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Article

Distribution Patterns and Diversity of Sedimental Microbial Communities in the Tianxiu Hydrothermal Field of Carlsberg Ridge

by
Fangru Li
1,
Xiaolei Liu
1,
Weiguo Hou
1,*,
Hailiang Dong
1,*,
Jinglong Hu
1,
Hongyu Chen
1,
Yi Ding
2,
Yuehong Wu
2 and
Xuewei Xu
2
1
Center for Geomicrobiology and Biogeochemistry Research, State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Beijing 100083, China
2
Key Laboratory of Marine Ecosystem Dynamics, Ministry of Natural Resources and Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
*
Authors to whom correspondence should be addressed.
Oceans 2025, 6(4), 61; https://doi.org/10.3390/oceans6040061
Submission received: 12 July 2025 / Revised: 15 September 2025 / Accepted: 18 September 2025 / Published: 24 September 2025
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Abstract

Hydrothermal vents, widely occurring along middle-ocean ridges and volcanic arcs, have been well-studied in vent-associated microbiology, mineralogy, and geochemistry. However, there are rarely investigations regarding the detailed microbial community in the hydrothermal vent-influenced sediment. To explore hydrothermal activities on microbial diversity at the Carlsberg Ridge in the northwestern Indian Ocean, four sediment cores were sampled from the near-vent fields to distant vent sedimentary fields in the Tianxiu hydrothermal field, and the microbial community compositions were analyzed. The sediment microorganisms closest to the hydrothermal vent were primarily composed of Acidimicrobiia, Gammaproteobacteria, Anaerolineae, and Planctomycetes. The microbial communities at the depth containing extensive signals of hydrothermal activity consisted mainly of Dehalococcoidia, Aerophoria, Anaerolineae, and Gammaproteobacteria. No significant differences in microbial composition were observed between the two weak hydrothermal sediment cores, primarily composed of Nitrososphaeria, Gammaproteobacteria, Alphaproteobacteria, and Acidimicrobiia. Moreover, heterogeneous selection substantially impacted the bacterial community assembly in near-vent sediments other than stochasticity. Multivariate statistical analysis identified that environmental fluctuations accounted for 55.59% of the community variation, with hydrothermal inputs (such as Fe, Pb, Cu, and Zn) being the primary factors shaping the construction of hydrothermal sediment microbial communities. These results enhance understanding of the response of deep-sea sediments to hydrothermal activity.

1. Introduction

Deep-sea hydrothermal environments, an example of extreme habitats, exhibit pronounced environmental gradients in temperature, pH, redox potential, and chemical composition [1,2]. These distinct environmental gradients give rise to unique microbial populations within hydrothermal systems [3,4]. With the discovery of an increasing number of hydrothermal areas, microbial communities inhabiting various niches of hydrothermal vents have been extensively explored using samples from hydrothermal chimneys, plumes, and surrounding water [1]. These include the 9°50′ N hydrothermal plume on the East Pacific Rise (EPR) [5], the Rainbow hydrothermal vent plume at the Mid-Atlantic Ridge [6], the Juan de Fuca Ridge [7], Guaymas Basin [8,9], and hydrothermal plumes across the Eastern Lau Spreading Center [10]. Previous research in Indian Ocean hydrothermal systems has predominantly concentrated on the Southwest Indian Ridge (SWIR) and Central Indian Ridge (CIR) [11,12]. The microbial populations in the Longqi hydrothermal field, located near the vent orifice of SWIR, were primarily composed of Aquificota and Campylobacterota [13], which were similar to those in hydrothermal fields from the Pacific Ocean. However, other microbial communities from the Indian Ocean, such as from the CIR with low abundances of Aquificota and Campylobacterota, exhibited extensive differences from the other hydrothermal microbial communities [14,15,16]. Microbial communities are mainly shaped by physical and geochemical parameters, as well as geologic settings [17,18,19].
Microbial communities display significant variations depending on the level of hydrothermal activity [4,20]. Recently, Hou et al. (2020) revealed that the microbial community in active sulfide chimneys predominantly consists of Campylobacteria, Aquificae, and Gammaproteobacteria, while inactive chimneys are mainly colonized by Deltaproteobacteria, Alphaproteobacteria, Betaproteobacteria, and Bacteroidetes [21]. The cessation of hydrothermal activity leads to the disappearance of previously available thermal gradients and energy, resulting in a significant shift in the microbial communities residing in inactive chimneys [22]. In addition, several studies elucidated the distribution patterns of microbial communities along a hydrothermal plume to surrounding seawater, which was also related to the chemical gradient referring to the energy supply [23,24,25,26,27].
Hydrothermal sediment receives varying volumes of plume materials, resulting in variations in the spatial and temporal distribution of microbial communities compared to those in the surrounding normal marine sediment [28]. For example, the sediment cores from the inactive Tianzuo vent field of SWIR harbored high abundances of Gammaproteobacteria, Nitrosococcus, and the JTB255 marine benthic group were the most prevalent bacterial taxa [3]. Meanwhile, the sediment samples from the Invent E and Onnuri Vent Field of CIR were dominated by Proteobacteria, Firmicutes, Bacteroidetes, and Euryarchaeota [29]. However, it is unknown how the hydrothermal activities influence the microbial community. In this study, we characterized microbial community structures in the sediments of the Tianxiu vent to explore the distribution of microorganisms as a function of distance from the vent. In particular, we assessed the effect of hydrothermal input on the microbial community. This research contributes to a deeper understanding of microbial communities inhabiting slow-to-intermediate-spreading hydrothermal systems.

2. Materials and Methods

2.1. Description of the Study Area

The Carlsberg Ridge (CR) in the Northwestern Indian Ocean is a slowly spreading ocean ridge, and Wocan, Tianxiu, Daxi, and Tianshi hydrothermal fields have been discovered [30,31,32,33]. The Tianxiu vent field is the first ultramafic-hosted hydrothermal site discovered on the CR [24,31], and previous studies have described the mineralogical, geochemical characteristics, and hydrothermal activity in detail [24,34,35,36].

2.2. Sampling and Geochemical Analysis

One 10 cm long box corer and three 30 cm long push-corers of sediment were collected from the Tianxiu hydrothermal field in Carlsberg Ridge of the Indian Ocean by “JiaoLong” Manned Submersible during the Chinese DY72 cruise in 2022. The four sediment cores, collected at different distances from the vent and at water depths of 3400–3640 m, were categorized into two groups (strong hydrothermal and weak hydrothermal), depending on the presence or absence of hydrothermal signals: strong hydrothermal influenced sediment cores (BC12 and JL218.H) and regular marine sediment cores (JL218.P and JL219) (Table 1, Figure 1 and Figure S1). The hydrothermal influenced sediment cores were reddish brown to dark brown in color and coarse particle size layers, while JL218.P and JL219 were mostly lighter colored fine-grained. The cores were subsampled at 1 cm intervals in an anaerobic chamber (Coy Laboratory Products, Grass Lake, MI, USA) of the laboratory. A total of 72 biological subsamples were taken from the inside of the core to avoid contamination from the edge of the pipe wall with sterilized spoons and preserved in sterile sampling bags (Table 1). Immediately, these subsamples were stored at −80 °C until further processing.
The pH of the sediments was measured using a portable pH meter (CLEAN PH200, Shanghai, China). Each sediment subsample was freeze-dried and ground to fine powder (<200 mesh) using an agate mortar. The subsamples were leached with an HF-HNO3 mixture, and elemental analyses of the ground sediment subsamples (major elements (Al2O3, CaO, Fe2O3, MgO, MnO, and TiO2), trace elements (V, Cr, Co, Ni, Cu, Zn, Mo, Pb, and U), and rare earth elements (La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu)) were performed using ICP-AES, ICP-MS at Tongji University. The analysis process included five standards of certified materials (BHVO-2, W2, GSP2, GSR-6, and GSD-9), along with duplicate and blank samples. The total carbon (TC) and total organic carbon (TOC) contents in freeze-dried samples were measured using an Analytik-Jena multi N/C 2100s series analyzer (Analytik Jena GmbH, Jena, Germany). The concentrations of SO42− and NO3− of the porewater, retrieved with a rhizon sampler [37], were analyzed using ion chromatography (Thermo ICS-600, Waltham, MA, USA).

2.3. DNA Extraction, PCR Amplification, and Sequencing

Total genomic DNA was extracted from each 0.5 g sediment subsample using the FastDNATM SPIN Kit for Soil (MP Biomedicals, Solon, OH, USA) according to the manufacturer’s protocol. The primer set 515F (GTGYCAGCMGCCGCGGTAA)/806R (GGACTACNVGGGTWTCTAAT), ligated with asymmetric barcode sequences, was used for 16S rRNA gene amplification. The gene amplification process was conducted in a 50 μL reaction system containing 5 μL of 10× Ex Taq buffer (20 mM), 4 μL of dNTP mixture (2.5 mM), 1 μL of each primer (final concentration 1 μM), 0.6 μL of Ex Taq HS (Hot Start), 1 μL of template DNA, and 37.4 μL of ddH2O. The PCR parameters were set as follows: 95 °C for 3 min, followed by 35 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 45 s, with a final extension step at 72 °C for 10 min. The PCR products were then subjected to electrophoresis through a 0.8% agarose gel, followed by purification using the Gel Extraction Kit (Omega, Norcross, GA, USA) for library preparation. Sequencing was performed on an Illumina Novaseq platform at Magigene Biotechnology Co., Ltd. (Guangzhou, China).

2.4. Bioinformatics and Statistical Analyses

The 16S rRNA gene sequencing data were processed using Quantitative Insights Into Microbial Ecology 2 (QIIME2) tools [38]. Briefly, the raw paired-end sequences were demultiplexed by the demux plugin, and primers were trimmed with the Cutadapt plugin [39]. The sequences were quality-filtered, denoised, chimera-checked, and clustered into amplicon sequence variants (ASVs) using the DADA2 software package (version 1.37.0) implemented in R [40]. Taxonomic assignment of representative sequences for each ASV was carried out via the Ribosomal Database Project classifier (RDP) [41] based on the SILVA database (version 138.1) via an accessible Galaxy Pipeline (https://dmap.denglab.org.cn, accessed on 18 March 2023). After taxonomic classification, taxa identified as mitochondria, chloroplasts, or those classified only to the domain level as “Bacteria;” or “Archaea;” were filtered out from the ASV table using the taxa filter-table function in QIIME2.
After filtering, the ASV table was rarefied to a minimum sequencing depth of 126,000 reads per sample to standardize the data for downstream analyses. The alpha diversity of the communities was evaluated by calculating the Ace, Richness, Shannon, and Simpson diversity indices. The differences among samples were compared by the Kruskal–Wallis test. Beta diversity was calculated using a Bray–Curtis distance matrix at the class level. These analyses were carried out using the R project (v 4.2.3, Vienna, Austria) with the Vegan package (v.2.6-10., available at https://cran.r-project.org/web/packages/vegan/index.html, accessed on 18 March 2023). Principal coordinates analysis (PCoA) based on the Bray–Curtis dissimilarity index was visualized to evaluate the beta diversity among different samples. Multiple regression permutation procedure (MRPP), analysis of similarities (ANOSIM), and permutational multivariate analysis of variance (PERMANOVA) were used to determine statistically significant differences in community structure among different samples. Pairwise geographic distance and environmental distance were analyzed using the vegan and geosphere (v1.5-20) packages [42,43]. Mantel test (999 iterations) was utilized to assess the fit of distance–decay patterns to Bray–Curtis dissimilarities. Non-metric multidimensional scaling (NMDS) was conducted to examine the relationships between microbial communities and key environmental factors. Redundant variables were eliminated by functions of envfit (permutations = 999). A variance partitioning analysis (VPA) was performed to quantify the relative contributions of environmental factors and geographic distance to differences in microbial community structure using the “vegan” packages [44]. To evaluate the relative importance of variables in shaping microbial communities, a random forest model was constructed using the “randomForest (v4.7-1.2) ” and “ggplot2 (v4.0.0) ” packages [45]. The functional potential of the microbial community was assessed by using FAPROTAX (v1.2.11) based on 16S rRNA data [46].
The neutral community model (NCM) was utilized to discern the contribution of stochastic processes to microbial community assembly [47]. Within this model, “Rsqr” signifies the goodness of fit, and “Nm” indicates the metacommunity size times immigration and denotes the correlation between a taxon’s relative abundance and its occurrence frequency.
To quantify the impact of assembly processes on bacterial community structure, we measured variations in both phylogenetic diversity and taxonomic diversity using null model-based phylogenetic and taxonomic β-diversity metrics: the β-nearest taxon index (βNTI) and Bray–Curtis-based Raup–Crick (RCbray) [48,49]. A βNTI < −2 indicates that homogeneous selection increases the community’s phylogenetic similarity, while a βNTI > +2 suggests that heterogeneous selection is dominant. We then defined homogeneous dispersal as |βNTI| < 2 and RCbray < –0.95. Dispersal limitation was quantified as the proportion of pairwise comparisons with |βNTI| < 2 and RCbray > 0.95. The proportions of all pairwise comparisons with |βNTI| < 2 and |RCbray| < 0.95 were used to estimate the influence of “undominated” assembly, which primarily consisted of weak selection, weak dispersal, diversification, and drift. All of the above analyses were performed using the Tutools platform (https://www.cloudtutu.com, accessed on 30 March 2023).

3. Results

3.1. Geochemical Characterization of the Sediments

A total of twenty geochemical parameters were derived from all sediment subsamples. Notably, these parameters demonstrated variation across the different sites (Figure 2A). The subsamples in this study exhibited a wide range of pH (6.5–8.6), SO42− (20.95–70.34 mM), and NO3− (0–37.45 mM). The hydrothermal sediments BC12 and JL218.H (the lower layers) were enriched in Fe, Cu, Mg, Zn, Co, Ni, U, and Cr but depleted in Al, Ca, and Ti. The far-vent sediments (JL218.P and JL219) were more enriched in Ca and TC. Spearman correlation analysis revealed significantly positive correlations between any pairs of hydrothermally derived elements (Fe, V, CO, Cu, Zn, Mo, Pb, and U) (p < 0.05) (Figure 2B). Meanwhile, the totals rare earth element (REE) contents of BC12, JL218.H, JL218.P, and JL219 sediment cores demonstrated a progressive increase. In particular, the near-vent sediments (BC12 and JL218.H) were characterized by significantly lower ΣREE contents and strong positive δEu than the sediment cores (JL218.P and JL219) (Table 2). Overall, the BC12 and JL218.H (especially the lower layer) sediments received more hydrothermal-derived metal input (Figure S2 and Table S1).

3.2. Microbial Community Diversity and Structures

Following the amplification and sequencing of the microbial 16S rRNA genes, a total of 126,000 sequences were randomly resampled for each sediment subsample. To evaluate the diversity of bacterial communities across various subsamples, we calculated the ACE, Simpson, and Shannon indices (Figure 3 and Table S2). The ACE species richness and Shannon index differed slightly among the four sites. The alpha diversity indices (ACE, Shannon, and Simpson) of two strong hydrothermal-influenced sediment cores (BC12 and JL218.H) were lower than those of the weak hydrothermal sediments (JL218.P and JL219). However, no difference was observed in Shannon and Richness indices between JL218.P and JL219. The Shannon, Simpson, and Niche width indices of JL218.P and JL219 did not show any significant variations along the length of the cores, as evidenced by tight groupings of all subsamples (Figure 3B–D).
The microbial community composition varied among the four cores (Figure 4). At the class level, BC12 was dominated by Acidimicrobiia, followed by Gammaproteobacteria, Anaerolineae, and Planctomycetes. Gammaproteobacteria, Dehalococcoidia, Acidimicrobiia, Alphaproteobacteria, Anaerolineae, and Aerophobia dominated the core of JL218.H. For the weak hydrothermal-influenced sediment cores, the community composition between JL218.P and JL219 exhibited minimal variation, primarily dominated by Nitrosophaeria, Gammaproteobacteria, Alphaproteobacteria, Acidimicrobia, and Dehalococcoidia. At the same time, the abundances of Anaerolineae, Aerophobia, and Planctomycetes in JL218.P and JL219 were low relative to those in the strongly hydrothermal-influenced sediment cores (BC12 and JL218.H). Notably, the dominant species in the subsamples exhibited apparent differences in the vertical variations in the cores. The upper layers (1–3 cm) showed a higher abundance of Nitrososphaeria (7.8~18.8%) compared to the lower layers, while the middle layer (4–7 cm) had a greater abundance of Acidimicrobiia (32.4~37.7%) in the BC12 site. In the JL218.H, the dominant microbial members in the upper layers (1–13 cm) were Gammaproteobacteria, Acidimimicrobia, Nitrosophaeria, Alphaproteobacteria, etc., while Dehalococcoidia, Aerophoria, Anaerolineae, and Americentenia dominated the lower layers (14–24 cm). There were little vertical variations in the dominant species for the weak hydrothermal-influenced sediment cores of JL218.P and JL219. JL219, the farthest core from the vent, exhibited higher abundances of common marine microorganisms such as Vicinamibacteria, Phycisphaerae, Nitrospiria, and BD2-11_terrestrial_group. Meanwhile, we conducted a functional prediction of the microbial communities using the FAPROTAX database to assess the potential putative metabolic functions of sediment microorganisms across different sampling sites. The results demonstrated substantial spatial heterogeneity in the metabolic functional profiles of sediment microbiota, with pronounced variations observed in key biogeochemical processes including carbon, nitrogen, and sulfur cycling (Figure S3).
Taxonomic distance was calculated across the four cores. The Bray–Curtis dissimilarity for the two strong hydrothermal-influenced sediment cores (BC12 and JL218.H) was significantly higher than that for the weak hydrothermal-influenced sediment cores (JL218.P and JL219) (Figure 5A). The PCoA analysis was used to explore the dissimilarity of the microbial community compositions across the four cores. The two axes (PCoA1 and PCoA2) together explained 55.22% of the variation, and similar sites (JL218.H and JL218.P) grouped. However, there was no obvious difference in the upper layer of the three stations (JL218.H, JL218.P, and JL219) (Figure 5B). Dissimilarity tests confirmed microbial groupings according to geographic distance, i.e., a substantial difference in microbial community structure between two cores at different locations, but no difference between two adjacent cores (i.e., JL218.H versus JL218.P based on ANOSIM). Overall, the four sites had significant differences in sediment communities (Table S3).

3.3. Environmental Factors Shaping the Community Compositions

The results of the distance–decay relationship revealed that the taxonomic composition dissimilarity (Bray–Curtis distance) of the microbial communities was significantly correlated with physical separation and environmental distance (Euclidean distance) (p < 0.001) (Figure 6A,B). This suggests that microbial communities became increasingly different with distance and environmental conditions. However, Bray–Curtis dissimilarity of microbial communities exhibited a stronger correlation with environmental distance (R = 0.73) than geographic distance (R = 0.32), suggesting that environmental condition was more important in shaping microbial community structure. VPA was carried out to quantify the relative contributions of environmental factors and spatial distance variables, as well as their interactions, to the sedimentary communities. These variables collectively accounted for 38.45% of the observed variation, with environmental factors explaining 12.03% and spatial distance variables explaining 2.94% independently (Figure 5C). The results are consistent, and the influence of environmental factors on community structure variation was significantly greater than that of spatial variables (Figure 6A,B). This implies that the spatiotemporal distribution of microorganisms was predominantly influenced by environmental factors. The RDA analysis revealed that all environmental parameters accounted for up to 46.71% of the overall variance in microbial communities (Figure 6C). U concentration had the greatest control on microbial dissimilarity, followed by Fe2O3, Cu, and V. The random forest model revealed that the U (11.41%), Cu (10.70%), Ni (9.54%), TiO2 (9.29%), and MnO (5.94%) significantly (p < 0.05) explained the variations in microbial diversity (Shannon diversity index) (Figure 6D).

3.4. Assembly Process of Microbial Community Structure

The assembly mechanism of the sediment microbial community across the four sites was examined via NCM (Figure 7A–D). The overall goodness of fit (R2) was ranked as follows: JL219 (R2 = 0.773) > JL218.P (R2 = 0.75) > JL218.H (R2 = 0.337) > BC12 (R2 = 0.332). The Nm-value was higher for microbial taxa in JL218.P and JL219 (Nm = 9451 and 3521) than those in JL218.H and BC12 (Nm = 753 and 1520). These results indicated that species dispersal of microbial communities was higher in the weak hydrothermal-influenced sediments than in the strong hydrothermal sediments.
Null model-based analyses indicated that the deterministic process predominantly shaped the microbial community construction in the BC12 and JL218.H sediments, as evidenced by the larger proportion of heterogeneous selection than in the JL218.P and JL219 sediments (Figure 7E,F). Conversely, dispersal limitations associated with stochastic processes primarily dictated the microbial community structure in the JL218.P and JL219 sediments.

4. Discussion

4.1. Microbial Community Composition in Response to Geochemistry

In this study, the geochemical parameters were closely related to the distances of these sediments to the hydrothermal vent. Higher concentrations of Fe and other elements from the adjacent sediments compared with those from the distant sediments (Figure 2), which also reflected the site sequence of the affecting degrees by the hydrothermal activities: BC12, JL218.H, JL218.P, and JL219. By comparison, the relative abundances of the two bacterial lineages Aerophobia (0.266% and 4.37% vs. 0.032% and 0.009%) and Anaerolineae (9.3% and 4.99% vs. 3.5% and 1.74%) obviously differed between the hydrothermal activity-affected sediments and the low-hydrothermal activity-affected sediments. The divergent distributions of Aerophibia and Anaerolineae between the hydrothermally influenced and background sediments are highly indicative of the contrasting redox regimes and energy sources across the gradient. Aerophobia (Aerophobetes) has been widely detected in deep-sea sediments [50,51] and harbors a genomic capability of hydrocarbon degradation [52]. Lagostina et al., (2021) found that Aerophobetes dominated in the seep area of the Guaymas Basin hydrothermal sediment [53]. The assembled genome of Aerophobia possessed both Wood-Ljungdahl (WL) and reductive citric acid cycle (rTCA) carbon fixation pathways. Furthermore, these taxa can degrade complex organic compounds in sediments, generating essential substrates such as H2 and acetic acid for methanogens and other microorganisms (Figure S3). These Aerophobetes bacteria prefer anoxic habitats near cold seeps [54,55]. The relative abundance of this taxon was consistently observed in the hydrothermal-influenced sediment cores (specifically in the deep layers of BC12 and JL218.H), indicating that sediment cores record preserves the anoxic environment of hydrothermal activity, and distinguishing the microbial communities of the Tianxiu field from other fields. Research had indicated that the Chloroflexi phylum (primarily the class Anaerolineae), primarily facultative anaerobes, are ubiquitously distributed across diverse habitats such as deep oceans [56], marine hydrothermal systems [57], and terrestrial hot springs [58]. Similarly, the class Anaerolineae demonstrated a relatively higher abundance in near-vent fields in our study. Numerous research showed that sediment depth and habitat significantly influence the spatial variation in this taxa [59,60]. Anaerolineae were abundant in iron-rich shallow-water hydrothermal vents [61], hydrothermal sediments of submarine volcanoes, and circumneutral hot springs [62]. Anaerolineae may couple the oxidation of organic matter with iron reduction, positioning them as key contributors to the biomineralization of organic matter in shallow-water vent environments [61]. Their significant enrichment in the hydrothermally impacted sediments is a direct response to the anoxic conditions and the abundance of organic carbon and fermentation products derived from the thermal alteration of organic matter. Members of this class are often syntrophic partners with methanogens, contributing to carbon cycling in anaerobic environments [63]. These results provide empirical support for our study’s conclusion that the spatial heterogeneity of sedimentary habitats, driven by the hydrothermal impact gradient, is a key driver of microbial community assembly. This is evidenced not only by the distribution of Anaerolineae but also by other responsive taxa. For instance, Aerophobia predominated in high-impact zones, while Nitrososphaeria (ammonia-oxidizing archaea) thrived in background sediments with weaker hydrothermal influence. The predictable distribution of these phylogenetically and functionally diverse taxa underscores that hydrothermal activity creates environmental filters that select for specific microbial lineages across the gradient.
The metabolism-functional prediction results exhibited relatively higher activities of putative Knallgas bacteria (H2-consuming bacteria) and methanogenesis by CO2 reduction with H2 from the BC12 and JL218.H sediments (Figure S3), which coincided with their close distances of the sites to the hydrothermal vents with H2 emission [64]. By comparing with regular seawater [65], the pore water samples from the JL218.H and JL218.P sediment cores enriched SO42− and NO3− (Figure 2). However, according to the functional prediction results, it pointed to different metabolic pathways. The JL218.H sediments contained both sulfate respiration and sulfate production processes, where the production processes may couple with nitrate reduction. While the JL218.P sediment only contained sulfate production processes probably coupled with nitrate reduction. In BC12 sediments, it also contained putative respiration of sulfate and production processes, but the concentrations of sulfate were similar to seawater. We argued that this sediment core was adjacent to the hydrothermal vent and received abundant reduced chemicals to reduce sulfate and offset the sulfate production process [66].
Furthermore, this study also observed the overall microbial community in the sediment of the Tianxiu hydrothermal field exhibited differences from the other hydrothermal systems. For example, hydrogen sulfide-oxidizing bacteria Campylobacterota (former Epsilonproteobacteria) and sulfate-reducing bacteria Deltaproteobacteria were present in significant numbers in a lot of hydrothermal surface sediments [67]. However, Campylobacterota had a relatively low overall abundance in this study, and it exhibited the highest content in BC12 (~0.32%) closest to the vent compared to the other three cores (0.04~0.07%). Campylobacterota has been reported to be one of the core groups in the plume of hydrothermal vents and played a major role in hydrogen conversion and hydrogen-based primary production [64]. The variations in the relative abundance of these taxa may reflect the differences in hydrothermal activity across distinct regions.

4.2. Microbial Community Assembly in Response to Hydrothermal Activity

As anticipated, the PCoA analysis revealed that microbial communities from the same site clustered together (Figure 5B), indicating clear compositional distinctions between hydrothermal and non-hydrothermal zones. This clear separation in community structure is likely driven by pronounced differences in sediment geochemistry between different sites. This divergence underscores the critical role of environmental factors in shaping microbial assembly. The geochemical characteristics of sediments near the hydrothermal zone were more representative of metals (such as Fe, Mn, Mg, Cu, Zn, Co, etc.) compared to sediments far away from the hydrothermal zone (Figure 2A). Hydrothermal fluids, enriched with acidic gases like hydrogen sulfide, typically exhibit lower pH values, forming more acidic sediments. However, as these fluids gradually mix with seawater, sediment pH values rise accordingly. The greater the degree of mixing with seawater, the closer the pH values approach the weakly alkaline nature of seawater [68]. This trend is consistently reflected in the observed pH variations within sediment samples. Accordingly, microbial communities’ assembly in the sediments affected by hydrothermal activity significantly differed from those in the regular ocean sediment samples.
The alpha diversity index for two strong hydrothermal-influenced sediments (BC12 and JL218.H) was lower than that of weak hydrothermal sediments (JL218.P and JL219). A correlation was observed between the geographic distances from the vent and the alpha diversities (Richness and Shannon) of the microbial communities (Figure 3E,F). We hypothesized that the reduced diversity in hydrothermal sediments results from the selective enrichment of specialized microbial taxa under high-energy hydrothermal conditions. The abundant supply of reduced chemicals (e.g., H2, H2S, CH4) from vent fluids [4,69] likely promoted the dominance of fast-growing chemolithoautotrophs (e.g., hydrogen-oxidizing Knallgas bacteria, and methanogens), leading to competitive exclusion of less-adapted taxa. Such energy-rich, chemically extreme environments often favor a limited subset of metabolically specialized microbes, thereby reducing overall community diversity. In contrast, the far-vent sediments, which were minimally influenced by hydrothermal flux, represented low-energy environments where microbial communities rely primarily on recalcitrant organic matter as an energy source. Under these energy-limited conditions, microbial survival likely depends on metabolic cooperation and niche partitioning, fostering a more stable and taxonomically diverse assemblage. The necessity for resource-sharing and functional redundancy in these environments may explain the higher alpha diversity observed in distal sediments (Figure 3).
In addition, the findings from community assembly analysis indicated that the deterministic process predominantly shaped the sediment microbial communities in hydrothermally influenced zones. Particles precipitated by hydrothermal plumes exhibit a notable enrichment in metals and sulfur, characterized by sizes considerably larger than those of the adjacent marine sediments [35,70]. The physical heterogeneity of these coarse particles creates micro-niches with varying permeability and surface area for colonization, thereby promoting a heterogeneous selection process by limiting the dispersal of microorganisms and creating diverse ecological opportunities. Moreover, the hydrothermal vent fluid introduces a steep geochemical gradient characterized by extreme conditions (high temperature and low oxygen), as well as a rich content of redox-sensitive metals and elements, including Fe, Mn, Cu, Zn, and Pb. We speculate that the long-term environmental filtering effect of multiple hydrothermal activities leads to the enrichment of more specialized and dominant species in the hydrothermal influence zone. The microbial communities in hydrothermal sediments are more complex and responsive to environmental changes, and their interactions contribute to the deterministic processes of their construction. Conversely, weak hydrothermal-influenced sediments tended to be more homogenized with calcareous oozes, with small mutation gradients of geochemical parameters, and provided homogenized ecological niches for the survival of stable microbial communities. As a result, they develop relatively diverse microbial structures compared to the sedimentary environments influenced by hydrothermal activities. Thereby, microbial communities formed through stochastic processes often exhibit higher species diversity compared to those dominated by determinism [71]. However, this study did not measure basic and key environmental parameters that drive the community assembly (temperature [18,72], pH [73], and dissolved oxygen content [74]), which may ultimately lead to an underestimation of the role of environmental factors in shaping microbial communities.

5. Conclusions

This article scrutinized the structural composition and diversity of microbial communities of four sites in the Tianxiu hydrothermal area. According to the results, there were significant differences in the composition of sedimental communities between near-vent sites and far-vent sites. The assembly of sediment microbial communities within the hydrothermal sites was predominantly determined by deterministic mechanisms, with only certain species capable of adapting to the distinct conditions of hydrothermalism. These results provide new perspectives to deepen our understanding of sediment microbial community distribution in hydrothermal systems and the contribution of environmental factors.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/oceans6040061/s1; Figure S1: The photos of the sediment cores. Figure S2: The relative contribution of hydrothermally active components in sediments. Fe/Ti ratio versus Al/(Al + Fe + Mn) ratio (A) and Al/(Al + Fe + Mn) ratio versus (Fe + Mn)/Al ratio (B) for Tianxiu sediments. Figure S3: The heatmap of the selected predicted functional potential using FAPROTAX analysis within four core sites. Table S1: Characteristics of twenty environmental variables in sediments. Table S2: Alpha diversity of all subsamples in different locations. Table S3: Dissimilarity test of microbial community composition based on Bray–Curtis between the sample groups.

Author Contributions

Conceptualization, F.L., X.L., J.H. and H.C.; methodology, F.L., X.L., W.H., J.H., H.C., Y.D., Y.W. and X.X.; Formal Analysis, F.L., X.L., J.H. and H.C.; resources, Y.D., Y.W. and X.X.; writing—original draft preparation, F.L.; writing—review and editing, W.H. and H.D.; supervision, H.D.; project administration, X.L., W.H. and H.D.; funding acquisition, X.L., W.H. and H.D. All authors have read and agreed to the published version of the manuscript.

Funding

The research was supported by the National Key Research and Development Program of China (Grant No. 2021YFF0501303), the National Natural Science Foundation of China (No. 92351303), and the Fundamental Research Funds for the Central Universities (No. 2652023001).

Data Availability Statement

Sequence data have been deposited in the Genome Sequence Archive in the National Genomics Data Center, Beijing Institute of Genomics (China National Center for Bioin-formation), Chinese Academy of Sciences, under accession number (CRA025471).

Acknowledgments

We thank all the scientists and crew members for the sampling support and collection during the Chinese cruise DY 72th. This work was supported by High-performance Computing Platform of China University of Geosciences Beijing. We thank all the anonymous reviewers for their valuable and insightful comments and suggestions that have helped us improve the quality of the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Map showing the Tianxiu vent filed (A) and location of four sediment core sites (B) in the Indian Ocean.
Figure 1. Map showing the Tianxiu vent filed (A) and location of four sediment core sites (B) in the Indian Ocean.
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Figure 2. Comparative analysis of twenty environmental variables. (A) Box plots of twenty environmental factors for each 1 cm subsample across the four sites. The box plot shows the distribution of numerical data. The whisker ends denote the minimum and maximum values, while the line inside the box is the median. (B) Spearman correlation analysis of environmental factors.
Figure 2. Comparative analysis of twenty environmental variables. (A) Box plots of twenty environmental factors for each 1 cm subsample across the four sites. The box plot shows the distribution of numerical data. The whisker ends denote the minimum and maximum values, while the line inside the box is the median. (B) Spearman correlation analysis of environmental factors.
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Figure 3. Alpha diversity of all samples in different locations. Box plots of Ace (A), Shannon (B), Simpson (C), and Niche width (D) indices of microbial community among all sites, respectively. The box plot shows the distribution of numerical data. The whisker ends denote the minimum and maximum values, while the line inside the box is the median. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. Relationship between Richness (E) and Shannon diversity (F) of microbial communities and geographical distance in all samples. Grey line, 95% confidence band. Blue line, linear regression line.
Figure 3. Alpha diversity of all samples in different locations. Box plots of Ace (A), Shannon (B), Simpson (C), and Niche width (D) indices of microbial community among all sites, respectively. The box plot shows the distribution of numerical data. The whisker ends denote the minimum and maximum values, while the line inside the box is the median. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. Relationship between Richness (E) and Shannon diversity (F) of microbial communities and geographical distance in all samples. Grey line, 95% confidence band. Blue line, linear regression line.
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Figure 4. Taxonomic compositions of the microbial community at the class level in different sedimental layers. Four sediment cores: (A) BC12. (B) JL218.H. (C) JL218.P. (D) JL219. The most abundant 10 taxa were shown, otherwise were assigned to others.
Figure 4. Taxonomic compositions of the microbial community at the class level in different sedimental layers. Four sediment cores: (A) BC12. (B) JL218.H. (C) JL218.P. (D) JL219. The most abundant 10 taxa were shown, otherwise were assigned to others.
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Figure 5. Analysis of differences in microbial community structure between all samples. (A) Bray–Curtis dissimilarities in four sedimental sites. The p-values between any two groups were all less than 0.005. (B) PCoA analysis based on Bray–Curtis dissimilarity of bacterial community structure. (C) VPA performed to quantify the relative contributions of environmental factors and geographic variables to alterations in microbial community structure.
Figure 5. Analysis of differences in microbial community structure between all samples. (A) Bray–Curtis dissimilarities in four sedimental sites. The p-values between any two groups were all less than 0.005. (B) PCoA analysis based on Bray–Curtis dissimilarity of bacterial community structure. (C) VPA performed to quantify the relative contributions of environmental factors and geographic variables to alterations in microbial community structure.
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Figure 6. Environmental factor analysis showing the correlation of microbial community with geochemical data and spatial variables. (A) Distance–decay curves showing Bray–Curtis dissimilarity of microbial communities against physical distances between sampling sites. (B) Distance–decay curves showing Bray–Curtis dissimilarity of microbial communities against environmental distances between sampling sites. (C) NMDS analysis shows the degree of influence of environmental factors on microbiota composition (p < 0.05). (D) The predicted effects of different geochemical factors on microbial diversity based on random forest analysis (* p < 0.05).
Figure 6. Environmental factor analysis showing the correlation of microbial community with geochemical data and spatial variables. (A) Distance–decay curves showing Bray–Curtis dissimilarity of microbial communities against physical distances between sampling sites. (B) Distance–decay curves showing Bray–Curtis dissimilarity of microbial communities against environmental distances between sampling sites. (C) NMDS analysis shows the degree of influence of environmental factors on microbiota composition (p < 0.05). (D) The predicted effects of different geochemical factors on microbial diversity based on random forest analysis (* p < 0.05).
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Figure 7. Neutral Community Model and Null model analysis revealing the assembly mechanism of the microbial community across all samples. Four sediment cores: (A) BC12. (B) JL218.H. (C) JL218.P. (D) JL219. Rsqr is the overall goodness of fit of the neutral community model, and Nm is the product of meta-community size (N) and mobility (m), quantifying the estimate of diffusion between community assembly. The solid blue lines indicate the best fit to the NCM and the dashed blue lines represent 95% confidence intervals around the model prediction. ASVs that occur more or less frequently than predicted by the NCM are shown in teal or red colors. (E) βNT index and (F) relative importance of different ecological processes structuring the microbial communities at the four sites.
Figure 7. Neutral Community Model and Null model analysis revealing the assembly mechanism of the microbial community across all samples. Four sediment cores: (A) BC12. (B) JL218.H. (C) JL218.P. (D) JL219. Rsqr is the overall goodness of fit of the neutral community model, and Nm is the product of meta-community size (N) and mobility (m), quantifying the estimate of diffusion between community assembly. The solid blue lines indicate the best fit to the NCM and the dashed blue lines represent 95% confidence intervals around the model prediction. ASVs that occur more or less frequently than predicted by the NCM are shown in teal or red colors. (E) βNT index and (F) relative importance of different ecological processes structuring the microbial communities at the four sites.
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Table 1. Sample geographic location.
Table 1. Sample geographic location.
Cores IDSample AbbreviationSampling MethodLatitude (°N)Longitude (°E)Water Depth (m)Distance from the Vent (m)Number of Subsamples
BC12_10 cmBC12box corer3.69189563.8317653400408
JL218_30 cmJL218.Hpush-corer3.69484463.8353523508.845024
JL218_10 cmJL218.Ppush-corer3.69554363.8334593524.150010
JL219_30 cmJL219push-corer3.69879663.8391033640110030
Table 2. Characteristics of rare earth elements in sediments.
Table 2. Characteristics of rare earth elements in sediments.
SampleLREEHREEΣREEδEu
BC12
(n = 8)		Min.7.192.8510.042.29
Max.25.417.5332.943.55
Average16.945.0121.952.86
Standard deviation1.525.987.480.50
JL218.H
(n = 24)		Min.16.636.1222.751.32
Max.49.7911.9261.711.90
Average39.119.9149.021.47
Standard deviation1.749.7411.420.17
JL218.P
(n = 10)		Min.36.129.6745.791.25
Max.47.3812.0359.401.37
Average43.3511.0754.431.31
Standard deviation0.623.323.920.04
JL219
(n = 30)		Min.32.157.9240.071.16
Max.49.0712.0261.091.32
Average40.109.7149.811.22
Standard deviation1.084.165.230.04
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Li, F.; Liu, X.; Hou, W.; Dong, H.; Hu, J.; Chen, H.; Ding, Y.; Wu, Y.; Xu, X. Distribution Patterns and Diversity of Sedimental Microbial Communities in the Tianxiu Hydrothermal Field of Carlsberg Ridge. Oceans 2025, 6, 61. https://doi.org/10.3390/oceans6040061

AMA Style

Li F, Liu X, Hou W, Dong H, Hu J, Chen H, Ding Y, Wu Y, Xu X. Distribution Patterns and Diversity of Sedimental Microbial Communities in the Tianxiu Hydrothermal Field of Carlsberg Ridge. Oceans. 2025; 6(4):61. https://doi.org/10.3390/oceans6040061

Chicago/Turabian Style

Li, Fangru, Xiaolei Liu, Weiguo Hou, Hailiang Dong, Jinglong Hu, Hongyu Chen, Yi Ding, Yuehong Wu, and Xuewei Xu. 2025. "Distribution Patterns and Diversity of Sedimental Microbial Communities in the Tianxiu Hydrothermal Field of Carlsberg Ridge" Oceans 6, no. 4: 61. https://doi.org/10.3390/oceans6040061

APA Style

Li, F., Liu, X., Hou, W., Dong, H., Hu, J., Chen, H., Ding, Y., Wu, Y., & Xu, X. (2025). Distribution Patterns and Diversity of Sedimental Microbial Communities in the Tianxiu Hydrothermal Field of Carlsberg Ridge. Oceans, 6(4), 61. https://doi.org/10.3390/oceans6040061

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