial communities, reducing their diversity and altering community composition, as documented in various studies exploring high-CO₂ environments [31,32,33,34,41].

For fungi, the correlation between CO₂ concentration and alpha diversity indices was weaker (Chao1: ρ = −0.41, p = 0.08; Fisher: ρ = −0.39, p = 0.09), indicating a negative but less robust trend compared to bacteria. These results suggest that fungal communities are also sensitive to elevated CO₂, though the strength of the relationship appears lower than in bacterial assemblages (

Table 4. Examples of bacterial and fungal genera detected in the study and their responses to CO₂ enrichment, with supporting references. Symbols and abbreviations: ↑ = increased/relative enrichment under high CO₂; ↓ = decreased/relative depletion under high CO₂. OM = organic matter. C-rich = carbon-rich. Anoxic = low-oxygen conditions. Mofetta = natural cold CO₂ emission site.

Domain	Genus	Trend Under High CO2	Reference(s)
Bacteria	Acidobacterium, Granulicella	↑ Enriched in acidic, CO2-rich soils	[37,38]
Bacteria	Streptomyces, Nocardia	↑ Tolerant, degrade complex OM	[39]
Bacteria	Flavobacterium, Chitinophaga	↑ Adapted to C-rich, anoxic niches	[40]
Bacteria	Rhizobium, Pseudomonas	↓ Sensitive to acidity/anoxia	[33,35]
Fungi	Thelebolus	↑ Ubiquitous in mofettas	[33,34]
Fungi	Mortierella	↓ Reduced under high CO2	[31]
Fungi	Cladosporium	↑ Tolerant, stress-adapted	[32]
Fungi	Cryptococcus, Vishniacozyma	↓ Sensitive, aerobic niches	[41]

).

4.6. Statistical Significance of Diversity Patterns

The statistical analyses reinforce the interpretation that CO₂ exerts a major influence on microbial communities at Băile Lăzărești. Welch’s ANOVA showed significant reductions in bacterial alpha diversity between low- and high-CO₂ soils (Chao1: p = 0.017; Fisher: p = 0.023), consistent with findings from other natural analog sites where prolonged CO₂ exposure reduces bacterial richness [33,35]. In contrast, fungal alpha diversity did not differ significantly between groups (p > 0.09), although a general decreasing trend was observed at high CO₂, in line with reports from mofetta soils in Italy and China where fungal responses were more variable [31,34]. PERMANOVA results demonstrated a significant effect of CO₂ concentration on bacterial community composition (p = 0.001), and a weaker but still significant effect on fungi (p = 0.045). This suggests that bacterial communities are more tightly structured by CO₂ gradients, while fungi show greater ecological plasticity and higher variability in their responses.

4.7. Site-Scale Ecological Implications

CO₂ migration along N–S oriented fractures is accompanied by vegetation stress/absence and mortality of small fauna (insects, rodents), signaling hostile conditions that reduce diversity and remodel microbial communities (Figure 21). These observations are consistent with the literature from other volcanic regions and support the use of natural sites as analogs for assessing risks in CO₂ leakage scenarios associated with geological storage [47].

Vegetation sparseness and plant stress observed at Băile Lăzărești coincide with zones of elevated CO₂ flux (>30,000 ppm), consistent with reports from natural analog sites where soil gas emissions suppress plant growth or cause complete absence of vegetation [6,32,46,48,49,50,51,52]. While gas emissions represent the primary stressor, other factors such as soil characteristics, seasonal humidity, or local hydrology may modulate vegetation responses. This interplay of drivers underscores the importance of integrated monitoring (gas flux, soil chemistry, vegetation surveys) for understanding ecosystem-level effects of CO₂ leakage.

We compared vegetation responses at Băile Lăzărești with those documented at several well-studied natural analog sites. As summarized in

Table 5. Summarizing CO₂ fluxes and vegetation responses at several natural analog sites.

Site (Reference)	Max CO2 Flux/Conc.	Vegetation Response
Latera, Italy [6]	>2000–3000 g m−2 d−1, >95% soil CO2	No vegetation in vent core; transition zone with grasses/clover
Laacher See, Germany [47,48]	Up to 95% soil CO2	Vegetation absent directly above vents
Florina, Greece [49]	Localized vents with high soil CO2 (>20%)	Grassland degraded; plant diversity reduced near vents
ASGARD, UK [50]	Injected 2–50% soil CO2	Crop stress, reduced biomass at >15% CO2
Australia [51]	Fluxes >10,000 g m−2 d−1	Vegetation death in high flux zones
Daepyeong, Korea [46]	29% soil CO2‚ in high flux group	Lower pH soils, vegetation stress and reduced cover

, high CO₂ fluxes (>2000–3000 g m⁻² d⁻¹ or >20% soil CO₂) consistently coincide with vegetation absence or stress, while transition zones with lower CO₂ concentrations often support grasses or mixed plant communities. These cross-site comparisons indicate that although soil properties, humidity, and local ecological factors can modulate vegetation cover, elevated CO₂ remains the dominant driver of vegetation sparseness in degassing areas [6,46,48,49,50,51].

4.8. Integration of Tracers

Together, the results paint a coherent picture: (i) CO₂ shapes microbial communities along the entire gradient; (ii) CH₄ gains weight in the intermediate zone (C2), generating distinct micro-niches (e.g., P16) with specific bacterial fingerprints; (iii) H₂S has a limited impact at the study scale; (iv) the decrease in diversity at high CO₂ implies possible effects on soil functions (C and nutrient cycling, organic matter decomposition)—more evident in fungi—relevant both for risk assessment in natural analogs of CO₂ leakage and, by extension, for the context of geological CO₂ storage.

The dominance of CO₂ at the ends of the gradient and the pronounced methane signature in C2 together explain the observed compositional and diversity changes. The results support the combined use of gas monitoring and metabarcoding as complementary tools for the ecological diagnosis of post-volcanic degassing areas and for establishing useful benchmarks in CO₂ leakage monitoring programs associated with geological CO₂ storage. These results align closely with findings from other volcanic regions [35,46], reinforcing the idea that sustained high CO₂ emissions create inhospitable conditions that lead to significant biodiversity losses.

The PCA including CO₂, CH₄, and H₂S concentrations (Figure Q) confirmed the dominant role of CO₂ as the main structuring factor across the study site, explaining 65.7% of the variance (PC1). CH₄ accounted for 33.2% of the variance (PC2) and highlighted local micro-niches such as sample P16, where elevated methane concentrations strongly influenced bacterial community structure. In contrast, H₂S contributed negligibly, consistent with its low absolute concentrations (<10 ppm) and limited spatial distribution. These results reinforce the decision to use CO₂ as the primary grouping variable, while acknowledging the secondary role of CH₄ in shaping outlier community responses. Together, the combined gas analyses confirm that the observed microbial community patterns are primarily controlled by the CO₂ gradient, with localized contributions from CH₄.

Given the large surface extent of the mofetta field (>1 ha, with several hundred square meters of active vents), low-emission soils within the area were used as controls. Selecting an external reference site with no gas influence would have required moving far away from the studied degassing zone, thereby introducing confounding differences in geology, soil type, and ecological conditions. For this reason, our design focused on internal contrasts along the CO₂ gradient, while future studies could expand to include more distant reference sites for comparison.

5. Conclusions

This study provides an integrated assessment of the spatial distribution and ecological impact of post-volcanic gas emissions (CO₂, CH₄, H₂S) at Băile Lăzărești. Gas measurements revealed substantial variability, with CO₂ dominating at the site scale (up to 46,220.71 ppm, ~4.62% v/v at P7), while CH₄ reached localized maxima of 8271.47 ppm (~0.83% v/v at P16) and H₂S remained low (10.12 ppm at P7). These spatially heterogeneous peaks reflect the influence of gas migration along fractures and microhabitat conditions. Soil eDNA metabarcoding identified 3064 OTUs in 19 samples (2463 bacterial and 601 fungal). Proteobacteria were the most diverse bacterial phylum, while Ascomycota dominated fungi. Thelebolus (Thelebolales) was nearly ubiquitous, indicating broad tolerance to acidic and CO₂-rich conditions. Species-level identifications were more frequent in fungi than in bacteria, reflecting uneven database coverage. Alpha diversity analyses (Welch’s ANOVA) demonstrated that bacterial richness (Chao1, Fisher indices) was significantly lower in high-CO₂ soils (>3000 ppm; p < 0.05) compared to low-CO₂ soils (<1000 ppm), with intermediate CO₂ concentrations showing heterogeneous responses. Fungal alpha diversity showed a decreasing trend at high CO₂ but without statistical significance. Beta diversity (PERMANOVA) confirmed significant differences in bacterial (p = 0.001) and fungal (p = 0.045) community composition between CO₂ categories. Taxonomic patterns revealed that bacterial genera such as Acidobacterium, Granulicella, Streptomyces, Nocardia, Flavobacterium, and Chitinophaga were enriched under high CO₂, whereas Rhizobium and Pseudomonas declined, reflecting sensitivity to acidity and oxygen limitation. Among fungi, Thelebolus and Cladosporium tolerated CO₂-rich soils, while Mortierella, Cryptococcus, and Vishniacozyma decreased, highlighting contrasting adaptive strategies. Sample P16, characterized by a methane peak, was an ecological outlier with distinct bacterial signatures (increased Actinomycetia and Planctomycetes). Multivariate analysis (PCA) including CO₂, CH₄, and H₂S concentrations confirmed that CO₂ is the dominant environmental driver (PC1, 65.7% variance), while CH₄ contributed to localized variability (PC2, 33.2%), particularly in sample P16. H₂S had negligible influence.

Overall, our findings show that post-volcanic gas emissions restructure microbial communities, reducing diversity at high CO₂ and establishing distinct ecological niches that can alter soil processes such as nutrient cycling and organic matter decomposition. These results emphasize the ecological risks of sustained CO₂ exposure and highlight the value of natural analog sites for assessing the potential impacts of CO₂ leakage in geological storage scenarios.