Depth-dependent distribution of prokaryotes in sediments of the manganese crust on Nazimov guyots of the Magellan seamounts

Depth-dependent distribution of prokaryotes in sediments of the manganese crust on Nazimov guyots of the Magellan seamounts | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Depth-dependent distribution of prokaryotes in sediments of the manganese crust on Nazimov guyots of the Magellan seamounts Jianxing Sun, Hongbo Zhou, Haina Cheng, Zhu Chen, Jichao Yang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2945198/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Oct, 2023 Read the published version in Microbial Ecology → Version 1 posted 3 You are reading this latest preprint version Abstract Deep ocean polymetallic nodules, rich in cobalt, nickel, and titanium which are commonly used in high-technology and biotechnology applications, are being eyed for green energy transition through deep-sea mining operations. Prokaryotic communities underneath polymetallic nodules could participate in deep-sea biogeochemical cycling, however, are not fully described. To address this gap, we collected sediment cores from Nazimov guyots, where polymetallic nodules exist, to explore the diversity and vertical distribution of prokaryotic communities. Our 16S rRNA amplicon sequencing data, quantitative PCR results and phylogenetic beta diversity indices showed that prokaryotic diversity in the surficial layers (0–8 cm) was > 4-fold higher compared to deeper horizons (8–26 cm), while heterotrophs dominated in all sediment horizons. Proteobacteria was the most abundant taxon (32–82%) across all sediment depths, followed by Thaumarchaeota (4–37%), Firmicutes (2–18%) and Planctomycetes (1–6%). Depth was the key factor controlling prokaryotic distribution, while heavy metals (e.g., iron, copper, nickel, cobalt, zinc) can also influence significantly the downcore distribution of prokaryotic communities. Analyses of phylogenetic diversity showed that deterministic processes governing prokaryotic assembly in surficial layers, contrasting with stochastic influences in deep layers. This was further supported from the detection of a more complex prokaryotic co-occurrence network in the surficial layer which suggested more diverse prokaryotic communities existed in the surface vs. deeper sediments. This study expands current knowledge on the vertical distribution of benthic prokaryotic diversity in deep sea settings underneath polymetallic nodules, and the results reported might set a baseline for future mining decisions. Prokaryotes Marine sediment Vertical distribution Community assembly Co-occurrence network Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Deep-ocean polymetallic nodules (also known as manganese nodules) are widely distributed below the vast, sediment-covered, abyssal plains of the global ocean [1, 2]. Polymetallic nodules are strategically important since they are rich in precious metals such as nickel, copper, cobalt, and manganese [3, 4]. Rising demand for metals and the depletion of land-based resources have led to a surge of interest in ocean mining operations [5]. However, ocean mining will inevitably damage the marine environment and biodiversity, which has been highlighted over the past several years [6, 7]. For instance, the dispersion of sediment plumes and the footprint of plumes have strong negative impacts on benthic organisms, even leading to biodiversity loss [6, 8]. Although extensive studies have revealed the geographical distribution and polymetallic composition of manganese nodules in marine sediments [2, 9, 10], the microbial diversity underneath sediments where polymetallic nodules exist is understudied [11]. Microorganisms are the major contributors of biogeochemical cycling in deep sea settings [12-14], while their distribution and presence/absence in both marine and terrestrial ecosystems can reflect anthropogenic activities [e.g., 15-17]. However, compared to other marine habits, such as seawater and marine sediments, our knowledge of microbial diversity and community distribution in the polymetallic nodule regions remains largely limited [14]. Using appropriate prokaryotic primers, 16S rRNA amplicon sequencing can estimate microbial diversity and can also capture taxa with low abundances [14]. Nonetheless, when combined with quantitative PCR (qPCR) it can provide a second line of evidence on microbial abundance [18, 19], and community structure [20, 21]. Deep-sea mining for polymetallic nodules could have a major impact on the abyssal environment and microbial diversity [7]. Furthermore, microbial community assembly and putative metabolic processes would be shifted under environmental disturbance [22, 23]. In subsurface sediments, both deterministic and stochastic processes can occur simultaneously and shape the assembly of in situ microbial communities. This is not surprising considering that essential environmental parameters, like O 2 , temperature, nutrient concentrations, and available nutrient pools, as well as physical disturbances at abyssal depths (e.g., earthquakes and hydrodynamic processes) can influence the spatial distribution of microorganisms along sediment depth. Co-occurrence networks can examine relationship between microbes and environmental factors [24, 25], and network analysis has been widely used to explore interactions between marine benthic microbes and microbe-environment relationships in shallow sediments [26, 27]. Nonetheless, prokaryotic co-occurrence networks in sediments underneath polymetallic nodules are not fully investigated. Magellan Seamounts are rich in manganese nodules [28, 29]. However, there are few studies on microbial diversity and community vertical distribution here [14]. In this study, core sediments were obtained from the Nazimov guyots of Magellan seamounts, and 16S rRNA amplicon sequencing and quantitative PCR (qPCR) methods were employed to explore prokaryotic diversity and community vertical distribution in Nazimov guyots. Our objectives were: (i) to reveal prokaryotic diversity and community compositions in different depth layers; (ii) to estimate the contribution of stochastic and deterministic processes in shaping the downcore prokaryotic diversity; and (iii) to investigate putative co-occurrence patterns at different examined sediment horizons. This study reveals the prokaryotic community structure in the Magellan seamounts and expands our understanding on the microbial vertical distribution in sediments where polymetallic nodules exist. Describing the microbial community underneath those nodules can be useful in future deep-sea mining operations. Materials And Methods Sample Collection and Environmental Characterization One sediment core was collected from surface to 26 cm below seafloor (cmbsf) at the Nazimov guyots in Magellan seamount (162.45 °E, 15.18 °N, 5365 m seawater depth) using a four-tube multitube sampler (MC-400, 10×58 cm, Ocean Environmental Sci.&Tech.) during the Dayang 61st-I cruise of P. R. China. Briefly, the sediment core was sliced at 2-cm intervals with a stainless-steel cutter at 13 different sediment intervals (n = 13 samples). A sterile spatula was used to carefully collect sediment from the center of each sediment horizon for DNA extraction to avoid potential contamination. Samples were transferred to sterilized plastic tubes and stored at -80 °C until DNA extraction, and analyses of selected environmental factors like pH, electrical conductivity (EC), available phosphorous (AP), available potassium (AK), available nitrogen (AN), and organic carbon (OC), according to [30, 31]. We note that our analyses on AN and AP provide bulk estimations of available N and P in the sediments without further speciation on the contribution of inorganic vs. organic sources. Briefly, samples were air-dried at room temperature in the shade and sieved through a 2-mm screen. The pH and EC were determined in a 1:2.5 sediment/water solution [31]. The contents of AP were determined by molybdenum blue colorimetry using a spectrophotometer (TAS-990, Persee, Beijing) according to a previous study [32], and concentrations of AK were measured by the ammonium acetate extraction flame photometer method [33]. The contents of AN were determined by the Kjeldahl procedure [30]. Sediment OC was determined by the rapid dichromate oxidation-titration method [34]. Concentrations of heavy metals including Mn, Fe, Co, Ni, Cu and Zn found underneath polymetallic nodules [35, 36], were determined according to [37]. Briefly, sediment samples were dried at 105 ◦C for 6 h and then ground to a fine powder using a Hard Tissue Homogenizer (VWR International, West Chester, PA, USA). Accordingly, 0.5 g of dried sample powder was dissolved in an acidic mixture of HF–HCl–HNO 3 (1:3:1) by microwave-assisted digestion, and the leachate was used to detect the concentrations of metallic ions by Inductively coupled plasma optical emission spectroscopy (ICP-AES) (Agilent 720ES, USA) [38, 39]. DNA Extraction, Sequencing, and Data Processing DNA was extracted from 0.25 g of homogenized sediment (wet weight) using the Power Soil DNA Isolation Kit (MoBio Laboratories, Inc., Carlsbad, CA, USA) as instructed by the manufacturer. DNA extraction, PCR amplification protocol, and data processing are described in [40]. The primer set of 515F (Parada) (5′-GTGYCAGCMGCCGCGGTAA-3′) and 806R (Apprill) (5′-GGACTACNVGGGTWTCTAAT-3′) was used to amplify the V4 hypervariable region of the 16S rRNA gene targeting both bacteria and archaea according to the recommendation of Earth Microbiome Project (EMP) [41-43]. Quantitative PCR (qPCR) was performed to estimate total abundances of bacteria and archaea. qPCR primers and conditions were described in detail in reference [44]. Briefly, qPCR was conducted as follows: 3 min for denaturation at 95°C; 29 cycles of 30 s at 95°C, 30 s for annealing at 55°C, 45 s for elongation at 72°C, and 10 min for a final extension at 72°C. qPCR reactions were performed in triplicate. Furthermore, the absolute abundances of targeted microbial populations were calculated by total DNA copies × their relative abundance. In all experiments, negative controls, which contained no template DNA, were qPCR amplified to detect putative contamination. Sequencing was conducted using the MiSeq paired-end 2 x 250 bp (PE250) platform (Illumina Inc., San Diego, CA, USA). Raw data were processed and analyzed using an in-house pipeline (http://mem.rcees.ac.cn:8080) as described by previous study [45]. Sequences with an average quality score below 20 and sequence length of fewer than 200 bp were discarded. And sequences were then split into operational taxonomic units (OTUs) at a 97% similarity level using the UPARSE pipeline. Furthermore, singleton OTUs were removed before downstream analyses since they may represent sequencing errors. Accordingly, the taxonomy of each 16S rRNA gene sequence was analyzed via the Ribosomal Database Project (RDP) classifier algorithm (version 2.11, http://rdp.cme.msu.edu/) [46] against the Silva rRNA database (Release132, http://www.arb-silva.de )[47]. Data Analysis To equalize sequencing depth, each sub-sample was rarefied to 118403 reads (the lowest sequence number across all samples) for further analysis. The alpha diversity was estimated using OTU richness, Shannon index, Chao1 index, and ACE index in this study. Overall differences in bacterial and archaeal community composition were visualized with principal coordinates analysis (PCoA) based on Bray-Curtis distance using R software (Version 4.0.1) base package ‘vegan’, and the statistically significant difference between any pair of regions was assessed using the analysis of similarities (ANOSIM), multiple response permutation procedure (MRPP) and permutational multivariate analysis of variance (PERMANOVA) (all run with 999 permutations). Moreover, the unweighted pair-group method with arithmetic means analysis (UPGMA analysis) was conducted to explore the vertical variation of prokaryotic communities with depth, and the 16S rRNA gene copy numbers of different prokaryotic populations were calculated by the total gene copy numbers multiplied by the corresponding relative abundance. Redundancy analysis (RDA) was performed to evaluate the relationships between prokaryotic communities and environmental factors using the ‘vegan’ package in R. RDA method was chosen because preliminary detrended correspondence analysis (DCA) on prokaryotic community data revealed that the longest gradient lengths were less than 3.0. Correlation analysis between prokaryotic phylum populations and environmental factors was conducted based on the Mantel test and visualized by a heatmap. Spearman’s rank correlations were used to determine the relationship between the Bray-Curtis similarity of bacterial and archaeal communities and the environmental factors based on OTU level. A mantel test was conducted (999 permutations) in conjunction with the OTU table to reveal how environmental factors affect bacterial or archaeal communities. Spearman’s correlations among environmental factors and their correlations to bacteria and archaea were analyzed using the ‘ggcor’ package of R. Prokaryotic Community Assembly Processes The relative importance of deterministic and stochastic processes in prokaryotic community assembly was evaluated using null model-based methods which detect nonrandom cooccurrence phylogeny patterns. These methods were conducted following the analytical framework analyses of phylogenetic beta diversity and taxonomic beta diversity [48]. First, a null distribution of the β-mean-nearest taxon distance (βMNTD) was generated among samples within different depths by re-dominating the taxa labels of the phylogenetic tree 999 times. Then, the β-nearest taxon index (βNTI) was calculated by comparing the difference between the observed βMNTD values and the mean of the null distribution of βMNTD normalized by its standard deviation. A βNTI value +2 indicates significantly higher than the expected phylogenetic turnover rate. When -2< βNTI < +2, it means that this is the stochastic process, including dispersal limitation, homogeneous dispersal, and undominant processes. To disentangle these cases, a further calculation based on the Bray-Curtis based Raup-Crick metric (RC bray ) as described by the previous study on the relative contribution to the assembly process with |βNTI| <2 [48]. The relative contribution of dispersal limitation was estimated in terms of percentages of paired comparisons with |βNTI| 0.95. The contribution of relative homogeneous dispersal was estimated in terms of percentages of paired comparisons with |βNTI| <2 and RC bray < -0.95. In contrast, not belonging to any of these categories suggested that the undominated process governed community assembly. Co-occurrence Network Analysis The co-occurrence patterns of prokaryotic communities were demonstrated by co-occurrence networks using the “igraph” and “Hmisc” packages in R and visualized by Gephi (version 0.9.2). It was noticeable that only OTUs occurring in more than 10 sequences (on average) of all samples were retained to avoid the influence caused by sequencing contamination [49]. The pairwise Spearman's correlations between OTUs were calculated, with a correlation coefficient > |0.7| and a p-value < 0.05 (Benjamini and Hochberg-adjusted, BH) being considered as a valid relationship. To describe the network-level topology of the networks, we calculated a set of metrics: average degree, modularity, average clustering coefficient ( AvgCC ), average path length ( APL ), network diameter, and graph density. Average degree refers to the average connections of each node with another unique node in the network; AvgCC represents the degree to which the nodes tend to cluster together; the term ‘ APL ’ has been used to describe the average network distance between all pairs of nodes; network diameter refers to the greatest distance between the nodes that exist in the network; and graph density refers to the intensity of connections among nodes. Therefore, higher average degree, AvgCC , and graph density suggest a more connected network. In addition, lower APL and diameters indicate closer associations within the network, higher modularity index with a higher clustering coefficient of nodes indicated that they were more likely to present in an interconnected “small world” [44, 50]. Additionally, the surficial layer and deep layer networks were constructed to visualize the correlations between the microbes and environmental factors. In the process of calculation, only strong and significant connections (Spearman correlation, |R| > 0.6 and P < 0.05) were kept in networks to find the key environmental factors [51]. These processes were calculated using R 4.0.5 with “psych” and “reshape2” packages. Results General Environmental Characterization Detailed sediment environmental parameters are shown in Table 1. Generally, the sediments are slightly alkaline, the pH ranged from 8.02 to 8.26. The EC values of the sediments ranged from 8.42 to 10.92 mS∙cm -1 . The AP and AK concentrations ranged from 13.17 to 16.61 mg∙kg -1 and 7.57 to 14.15 g∙kg -1 , respectively. The contents of AN and OC were in a range of 6.51 to 42.90 mg∙kg -1 and 22.18 to 26.86 g∙kg -1 , respectively. We found that the EC value and the contents of AP and AK were clustered in the upper sediment (top 10 cmbsf) and deeper sediment (10–26 cmbsf) samples, while other parameters were not showed the same tendency. Moreover, the contents of Mn, Fe, and Zn does not appear to exhibit a significant trend between the surface and deep layers. However, some kinds of metals, such as Co, Ni, and Cu were higher in the surficial layer than those in the deep layer. Table 1 Environmental parameters of sediment in all samples Sample Layer Depth a pH EC b AP c AK d AN c OC d Mn d Fe d Co c Ni d Cu d Zn d S0_2 Surficial 2 8.22 10.68 14.53 10.35 21.34 22.99 8.23 57.63 20.88 0.27 0.36 0.16 S2_4 Surficial 4 8.23 8.42 14.29 10.44 32.12 24.5 7.64 58.26 19.55 0.27 0.43 0.18 S4_6 Surficial 6 8.11 8.54 16.61 10.00 13.25 23.7 7.72 63.00 11.56 0.27 0.38 0.16 S6_8 Surficial 8 8.22 9.37 14.81 11.79 21.34 26.4 6.98 54.14 26.22 0.26 0.38 0.16 S8_10 Deep 10 8.2 9.73 14.21 12.31 75.24 23.3 10.56 58.05 46.76 0.35 0.45 0.17 S10_12 Deep 12 8.24 10.79 13.59 13.48 19.99 23.53 6.53 57.16 17.43 0.26 0.41 0.16 S12_14 Deep 14 8.24 10.82 13.53 13.16 21.34 26.03 8.72 54.81 32.33 0.38 0.44 0.17 S14_16 Deep 16 8.18 10.74 13.81 14.15 7.19 22.8 6.65 49.37 24.73 0.27 0.38 0.15 S16_18 Deep 18 8.24 10.32 13.41 13.45 6.51 22.48 8.30 57.87 33.06 0.37 0.45 0.18 S18_20 Deep 20 8.23 10.18 13.25 13.55 18.64 22.18 7.40 53.80 31.56 0.31 0.42 0.17 S20_22 Deep 22 8.26 10.92 13.31 13.48 7.19 23.19 6.66 56.53 20.02 0.29 0.41 0.17 S22_24 Deep 24 8.22 10.36 13.17 7.57 11.9 26.05 8.75 58.33 31.56 0.37 0.46 0.19 S24_26 Deep 26 8.02 9.59 12.89 13.24 42.9 26.86 6.65 58.89 10.40 0.25 0.34 0.16 (Note: a cm; b mS/cm; c mg/kg; d g/kg) Prokaryotic Alpha and Beta Diversity After sequences processing, all sequences were clustered into 6447 OTUs (5770 bacterial OTUs and 677 archaeal OTUs) at a 97% similarity level. The OTUs rarefaction curves for all 13 samples showed a tendency to plateau, which indicated that the sequencing depth was enough to capture all prokaryotes, and the species rarefaction curves showed that species richness was much high in the top 8 cmbsf than that in the deeper sediments (Fig. S1). Moreover, the prokaryotic quantity expresses as gene copy numbers declined one order of magnitude from the top 8 cmbsf to the 8–26 cmbsf layer (Table S1). Thus, according to the species richness and gene copies, we divided the whole sediment core into the surficial layer (top 8 cmbsf) and the deep layer (8–26 cmbsf) for further analysis. Our results indicated that the OTU number, Shannon index, Chao 1 index, and ACE index in the surficial layers were significantly higher than those in the deep layer (Fig. 2 and Fig. S2). The overall variability of prokaryotic distribution patterns was analyzed by PCoA based on Bray-Curtis distance, and the samples clustered basically according to different depth layers as shown in Fig. S3, and the first two PCoA axes could explain 59.3% of the total prokaryotic community variations. In specific, the surficial layer samples clustered more tightly than that in the deep layer, which suggested that higher community variation existed in the deep layer than that in the surficial layer. Furthermore, three different statistical analyses, including MRPP, ANOSIM, and PERMANOVA, revealed that prokaryotic community structures were significantly different between the surficial layer and the deep layer ( P < 0.01, Table 2). Prokaryotic Community Compositions at Different Sediment Layers The major prokaryotic community compositions at phylum level (relative abundance over 0.1% on average) were shown in Fig. 3. Our results demonstrated that bacterial communities mainly consisted of Proteobacteria (31.8–82.4%), Firmicutes (2.05–8.8%), Planctomycetes (0.8–6.0%), Actinobacteria (0.6–5.8%), Acidobacteria (0.3–7.2%) and Bacteroidetes (0.6–5.4%) at phylum level across all samples (Fig. 3A). Moreover, Archaea were mainly composed of Thaumarchaeota (3.7–36.8%), Pacearchaeota (0.1–2.0%) and Woesearchaeota (0.1–1.0%) in sediments (Fig. 3A). The 16S rRNA gene abundances of prokaryotes were much higher in the surficial layer (1.36–7.11× 10 9 copies g -1 , dry weight), and then declined nearly ten-fold in the deep layer (1.57–5.68 × 10 8 copies g -1 ) (Fig. 3B). In the context of absolute abundance (Fig. 3B), Proteobacteria (7.31 × 10 8 –3.38 × 10 9 copies∙g -1 in the surficial layer and 6.82 × 10 7 –3.06 × 10 8 copies∙g -1 in the deep layer) and Thaumarchaeota (2.98 × 10 8 –1.54 × 10 9 copies∙g -1 in the surficial layer and 1.39 × 10 7 –1.02 × 10 8 copies∙g -1 in the deep layer) were the most prevalent populations at phylum level. Interestingly, the 24–26 cm depth was unique among these samples, where habitat-abundant Proteobacteria (mainly Gammaproteobacteria ) but rare Thaumarchaeota , Firmicutes , and Actinobacteria . At class level (Fig. S4A), Proteobacteria was mainly composed of Gammaproteobacteria (10.6–76.6%), Alphaproteobacteria (3.5–22.8%), Betaproteobacteria (0.3–10.9%) and Deltaproteobacteria (0.2–2.4%). Firmicutes were mainly divided into classes Bacilli (0.1–15.4%) and Clostridia (0.1–1.5%). Planctomycetes consisted of class Planctomycetacia (0.1–5.1%). At the order level, the abundances of Nitrosopumilales and Alteromonadales generally decreased with depth. In contrast to Chromatiales , the abundances of Pseudomonadales were much lower in the surficial layer than those in the deep layer (Fig. S4B). Interestingly, our results indicated that prokaryotic abundances in the deepest sample (>24 cmbsf) were significantly distinct (e.g., orders Alteromonadales , Rhodospirillales , Pseudomonadales , and Oceanospirillales ) with other samples (Fig. S4B). Among the 5770 bacterial OTUs, 440 OTUs belong to class Gammaproteobacteria , which mainly consisted of genera Acidibacter (1.6%), Thioprofundum (1.6%), Pseudomonas (3.6%), Colwellia (1.6%) and unclassified populations (58.9%, Fig. S5A). As for Archaea, there were 90, 189 and 337 OTUs affiliated to phyla Thaumarchaeota (including 76.7% Nitrosopumilus and 23.3% Nitrososphaera at genus level), Pacearchaeota (all OTUs belong to Pacearchaeota Incertae Sedis AR13 at genus level) and Woesearchaeota (including 9.2% Woesearchaeota Incertae Sedis AR15, 37.4% Woesearchaeota Incertae Sedis AR16, and 8.0% Woesearchaeota Incertae Sedis AR18 at genus level), respectively (Figs. S5B–5D). Moreover, our results demonstrated that the abundances of genus Nitrosopumilus were much lower when the depth was over 20 cmbsf in sediments (Fig. S4E), and in contrast to genus Colwellia , the abundances of the genus Acinetobacter were much higher in the deep layer than those in the surficial layer. Interestingly, the abundance of the genus Pseudomonas was much higher in the deepest sample (26 cmbsf, Fig. S5E). The Influences of Environmental Factors on the Prokaryotic Community Our results indicated that only the contents of AP, AK, Ni and Cu were significantly different from the surficial to deep layer ( P <0.05), while other environmental parameters were not significantly changed (Fig. 4A). The first two axes of RDA jointly explained 41.6% of the total prokaryotic variation by all selected environmental factors (Fig. 4B). The Monte Carlo permutation tests indicated that prokaryotic community variation showed strong correlated with depth (r = 0.58, P = 0.007 < 0.05, 999 permutations, Table S2) and Cu (r=0.24, P =0.04<0.05). Furthermore, the abundances of Thaumarchaeota , Acidobacteria , and Nitrospirae showed a significant and positive correlation with AP, conversely, Actinobacteria , Bacteroidetes and Cyanobacteria showed a significant and negative correlation with AP (Fig. 4C). At genus level, the abundances of Pseudohongiella , Colwellia , Nitrosopumilus , and Woesearchaeota Incertae Sedis AR18 showed significant and negative correlation with depth but significant and positive Correlation with AP. Moreover, EC, AK, AN and OC also exerted significant and negative influences on some genera (e.g., Thioprofundum , Pseudohongiella , Luteimonas , Woesearchaeota Incertae Sedis AR15 and Woesearchaeota Incertae Sedis AR16). In addition, our results also indicated that heavy metals, such as Co, Ni, and Cu showed significant and positive correlations on genera Acidibacter , Acinetobacter , Cu and Zn showed significant positive influence on Woesearchaeota Incertae Sedis AR15 and Pacearchaeota Incertae Sedis AR13, respectively (Fig. S5F). Accordingly, to distinguish the different effects of environmental factors on bacterial and archaeal communities, all environmental factors were estimated through the Mantel test and as shown in Fig. 4D. Generally, environmental factors had distinct effects on bacteria and archaea. Archaea was significantly influenced by depth (r > 0.5, P 0.5, P 0.25, P 0.25, P 0.5, P 0.5, P <0.05). Fig. 4 Effects of environmental factors on prokaryotic communities. A) compared sediment properties between surficial and deep layers based on T-test. B) RDA analysis illustrating the relationship between prokaryotic communities at the OTU level. C) Correlation between environmental factors and prokaryotes at phylum level (the color gradient on the right indicates Spearman’s rank correlation coefficients). D) Correlation between environmental factors and prokaryotic community structure based on OTU level (Pairwise comparisons between environmental factors and Archaea/Bacteria. The color gradient and circle denote Spearman’s rank correlation coefficients. The line width represents the Mantel’s statistic for the corresponding correlation coefficient, and the line color means that significance which is tested based on 999 permutations). (*, ** and ‘ns’ represent P < 0.05, P < 0.01 and no significant, respectively.) Abbreviations, EC: electrical conductivity, AP: Available phosphorous, AK: available potassium, AN: available nitrogen, OC: organic carbon. Prokaryotic Community Assembly Processes Based on the null model, our results indicated that the proportions of absolute phylogenetic turnover (βNTI) values > 2 in the surficial and deep layers were 100% and 41%, respectively (Fig. 5A), and the proportions of absolute taxonomic turnover (RC bray ) values < 0.95 in the two sediment layers were 100% and 42%, respectively (Fig. 5B). Above results indicated that the role of deterministic processes, especially variable selection, dominated prokaryotic assembly process in the surficial layer. Whereas, variable selection, dispersal limitation and undominated processes explained 42%, 22% and 36% of prokaryotic community assembly in the deep layer, respectively (Figs. 5C). Co-occurrence Networks in Different Sediment Layers Co-occurrence networks were built based on correlation relationships across all samples from the surficial and deep layers, respectively (Fig. 4). The resulting network of the surficial and deep layers consisted of 851 and 860 nodes linked by 22057 and 10677 edges, respectively. To depict the complex co-occurrence patterns of the networks, the topological characteristics were calculated as shown in Fig. S5. We found that the average degree and avgCC were much lower in the deep layer, as did the modularity and graph density. Besides, compared with the surficial layer, the entire network of the deep layer showed bigger APL and network diameter. Notably, all the links between each pair of nodes consisted of 53.3% positive edges and 46.7% negative edges in the surficial layer network, while the positive edges occupied 99.2% of the total connections within the deep layer network. The major prokaryotes and their connected edges and average degree in different co-occurrence networks were shown in Table S3. We found that Proteobacteria was the most abundant population within both the surficial layer (30.8%) and the deep layer (30.1%), while its average degree decreased by more than half from the surficial layer to the deep layer network, and the similar trend was observed in Thaumarchaeota . Although Chloroflexi owned higher average degree edges in two co-occurrence networks, its abundances were always much low (around 2%). As the second dominant bacterial and archaeal phyla, Planctomycetes and Woesearchaeota owned a much higher average degree in the surficial layer than that in the deep layer. Interestingly, although the abundance of Firmicutes increased from the surficial layer (5.3%) to the deep layer (6.1%), its average degree was sharply decreased from 47.9 to 16.2, as did as Bacteroidetes , Actinobacteria and Pacearchaeota . To further reveal the relationships between prokaryotic species and environmental factors, correlation analysis was examined based on Person correlation and only significant and strong correlations (r > 0.6, P < 0.05) were visualized by the network (Figs. 6C and 6D). The results showed that depth, AK and OC had significantly strong negative effects on prokaryotes within the surficial layer, followed by pH and AP. Interestingly, only a few species were positively influenced by AP. In the deep layer, AK showed a significantly strong negative effect on more species, while OC and depth only impacted a few species. Conversely, AP and AN showed a positive influence. Our results also indicated that heavy metals showed different influences on prokaryotes between surficial and deep layers (except Mn, which showed positive influences on prokaryotes on both surficial and deep layers). For example, Fe, Cu, Ni and Zn showed positive influences on prokaryotes in the surficial layer but showed negative influences on some prokaryotes in the deep layer. And Co also exerted negative effect on prokaryotes in the surficial but some positive influences in the deep layer (Figs. 6C and 6D). Discussion Environmental Heterogeneity Determined Prokaryotic Distribution The knowledge of the diversity and vertical distribution of prokaryotes in deep-sea sediment is crucial for better understanding their ecological roles in driving biochemical processes [52, 53]. Combined relative abundance with absolute abundance could describe microbial distribution more precisely. Our results indicated that prokaryotic diversity and absolute abundance dramatically decreased from the surficial layer to the deep layer (Fig. 2). This is not surprising considering that in situ environmental factors (e.g., nutrient availability, O 2 concentrations, temperature) shape the prokaryotic community assembly in sediments at abyssal and hadal depths [54-57]. Nonetheless, studies at those isolated marine settings have also suggested that certain factors (e.g., nitrate concentrations) can support/explain the high bacterial α- diversity along the sediment vertical profile (e.g., Izu-Boin Trench ~10 km water depth [58]). Our study results indicated that prokaryotic α- diversity decreased sharply from the surficial layer to the deep layer, which agrees with previous published literature for deep marine settings [44, 53, 59]. As mentioned, decline in the microbial diversity and abundance is related to O 2 content, nutrient pools and their downcore distribution along sediments [54, 58, 60, 61]. Our results showed that the abundances of both heterotrophs and autotrophs including Proteobacteria and Thaumarchaeota reduced dramatically from the surficial layer to the deep layer, while the abundance of commonly identified deep sea linages (e.g., Chloroflexi ) increased in the deep layer (Figs. 3B and 4C). Although, the OC content remained high throughout the examined depths (Table 1), almost 59% of the OTUs belonging to Gammaproteobacteria (dominated among Proteobacteria phylum) appeared to be unclassified (according to Silva v.132 database). This does not allow us to interpret their metabolic potential (e.g., sulfur oxidizers, heterotrophs etc), nonetheless, we observed that the downcore distribution of Gammaproteobacteria can be driven by iron availability (Fig 4A) which agrees with previous studies from dee sea settings (e.g., Kermadec Trench; see in reference [62]). The overall distribution of Chloroflexi seems to be influenced by AN (Fig. 4C), however, deep sea Chloroflexi are known to have a suite of metabolic potentials including both autotrophic and heterotrophic feeding modes [63-65]. Genera Acinetobacter and Pseudomonas , both affiliated to class Gammaproteobacteria , can degrade organic matters in marine sediments [66, 67]. Moreover, as one of the key members of P-solubilizing bacteria [68], the abundances of Pseudomonas were much higher in the deep layer, especially in the deepest sample (>24 cmbsf, Fig. S5E). This could be due to Pseudomonas having strong environmental adaptability in the deep layer, since it was reported that Pseudomonas species exhibit remarkable nutritional versatility and may utilize up to 100 different carbon sources for growth [69]. Conversely, as an autotrophic ammonia-oxidizing archaeon (AOA) of phylum Thaumarchaeota [70], Nitrosopumilus was the key population which plays important roles in N cycling in marine ecosystems [71-73], Our results indicated that the abundances of Nitrosopumilus generally decreased with depth and showed significant negative correlation with dept (Figs. S5E and 5F), that was well accordance with the distribution of Fe, because previous study had revealed that the growth of Nitrosopumilus may be facilitated by Fe [73], and our results also indicated that the contents of Fe were higher in the surficial layer (Fig. 4A) and Nitrosopumilus showed a positive correlation with Fe (although it was not significant, Fig. S5F). Additionally, although the content of oxygen may decrease with depth in deep-sea sediments [58], previous study had revealed that Nitrosopumilus not only consumes oxygen but can also produce oxygen in dark sea, which is used for ammonia oxidation [74]. Therefore, the presence or absence of oxygen may not have a significant impact on its growth and ammonia oxidation process. As the members of the superphylum DPANN ( Diapherotrites , Parvarchaeota , Aenigmarchaeota , Nanoarchaeota and Nanohaloarchaea ) [75, 76], Pacearchaeota and Woesearchaeota which are among the archaea with much smaller cellular and genome size [77] and with evident metabolic flexibility [78, 79]. It was reported that Pacearchaeota Incertae Sedis AR13 was a potential hydrogenotrophic methanogen and had the potential for converting CO 2 and H 2 into CH 4 [80], which indicated that Pacearchaeota Incertae Sedis AR13 was one of the key archaea in driving carbon cycle in marine sediments [16]. Although intriguing, we cannot ascertain if this metabolic potential of Pacearchaeota Incertae Sedis AR13 occurs in our sediments, nor its role on carbon cycling underneath polymetallic nodules, as CH 4 concentrations were not measured on our investigated sediments . Woesearchaeota plays a key role in the cycling of carbon, nitrogen, and sulfur [81, 82]. Our results demonstrated that genera Woesearchaeota Incertae Sedis AR15, Woesearchaeota Incertae Sedis AR16 and Woesearchaeota Incertae Sedis AR18 totally occupied 54.6% of the phylum Woesearchaeota , which suggested that these three genera are the major drivers of C, N and S cycles in manganese nodules sediments [83, 84]. Moreover, compared to the almost same depth (5086 m) of Mariana Trench, our results found that much a higher abundance of Woesearchaeota was detected in manganese nodules sediments (0.46% vs. 0.19% on average), and no Pacearchaeota populations were detected in Mariana Trench [85]. That suggested that there exist unique archaeal resources in the manganese nodules sediments and these archaea played crucial roles in maintaining geochemical elements cycling. Depth was the predominant factor influencing prokaryotic distribution, with two key contributing factors. Firstly, previous studies had proved that the O 2 content declined with sediment depth (although it was not determined in this study) [54, 60], and less available oxygen in deeper sediments led to a decline of microbial richness [54, 58, 86]. Secondly, weaker water fluidity hinders nutrient exchange and microbial diffusion in the deeper layer [44, 87, 88]. In addition, some studies also showed that pH and EC had important influences on microbial diversity and community structure [31, 89-91]. However, we only found that pH and EC significantly affected the archaeal community, but not the bacterial community. This might be owing to differences in environmental adaptations, cellular structure, and cellular metabolisms between bacteria and archaea [92]. As the primary source of electron donors for microbes [93], many studies indicated that microbial abundances were strongly correlated with OC concentration [19, 52]. However, our results showed that prokaryotes were not significantly correlated with the content of OC except Firmicutes , which conflicted with earlier reports on the global distribution pattern of benthic microbes [94, 95]. This difference may be caused by the content of OC in this study (22–27 g∙kg -1 ) was much higher than the pioneers’ results [44, 54, 61]. In addition, our results also indicated that the contents of Cu in the deep layer were significantly higher than those in the surficial layer (Fig. 4A), and exerted significant influence on bacteria (Fig. 4D), those results indicated that heavy metals may also shift prokaryotic distribution in sediments. Shifting in Community Assembly Processes from Surficial to Deep Layer Environmental filtering can lead to different community assemblies [48, 96]. Variable selection implies that spatial or temporal environmental changes select dominated species from the local species pool [16, 97]. Our findings revealed a significant difference in variable selection, with nearly 60% higher levels observed in the surficial layer than that in the deep layer (Fig. 5C). This discrepancy may be linked to more sensitivity of surficial sediment prokaryotes to the influence of neighboring aquatic organisms and environmental fluctuations, in contrast to their counterparts in the deeper horizons [98]. Unlike the surficial layer, there was a shift in the assembly mechanism of prokaryotic communities in the deep layer, remarkably showing a stochasticity-dominated mechanism driven by dispersal limitation and undominated processes (Fig. 5C). Dispersal refers to the movement of organisms across space [99], dispersal events can alter the rate of prokaryotic colony information, thereby impacting the compositions and distribution of prokaryotic communities [97, 100]. Notably, microbial dispersal rates are contingent on the surrounding environmental conditions [101, 102]. It's worth highlighting that water can serve as a medium for microbial diffusion, and as sediments deepen, water mobility diminishes. This explains why the community assembly processes for deep-layer prokaryotic communities were significantly influenced by dispersal limitation (22%, Fig. 5C). Undominated (primarily including drift, weak selection, and weak dispersal) process, it was used to estimate that neither selection nor dispersal is the primary cause of between-community compositional differences [48, 103]. Although our results showed that variable selection explained 40% of prokaryotic community assembly processes in the deep layer, dispersal limitation (22%) and undominated (36%) also played considerable contributions in community assembling. This indicated that prokaryotic community diversity and taxonomic compositions in this layer could not be solely attributed to environmental selection [104]. In addition, a higher proportion of undominated processes in shaping prokaryotic community assembly may suggest that the lower prokaryotic abundance may also contribute to the stochastic assembly in the deep layer [105]. Co-occurrence Networks Shaped by Depth and Influenced by Environmental Factors Microbial co-occurrence patterns are intricately linked to interspecies diversity, abundance and interactions [106]. Compared with the surficial layer, a reduced complex co-occurrence network was observed within the deep layer. That might be due to i) the reduction of microbial abundance and diversity in the deep layer compared with the surficial layer; and ii) some microorganisms in the deep layer adopt a dormancy state for survival, characterized by slower growth rates and metabolism [107], this dormancy state can reduce their interactions and connections with other species. In addition, environmental perturbations in the surficial layer were more frequent compared to the deep layer. This led to higher clustering coefficients and modularity in the surficial layer (Fig. S6), these factors are crucial for maintaining community stability [108-111]. Module, signifies the division of microbial groups, where species within a module are closely related to each other but less so to species outside the module. This arises due to differences in ecological functions, niches and/or divergent selection [59, 112]. Due to one module having little or no influence on another module, and the influence of environmental perturbations within one module is unlikely to be transmitted to the other, that could reduce the impact of environmental perturbations on the whole microbial community. Therefore, high modularity is help to improve prokaryotic environmental adaptation in the surficial layer [111]. Moreover, it was proved that a high proportion of negative links among nodes indicated that the community returns to stability more quickly within a network [113]. We found approximately 46% more negative links in the surface layer compared to the deep layer, suggesting that prokaryotes within the surficial layer would increase negative relationships to enhance microbial community stability. In addition, our results indicated that environmental factors, especially for Fe, Co, Ni, Cu and Zn, may exert distinct effects on some prokaryotes in the surficial layer and deep layers (Figs. 6C and 6D). That may be attributed to differences in heavy metal content between the surficial and deep layers (Fig. 4A) and variations in heavy metal tolerance of prokaryotes in different layers. These findings suggested that heavy metals may be the key factors of driving prokaryotic community structure shift in manganese nodules sediments. Conclusion This study presented a depth-layer distribution of prokaryotic diversity and community assembly patterns in sediments of Nazimov guyots. Generally, prokaryotic diversity, microbial abundance and community structure were significant higher and different in the surficial layer (0–8 cm) than those in the deep layer (8–26 cm). Prokaryotic community assemblies were governed by deterministic and stochastic processes in the surficial and deep layers, respectively. And co-occurrence network analysis further showed that network in the surficial layer was much complex than that in the deep layer, and more modules were observed in the surficial layer to enhance prokaryotic environmental resistance in the surficial layer. And heavy metals may exert distinct influences on prokaryotes in different layers. Such results described the vertical distribution of prokaryotes and their environmental response of in polymetallic nodules area, another important practical implication is that providing some basal reference for marine ecosystem protection. Declarations Data Accessibility The raw sequence data were submitted to the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (accession No.: PRJNA824224). Author Contribution JX Sun ： Methodology, Software, Investigation, Funding Acquisition, Formal Analysis, Writing - Original Draft. HB Zhou ： Writing- Reviewing & Editing. HN Cheng ： Software， Funding Acquisition. Z Chen ： Data Curation, Formal analysis. JC Yang: Data Curation, Formal analysis. YG Wang ： Resources, Conceptualization, Writing- Reviewing & Editing, Funding Collection. CL Jing: Data Curation, Writing - Review & Editing. Declaration of Competing Interest All authors declare that they have no known competing financial interests that influence the work reported in this paper. Consent to Publish All authors have given their consent to publish this research article. Acknowledgments The work was supported by the National Nature Science Foundation of China (41706221 and 42073079), the Open Funding Project of National Key Laboratory of Human Factors Engineering (GJSD22008 and SYFD062009K) and Fundamental Research Funds for the Central Universities of Central South University (2022ZZTS0434). References Hein JR, Koschinsky A, Kuhn T (2020) Deep-ocean polymetallic nodules as a resource for critical materials. 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Wang K, Yan H, Peng X, Hu H, Zhang H, Hou D, Chen W, Qian P, Liu J, Cai J, Chai X, Zhang D (2020) Community assembly of bacteria and archaea in coastal waters governed by contrasting mechanisms: A seasonal perspective. Mol Ecol 29: 3762−3776. doi: 10.1111/mec.15600 Gorter FA, Manhart M, Ackermann M (2020) Understanding the evolution of interspecies interactions in microbial communities. Philosophical Transactions of the Royal Society B 375: 20190256. Jørgensen BB, Marshall IP (2016) Slow microbial life in the seabed. Annu Rev Mar Sci 8: 311-332. Li C, Wang L, Ji S, Chang M, Wang L, Gan Y, Liu J (2021) The ecology of the plastisphere: Microbial composition, function, assembly, and network in the freshwater and seawater ecosystems. Water Res 202: 117428. doi: 10.1016/j.watres.2021.117428 Wan X, Gao Q, Zhao J, Feng J, Nostrand JDv, Yang Y, Zhou J (2020) Biogeographic patterns of microbial association networks in paddy soil within Eastern China. Soil Biol Biochem 142: 107696. doi: 10.1016/j.soilbio.2019.107696 Liu S, Yu H, Yu Y, Huang J, Zhou Z, Zeng J, Chen P, Xiao F, He Z, Yan Q (2022) Ecological stability of microbial communities in Lake Donghu regulated by keystone taxa. Ecol Indicators 136: 108695. doi: 10.1016/j.ecolind.2022.108695 Li W, Kuzyakov Y, Zheng Y, Li P, Li G, Liu M, Alharbi HA, Li Z (2022) Depth effects on bacterial community assembly processes in paddy soils. Soil Biol Biochem 165. doi: 10.1016/j.soilbio.2021.108517 Olesen JM, Bascompte J, Dupont YL, Jordano P (2007) The modularity of pollination networks. Proc Natl Acad Sci USA 104: 19891-19896. Coyte KZ, Schluter J, Foster KR (2015) The ecology of the microbiome: Networks, competition, and stability. Science 350: 663-666. Additional Declarations No competing interests reported. Supplementary Files RevisedSupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 04 Oct, 2023 Read the published version in Microbial Ecology → Version 1 posted Editorial decision: Accepted 04 Sep, 2023 Submission checks completed at journal 04 Sep, 2023 First submitted to journal 03 Sep, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2945198","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":230830013,"identity":"0303c622-fcda-4e9d-8b49-c5f2507e046a","order_by":0,"name":"Jianxing Sun","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jianxing","middleName":"","lastName":"Sun","suffix":""},{"id":230830014,"identity":"d7295485-7ee6-47e2-9590-f88dd0e8f24a","order_by":1,"name":"Hongbo Zhou","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hongbo","middleName":"","lastName":"Zhou","suffix":""},{"id":230830015,"identity":"5f381d49-0f3d-4f6e-a96f-cc639f9b5fe5","order_by":2,"name":"Haina Cheng","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haina","middleName":"","lastName":"Cheng","suffix":""},{"id":230830016,"identity":"6261ba17-f87a-4ff5-bd50-2799f863db0b","order_by":3,"name":"Zhu Chen","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhu","middleName":"","lastName":"Chen","suffix":""},{"id":230830017,"identity":"9012d3b4-9bd6-4c15-873d-1829d85bf81d","order_by":4,"name":"Jichao Yang","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jichao","middleName":"","lastName":"Yang","suffix":""},{"id":230830018,"identity":"bb14e474-ca65-471c-a316-9c4bd1d7aa53","order_by":5,"name":"Yuguang Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYPACGwjFQ4KWNNK1HCZBi8H5NYafC36dl5efkcD44G0bg7w5QS033hhLz+y7bbjhRgKz4dw2BsOdDQS1nN0gzdtzO8FAIoFNmreNIcHgAGEtm3/z9pxLADqM/TdxWs73bpPm+XEggeFGAhszUVokb/B/s+ZtSDbccOZhs+SccxKGGwhp4Tt/LPk2zx87efn25IMf3pTZyBO0ReFGAgMDYxuIydgAJCQIqAcC+X6QoX8IKxwFo2AUjIIRDAApjUOYeeI7jAAAAABJRU5ErkJggg==","orcid":"","institution":"Central South University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yuguang","middleName":"","lastName":"Wang","suffix":""},{"id":230830019,"identity":"e5c1a00b-a62e-47f9-9ecc-713e5b25d84f","order_by":6,"name":"Chunlei Jing","email":"","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chunlei","middleName":"","lastName":"Jing","suffix":""}],"badges":[],"createdAt":"2023-05-17 03:44:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2945198/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2945198/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00248-023-02305-8","type":"published","date":"2023-10-04T15:02:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":43149984,"identity":"34b698bf-410c-4d3e-bf1d-5ad9d2a4a3c8","added_by":"auto","created_at":"2023-09-14 17:47:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":574964,"visible":true,"origin":"","legend":"\u003cp\u003eSampling site on Nazimov guyots of Magellan Seamounts area.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2945198/v1/aeff5c3e3b490b6aad536a3b.png"},{"id":43150820,"identity":"6d028c81-85d3-4c80-bec6-ced9b6e46719","added_by":"auto","created_at":"2023-09-14 17:55:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66826,"visible":true,"origin":"","legend":"\u003cp\u003eT-test between the surficial and the deep layers of prokaryotic A) richness, B) Shannon index, C) Chao1 index, and D) ACE index in sediments (*, and *** represent \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, respectively).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2945198/v1/fe5d06dc81f7445d361a043a.png"},{"id":43151238,"identity":"5354735e-93b5-4c39-96dd-5b618fccbac2","added_by":"auto","created_at":"2023-09-14 18:03:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":101823,"visible":true,"origin":"","legend":"\u003cp\u003eStacking histogram of prokaryotic major composition at phylum level based on (A) relative abundance and (B) absolute abundance (UPGMA analysis).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2945198/v1/3a4faeedd851aa2c94f3b55c.png"},{"id":43149228,"identity":"a669c1d9-132e-4913-92d4-cd998aaef707","added_by":"auto","created_at":"2023-09-14 17:39:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":280642,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of environmental factors on prokaryotic communities. A) compared sediment properties between surficial and deep layers based on T-test. B) RDA analysis illustrating the relationship between prokaryotic communities at the OTU level. C) Correlation between environmental factors and prokaryotes at phylum level (the color gradient on the right indicates Spearman’s rank correlation coefficients). D) Correlation between environmental factors and prokaryotic community structure based on OTU level (Pairwise comparisons between environmental factors and Archaea/Bacteria. The color gradient and circle denote Spearman’s rank correlation coefficients. The line width represents the Mantel’s statistic for the corresponding correlation coefficient, and the line color means that significance which is tested based on 999 permutations). (*, ** and ‘ns’ represent \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01 and no significant, respectively.) Abbreviations, EC: electrical conductivity, AP: Available phosphorous, AK: available potassium, AN: available nitrogen, OC: organic carbon.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2945198/v1/378bc6b5df8126cd3a8d5f57.png"},{"id":43149233,"identity":"14f7507f-8cbb-46da-96bb-251f2d16f536","added_by":"auto","created_at":"2023-09-14 17:39:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":140006,"visible":true,"origin":"","legend":"\u003cp\u003eA) Distribution of standardized phylogenetic turnover (βNTI) and B) taxonomic turnover (RC\u003csub\u003ebray\u003c/sub\u003e) and C) the percentages of the community assembly processes. The vertical dashed black lines mark the positions of − 2 and 2 A) and − 0.95 and 0.95 B).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2945198/v1/db9eb06894dfb95941e8b436.png"},{"id":43149229,"identity":"14dc704a-84ce-4649-8efc-70e70c61e3cd","added_by":"auto","created_at":"2023-09-14 17:39:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":498240,"visible":true,"origin":"","legend":"\u003cp\u003eCo-occurrence networks of the microbial community in A) surficial and B) deep depth networks. Co-occurrence networks with OTUs colored by prokaryotic communities at phylum. Each connection shown has a positive (red) or negative (blue) correlation coefficient \u0026gt; |0.7| and a \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.05. The size of each node is proportional to the number of connections (degree). Co-occurrence networks of environment-prokaryote interactions (OTU level) for C) surficial and D) deep layers. The correlations between significant environmental variables and OTUs with strong correlations (r = 0.68 ~ 0.99, \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, the small nodes represent OTUs).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2945198/v1/7d67eb21a19c6bf2890b0aad.png"},{"id":44302534,"identity":"849b6ad7-1700-422f-9a99-ea874c42c20f","added_by":"auto","created_at":"2023-10-09 15:10:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2086855,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2945198/v1/c731045b-21c5-48dc-8b98-08d3ded0bb02.pdf"},{"id":43149234,"identity":"5f3505a3-8982-49a6-a8d5-6ecb83441b86","added_by":"auto","created_at":"2023-09-14 17:39:53","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":955225,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedSupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-2945198/v1/d52e179e02cd3714bf5c708a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Depth-dependent distribution of prokaryotes in sediments of the manganese crust on Nazimov guyots of the Magellan seamounts","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDeep-ocean polymetallic nodules (also known as manganese nodules) are widely distributed below the vast, sediment-covered, abyssal plains of the global ocean\u0026nbsp;[1, 2]. Polymetallic nodules are strategically important since they are rich in precious metals such as nickel, copper, cobalt, and manganese\u0026nbsp;[3, 4]. Rising demand for metals and the depletion of land-based resources have led to a surge of interest in ocean mining operations\u0026nbsp;[5]. However, ocean mining will inevitably damage the marine environment and biodiversity, which has been highlighted over the past several years\u0026nbsp;[6, 7]. For instance, the dispersion of sediment plumes and the footprint of plumes have strong negative impacts on benthic organisms, even leading to biodiversity loss\u0026nbsp;[6, 8]. Although extensive studies have revealed the geographical distribution and polymetallic composition of manganese nodules in marine sediments\u0026nbsp;[2, 9, 10], the microbial diversity underneath sediments where polymetallic nodules exist is understudied\u0026nbsp;[11]. Microorganisms are the major contributors of biogeochemical cycling in deep sea settings\u0026nbsp;[12-14], while their distribution and presence/absence in both marine and terrestrial ecosystems can reflect anthropogenic activities\u0026nbsp;[e.g., 15-17].\u0026nbsp;However, compared to other marine habits, such as seawater and marine sediments, our knowledge of microbial diversity and community distribution in the polymetallic nodule regions remains largely limited\u0026nbsp;[14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing appropriate prokaryotic primers, 16S rRNA amplicon sequencing can estimate microbial diversity and can also capture taxa with low abundances\u0026nbsp;[14]. Nonetheless, when combined with quantitative PCR (qPCR) it can provide a second line of evidence on microbial abundance\u0026nbsp;[18, 19], and community structure\u0026nbsp;[20, 21].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDeep-sea mining for polymetallic nodules could have a major impact on the abyssal environment and microbial diversity\u0026nbsp;[7]. Furthermore, microbial community assembly and putative metabolic processes would be shifted under environmental disturbance\u0026nbsp;[22, 23].\u0026nbsp;In subsurface sediments, both deterministic and stochastic processes can occur simultaneously and shape the assembly of in situ microbial communities. This is not surprising considering that essential environmental parameters, like O\u003csub\u003e2\u003c/sub\u003e, temperature, nutrient concentrations, and available nutrient pools, as well as physical disturbances at abyssal depths (e.g., earthquakes and hydrodynamic processes) can influence the spatial distribution of microorganisms along sediment depth. Co-occurrence networks can examine relationship between microbes and environmental factors\u0026nbsp;[24, 25], and network analysis has been widely used to explore interactions between marine benthic microbes and microbe-environment relationships in shallow sediments\u0026nbsp;[26, 27]. Nonetheless, prokaryotic co-occurrence networks in sediments underneath polymetallic nodules are not fully investigated.\u003c/p\u003e\n\u003cp\u003eMagellan Seamounts are rich in manganese nodules [28, 29]. However, there are few studies on microbial diversity and community vertical distribution here [14]. In this study, core sediments were obtained from the Nazimov guyots of Magellan seamounts, and 16S rRNA amplicon sequencing and quantitative PCR (qPCR) methods were employed to explore prokaryotic diversity and community vertical distribution in Nazimov guyots. Our objectives were: (i) to reveal prokaryotic diversity and community compositions in different depth layers; (ii) to estimate the contribution of stochastic and deterministic processes in shaping the downcore prokaryotic diversity; and (iii) to investigate putative co-occurrence patterns at different examined sediment horizons. This study reveals the prokaryotic community structure in the Magellan seamounts and expands our understanding on the microbial vertical distribution in sediments where polymetallic nodules exist. Describing the microbial community underneath those nodules can be useful in future deep-sea mining operations. \u0026nbsp;\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003ch2\u003eSample Collection and Environmental Characterization\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eOne sediment core was collected from surface to 26 cm below seafloor (cmbsf) at the Nazimov guyots in Magellan seamount\u0026nbsp;(162.45 \u0026deg;E, 15.18 \u0026deg;N, 5365 m seawater depth) using a four-tube multitube sampler (MC-400, 10\u0026times;58 cm, Ocean Environmental Sci.\u0026amp;Tech.) during the Dayang 61st-I cruise of P. R. China. Briefly, the sediment core was sliced at 2-cm intervals with a stainless-steel cutter at 13 different sediment intervals (n = 13 samples). A sterile spatula was used to carefully collect sediment from the center of each sediment horizon for DNA extraction to avoid potential contamination.\u0026nbsp;Samples were transferred to sterilized plastic tubes and stored at -80 \u0026deg;C until DNA extraction, and analyses of selected environmental factors like pH, electrical conductivity (EC), available phosphorous (AP), available potassium (AK), available nitrogen (AN), and organic carbon (OC), according to\u0026nbsp;[30, 31]. We note that our analyses on AN and AP provide bulk estimations of available N and P in the sediments without further speciation on the contribution of inorganic vs. organic sources.\u0026nbsp;Briefly, samples were air-dried at room temperature in the shade and sieved through a 2-mm screen. The pH and EC were determined in a 1:2.5 sediment/water solution\u0026nbsp;[31]. The contents of AP were determined by molybdenum blue colorimetry using a spectrophotometer (TAS-990, Persee, Beijing) according to a previous study\u0026nbsp;[32], and concentrations of AK were measured by the ammonium acetate extraction flame photometer method\u0026nbsp;[33]. The contents of AN were determined by the Kjeldahl procedure\u0026nbsp;[30]. Sediment OC was determined by the rapid dichromate oxidation-titration method\u0026nbsp;[34].\u0026nbsp;Concentrations of heavy metals including Mn, Fe, Co, Ni, Cu and Zn found underneath polymetallic nodules\u0026nbsp;[35, 36], were determined according to\u0026nbsp;[37]. Briefly, sediment samples were dried at 105 ◦C for 6 h and then ground to a fine powder using a Hard Tissue Homogenizer (VWR International, West Chester, PA, USA). Accordingly, 0.5 g of dried sample powder was dissolved in an acidic mixture of HF\u0026ndash;HCl\u0026ndash;HNO\u003csub\u003e3\u003c/sub\u003e (1:3:1) by microwave-assisted digestion, and the leachate was used to detect the concentrations of metallic ions by Inductively coupled plasma optical emission spectroscopy (ICP-AES) (Agilent 720ES, USA) [38, 39].\u003c/p\u003e\n\u003ch2\u003eDNA Extraction, Sequencing, and Data Processing\u003c/h2\u003e\n\u003cp\u003eDNA was extracted from 0.25 g of homogenized sediment (wet weight) using the Power Soil DNA Isolation Kit (MoBio Laboratories, Inc., Carlsbad, CA, USA) as instructed by the manufacturer. DNA extraction, PCR amplification protocol, and data processing are described in\u0026nbsp;[40].\u0026nbsp;The primer set of 515F (Parada) (5\u0026prime;-GTGYCAGCMGCCGCGGTAA-3\u0026prime;) and 806R (Apprill) (5\u0026prime;-GGACTACNVGGGTWTCTAAT-3\u0026prime;) was used to amplify the V4 hypervariable region of the 16S rRNA gene\u0026nbsp;targeting both bacteria and archaea\u0026nbsp;according to the recommendation of Earth Microbiome Project (EMP)\u0026nbsp;[41-43]. Quantitative PCR (qPCR) was performed to estimate total abundances of bacteria and archaea. qPCR primers and conditions were described in detail in reference\u0026nbsp;[44].\u0026nbsp;Briefly, qPCR was conducted as follows: 3 min for denaturation at 95\u0026deg;C; 29 cycles of 30 s at 95\u0026deg;C, 30 s for annealing at 55\u0026deg;C, 45 s for elongation at 72\u0026deg;C, and 10 min for a final extension at 72\u0026deg;C. qPCR reactions were performed in triplicate. Furthermore, the absolute abundances of targeted microbial populations were\u0026nbsp;calculated by total DNA copies \u0026times; their relative abundance. In all experiments, negative controls, which contained no template DNA, were qPCR amplified to detect putative contamination. Sequencing was conducted using the MiSeq paired-end 2 x 250 bp (PE250) platform (Illumina Inc., San Diego, CA, USA). Raw data were processed and analyzed using an in-house pipeline (http://mem.rcees.ac.cn:8080) as described by previous study\u0026nbsp;[45]. Sequences with an average quality score below 20 and sequence length of fewer than 200 bp were discarded. And sequences were then split into operational taxonomic units (OTUs) at a 97% similarity level using the UPARSE pipeline. Furthermore, singleton OTUs were removed before downstream analyses since they may represent sequencing errors. Accordingly, the taxonomy of each 16S rRNA gene sequence was analyzed via the Ribosomal Database Project (RDP) classifier algorithm (version 2.11, http://rdp.cme.msu.edu/)\u0026nbsp;[46]\u0026nbsp;against the Silva rRNA database (Release132, http://www.arb-silva.de )[47].\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData Analysis\u003c/h2\u003e\n\u003cp\u003eTo equalize sequencing depth, each sub-sample was rarefied to 118403 reads (the lowest sequence number across all samples) for further analysis. The alpha diversity was estimated using OTU richness, Shannon index, Chao1 index, and ACE index in this study. Overall differences in bacterial and archaeal community composition were visualized with principal coordinates analysis (PCoA) based on Bray-Curtis distance using R software (Version 4.0.1) base package \u0026lsquo;vegan\u0026rsquo;, and the statistically significant difference between any pair of regions was assessed using the analysis of similarities (ANOSIM), multiple response permutation procedure (MRPP) and permutational multivariate analysis of variance (PERMANOVA) (all run with 999 permutations). Moreover, the unweighted pair-group method with arithmetic means analysis (UPGMA analysis) was conducted to explore the vertical variation of prokaryotic communities with depth, and the 16S rRNA gene copy numbers of different prokaryotic populations were calculated by the total gene copy numbers multiplied by the corresponding relative abundance. Redundancy analysis (RDA) was performed to evaluate the relationships between prokaryotic communities and environmental factors using the \u0026lsquo;vegan\u0026rsquo; package in R. RDA method was chosen because preliminary detrended correspondence analysis (DCA) on prokaryotic community data revealed that the longest gradient lengths were less than 3.0. Correlation analysis between prokaryotic phylum populations and environmental factors was conducted based on the Mantel test and visualized by a heatmap. Spearman\u0026rsquo;s rank correlations were used to determine the relationship between the Bray-Curtis similarity of bacterial and archaeal communities and the environmental factors based on OTU level. A mantel test was conducted (999 permutations) in conjunction with the OTU table to reveal how environmental factors affect bacterial or archaeal communities. Spearman\u0026rsquo;s correlations among environmental factors and their correlations to bacteria and archaea were analyzed using the \u0026lsquo;ggcor\u0026rsquo; package of R.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eProkaryotic Community Assembly Processes\u003c/h2\u003e\n\u003cp\u003eThe relative importance of deterministic and stochastic processes in prokaryotic community assembly was evaluated using null model-based methods which detect nonrandom cooccurrence phylogeny patterns. These methods were conducted following the analytical framework analyses of phylogenetic beta diversity and taxonomic beta diversity\u0026nbsp;[48]. First, a null distribution of the \u0026beta;-mean-nearest taxon distance (\u0026beta;MNTD) was generated among samples within different depths by re-dominating the taxa labels of the phylogenetic tree 999 times. Then, the \u0026beta;-nearest taxon index (\u0026beta;NTI) was calculated by comparing the difference between the observed \u0026beta;MNTD values and the mean of the null distribution of \u0026beta;MNTD normalized by its standard deviation. A \u0026beta;NTI value \u0026lt; -2 means significantly lower than the expected phylogenetic turnover rate whereas a \u0026beta;NTI value \u0026gt;+2 indicates significantly higher than the expected phylogenetic turnover rate. When -2\u0026lt; \u0026beta;NTI \u0026lt; +2, it means that this is the stochastic process, including dispersal limitation, homogeneous dispersal, and undominant processes. To disentangle these cases, a further calculation based on the Bray-Curtis based Raup-Crick metric (RC\u003csub\u003ebray\u003c/sub\u003e) as described by the previous study on the relative contribution to the assembly process with |\u0026beta;NTI| \u0026lt;2\u0026nbsp;[48]. The relative contribution of dispersal limitation was estimated in terms of percentages of paired comparisons with |\u0026beta;NTI| \u0026lt;2 and RC\u003csub\u003ebray\u003c/sub\u003e \u0026gt;0.95. The contribution of relative homogeneous dispersal was estimated in terms of percentages of paired comparisons with |\u0026beta;NTI| \u0026lt;2 and RC\u003csub\u003ebray\u003c/sub\u003e \u0026lt; -0.95. In contrast, not belonging to any of these categories suggested that the undominated process governed community assembly.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCo-occurrence Network Analysis\u003c/h2\u003e\n\u003cp\u003eThe co-occurrence patterns of prokaryotic communities were demonstrated by co-occurrence networks using the \u0026ldquo;igraph\u0026rdquo; and \u0026ldquo;Hmisc\u0026rdquo; packages in R and visualized by Gephi (version 0.9.2). It was noticeable that only OTUs occurring in more than 10 sequences (on average) of all samples were retained to avoid the influence caused by sequencing contamination\u0026nbsp;[49]. The pairwise Spearman\u0026apos;s correlations between OTUs were calculated, with a correlation coefficient \u0026gt; |0.7| and a p-value \u0026lt; 0.05 (Benjamini and Hochberg-adjusted, BH) being considered as a valid relationship. To describe the network-level topology of the networks, we calculated a set of metrics: average degree, modularity, average clustering coefficient (\u003cem\u003eAvgCC\u003c/em\u003e), average path length (\u003cem\u003eAPL\u003c/em\u003e), network diameter, and graph density. Average degree refers to the average connections of each node with another unique node in the network; \u003cem\u003eAvgCC\u003c/em\u003e represents the degree to which the nodes tend to cluster together; the term \u0026lsquo;\u003cem\u003eAPL\u003c/em\u003e\u0026rsquo; has been used to describe the average network distance between all pairs of nodes; network diameter refers to the greatest distance between the nodes that exist in the network; and graph density refers to the intensity of connections among nodes. Therefore, higher average degree, \u003cem\u003eAvgCC\u003c/em\u003e, and graph density suggest a more connected network. In addition, lower \u003cem\u003eAPL\u003c/em\u003e and diameters indicate closer associations within the network, higher modularity index with a higher clustering coefficient of nodes indicated that they were more likely to present in an interconnected \u0026ldquo;small world\u0026rdquo;\u0026nbsp;[44, 50]. Additionally, the surficial layer and deep layer networks were constructed to visualize the correlations between the microbes and environmental factors. In the process of calculation, only strong and significant connections (Spearman correlation, |R| \u0026gt; 0.6 and \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) were kept in networks to find the key environmental factors [51]. These processes were calculated using R 4.0.5 with \u0026ldquo;psych\u0026rdquo; and \u0026ldquo;reshape2\u0026rdquo; packages.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eGeneral Environmental Characterization\u003c/h2\u003e\n\u003cp\u003eDetailed sediment environmental parameters are shown in Table 1. Generally, the sediments are slightly alkaline, the pH ranged from 8.02 to 8.26. The EC values of the sediments ranged from 8.42 to 10.92 mS∙cm\u003csup\u003e-1\u003c/sup\u003e. The AP and AK concentrations ranged from 13.17 to 16.61 mg∙kg\u003csup\u003e-1\u003c/sup\u003e and 7.57 to 14.15 g∙kg\u003csup\u003e-1\u003c/sup\u003e, respectively. The contents of AN and OC were in a range of 6.51 to 42.90 mg∙kg\u003csup\u003e-1\u003c/sup\u003e and 22.18 to 26.86 g∙kg\u003csup\u003e-1\u003c/sup\u003e, respectively. We found that the EC value and the contents of AP and AK were clustered in the upper sediment (top 10 cmbsf) and deeper sediment (10\u0026ndash;26 cmbsf) samples, while other parameters were not showed the same tendency. Moreover, the contents of Mn, Fe, and Zn does not appear to exhibit a significant trend between the surface and deep layers. However, some kinds of metals, such as Co, Ni, and Cu were higher in the surficial layer than those in the deep layer.\u003c/p\u003e\n\u003cp\u003eTable 1 Environmental parameters of sediment in all samples\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.461538461538462%\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.076923076923077%\"\u003e\n \u003cp\u003eLayer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.538461538461538%\"\u003e\n \u003cp\u003eDepth\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003eEC\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003eAP\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003eAK\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003eAN\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003eOC\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003eMn\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003eFe\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003eCo\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003eNi\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003eCu\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.230769230769231%\"\u003e\n \u003cp\u003eZn\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.461538461538462%\"\u003e\n \u003cp\u003eS0_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.076923076923077%\"\u003e\n \u003cp\u003eSurficial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.538461538461538%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e8.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e10.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e14.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e10.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e21.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e22.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e8.23\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e57.63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e20.88\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.230769230769231%\"\u003e\n \u003cp\u003e0.16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.461538461538462%\"\u003e\n \u003cp\u003eS2_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.076923076923077%\"\u003e\n \u003cp\u003eSurficial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.538461538461538%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e14.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e10.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e32.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e7.64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e58.26\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e19.55\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.43\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.230769230769231%\"\u003e\n \u003cp\u003e0.18\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.461538461538462%\"\u003e\n \u003cp\u003eS4_6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.076923076923077%\"\u003e\n \u003cp\u003eSurficial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.538461538461538%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e8.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e16.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e13.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e23.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e7.72\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e63.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e11.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.38\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.230769230769231%\"\u003e\n \u003cp\u003e0.16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.461538461538462%\"\u003e\n \u003cp\u003eS6_8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.076923076923077%\"\u003e\n \u003cp\u003eSurficial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.538461538461538%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e8.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e9.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e14.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e11.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e21.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e6.98\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e54.14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e26.22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.26\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.38\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.230769230769231%\"\u003e\n \u003cp\u003e0.16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.461538461538462%\"\u003e\n \u003cp\u003eS8_10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.076923076923077%\"\u003e\n \u003cp\u003eDeep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.538461538461538%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e9.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e14.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e12.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e75.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e10.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e58.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e46.76\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.35\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.230769230769231%\"\u003e\n \u003cp\u003e0.17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.461538461538462%\"\u003e\n \u003cp\u003eS10_12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.076923076923077%\"\u003e\n \u003cp\u003eDeep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.538461538461538%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e10.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e13.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e13.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e19.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e23.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e6.53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e57.16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e17.43\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.26\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.230769230769231%\"\u003e\n \u003cp\u003e0.16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.461538461538462%\"\u003e\n \u003cp\u003eS12_14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.076923076923077%\"\u003e\n \u003cp\u003eDeep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.538461538461538%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e10.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e13.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e13.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e21.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e26.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e8.72\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e54.81\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e32.33\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.38\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.230769230769231%\"\u003e\n \u003cp\u003e0.17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.461538461538462%\"\u003e\n \u003cp\u003eS14_16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.076923076923077%\"\u003e\n \u003cp\u003eDeep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.538461538461538%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e8.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e10.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e13.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e14.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e22.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e6.65\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e49.37\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e24.73\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.38\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.230769230769231%\"\u003e\n \u003cp\u003e0.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.461538461538462%\"\u003e\n \u003cp\u003eS16_18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.076923076923077%\"\u003e\n \u003cp\u003eDeep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.538461538461538%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e10.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e13.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e13.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e22.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e8.30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e57.87\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e33.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.37\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.230769230769231%\"\u003e\n \u003cp\u003e0.18\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.461538461538462%\"\u003e\n \u003cp\u003eS18_20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.076923076923077%\"\u003e\n \u003cp\u003eDeep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.538461538461538%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e10.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e13.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e13.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e18.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e22.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e7.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e53.80\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e31.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.31\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.230769230769231%\"\u003e\n \u003cp\u003e0.17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.461538461538462%\"\u003e\n \u003cp\u003eS20_22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.076923076923077%\"\u003e\n \u003cp\u003eDeep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.538461538461538%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e8.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e10.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e13.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e13.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e23.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e6.66\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e56.53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e20.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.230769230769231%\"\u003e\n \u003cp\u003e0.17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.461538461538462%\"\u003e\n \u003cp\u003eS22_24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.076923076923077%\"\u003e\n \u003cp\u003eDeep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.538461538461538%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e8.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e10.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e13.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e7.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e26.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e8.75\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e58.33\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e31.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.37\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.230769230769231%\"\u003e\n \u003cp\u003e0.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.461538461538462%\"\u003e\n \u003cp\u003eS24_26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.076923076923077%\"\u003e\n \u003cp\u003eDeep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.538461538461538%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e8.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e9.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e12.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e13.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e26.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e6.65\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e58.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.769230769230769%\"\u003e\n \u003cp\u003e10.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.846153846153846%\"\u003e\n \u003cp\u003e0.34\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.230769230769231%\"\u003e\n \u003cp\u003e0.16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e(Note: \u003csup\u003ea\u003c/sup\u003ecm; \u003csup\u003eb\u003c/sup\u003emS/cm; \u003csup\u003ec\u003c/sup\u003emg/kg; \u003csup\u003ed\u003c/sup\u003eg/kg)\u003c/p\u003e\n\u003ch2\u003eProkaryotic Alpha and Beta Diversity\u003c/h2\u003e\n\u003cp\u003eAfter sequences processing, all sequences were clustered into 6447 OTUs (5770 bacterial OTUs and 677 archaeal OTUs) at a 97% similarity level. The OTUs rarefaction curves for all 13 samples showed a tendency to plateau, which indicated that the sequencing depth was enough to capture all prokaryotes, and the species rarefaction curves showed that species richness was much high in the top 8 cmbsf than that in the deeper sediments (Fig. S1). Moreover, the prokaryotic quantity expresses as gene copy numbers declined one order of magnitude from the top 8 cmbsf to the 8\u0026ndash;26 cmbsf layer (Table S1). Thus, according to the species richness and gene copies, we divided the whole sediment core into the surficial layer (top 8 cmbsf) and the deep layer (8\u0026ndash;26 cmbsf) for further analysis. Our results indicated that the OTU number, Shannon index, Chao 1 index, and ACE index in the surficial layers were significantly higher than those in the deep layer (Fig. 2 and Fig. S2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe overall variability of prokaryotic distribution patterns was analyzed by PCoA based on Bray-Curtis distance, and the samples clustered basically according to different depth layers as shown in Fig. S3, and the first two PCoA axes could explain 59.3% of the total prokaryotic community variations. In specific, the surficial layer samples clustered more tightly than that in the deep layer, which suggested that higher community variation existed in the deep layer than that in the surficial layer. Furthermore, three different statistical analyses, including MRPP, ANOSIM, and PERMANOVA, revealed that prokaryotic community structures were significantly different between the surficial layer and the deep layer (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1694675633.png\"\u003e\u003c/p\u003e\n\u003ch2\u003eProkaryotic Community Compositions at Different Sediment Layers\u003c/h2\u003e\n\u003cp\u003eThe major prokaryotic community compositions at phylum level (relative abundance over 0.1% on average) were shown in Fig. 3. Our results demonstrated that bacterial communities mainly consisted of \u003cem\u003eProteobacteria\u003c/em\u003e (31.8\u0026ndash;82.4%), \u003cem\u003eFirmicutes\u003c/em\u003e (2.05\u0026ndash;8.8%), \u003cem\u003ePlanctomycetes\u003c/em\u003e (0.8\u0026ndash;6.0%), \u003cem\u003eActinobacteria\u003c/em\u003e (0.6\u0026ndash;5.8%), \u003cem\u003eAcidobacteria\u003c/em\u003e (0.3\u0026ndash;7.2%) and \u003cem\u003eBacteroidetes\u003c/em\u003e (0.6\u0026ndash;5.4%) at phylum level across all samples (Fig. 3A). Moreover, Archaea were mainly composed of \u003cem\u003eThaumarchaeota\u003c/em\u003e (3.7\u0026ndash;36.8%), \u003cem\u003ePacearchaeota\u003c/em\u003e (0.1\u0026ndash;2.0%) and \u003cem\u003eWoesearchaeota\u003c/em\u003e (0.1\u0026ndash;1.0%) in sediments (Fig. 3A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 16S rRNA gene abundances of prokaryotes were much higher in the surficial layer (1.36\u0026ndash;7.11\u0026times; 10\u003csup\u003e9\u003c/sup\u003e copies g\u003csup\u003e-1\u003c/sup\u003e, dry weight), and then declined nearly ten-fold in the deep layer (1.57\u0026ndash;5.68 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e copies g\u003csup\u003e-1\u003c/sup\u003e) (Fig. 3B). In the context of absolute abundance (Fig. 3B), \u003cem\u003eProteobacteria\u003c/em\u003e (7.31 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e\u0026ndash;3.38 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e copies∙g\u003csup\u003e-1\u003c/sup\u003e in the surficial layer and 6.82 \u0026times; 10\u003csup\u003e7\u003c/sup\u003e\u0026ndash;3.06 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e copies∙g\u003csup\u003e-1\u003c/sup\u003e in the deep layer) and \u003cem\u003eThaumarchaeota\u003c/em\u003e (2.98 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e\u0026ndash;1.54 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e copies∙g\u003csup\u003e-1\u003c/sup\u003e in the surficial layer and 1.39 \u0026times; 10\u003csup\u003e7\u003c/sup\u003e\u0026ndash;1.02 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e copies∙g\u003csup\u003e-1\u003c/sup\u003e in the deep layer) were the most prevalent populations at phylum level. Interestingly, the 24\u0026ndash;26 cm depth was unique among these samples, where habitat-abundant \u003cem\u003eProteobacteria\u003c/em\u003e (mainly \u003cem\u003eGammaproteobacteria\u003c/em\u003e) but rare \u003cem\u003eThaumarchaeota\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, and \u003cem\u003eActinobacteria\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt class level (Fig. S4A), \u003cem\u003eProteobacteria\u003c/em\u003e was mainly composed of \u003cem\u003eGammaproteobacteria\u003c/em\u003e (10.6\u0026ndash;76.6%), \u003cem\u003eAlphaproteobacteria\u003c/em\u003e (3.5\u0026ndash;22.8%), \u003cem\u003eBetaproteobacteria\u003c/em\u003e (0.3\u0026ndash;10.9%) and \u003cem\u003eDeltaproteobacteria\u003c/em\u003e (0.2\u0026ndash;2.4%). \u003cem\u003eFirmicutes\u003c/em\u003e were mainly divided into classes \u003cem\u003eBacilli\u003c/em\u003e (0.1\u0026ndash;15.4%) and \u003cem\u003eClostridia\u003c/em\u003e (0.1\u0026ndash;1.5%). \u003cem\u003ePlanctomycetes\u003c/em\u003e consisted of class \u003cem\u003ePlanctomycetacia\u003c/em\u003e (0.1\u0026ndash;5.1%). At the order level, the abundances of \u003cem\u003eNitrosopumilales\u003c/em\u003e and \u003cem\u003eAlteromonadales\u003c/em\u003e generally decreased with depth. In contrast to \u003cem\u003eChromatiales\u003c/em\u003e, the abundances of \u003cem\u003ePseudomonadales\u003c/em\u003e were much lower in the surficial layer than those in the deep layer (Fig. S4B). Interestingly, our results indicated that prokaryotic abundances in the deepest sample (\u0026gt;24 cmbsf) were significantly distinct (e.g., orders \u003cem\u003eAlteromonadales\u003c/em\u003e, \u003cem\u003eRhodospirillales\u003c/em\u003e, \u003cem\u003ePseudomonadales\u003c/em\u003e, and \u003cem\u003eOceanospirillales\u003c/em\u003e) with other samples (Fig. S4B). Among the 5770 bacterial OTUs, 440 OTUs belong to class \u003cem\u003eGammaproteobacteria\u003c/em\u003e, which mainly consisted of genera \u003cem\u003eAcidibacter\u003c/em\u003e (1.6%), \u003cem\u003eThioprofundum\u003c/em\u003e (1.6%),\u003cem\u003e\u0026nbsp;Pseudomonas\u003c/em\u003e (3.6%),\u003cem\u003e\u0026nbsp;Colwellia\u003c/em\u003e (1.6%) and unclassified populations (58.9%, Fig. S5A). As for Archaea, there were 90, 189 and 337 OTUs affiliated to phyla \u003cem\u003eThaumarchaeota\u003c/em\u003e (including 76.7% \u003cem\u003eNitrosopumilus\u003c/em\u003e and 23.3% \u003cem\u003eNitrososphaera\u003c/em\u003e at genus level), \u003cem\u003ePacearchaeota\u003c/em\u003e (all OTUs belong to \u003cem\u003ePacearchaeota Incertae Sedis\u003c/em\u003e AR13 at genus level) and \u003cem\u003eWoesearchaeota\u003c/em\u003e (including 9.2% \u003cem\u003eWoesearchaeota Incertae Sedis\u003c/em\u003e AR15, 37.4% \u003cem\u003eWoesearchaeota Incertae Sedis\u003c/em\u003e AR16, and 8.0% \u003cem\u003eWoesearchaeota Incertae Sedis\u003c/em\u003e AR18 at genus level), respectively (Figs. S5B\u0026ndash;5D). Moreover, our results demonstrated that the abundances of genus \u003cem\u003eNitrosopumilus\u003c/em\u003e were much lower when the depth was over 20 cmbsf in sediments (Fig. S4E), and in contrast to genus \u003cem\u003eColwellia\u003c/em\u003e, the abundances of the genus \u003cem\u003eAcinetobacter\u003c/em\u003e were much higher in the deep layer than those in the surficial layer. Interestingly, the abundance of the genus \u003cem\u003ePseudomonas\u003c/em\u003e was much higher in the deepest sample (26 cmbsf, Fig. S5E).\u003c/p\u003e\n\u003ch2\u003eThe Influences of Environmental Factors on the Prokaryotic Community\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eOur results indicated that only the contents of AP, AK, Ni and Cu were significantly different from the surficial to deep layer (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), while other environmental parameters were not significantly changed (Fig. 4A). The first two axes of RDA jointly explained 41.6% of the total prokaryotic variation by all selected environmental factors (Fig. 4B). The Monte Carlo permutation tests indicated that prokaryotic community variation showed strong correlated with depth (r = 0.58, \u003cem\u003eP\u003c/em\u003e = 0.007 \u0026lt; 0.05, 999 permutations, Table S2) and Cu (r=0.24, \u003cem\u003eP\u003c/em\u003e=0.04\u0026lt;0.05). Furthermore, the abundances of \u003cem\u003eThaumarchaeota\u003c/em\u003e, \u003cem\u003eAcidobacteria\u003c/em\u003e, and \u003cem\u003eNitrospirae\u003c/em\u003e showed a significant and positive correlation with AP, conversely, \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eCyanobacteria\u003c/em\u003e showed a significant and negative correlation with AP (Fig. 4C). At genus level, the abundances of \u003cem\u003ePseudohongiella\u003c/em\u003e, \u003cem\u003eColwellia\u003c/em\u003e, \u003cem\u003eNitrosopumilus\u003c/em\u003e, and \u003cem\u003eWoesearchaeota Incertae Sedis\u003c/em\u003e AR18 showed significant and negative correlation with depth but significant and positive Correlation with AP. Moreover, EC, AK, AN and OC also exerted significant and negative influences on some genera (e.g., \u003cem\u003eThioprofundum\u003c/em\u003e, \u003cem\u003ePseudohongiella\u003c/em\u003e, \u003cem\u003eLuteimonas\u003c/em\u003e, \u003cem\u003eWoesearchaeota Incertae Sedis\u003c/em\u003e AR15 and \u003cem\u003eWoesearchaeota Incertae Sedis\u003c/em\u003e AR16). In addition, our results also indicated that heavy metals, such as Co, Ni, and Cu showed significant and positive correlations on genera \u003cem\u003eAcidibacter\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, Cu and Zn showed significant positive influence on \u003cem\u003eWoesearchaeota Incertae Sedis\u003c/em\u003e AR15 and \u003cem\u003ePacearchaeota Incertae Sedis\u003c/em\u003e AR13, respectively (Fig. S5F). Accordingly, to distinguish the different effects of environmental factors on bacterial and archaeal communities, all environmental factors were estimated through the Mantel test and as shown in Fig. 4D. Generally, environmental factors had distinct effects on bacteria and archaea. Archaea was significantly influenced by depth (r \u0026gt; 0.5, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01), AP (r \u0026gt; 0.5, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01), pH (r \u0026gt; 0.25, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) and EC (r \u0026gt; 0.25, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). However, the bacterial community was only strongly influenced by depth (r \u0026gt; 0.5, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) and Cu (r\u0026gt;0.5, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFig. 4 Effects of environmental factors on prokaryotic communities. A) compared sediment properties between surficial and deep layers based on T-test. B) RDA analysis illustrating the relationship between prokaryotic communities at the OTU level. C)\u0026nbsp;Correlation between environmental factors and prokaryotes at phylum level (the color gradient on the right indicates Spearman\u0026rsquo;s rank correlation coefficients). D) Correlation between environmental factors and prokaryotic community structure based on OTU level (Pairwise comparisons between environmental factors and Archaea/Bacteria. The color gradient and circle denote Spearman\u0026rsquo;s\u0026nbsp;rank\u0026nbsp;correlation coefficients. The line width represents the Mantel\u0026rsquo;s statistic for the corresponding correlation coefficient, and the line color means that significance which is tested based on 999 permutations).\u0026nbsp;(*, ** and \u0026lsquo;ns\u0026rsquo; represent \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01 and no significant, respectively.)\u0026nbsp;Abbreviations, EC: electrical conductivity, AP: Available phosphorous, AK: available potassium, AN: available nitrogen, OC: organic carbon.\u003c/p\u003e\n\u003ch2\u003eProkaryotic Community Assembly Processes\u003c/h2\u003e\n\u003cp\u003eBased on the null model, our results indicated that the proportions of absolute phylogenetic turnover (\u0026beta;NTI) values \u0026gt; 2 in the surficial and deep layers were 100% and 41%, respectively (Fig. 5A), and the proportions of absolute taxonomic turnover (RC\u003csub\u003ebray\u003c/sub\u003e) values \u0026lt; 0.95 in the two sediment layers were 100% and 42%, respectively (Fig. 5B). Above results indicated that the role of deterministic processes, especially variable selection, dominated prokaryotic assembly process in the surficial layer. Whereas, variable selection, dispersal limitation and undominated processes explained 42%, 22% and 36% of prokaryotic community assembly in the deep layer, respectively (Figs. 5C).\u003c/p\u003e\n\u003ch2\u003eCo-occurrence Networks in Different Sediment Layers\u003c/h2\u003e\n\u003cp\u003eCo-occurrence networks were built based on correlation relationships across all samples from the surficial and deep layers, respectively (Fig. 4). The resulting network of the surficial and deep layers consisted of 851 and 860 nodes linked by 22057 and 10677 edges, respectively. To depict the complex co-occurrence patterns of the networks, the topological characteristics were calculated as shown in Fig. S5. We found that the average degree and \u003cem\u003eavgCC\u003c/em\u003e were much lower in the deep layer, as did the modularity and graph density. Besides, compared with the surficial layer, the entire network of the deep layer showed bigger \u003cem\u003eAPL\u003c/em\u003e and network diameter. Notably, all the links between each pair of nodes consisted of 53.3% positive edges and 46.7% negative edges in the surficial layer network, while the positive edges occupied 99.2% of the total connections within the deep layer network.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe major prokaryotes and their connected edges and average degree in different co-occurrence networks were shown in Table S3. We found that \u003cem\u003eProteobacteria\u003c/em\u003e was the most abundant population within both the surficial layer (30.8%) and the deep layer (30.1%), while its average degree decreased by more than half from the surficial layer to the deep layer network, and the similar trend was observed in \u003cem\u003eThaumarchaeota\u003c/em\u003e. Although \u003cem\u003eChloroflexi\u003c/em\u003e owned higher average degree edges in two co-occurrence networks, its abundances were always much low (around 2%). As the second dominant bacterial and archaeal phyla, \u003cem\u003ePlanctomycetes\u003c/em\u003e and \u003cem\u003eWoesearchaeota\u003c/em\u003e owned a much higher average degree in the surficial layer than that in the deep layer. Interestingly, although the abundance of \u003cem\u003eFirmicutes\u003c/em\u003e increased from the surficial layer (5.3%) to the deep layer (6.1%), its average degree was sharply decreased from 47.9 to 16.2, as did as \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e and \u003cem\u003ePacearchaeota\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further reveal the relationships between prokaryotic species and environmental factors, correlation analysis was examined based on Person correlation and only significant and strong correlations (r \u0026gt; 0.6, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05) were visualized by the network (Figs. 6C and 6D). The results showed that depth, AK and OC had significantly strong negative effects on prokaryotes within the surficial layer, followed by pH and AP. Interestingly, only a few species were positively influenced by AP. In the deep layer, AK showed a significantly strong negative effect on more species, while OC and depth only impacted a few species. Conversely, AP and AN showed a positive influence. Our results also indicated that heavy metals showed different influences on prokaryotes between surficial and deep layers (except Mn, which showed positive influences on prokaryotes on both surficial and deep layers). For example, Fe, Cu, Ni and Zn showed positive influences on prokaryotes in the surficial layer but showed negative influences on some prokaryotes in the deep layer. And Co also exerted negative effect on prokaryotes in the surficial but some positive influences in the deep layer (Figs. 6C and 6D).\u003c/p\u003e"},{"header":"Discussion","content":"\u003ch2\u003eEnvironmental Heterogeneity Determined Prokaryotic Distribution\u003c/h2\u003e\n\u003cp\u003eThe knowledge of the diversity and vertical distribution of prokaryotes in deep-sea sediment is crucial for better understanding their ecological roles in driving biochemical processes\u0026nbsp;[52, 53]. Combined relative abundance with absolute abundance could describe microbial distribution more precisely. Our results indicated that prokaryotic diversity and absolute abundance dramatically decreased from the surficial layer to the deep layer (Fig. 2). This is not surprising considering that in situ environmental factors (e.g., nutrient availability, O\u003csub\u003e2\u003c/sub\u003e concentrations, temperature) shape the prokaryotic community assembly in sediments at abyssal and hadal depths\u0026nbsp;[54-57]. Nonetheless, studies at those isolated marine settings have also suggested that certain factors (e.g., nitrate concentrations) can support/explain the high bacterial \u0026alpha;- diversity along the sediment vertical profile (e.g., Izu-Boin Trench ~10 km water depth\u0026nbsp;[58]). Our study results indicated that prokaryotic \u0026alpha;- diversity decreased sharply from the surficial layer to the deep layer, which agrees with previous published literature for deep marine settings\u0026nbsp;[44, 53, 59]. As mentioned, decline in the microbial diversity and abundance is related to O\u003csub\u003e2\u003c/sub\u003e content, nutrient pools and their downcore distribution along sediments\u0026nbsp;[54, 58, 60, 61].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur results showed that the abundances of both heterotrophs and autotrophs including \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eThaumarchaeota\u003c/em\u003e reduced dramatically from the surficial layer to the deep layer, while the abundance of commonly identified deep sea linages (e.g., \u003cem\u003eChloroflexi\u003c/em\u003e) increased in the deep layer (Figs. 3B and 4C). Although, the OC content remained high throughout the examined depths (Table 1), almost 59% of the OTUs belonging to \u003cem\u003eGammaproteobacteria\u003c/em\u003e (dominated among \u003cem\u003eProteobacteria\u003c/em\u003e phylum) appeared to be unclassified (according to Silva v.132 database). This does not allow us to interpret their metabolic potential (e.g., sulfur oxidizers, heterotrophs etc), nonetheless, we observed that the downcore distribution of \u003cem\u003eGammaproteobacteria\u0026nbsp;\u003c/em\u003ecan be driven by iron availability (Fig 4A) which agrees with previous studies from dee sea settings (e.g., Kermadec Trench; see in reference\u0026nbsp;[62]). The overall distribution of \u003cem\u003eChloroflexi\u003c/em\u003e seems to be influenced by AN (Fig. 4C), however, deep sea \u003cem\u003eChloroflexi\u003c/em\u003e are known to have a suite of metabolic potentials including both autotrophic and heterotrophic feeding modes\u0026nbsp;[63-65].\u0026nbsp;Genera \u003cem\u003eAcinetobacter\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e, both affiliated to class \u003cem\u003eGammaproteobacteria\u003c/em\u003e, can degrade organic matters in marine sediments\u0026nbsp;[66, 67]. Moreover, as one of the key members of P-solubilizing bacteria\u0026nbsp;[68], the abundances of \u003cem\u003ePseudomonas\u003c/em\u003e were much higher in the deep layer, especially in the deepest sample (\u0026gt;24\u0026nbsp;cmbsf, Fig. S5E).\u0026nbsp;This could be due to \u003cem\u003ePseudomonas\u003c/em\u003e having strong environmental adaptability in the deep layer, since it was reported that \u003cem\u003ePseudomonas\u003c/em\u003e species exhibit remarkable nutritional versatility and may utilize up to 100 different carbon sources for growth\u0026nbsp;[69]. Conversely, as an autotrophic ammonia-oxidizing archaeon (AOA) of phylum \u003cem\u003eThaumarchaeota\u003c/em\u003e [70], \u003cem\u003eNitrosopumilus\u003c/em\u003e was the key population which plays important roles in N cycling in marine ecosystems\u0026nbsp;[71-73], Our results indicated that the abundances of \u003cem\u003eNitrosopumilus\u003c/em\u003e generally decreased with depth and showed significant negative correlation with dept (Figs. S5E and 5F), that was well accordance with the distribution of Fe, because previous study had revealed that the growth of \u003cem\u003eNitrosopumilus\u003c/em\u003e may be facilitated by Fe\u0026nbsp;[73], and our results also indicated that the contents of Fe were higher in the surficial layer (Fig. 4A) and \u003cem\u003eNitrosopumilus\u003c/em\u003e showed a positive correlation with Fe (although it was not significant, Fig. S5F). Additionally, although the content of oxygen may decrease with depth in deep-sea sediments\u0026nbsp;[58], previous study had revealed that \u003cem\u003eNitrosopumilus\u003c/em\u003e not only consumes oxygen but can also produce oxygen in dark sea, which is used for ammonia oxidation\u0026nbsp;[74]. Therefore, the presence or absence of oxygen may not have a significant impact on its growth and ammonia oxidation process. As the members of the superphylum DPANN (\u003cem\u003eDiapherotrites\u003c/em\u003e, \u003cem\u003eParvarchaeota\u003c/em\u003e, \u003cem\u003eAenigmarchaeota\u003c/em\u003e, \u003cem\u003eNanoarchaeota\u003c/em\u003e and \u003cem\u003eNanohaloarchaea\u003c/em\u003e)\u0026nbsp;[75, 76], \u003cem\u003ePacearchaeota\u003c/em\u003e and \u003cem\u003eWoesearchaeota\u003c/em\u003e which are among the archaea with much smaller cellular and genome size\u0026nbsp;[77]\u0026nbsp;and with evident metabolic flexibility\u0026nbsp;[78, 79]. It was reported that \u003cem\u003ePacearchaeota Incertae Sedis\u003c/em\u003e AR13 was a potential hydrogenotrophic methanogen and had the potential for converting CO\u003csub\u003e2\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003e into CH\u003csub\u003e4\u003c/sub\u003e [80], which indicated that \u003cem\u003ePacearchaeota Incertae Sedis\u003c/em\u003e AR13 was one of the key archaea in driving carbon cycle in marine sediments\u0026nbsp;[16]. Although intriguing, we cannot ascertain if this metabolic potential of \u003cem\u003ePacearchaeota Incertae Sedis\u003c/em\u003e AR13 occurs in our sediments, nor its role on carbon cycling underneath polymetallic nodules, as CH\u003csub\u003e4\u003c/sub\u003e concentrations were not measured on our investigated sediments\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003cem\u003eWoesearchaeota\u003c/em\u003e plays a key role in the cycling of carbon, nitrogen, and sulfur\u0026nbsp;[81, 82]. Our results demonstrated that genera \u003cem\u003eWoesearchaeota Incertae Sedis\u003c/em\u003e AR15, \u003cem\u003eWoesearchaeota Incertae Sedis\u003c/em\u003e AR16 and \u003cem\u003eWoesearchaeota Incertae Sedis\u003c/em\u003e AR18 totally occupied 54.6% of the phylum \u003cem\u003eWoesearchaeota\u003c/em\u003e, which suggested that these three genera are the major drivers of C, N and S cycles in manganese nodules sediments\u0026nbsp;[83, 84]. Moreover, compared to the almost same depth (5086 m) of Mariana Trench, our results found that much a higher abundance of \u003cem\u003eWoesearchaeota\u003c/em\u003e was detected in manganese nodules sediments (0.46% vs. 0.19% on average), and no \u003cem\u003ePacearchaeota\u003c/em\u003e populations were detected in Mariana Trench\u0026nbsp;[85]. That suggested that there exist unique archaeal resources in the manganese nodules sediments and these archaea played crucial roles in maintaining geochemical elements cycling. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDepth was the predominant factor influencing prokaryotic distribution, with two key contributing factors. Firstly, previous studies had proved that the O\u003csub\u003e2\u003c/sub\u003e content declined with sediment depth (although it was not determined in this study)\u0026nbsp;[54, 60], and less available oxygen in deeper sediments led to a decline of microbial richness\u0026nbsp;[54, 58, 86]. Secondly, weaker water fluidity hinders nutrient exchange and microbial diffusion in the deeper layer\u0026nbsp;[44, 87, 88]. In addition, some studies also showed that pH and EC had important influences on microbial diversity and community structure\u0026nbsp;[31, 89-91]. However, we only found that pH and EC significantly affected the archaeal community, but not the bacterial community. This might be owing to differences in environmental adaptations, cellular structure, and cellular metabolisms between bacteria and archaea\u0026nbsp;[92]. As the primary source of electron donors for microbes\u0026nbsp;[93], many studies indicated that microbial abundances were strongly correlated with OC concentration\u0026nbsp;[19, 52]. However, our results showed that prokaryotes were not significantly correlated with the content of OC except \u003cem\u003eFirmicutes\u003c/em\u003e, which conflicted with earlier reports on the global distribution pattern of benthic microbes\u0026nbsp;[94, 95]. This difference may be caused by the content of OC in this study (22\u0026ndash;27 g∙kg\u003csup\u003e-1\u003c/sup\u003e) was much higher than the pioneers\u0026rsquo; results\u0026nbsp;[44, 54, 61]. In addition, our results also indicated that the contents of Cu in the deep layer were significantly higher than those in the surficial layer (Fig. 4A), and exerted significant influence on bacteria (Fig. 4D), those results indicated that heavy metals may also shift prokaryotic distribution in sediments.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eShifting in Community Assembly Processes from Surficial to Deep Layer\u003c/h2\u003e\n\u003cp\u003eEnvironmental filtering can lead to different community assemblies\u0026nbsp;[48, 96]. Variable selection implies that spatial or temporal environmental changes select dominated species from the local species pool\u0026nbsp;[16, 97]. Our findings revealed a significant difference in variable selection, with nearly 60% higher levels observed in the surficial layer than that in the deep layer (Fig. 5C). This discrepancy may be linked to more sensitivity of surficial sediment prokaryotes to the influence of neighboring aquatic organisms and environmental fluctuations, in contrast to their counterparts in the deeper horizons\u0026nbsp;[98]. Unlike the surficial layer, there was a shift in the assembly mechanism of prokaryotic communities in the deep layer, remarkably showing a stochasticity-dominated mechanism driven by dispersal limitation and undominated processes (Fig. 5C). Dispersal refers to the movement of organisms across space\u0026nbsp;[99], dispersal events can alter the rate of prokaryotic colony information, thereby impacting the compositions and distribution of prokaryotic communities\u0026nbsp;[97, 100]. Notably, microbial dispersal rates are contingent on the surrounding environmental conditions\u0026nbsp;[101, 102]. It\u0026apos;s worth highlighting that water can serve as a medium for microbial diffusion, and as sediments deepen, water mobility diminishes. This explains why the community assembly processes for deep-layer prokaryotic communities were significantly influenced by dispersal limitation (22%, Fig. 5C). Undominated (primarily including drift, weak selection, and weak dispersal) process, it was used to estimate that neither selection nor dispersal is the primary cause of between-community compositional differences\u0026nbsp;[48, 103]. Although our results showed that variable selection explained 40% of prokaryotic community assembly processes in the deep layer, dispersal limitation (22%) and undominated (36%) also played considerable contributions in community assembling. This indicated that prokaryotic community diversity and taxonomic compositions in this layer could not be solely\u0026nbsp;attributed to environmental selection\u0026nbsp;[104]. In addition, a higher proportion of undominated processes in shaping prokaryotic community assembly may suggest that the lower prokaryotic abundance may also contribute to the stochastic assembly in the deep layer\u0026nbsp;[105].\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCo-occurrence Networks Shaped by Depth and Influenced by Environmental Factors\u003c/h2\u003e\n\u003cp\u003eMicrobial co-occurrence patterns are intricately linked to interspecies diversity, abundance and interactions [106]. Compared with the surficial layer, a reduced complex co-occurrence network was observed within the deep layer. That might be due to i) the reduction of microbial abundance and diversity in the deep layer compared with the surficial layer; and ii) some microorganisms in the deep layer adopt a dormancy state for survival, characterized by slower growth rates and metabolism [107], this dormancy state can reduce their interactions and connections with other species. In addition, environmental perturbations in the surficial layer were more frequent compared to the deep layer. This led to higher clustering coefficients and modularity in the surficial layer (Fig. S6), these factors are crucial for maintaining community stability [108-111]. Module, signifies the division of microbial groups, where species within a module are closely related to each other but less so to species outside the module. This arises due to differences in ecological functions, niches and/or divergent selection [59, 112]. Due to one module having little or no influence on another module, and the influence of environmental perturbations within one module is unlikely to be transmitted to the other, that could reduce the impact of environmental perturbations on the whole microbial community. Therefore, high modularity is help to improve prokaryotic environmental adaptation in the surficial layer [111]. Moreover, it was proved that a high proportion of negative links among nodes indicated that the community returns to stability more quickly within a network [113]. We found approximately 46% more negative links in the surface layer compared to the deep layer, suggesting that prokaryotes within the surficial layer would increase negative relationships to enhance microbial community stability. In addition, our results indicated that environmental factors, especially for Fe, Co, Ni, Cu and Zn, may exert distinct effects on some prokaryotes in the surficial layer and deep layers (Figs. 6C and 6D). That may be attributed to differences in heavy metal content between the surficial and deep layers (Fig. 4A) and variations in heavy metal tolerance of prokaryotes in different layers. These findings suggested that heavy metals may be the key factors of driving prokaryotic community structure shift in manganese nodules sediments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presented a depth-layer distribution of prokaryotic diversity and community assembly patterns in sediments of Nazimov guyots. Generally, prokaryotic diversity, microbial abundance and community structure were significant higher and different in the surficial layer (0\u0026ndash;8 cm) than those in the deep layer (8\u0026ndash;26 cm). Prokaryotic community assemblies were governed by deterministic and stochastic processes in the surficial and deep layers, respectively. And co-occurrence network analysis further showed that network in the surficial layer was much complex than that in the deep layer, and more modules were observed in the surficial layer to enhance prokaryotic environmental resistance in the surficial layer. And heavy metals may exert distinct influences on prokaryotes in different layers. Such results described the vertical distribution of prokaryotes and their environmental response of in polymetallic nodules area, another important practical implication is that providing some basal reference for marine ecosystem protection.\u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003eData Accessibility\u003c/p\u003e\n\u003cp\u003eThe raw sequence data were submitted to the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (accession No.: PRJNA824224).\u003c/p\u003e\n\u003cp\u003eAuthor Contribution\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJX Sun\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e Methodology, Software, Investigation, Funding Acquisition, Formal Analysis, Writing - Original Draft.\u003cstrong\u003e\u0026nbsp;HB Zhou\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eWriting- Reviewing \u0026amp; Editing. \u003cstrong\u003eHN Cheng\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eSoftware，\u0026nbsp;Funding Acquisition. \u003cstrong\u003eZ Chen\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eData Curation, Formal analysis.\u0026nbsp;\u003cstrong\u003eJC Yang:\u003c/strong\u003e Data Curation, Formal analysis.\u0026nbsp;\u003cstrong\u003eYG Wang\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eResources, Conceptualization, Writing- Reviewing \u0026amp; Editing, Funding Collection.\u0026nbsp;\u003cstrong\u003eCL Jing:\u003c/strong\u003e Data Curation, Writing - Review \u0026amp; Editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDeclaration of Competing Interest\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no known competing financial interests that influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eConsent to Publish\u003c/p\u003e\n\u003cp\u003eAll authors have given their consent to publish this research article.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe work was supported by the National Nature Science Foundation of China (41706221 and 42073079), the Open Funding Project of National Key Laboratory of Human Factors Engineering (GJSD22008 and SYFD062009K) and Fundamental Research Funds for the Central Universities of Central South University (2022ZZTS0434).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHein JR, Koschinsky A, Kuhn T (2020) Deep-ocean polymetallic nodules as a resource for critical materials. 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Science 350: 663-666.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Prokaryotes, Marine sediment, Vertical distribution, Community assembly, Co-occurrence network ","lastPublishedDoi":"10.21203/rs.3.rs-2945198/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2945198/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Deep ocean polymetallic nodules, rich in cobalt, nickel, and titanium which are commonly used in high-technology and biotechnology applications, are being eyed for green energy transition through deep-sea mining operations. Prokaryotic communities underneath polymetallic nodules could participate in deep-sea biogeochemical cycling, however, are not fully described. To address this gap, we collected sediment cores from Nazimov guyots, where polymetallic nodules exist, to explore the diversity and vertical distribution of prokaryotic communities. Our 16S rRNA amplicon sequencing data, quantitative PCR results and phylogenetic beta diversity indices showed that prokaryotic diversity in the surficial layers (0–8 cm) was \u003e 4-fold higher compared to deeper horizons (8–26 cm), while heterotrophs dominated in all sediment horizons. Proteobacteria was the most abundant taxon (32–82%) across all sediment depths, followed by Thaumarchaeota (4–37%), Firmicutes (2–18%) and Planctomycetes (1–6%). Depth was the key factor controlling prokaryotic distribution, while heavy metals (e.g., iron, copper, nickel, cobalt, zinc) can also influence significantly the downcore distribution of prokaryotic communities. Analyses of phylogenetic diversity showed that deterministic processes governing prokaryotic assembly in surficial layers, contrasting with stochastic influences in deep layers. This was further supported from the detection of a more complex prokaryotic co-occurrence network in the surficial layer which suggested more diverse prokaryotic communities existed in the surface vs. deeper sediments. This study expands current knowledge on the vertical distribution of benthic prokaryotic diversity in deep sea settings underneath polymetallic nodules, and the results reported might set a baseline for future mining decisions.","manuscriptTitle":"Depth-dependent distribution of prokaryotes in sediments of the manganese crust on Nazimov guyots of the Magellan seamounts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-14 17:39:48","doi":"10.21203/rs.3.rs-2945198/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2023-09-04T17:06:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-09-04T13:15:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbial Ecology","date":"2023-09-03T12:41:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f7e38c3d-9258-4ecb-82af-c87074b4853f","owner":[],"postedDate":"September 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-09T15:10:09+00:00","versionOfRecord":{"articleIdentity":"rs-2945198","link":"https://doi.org/10.1007/s00248-023-02305-8","journal":{"identity":"microbial-ecology","isVorOnly":false,"title":"Microbial Ecology"},"publishedOn":"2023-10-04 15:02:49","publishedOnDateReadable":"October 4th, 2023"},"versionCreatedAt":"2023-09-14 17:39:48","video":"","vorDoi":"10.1007/s00248-023-02305-8","vorDoiUrl":"https://doi.org/10.1007/s00248-023-02305-8","workflowStages":[]},"version":"v1","identity":"rs-2945198","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2945198","identity":"rs-2945198","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}