Sediment microbial community structure, enzymatic activities and functional gene abundance in the coastal hypersaline habitats

Sediment microbial community structure, enzymatic activities and functional gene abundance in the coastal hypersaline habitats | 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 Sediment microbial community structure, enzymatic activities and functional gene abundance in the coastal hypersaline habitats Doongar Chaudhary, Madhav Kumar, Vandana Kalla This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2098972/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Jan, 2023 Read the published version in Archives of Microbiology → Version 1 posted 7 You are reading this latest preprint version Abstract Salt marsh vegetation, mudflat and salt production are common features in worldwide coastal areas; however, their influence on microbial community composition and structure has been poorly studied and rarely compared. In the present study, microbial community composition (phospholipid fatty acid (PLFA) profiling and 16S rRNA gene sequencing (bacterial and archaeal)), enzymatic activities and abundance of functional genes in the sediments of salt ponds (crystallizer, condenser and reservoir), mudflat and vegetated mudflat were determined. Physicochemical characteristics of the sediments were also studied. Enzyme activities (β-glucosidase, urease and alkaline phosphatase) were considerably decreased in saltpan sediments because of elevated salinity while sediment of vegetated mudflat showed the highest enzyme activities. Concentrations of total and microbial biomarker PLFAs (total bacterial, Gram-positive, Gram-negative, fungal and actinomycetes) were the highest in vegetated mudflat sediments and the lowest in crystallizer sediments. Nonmetric-multidimensional scaling (NMS) analysis of PLFA data revealed that the microbial community of crystallizer, mudflat and vegetated mudflat was significantly different from each other as well as different from condenser and reservoir. The most predominant phyla within the classified bacterial fractions were Proteobacteria followed by Firmicutes, Bacteroidetes and Planctomycetes, while Euryarchaeota and Crenarchaeota phyla dominated the classified archaeal fraction. Cyanobacterial genotypes were the most dominant in the condenser. Mudflat and vegetated mudflat supported a greater abundance of Bacteroidetes and Actinobacteria, respectively. The results of the present study suggest that salt ponds had significantly decreased the microbial and enzyme activities in comparison to mudflat and vegetated mudflat sediments due to very high salinity, ionic concentrations and devoid of vegetation. Biogeochemical Cycling Coastal Sediments Enzymes Functional Gene Next-Generation Sequencing PLFA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Intertidal coastal zones constitute the interface between terrestrial and aquatic ecosystems which is an important ecosystem for carbon and nutrient cycling and natural resources with higher biodiversity (Li et al. 2017 ). The coastal ecosystem is the most productive system which provides services like climate regulation/buffering from natural hazards, nutrient cycling, carbon sequestration, shoreline stabilization, food and fodder, habitat for marine life and biodiversity (Calvão et al. 2013 ; Thompson and Schlacher 2008 ). Coastal areas are under the influence of various pressure (natural and anthropogenic), such as mariculture, solar-salt production, coastal mining, erosion, pollution, grazing of riparian vegetation and deforestation of mangroves (Calvão et al. 2013 ). Microbes play a fundamental role in ecological processes, including organic matter decomposition, nutrient cycling and fixation which are necessary for the functions and services of the coastal ecosystem (Rathore et al. 2017 ; Wang and Wang 2018 ). Microbial activities greatly depend on the environmental conditions of sediments, including carbon substrate and nutrient availability, salt concentration, water content, temperature and pH (Li et al. 2017 ; Rathore et al. 2017 ). Therefore, it is necessary to investigate the influence of coastal vegetation, mudflats and salt production on the microbial community composition of sediments that generally exist in a coastal ecosystem. India ranks the third position in global salt production after China and USA (Bhat et al. 2015 ) and Gujarat state shares the longest coastline (1600 km) which has a diversity of habitats like mangroves, halophytic vegetation, mudflats, salt marshes, coral reefs, wetlands, salt pans etc. Salt ponds are shallow pools with elevated borders designed to store seawater or other brines to produce salts. The salt ponds consist of three distinct interconnected ponds, namely; reservoir, condenser and crystallizer pond (Mani et al. 2012 ). During the tidal influxes or mechanical pumping, the seawater is collected into the reservoir pond and then seawater is transferred into the condenser ponds, where seawater evaporates by solar radiation. The condenser feeds crystallizer ponds and the salinity of crystallizer ponds is much higher than the rest of the two. The salt concentration is a crucial component of these salt ponds. During salt production, calcium carbonate is initially precipitated when salinity is two times seawater, calcium sulfate (gypsum) precipitation occurs when salt content reaches three times seawater and sodium chloride precipitates when salinity approaches ten times to seawater (Davis 1990 ; Williams 1998 ). Salt production ponds invariably increase the concentration of salt (salinity) in the sediments as compared to coastal intertidal sediments (Song et al. 2022 ; Wei et al. 2022 ); however, the influence of increased salinity on sediment community composition/structure of microbes is poorly understood, particularly in the hypersaline environments. Salt pans and marshes are attractive model systems for the investigation of halophilic microorganisms and also the habitat for novel organisms (Berrada et al. 2012 ; Wei et al. 2022 ). Higher salt concentrations generate stress for the microorganisms and only tolerant organisms survive under such circumstances (Oren 2009 ; Wei et al. 2022 ). Sfax solar saltern in Tunisia, Mediterranean coastal salterns and saltern of South China have been studied widely for microbial communities (Boujelben et al. 2014 ; Oren 2009 ; Wei et al. 2022 ). However, very few studies compared the microbial communities in sediments of coastal vegetation and salt ponds; further, these informations are scarce in Indian salterns. Soil/sediments microorganisms can be characterized using a variety of methods, including phospholipid fatty acid profiling (PLFA), fluorescence in situ hybridization (FISH), denaturing gradient gel electrophoresis (DGGE), 16S rRNA gene clone libraries, and next-generation sequencing (NGS). Salinity depressed the bacterial and actinomycetes biomarker PLFA concentrations but slightly influenced the amount of fungal PLFA in salinized sediments (Wang and Wang 2018 ). In contrast, elevated salinity had increased the bacterial, fungal and total PLFAs concentrations as observed by Li et al. ( 2017 ). However, salinity exerts pressure on microorganisms and only tolerant microbes can survive under high salinity (Boujelben et al. 2014 ). Bacterial diversity decrease with an increase in salinity due to osmotic stress (Baati et al. 2008 ; Berrada et al. 2012 ; Song et al. 2022 ). Bacteria in a hypersaline environment are more susceptible to the shifts in salinity than archaea (Leoni et al. 2020 ). Also, the archaeal community can withstand wide salinity fluctuations and remain stable (Mani et al. 2012 ; Leoni et al. 2020 ). Studies carried out in hypersaline conditions exhibited that archaea followed by bacteria were dominant halophilic members (Baati et al. 2008 ; Baati et al. 2010 ; Trigui et al. 2011 ). However, many studies demonstrated that the bacterial domain plays a vital role in hypersaline environments (Berrada et al. 2012 ; Li et al. 2017 ; Morrissey et al. 2014 ; Wang et al. 2012 ). Salicola marasensis belonged to the ɣ-Proteobacteria subdivision and was found predominantly in the non-crystallizer pond (Boujelben et al. 2014 ). Culturable bacteria in hypersaline environments showed the predominance of Gram-positive ( Bacillus species, which can grow up to 25% salinity) and Gram-negative ( Vibrio species) bacteria (Berrada et al. 2012 ). In the salt crystals of salterns, Bacteroidetes were dominant, while ɣ-Proteobacteria and α-Proteobacteria were equally distributed (Baati et al. 2010 ). Euryarchaeota and Crenarchaeota phyla were previously reported as the dominant phyla in solar salterns (Ahmad et al. 2011 ; Xie et al. 2017 ). Carbon (C) and nitrogen (N) cycling are the key processes carried out by the microorganisms which are involved in biogeochemical cycling in the coastal sediments. The RuBisCO (ribulose-1,5-bisphosphate carboxylase/ oxygenase) is a notably recognized protein for CO 2 assimilation through the Calvin cycle which is encoded by the cbbL gene (Spiridonova et al. 2006 ). The nitrogen assimilation process is governed by the diazotrophic bacteria and the nifH gene which encodes the dinitrogenase reductase enzyme involved in N-fixation (Poly et al. 2001 ). The 16S rRNA, cbbL and nifH were quantified using a quantitative real-time polymerase chain reaction to enumerate the abundance of bacteria, C- and N-fixer, respectively. However, how sediments from the salt pond, mudflat and coastal vegetation affect the abundance of functional genes (16S rRNA, cbbL and nifH ) in the coastal ecosystem is still indistinct. We hypothesized that the diverse microbial community exists in the hypersaline environment, and microbial community diversity and functional gene abundance would decrease at higher salinity. Hence, the objective of the present investigation was to elucidate how microbial community structure (phospholipid fatty acid (PLFA) profiling and 16S rRNA gene sequencing ), enzyme activities (β glucosidase, urease, phosphatase, and sulfatase) and functional genes (16S rRNA, nifH and cbbL ) abundance influenced by the different type of coastal sediments (salt ponds, mudflat and vegetated mudflat). Materials And Methods Sites and sediment sampling The present study was carried out at the Experimental Salt Farm of CSIR-CSMCRI, located at the coastal intertidal area (N21° 47.521’ to N21° 47.732’; E72°07.417’ to E72°07.644’) of Bhavnagar district of Gujarat, India (Fig. S1). The study site is located in hot semi-arid, with minimum and maximum temperatures of 24- 44° C and 15-32° C in summer and winter, respectively. The annual average rainfall is 593 mm and is unevenly distributed. The usual onset and cessation of rain occur during the second week of June and second week of September, respectively. Winter begins in November and extends up to February followed by the summer season. The texture of sediment ranged from clay loam to clay. Five different types of sediments were collected for the present study. Among the five sediments, three sediment samples were collected from different ponds of solar salt pans (reservoir, condenser and crystallizer ponds) and one each from mudflat (without vegetation) and vegetated mudflat (halophyte growing area) (four replicates of each; total samples: 20). Mudflat and vegetated mudflat had similar seawater flooding frequencies. Sediments from halophyte growing areas were compared with sediments from other habitats. At the experimental salt farm, salt is produced by several interconnected ponds. First seawater is collected in reservoir ponds (N 21° 47.73’, E 072°07.64’; area: 8.29 ha) then water flows through successive ponds, condenser (N21° 47.66’, E 072°07.42’; area 1.52 ha) and crystallizer (N 21° 47.604’, E 072°07.461’; area: 1.52 ha), these salt ponds are approximately 30 years old. Mudflats (N 21° 47.53, E 072°07.52; area: 2 ha) and halophyte growing area (N 21° 47.53, E 072°07.53) were also located near the salt pans and had similar diurnal tidal amplitude. In the halophyte vegetated area, Suaeda species were predominantly growing. In each sediment type (crystallizer, condenser, reservoir, mudflat and vegetated mudflat), four replicate quadrats (1x1 m) were constructed and sampled from 0-20 cm depth in January 2018. Distance between replicate quadrats was more than 20 m and a composite sediment sample was prepared by combining three adjacent cores from each replicate quadrat. Sediment samples were immediately brought to the laboratory after collection. Sediment samples were separated into two subsets; one set was used for physicochemical analysis after air-drying and the second subset was kept at -20 °C for analysis of microbial assay (enzyme activities, PLFA and genomic DNA analysis). Physico-chemical characteristics of sediments Moist sediment samples were oven-dried at 105 °C (24 hours) to measure moisture content. Sediment samples were passed through a 2 mm sieve after air drying and stored for chemical analysis. Salinity and pH of sediment samples were determined in a 1:2.5 (w:v; sediment : water) suspension using electrical conductivity and pH meters, respectively. Organic carbon (OC) content in sediments was measured by chromic acid oxidation method and back titrated with ferrous ammonium sulfate (Nelson and Sommers 1982). Water (40 mL) was used to extract dissolved organic carbon (DOC) from sediments (4 g) followed by determination through TOC analyzer (Liqui TOC, Elementar, Germany). Potassium chloride (2M KCl) was used to extract ammonium (NH 4 + -N) and nitrate (NO 3 - -N) and were measured colorimetrically using a spectrophotometer (Keeney and Nelson 1982). Phosphorus (P) was extracted with 0.5M NaHCO 3 solution (Olsen et al. 1954) and quantified by the inductively coupled plasma spectrometer (Optima 2000, PerkinElmer Inc., USA). Neutral normal ammonium acetate (1N NH 4 OAc, pH 7.0) was used to extract potassium (K + ), sodium (Na + ), calcium (Ca 2+ ) and magnesium (Mg 2+ ) (Hanway and Heidel 1952). The concentrations of K + and Na + were quantified using a flame photometer. An inductively coupled plasma spectrometer was used for the estimation of Ca 2+ and Mg 2+ . Sulfate (SO 4 2- ) was extracted with 0.15% calcium chloride and was determined turbidimetrically using spectrophotometer (Chesnin and Yien 1950). The chloride from the sediments was extracted with water (Lundmark and Olofsson 2007) and estimated by titration with AgNO 3 . Microbial activities in sediments Basal respiration and enzyme assay : The basal respiration rate of sediments was assessed by determining the evolved CO 2 in the headspace of the incubation bottle for 24 h using the alkali (NaOH) absorption method (Vogeler et al. 2008). The β-glucosidase (carbon cycling enzyme) enzyme activity was measured using p-nitrophenyl-β-D-glucopyranoside as substrate (Tabatabai 1994) with slight adaptations: toluene was not used due to the short incubation time (Lee et al. 2007). Activity of urease (nitrogen cycling enzyme) was measured using a method of Tabatabai (1994) and the released NH 4 + -N was extracted with 2M KCl and measured spectrophotometrically (Keeney and Nelson 1982). Alkaline phosphatase (phosphorus cycling enzyme) activity was analysed using p-nitrophenyl phosphate as a substrate (Tabatabai and Bremner 1969). p-nitrophenyl sulfate was used as a substrate to measure the activity of sulfatase (sulfur cycling enzyme) using the method of Tabatabai and Bremner (1970). Phospholipid fatty acid (PLFA) : PLFAs were extracted from sediments (5g) using the procedures of Bardgett et al. (1996) and Frostegård et al. (1993). In brief, lipids were extracted from sediments in three steps, using citrate buffer, chloroform and methanol in the ratio of 0.8:1:2 followed by separation of phospho-, neutral- and glycolipids using a silicic acid column. The esterification of phospholipids was carried out using alkaline methanol and then analysed by the gas chromatograph (GC) system (Agilent 6850, Agilent Technologies, Inc.) fitted with a capillary column and FID (flame ionization detector). The identification and quantification of PLFAs were carried out with MI System (MIDI Inc., Newark, DE) and internal standard (methyl nonadecanoate; 19:0), respectively. The concentration of PLFAs was expressed as nmol PLFA g -1 sediment which indicating the microbial biomass. Taxonomic groups of microbes such as bacteria (15:0i, 15:0a, 16:0i, 16:0a, 17:0i, 17:0a, 17:0, 17:0cy, 19:0cy, 16:1ω7, 16:1ω5, 18:1ω7, 17:1ω9), Gram-positive (GM+ve) bacteria (15:0i, 15:0a, 16:0i, 16:0a, 17:0i, 17:0a), Gram negative (GM-ve) bacteria (17:0cy, 19:0cy, 16:1ω7, 16:1ω5, 18:1ω7, 17:1ω9), fungi (18:2ω6,9), and actinomycetes (16:0 (10Me), 17:0 (10Me), 18:0 (10Me)) were indicated by individual PLFA biomarkers (Zelles 1996; Kaur et al. 2005). The ratios of fungal to bacterial PLFAs (F/B) and GM+ve/GM-ve were used to indicate the shift in microbial community structure (Bååth 2003; Li et al. 2017; Wang and Wang 2018). Specific microbial groups (bacterial, fungal and actinomycetes) PLFAs were computed using both absolute concentration (nmole g -1 soil) as well as relative abundances (molar percent of total PLFA). DNA extraction, quantitative real ‐ time polymerase chain reaction (qRT-PCR) and amplification of 16S rRNA gene : Microbial genomic DNA was extracted from 0.5 g of sediment samples using FastDNA™ SPIN Kit for soil (MP Biomedicals, USA) according to instructions of manufacturer’s protocol and using FastPrep bead beating instrument (MP Biomedicals, USA). DNA was quantified by Nanodrop (NanoDrop Technologies, USA) and quality was determined on the agarose gel (0.8% w/v). Extracted genomic DNA was stored at -20 °C for amplification of functional gene(s) and 16S rRNA gene. Previously designed primers for phylogenetic marker and functional genes ( cbbL and nifH ) involved in biogeochemical cycles (C and N cycling) were used for PCR amplification with slight alteration in the PCR conditions (Table S1). Quantitative real-time PCR was conducted on a Bio-Rad thermal cycler (CFX96; Bio-Rad, USA) using QuantiFast SYBR Green PCR Kit (Qiagen, USA). The RT-PCR reactions were performed in 20 μL volume, containing 10μL of 2x qPCR-SYBR green master mix (premix of dNTPs, Taq DNA polymerase, PCR buffers and SYBR green), 0.2 μL of each gene-specific primers of 20 μM, 8.6 μL MQ and 1μL template DNA (diluted) for nifH and 16S rRNA genes. For cbbL , the PCR reactions were achieved in 20 μL volume, consisting 10μL of 2x qPCR-SYBR green master mix, 0.3 μL of each gene-specific primers of 20 μM, 7.4 μL MQ and 2μL template DNA (diluted). Recombinant plasmids comprising one copy of respective gene fragments (16S rRNA/ cbbL / nifH ) were 10-fold diluted and standard curves were prepared. Microbial metagenomics DNA from samples of the same origin was pooled and concentrated in the concentrator and around 1000 μg of DNA was shipped to Macrogen Ltd., South Korea, for paired-end sequencing using the Illumina MiSeq platform. For library preparation, the target-specific primers, Bakt_341F (5’-CCTACGGGNGGCWGCAG-3’) and Bakt_805R (3’-GACTACHVGGGTATCTAATCC-5’) were used to amplify the V3-V4 hypervariable regions of the bacterial 16S rRNA gene (Mizrahi-Man et al. 2013; Sinclair et al. 2015). Sequence files obtained from Macrogen Ltd. were checked for their quality using FastQC software (Galaxy version 19.01). Low-quality sequences with a Phred score lower than 25 were excluded using the Trimmomatic package listed under galaxy server (www.usegalaxy.com; Galaxy version 19.01). Further, the sequences were analyzed using Mothur software package (version 1.39.1), according to the MiSeq Standard operating procedure (SOP) (http://www.mothur.org/wiki/MiSeq_SOP). Paired-end reads were joined to form contigs using both forward and reverse sequence files (Kumar et al. 2022). Both forward and reverse primers were trimmed and sequences were further screened for the following parameters: minimum length: 403 bp, maximum length: 429 bp, maximum homopolymers: 8, maxambig: 0 and pdiffs: 3. Unique sequences from each file were screened in order to enhance the speed and avoid computational problems. Chimera detection was performed with the UCHIME algorithm (Edgar et al. 2011) and removed. The resulting sequences were aligned against SILVA bacterial reference database. Archaeal communities were analysed by aligning the processed sequences against SILVA archaeal reference database. The taxonomic assignment to the sequences was done according to SILVA taxonomy. Sequences flagged as chloroplasts, mitochondria or eukaryotes were also excluded by using the remove lineage command. Further, the remaining sequences were clustered de novo using a distance matrix algorithm at a distance cut-off of 97% similarity. A total of 12031 and 9745 OTUs were obtained for bacteria and archaea, respectively. Rarefaction curves for the observed OTUs were generated using “rarefaction.single” command in Mothur to evaluate the alpha diversity and sampling effort (Fig. S2). Statistical analyses were performed with the web-based microbiome analyst software (Dhariwal et al. 2017). InteractiVenn software (http://www.interactivenn.net/) was used to construct the Venn diagram. The sequences were submitted in the NCBI SRA database under bio-project number PRJNA521453. Statistical analysis The results of the study are presented as means (n = 4) ± standard errors (SE) and ANOVA (one-way analysis of variance) was carried out to differentiate the significant differences (Tukey's honest significance test; p<0.05) between treatment means using SPSS version 19.0 (IBM Corp, NY). Nonmetric-multidimensional scaling (NMS) analysis was carried out using PC-ORD software (McCune and Mefford 2006). Molar per cent of PLFAs was used for NMS analysis using the Sorensen distance measure and a second matrix was prepared by summed values for microbial groups and soil physicochemical properties which was used for creating the joint plot overlaid on the NMS plot. The NMS scores were analysed by one-way ANOVA. The relationships between sediment microbial community and physicochemical characteristics were examined using correlation coefficients (p<0.05). Results Sediment characteristics Chemical characteristics and nutrient contents of sediments showed significant variability (except dissolved organic carbon) (Table S2 and S3). Moisture content was the highest in the sediments from the condenser (42.2%) and the lowest in the vegetated mudflat (27.3%). The pH and EC varied from 8.1 (mudflat) to 8.4 (condenser) and from 12.3 (vegetated mudflat) to 49.8 dS m -1 (crystallizer), respectively. Organic carbon content was the highest in sediments from the condenser (1.2%) and crystallizer (1.1%) while the lowest was in vegetated mudflat (0.7%). Salt production significantly influenced the nutrient content of the sediments from salt ponds. Sediments from crystallizer had much highest contents of NH 4 + (6.9 mg kg -1 ), K + (4.1 g kg -1 ), SO 4 2- (2.5 g kg -1 ), Cl ‑ (67.5 g kg -1 ) and Mg 2+ (8.5 g kg -1 ) than other sediments. Sediments from mudflat and vegetated mudflat had remarkably higher contents of NO 3 - (1.6-1.92 mg kg -1 ) and P (19.3-20.7 mg kg -1 ) while concentrations of NH 4 + (3.6-4.4 mg kg -1 ), K + (1.1-1.6 g kg -1 ), Na + (8.8-21.3 g kg -1 ), SO 4 2- (1.2-1.5 g kg -1 ), Cl - (11.6-32.8 g kg -1 ), Ca 2+ (3.6-4.6 mg kg -1 ) and Mg 2+ (1.4-2.6 mg kg -1 ) were the lowest in comparison with salt pan sediments. Microbial respiration and enzyme activities in sediments Basal respiration and enzyme activities were significantly influenced by different sediment sources (Table 1). Basal respiration was the highest in sediments from vegetated mudflat and the lowest in crystallizer and condenser. β-glucosidase and urease activities were noticeably lower in the sediments of saltpan and mudflat than in vegetated mudflat. Sediments from mudflat and vegetated mudflat had much higher alkaline phosphatase activity than the other sediments. Unlike other enzyme activities, the activities of the sulfatase enzyme were the highest in the reservoir sediment and the lowest in mudflats. Correlation analysis between sediment characteristics and enzyme activities showed that β-glucosidase was positively correlated with pH (Table S4). The activities of urease and alkaline phosphatase, as well as the rate of basal respiration were positively correlated with NO 3 - and P while it was negatively correlated with sediment moisture content (except basal respiration), EC, OC, K + , Na + , SO 4 2- , Cl - , Ca 2+ and Mg 2+ . Sulfatase activity showed positive correlation with pH, OC and Ca 2+ while negative correlation with P. Microbial community composition/structure of sediments (PLFA) Different sediment samples significantly affected the amount of total PLFAs, Gram-positive, Gram-negative, total bacterial and actinomycetes biomarker PLFAs (Table 2). The highest concentrations of PLFAs microbial biomarkers were exhibited in vegetated mudflat sediments, whereas the lowest were in crystallizer sediments. There was no significant influence of different sediments on the fungi/bacterial ratio of PLFAs. The ratios of GM+ve /GM-ve PLFAs showed significant differences among the sediments and the ratio was the highest in crystallizer and mudflat while the lowest in vegetated mudflat sediments. Further, attempts were made to correlate the PLFA concentrations of biomarkers of microbial groups with sediment characteristics (Table S5). Amounts of total PLFAs, Gram-positive and actinomycetes biomarkers were negatively correlated with EC, K + , Na + SO 4 2- , Cl ‑ and Mg 2+ while positively correlated with NO 3 - . The EC, K + , Na + SO 4 2- , Cl ‑ , Ca 2+ and Mg 2+ were negatively correlated with the amount of Gram-negative, total bacterial and fungal PLFAs while the same microbial groups were positively correlated with NO 3 - . Furthermore, biomarker PLFA of fungi had a positive relationship with the P concentration of sediments. Amounts of total PLFAs and biomarkers of microbial groups were positively correlated with activities of all enzymes (except sulfatase) (Table S6). Molar per cent of PLFAs was subjected to NMS (Nonmetric-multidimensional scaling) ordination analysis and resulted in a two-dimensional elucidation (Fig. 1). The ordination of both axes (2 and 3) explained 93.2% of the total variability (70.5 and 22.7% by axis two and three, respectively; Fig. 2). Gram-positive, Gram-negative, total bacteria, fungi and actinomycetes had a negative relation with axis 2. Among sediment characteristics, EC, OC, NH 4 + , K + , Na + , SO 4 2- , Cl - , Ca 2+ and Mg 2+ were positively correlated while NO 3 - and P were negatively correlated with axis 2. Axis 3 was positively associated with the F/B ratio while negatively associated with Gram-negative and total bacteria. The sediment microbial communities of crystallizer, mudflat and vegetated mudflat were significantly different from each other as well as distinct from the condenser and reservoir. However, the microbial community of condenser and reservoir was similar to each other. The molar percent (relative abundance) of different microbial groups is presented in Fig. 2 and was significantly influenced by the sediment sources. The molar percent of Gram-positive was similar in the vegetated mudflat, mudflat and condenser while significantly lower in the crystallizer. Vegetated mudflat sediments had the highest molar percent of Gram-negative, total bacteria and actinomycetes while the lowest were in sediments from crystallizer. A similar abundance of fungi was observed in crystallizer, reservoir, mudflat and vegetated mudflat. Bacterial community structure A total of 492804 contigs were obtained after aligning paired-end raw sequence reads. The number of contigs per sample ranged from 161398 to 172103 (average length ~400 bp). Further, 252238 unique sequences were produced by Mothur software after aligning the sequences with SILVA reference database. Finally, 63384 sequence reads were obtained after excluding low-quality sequence reads, pre-clustering and chimera removal. Sequences were classified into 12031 OTUs at 97% similarity cutoff. The observed OTUs for bacteria were assigned to 22 Phyla, 52 classes, 98 orders and 154 families. The rarefaction curves of the observed OTUs combined with the goods coverage values reached saturation for all samples indicating sufficient sampling depth and large library size to capture a vast majority of the bacterial diversity in all samples (Fig. S2). The rarefaction curves suggested that the highest bacterial OTUs observed were in vegetated mudflat (4521) followed by mudflat (4372), reservoir (4315), condenser (3529) and crystallizer (2250). The observed OTUs (after quality filtering) were used to calculate bacterial community composition. More than 99% of the bacterial OTUs were successfully classified at the phylum level into 17 different phyla and six candidate divisions (Fig. 3). All samples showed a high percentage of unclassified bacteria (ranging from 16%-37%). The unclassified bacterial phyla clustered into 4023 OTUs. Crystallizer and reservoir harbored the highest percentage of unclassified bacterial phyla (37 and 26%) followed by Proteobacteria (22 and 17%) and Bacteroidetes (17 and 14%, respectively). Similarly, a high percentage of unclassified bacterial phyla (28%) were also observed in condenser followed by Cyanobacteria (20%), Bacteroidetes (19%) and Proteobacteria (15%). Vegetated mudflat showed a similar percentage of Actinobacteria (19%) and unclassified bacteria (19%) followed by Proteobacteria (15%), Bacteroidetes (15%) and Firmicutes (14%). Bacteroidetes (18%), unclassified Bactria (17%), Cyanobacteria (16%), Actinobacteria (14%) and Proteobacteria (10%) were dominant phyla in mudflat samples. All the samples were clustered into two groups based on 16S rRNA gene sequence dendrogram (OTU level) (Fig. S3). Samples from the reservoir, condenser and crystallizer were clustered in the same group while samples from vegetated mudflat and mudflat were clustered in another group. In the both groups, unclassified bacteria (30 and 18%) were predominantly observed. First group (reservoir, condenser and crystallizer) was dominated by Proteobacteria (18%), Bacteroidetes (17%), Cyanobacteria (9%) and Firmicutes (5%) phyla, while another group (vegetated mudflat and mudflat) was dominated by Actinobacteria (16%), Bacteroidetes (16%), Proteobacteria (13%), Firmicutes (11%), Cyanobacteria (8%), Chloroflexi (6%) and Planctomycetes (5%). Clustering of top bacterial classes also showed that a number of the bacteria belong to unclassified groups (Fig. S4). In the crystallizer, Bacteroidia, Deltaproteobacteria, Lentisphaeria were mainly distributed while Sphingobacteria, Chlorobia and Cyanobacteria SubsectionIII dominated in the condenser. Similarly, mudflat had more bacteria belonging to Phycisphaerae, Optitutae, Flavobacteria and Verrucomicrobia OPB35 classes. The reservoir was dominated by Gemmantimonadetes, Firmicutes RF3, Planctomycetes MBMPE71, Acidobacteria, Alphaproteobacteria and Anaerolineae while vegetated mudflat showed a dominance of Clostridia, Betaproteobacteria, Actinobacteria, Caldilineae, Bacilli, Holophagae and Thermomicrobia. At class level, condenser showed a similar distribution of bacterial classes with crystallizer while reservoir showed similarity with vegetative mudflat (Fig. S4). Family level analysis showed a wide range of distribution among the samples (Fig. S5). Acidobacteriaceae and Opitutaceae were found to be present in the reservoir, mudflat and vegetated mudflat, while the same families were absent in crystallizer and condenser. Rhodothermaceae, Bacillaceae, Paenibacillaceae, Peptococcaceae, Rhodobacteriaceae, Bdellovibrionaceae, Bacteriovoraceae, Halomonadaceae, Alteromonadaceae, Oceanospirillaceae, Punniceicoccaceae and Sinobacteraceae were present in all the samples with higher abundance in vegetated mudflat and mudflat. Members of Desulfobacteriaceae, Desulfovibrionaceae, Desulfobulbaceae, Geobacteriaceae, Spirochaetaceae, Victivallaceae and Halanaerobiaceae were relatively found in abundance in crystallizer, condenser and reservoir. At the genera level majority of the bacterial OTUs were unclassified. Altogether 18,987 bacterial OTUs were detected across all libraries, with 169 OTUs common to all (Fig. 4). The commonly shared OTUs represented unclassified Bacteria (27%), Proteobacteria (24%), Firmicutes (17%), Bacteroidetes (11%), Planctomycetes (7%), Actinobacteria (5%) and other Bacteria (9%) at phylum level. The numbers of OTUs exclusive to the reservoir, condenser, crystallizer, mudflat and vegetated mudflat samples were 2157, 1272, 680, 1306 and 1963, respectively. The bacterial community richness and diversity values are shown in Table 3. The reads obtained from different samples were compared for alpha diversity measures such as richness (Chao1), abundance-based coverage estimator (ACE) and species diversity (Shannon index). The values of Chao1 and ACE were the highest in mudflat and vegetated mudflat, whereas the lowest in the crystallizer. The highest Shannon index was observed in the reservoir and lowest was in condenser and crystallizer. Simpson index (calculates a measure of diversity) was highest in condenser followed by mudflat, crystallizer, vegetated mudflat and reservoir. Archaeal community structure After aligning the sequences with the archaeal silva reference database, taxonomic affiliation with 97% similarity threshold yielded 9745 OTUs. From the observed OTUs, archaeal community composition was calculated. More than 97% of archaeal OTUs were unclassified and the remaining OTUs were classified into two different phyla (Fig. 5). In classified archaeal phyla, Euryarchaeota was the dominant followed by Crenarchaeota in all the samples. The highest percentage of Euryarchaeota phyla was observed in the condenser followed by crystallizer and mudflat, while the lowest percentage was observed in the reservoir. For the Crenarchaeota phyla, the highest percentage was observed in the reservoir followed by crystallizer and condenser while the lowest percentage was observed in mudflat samples. Further on the class level, the phyla Euryarchaeota was classified into Halobacteria and Methanomicrobia while a large fraction of the same phyla was unclassified (Fig. 5). This showed that the saltpan archaeal community has been poorly studied using the high-throughput NGS technique and needs to be studied. The OTUs from the Crenarchaeota phyla were further classified into Thermoprotei class (18%) and the rest OTUs remain unclassified (Fig. 5). Thermoprotei class was most abundant in crystallizer and condenser while it was absent in mudflat samples. The highest percentage of Halobacteria was observed in the condenser followed by mudflat and crystallizer while the same class was absent in the reservoir and vegetated mudflat. Methanomicrobia class was detected in mudflat, vegetated mudflat and crystallizer while it was absent in condenser and reservoir samples. Venn diagram for archaeal OTUs showed 91 common among the samples. The OTUs exclusive to the reservoir, condenser, crystallizer, mudflat and vegetated mudflat samples were 1758, 1143, 975, 1073 and 1414, respectively (Fig. S6). The community richness and diversity index values for archaeal communities were calculated (Table S7). Chao1 and ACE values were observed highest in the reservoir followed by condenser and mudflat. The highest Shannon index value was observed in the reservoir, mudflat and vegetated mudflat, while the lowest in the crystallizer. Simpson index values were highest in condenser and crystallizer. Functional g ene abundance s in sediments The genomic DNA concentration varied from 7.4 to 20.4 ng µl -1 and the highest concentration was observed in the condenser followed by vegetated mudflat, reservoir, mudflat and crystallizer sediments. The copies of the bacterial 16S rRNA gene varied from 1.8x10 7 (crystallizer) to 153.0x10 7 (vegetated mudflat) per g of sediment. The abundance of cbbL gene was markedly different in sediments and the highest copy number was exhibited in vegetated mudflat, whereas the lowest in crystallizer sediments. Sediments significantly affected the abundance of nifH gene and the highest abundance was observed in the condenser followed by vegetated mudflat, mudflat, reservoir and crystallizer sediments (Fig. 6). The copies of the bacterial 16S rRNA and cbbL genes showed a positive correlation with all enzyme activities and microbial biomarker PLFA concentrations (Table S8). Discussion Microbial respiration and enzyme activities Basal respiration is extensively used to assess the microbial activity in the sediments/soils and measures the overall decomposition rate of the organic carbon (Vogeler et al. 2008). The basal respiration rate was higher in vegetated mudflat sediments than any other sediments, even though other sediments (crystallizer and condenser) had higher organic carbon which is burial and preserved C in the precipitated salts (Table 1). This indicates that the higher sediment salinity significantly reduced the decomposition rate of organic matter which is confirmed by negative relationship between basal respiration rate and salinity (Table S4). Previous studies have also shown that basal respiration was reduced considerably with increased salinity (Mahajan et al. 2015). Enzyme activities are sensitive indicators of the quality of sediments and were closely related to physico-chemical characteristics of sediments (Mahajan et al. 2015; Yang et al. 2017). The enzymes activities (β-glucosidase, urease and alkaline phosphatase) considerably decreased in sediments of salt ponds while vegetated mudflat sediment had the highest enzyme activities. The decreased enzyme activities in salt ponds (with higher salinity) might be due to increased osmotic stress on microbes (Frankenberger and Bingham 1982; Wei et al. 2022) which shows the reduced capability of sediments to mineralize nutrients (C, N and P) as evident in table (S2 and S3) as well as negative correlation with salinity. Enzyme activities under vegetated mudflats could be stimulated by the higher microbial activities sustained by root exudation and litter decomposition (Yang et al. 2017). The salinity did not affect the sulfatase activity; similar results were also observed by Oshrain and Wiebe (1979). Sediment microbial community composition/structure (PLFA) The biomass of microbes and their activities show the size and magnitude of the microbial population associated with nutrient cycling and biogeochemical processes occurring in the sediments. Sediment microbial activities can be enumerated by the culture-dependent as well as culture-independent approaches. In the present study, culture-independent (PLFA analysis) approach was used for estimation of microbial biomass and microbial community structure which is extensively used for sediment-microbial interaction studies (Li et al. 2017; Rathore et al. 2017; Wang and Wang 2018). The values of total PLFAs, Gram-positive, Gram-negative, total bacterial and actinomycetes biomarker PLFAs were the lowest in crystallizer and the highest in vegetated mudflat sediments (Table 2). The increased content of PLFA in vegetated mudflat sediments was mainly attributed to higher contents of NO 3 - and lower salinity (salinity-associated ions) as revealed by the correlation study (Table S6). These results are supported by earlier studies (Wang and Wang 2018) which described that the rise in salinity would depress microbial activity and biomass. The crystallizer and condenser had four times; and the reservoir and mudflat had two times higher salinity than the sediments from the vegetated mudflat (Table S2). Furthermore, crystallizer and condenser had a significantly higher amount of organic carbon than vegetated mudflat sediments; however, there was no relationship between organic carbons on microbial biomass, as observed by Li et al. (2017) in coastal sediments. It shows a more dominant effect of salt content than organic carbon on microbial biomass. Further, fungal biomass was positively correlated to the P concentration of sediments which implying that adequate availability of P is necessary to sustain fungal biomass (Teste et al. 2016). Different sediments did not influence the ratio of F/B. Similar results were observed in earlier studies (Li et al. 2017; Wang and Wang 2018) which might be due to the adaptation of fungus to the high salt concentration of the coastal wetlands. In all studied sediments, the biomass of Gram-negative bacteria was higher than Gram-positive bacteria and the ratio of Gram-positive /Gram-negative was significantly affected by different sediments (Table 2). Berrada et al. (2012) also observed that Gram-positive bacteria were widely represented in higher salinity which agreed with the highest Gram-positive /Gram-negative ratio in crystallizer sediments. Sediment microbial communities of crystallizer, mudflat and vegetated mudflat have varied from each other and also differed from condenser and reservoir (Fig. 1) due to the changes in the abundance of bacterial, fungal and actinomycetes PLFA biomarkers (Fig. 2). The increased salinity in sediments from mudflat and salt ponds had shifted the bacterial abundance (dominance of Gram-positive) (Morrissey et al. 2014). There was also a marginal reduction in the abundance of fungus at higher salinity. The Gram-positive bacteria are slow-growing as compared to Gram-negative bacteria and adopt k-strategists (low growth rate with high resource use efficiency) which is related to the resistance of the bacterial community to salinity and increased the ratio of Gram-positive /Gram-negative with a rise in salinity (de Vries and Shade 2013). In earlier studies, it was observed that the genera and species numbers decreased from marsh to salterns at Lower Loukkos (Morocco) (Berrada et al. 2012) and Wendeng salterns of China (Song et al. 2022). Similarly, elevated salinity reduced the abundance of actinomycetes. Actinomycetes were higher in mangrove sediments followed by mudflat and saltpan sediments (Vijayakumar et al. 2007). Bacterial community structure (16S rRNA sequencing) Bacterial 16S rRNA gene OTUs rarefaction curve reached saturation depicting sufficient sampling depth and large library size to capture a vast majority of the diversity in all samples (Fig. S2). Proteobacteria were detected as the most dominant phyla in crystallizer and reservoir after unclassified bacteria (Fig. 3). Core microbiome analysis of the selected phyla showed that most of the bacterial OTUs belonged to unclassified phyla, indicating that the diversity of the hypersaline ecosystem is poorly studied (Najjari et al. 2015; Zhong et al. 2016). This finding is similar to previous studies on coastal wetlands and intertidal soils (Li et al. 2017; Wang and Wang 2018). Further, Hu et al. (2014) reported that the abundance of Proteobacteria is influenced by a change in salinity and is directly proportional to salinity. Apart from Proteobacteria phyla, Bacteroidetes, Firmicutes, Chloroflexi, Actinobacteria, Cyanobacteria and Planctomycetes were also observed in all the samples; this finding is supported by Trigui et al. (2011), Wang et al. (2012) and Wei et al. (2022). Firmicutes produce spores under extreme conditions for their survival (Yu et al. 2012). In mudflat and vegetated mudflat samples, Bacteroidetes (in mudflat) and Actinobacteria (in vegetated mudflat) were the most abundant phyla. Actinobacteria can withstand harsh environmental conditions in the dormant stage or sporulation, or in an inactive but viable form. When the condition becomes favourable the Actinobacterial cells start dividing again (Jones and Lennon 2010; Crits-Christoph et al. 2013). Gammaproteobacteria, the most predominant Proteobacterial class observed in this study (Fig. S4) which is phylogenetically and physiologically diverse and involved in the nutrients cycling (Evans et al. 2008). The absence of Acidobacteriaceae and Opitutaceae family in the crystallizer and condenser showed that these bacterial families could not tolerate higher salinity (Fig. S5). Desulfovibrionaceae and Desulfobulbaceae represent incomplete oxidizers while Desulfobacteriaceae represents members of complete oxidizers in a hypersaline environment (Foti et al. 2007) which were detected abundantly in crystallizer, condenser and reservoir. Sorokin et al. (2004) demonstrated the sulfur-reducing activity in the Siberian soda lakes with saturated salinity. Many sulfur-reducing bacteria have previously been reported in hypersaline environments (Foti et al. 2007; Song et al. 2022; Wei et al. 2022). Mudflat and vegetated mudflat shared the highest OTUs between them while crystallizer and condenser shared the second-highest OTUs (Fig. 4). This indicated that the bacterial communities in mudflat and vegetated mudflat were similar while the bacterial communities in crystallizer showed similarity with condenser. In the present study, bacterial diversity was decreased with an increase in salinity (Table 3) which is also supported by previous studies (Baldwin et al. 2006; Song et al. 2022). Based on the OTU analysis and the different diversity indices (Shannon), the highest diversity was observed in the reservoir and vegetated mudflat samples while the lowest diversity was observed in crystallizer and condenser which is due to the higher concentration of salt (Baldwin et al. 2006; Song et al. 2022). Archaeal community structure (16S rRNA sequencing) Archaeal 16S rRNA gene sequencing results depicted a higher percentage of unclassified archaeal sequences at the phylum level which shows that the diversity in the salt ponds is poorly studied using the NGS technique (Fig. 5). We found that the Euryarchaeota was the most dominant phyla followed by Crenararchaeta in all the samples (Fig. S5). The abundance of Euryarchaeota has been reported by many researchers in saline soils (Xie et al. 2017; Walsh et al. 2005). The presence of halophilic archaea such as Halobacteria (Fig. 5) in this study is in accordance with other reported studies in hypersaline environments (Maturrano et al. 2006; Youssef et al. 2012; Weigold et al. 2016). The halophilic archaea (Halobacteria) adopt a salt-in strategy for osmoregulation which requires less metabolic energy compared with the synthesis of compatible solutes (Kulp et al. 2007; Genderjahn et al. 2018), therefore, detected only in condenser, crysytallizer and mudflat. The Crenarchaeota phyla observed in the present study is very diverse, ranging from chemolithoautotrophs to chemoorganotrophs which also vary from aerobes to facultative anaerobes to anaerobes (Ahmad et al. 2011). The majority of the Crenarchaeota phyla were unclassified at the class level while classified sequences showed the abundance of Thermoprotei class in all the samples except for mudflat which showed that the class Thermoprotei could also withstand a wide salinity range (Yan et al. 2018). Venn diagram analysis of the archaeal community showed that the mudflat shared maximum OTUs with vegetated mudflat and reservoir (Fig. S6). This may be because of the lower salinity in both vegetated mudflat and reservoir. Archaeal diversity was the highest in the reservoir followed by mudflat, vegetated mudflat and condenser while the lowest diversity was observed in crystallizer (Table. S6). The diversity pattern indicated that the archaeal communities did not follow the same pattern as bacteria and did not show a reduced diversity pattern with respect to salinity. Taxonomic assignment and community composition depend on the choice of variable regions. In the present study, we have targeted V3-V4 regions for the amplification of microbial 16S rRNA gene analysis, while studies suggest that targeting V4-V5 regions gives superior recognition of Archaea (Willis et al. 2019; Parada et al. 2016; Satari et al. 2021). A large number of unclassified archaeal communities on the phylum level may be overcome by targeting the archaeal-specific V4-V5 regions. Functional gene abundances in sediments Quantification of key functional genes offers an exceptional tool to study sediment microbial communities in-situ without cultivation biases for environmental samples (Spring et al. 2000). Abundances of bacterial 16S rRNA gene and two functional biomarker genes ( cbbL and nifH ) involved in C and N cycling were quantified (Fig. 6). The relative abundance of gene copy numbers (per gram sediment) occurring in different coastal sediments (crystallizer, condenser, mudflat and vegetated mudflat) was significantly affected by the sediment types. Relatively low copy numbers of the 16S rRNA gene in the crystalline sediment indicates hostile habitat condition due to the high salt concentration and nutrient-deficient environment in comparison to other sediments which imposed additional stress conditions on microbes so that the consumption of C substrate will not be efficient (Marinari et al. 2012; Keshri et al. 2015). Sediments from vegetated mudflats appeared to have a higher abundance of bacterial flora which can be helpful in improving stressed environmental conditions. The carbon-fixing bacterial communities (abundance of cbbL gene) were most abundant in vegetated mudflat sediments and its copy number was inversely proportional to the sediment salinity (lowest abundance in crystalline sediments). Similar results have also been reported by Keshri et al. (2015) in sediments of the Arabian Sea. The abundance of nifH gene was the lowest in crystallizer sediments while remaining sediments had a similar abundance. These results align with other reports observed in coastal sediments (Sorokin et al. 2008; Keshri et al. 2013). Conclusion The present study found notable differences in the chemical and microbial characteristics of sediments collected from salt ponds (crystallizer, condenser and reservoir), mudflat and vegetated mudflat. These changes advocate that salt production processes strongly affect the biogeochemical processes and nutrient cycling in the coastal ecosystem. It was established from the present study that the key controller of the microbial community structure and enzyme activities in sediments are sediment salinity and ionic concentration. Vegetation (halophyte) created the most conducive environment for microbial activities in the sediments. The majority of phyla belonged to the unclassified bacteria and archaea which shows that the diversity of hypersaline ecosystems is poorly studied. The bacterial population was dominated by Proteobacteria, Bacteroidetes, Firmicutes and Chloroflexi while Euryarchaeota and Crenarchaeota dominated archaeal communities. The abundance of genes in sediments enhances our information and understanding of the role of microbes in the nutrients biogeochemical cycling. Declarations Conflict of interest: The authors declare that there is no conflict of interest (financial or non-financial). This MS did not include any published work. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Acknowledgments The authors gratefully acknowledge the financial assistance GAP2012 and GAP2125/CRG/2020/000542 rendered by the Ministry of Earth Sciences (MoES) and Science and Engineering Research Board (SERB), New Delhi, respectively. The authors are thankful to Mr. S. C. Upadhyay and Aditya P. Rathore for the help received during sample collection and analysis. CSIR-CSMCRI communication No.: 115/2018. Funding Declaration The funding was received from the Ministry of Earth Sciences (MoES) (GAP2012) and the Science and Engineering Research Board (SERB), (GAP2125/CRG/2020/000542), New Delhi to carry out the study. Data Availability Statements The datasets generated during and/or analysed in the current study are available from the corresponding author upon reasonable request. Author’s contributions MK and VK did analytical work, and MK and DRC wrote the manuscript, prepared the figures and tables. Conception and study designed by DRC. Acquisition of data and statistical analysis carried out by the MK, VK and DRC. All authors reviewed the manuscript. References Ahmad N, Johri S, Sultan P, Abdin MZ, Qazi GN (2011) Phylogenetic characterization of archaea in saltpan sediments. 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Plant soil 434:245-262. https://doi.org/10.1007/s11104-017-3546-9 Youssef NH, Ashlock-Savage KN, Elshahed MS (2012) Phylogenetic diversities and community structure of members of the extremely halophilic Archaea (order Halobacteriales) in multiple saline sediment habitats. Appl Environ Microbiol 78:1332-1344. https://doi.org/10.1128/AEM.07420-11 Yu Y, Wang H, Liu J et al (2012) Shifts in microbial community function and structure along the successional gradient of coastal wetlands in Yellow River Estuary. Eur J Soil Biol 49:12-21. https://doi.org/10.1016/j.ejsobi.2011.08.006 Zelles L (1996) Fatty acid patterns of microbial phospholipids and lipopolysaccharides. In: Schinner F, Ohlinger R, Kandeler E, Margesin R. (eds) Methods in soil biology. Springer-Verlag, Berlin, pp 80-92 Zhong ZP, Liu Y, Miao LL et al (2016) Prokaryotic community structure driven by salinity and ionic concentrations in plateau lakes of the Tibetan plateau. Appl Environ Microbiol 82:1846-1858. https://doi.org/10.1128/AEM.03332 -15 Tables Table 1 Basal respiration and enzyme activities in the sediments Treatments Basal respiration (mg CO 2 kg -1 hr -1 ) β-glucosidase (µg PNP g -1 hr -1 ) Urease (µg N g -1 hr -1 ) Phosphatase (µg PNP g -1 hr -1 ) Sulfatase (µg PNP g -1 hr -1 ) Crystallizer 5.28±0.46b* 1.67±0.29b 3.87±0.30b 40.29±2.76b 6.98±0.52ab Condenser 5.75±0.32b 3.87±0.41b 7.48±0.70b 42.30±5.66b 10.26±0.93ab Reservoir 6.05±0.35ab 2.00±0.33b 4.28±0.15b 49.97±8.01b 12.21±2.71a Mudflat 6.13±0.62ab 1.72±0.26b 11.49±1.79b 77.73±6.63a 4.98±1.34b Vegetated mudflat 7.62±0.14a 8.80±0.98a 23.11±4.49a 95.29±3.17a 9.42±0.55ab *Different letters denote statistical significant differences (Tukey's test) between treatments (within column) at p<0.05 level; mean + standard error (n=4). Table 2 PLFA (nmol g -1 soil) concentration and F/B ratio in the sediments Treatments Total PLFA (nmol g -1 ) GM+ve (nmol g -1 ) GM-ve (nmol g -1 ) Bacteria (nmol g -1 ) Fungi (nmol g -1 ) Actinomycetes (nmol g -1 ) F/B GM+ve/GM-ve Crystallizer 11.63±0.56d* 0.76±0.10d 1.76±0.19d 2.52±0.44d 0.42±0.05b 0.49±0.03d 0.15±0.02a 0.76±0.08a Condenser 28.99±0.83b 3.79±0.37b 6.26±0.37b 10.04±0.63b 0.81±0.14b 2.60±0.34b 0.08±0.01a 0.61±0.05ab Reservoir 18.24±1.11c 2.08±0.28cd 4.01±0.38c 6.09±0.65c 0.60±0.14b 1.74±0.18bc 0.10±0.01a 0.48±0.06ab Mudflat 20.28±1.92c 2.90±0.28bc 4.59±0.50bc 7.49±0.68bc 0.70±0.07b 1.36±0.19cd 0.10±0.01a 0.75±0.07a Vegetated mudflat 49.33±2.08a 6.40±0.40a 14.75±0.72a 21.66±0.77a 2.51±0.37a 4.65±0.36a 0.12±0.01a 0.43±0.09b *Different letters denote statistical significant differences (Tukey's test) between treatments (within column) at p<0.05 level; mean+standard error (n=4). Table 3 Operational taxonomic unit (OTU), richness and diversity indices of bacteria in different samples with a 97% similarity cut-off Treatments Nseqs* Coverage Sobs Chao 1 ACE Shannon Simpson Crystallizer 83129 0.99 2250 2322 2395 5.94 0.010 Condenser 66589 0.99 3529 3714 3936 5.78 0.020 Reservoir 77146 0.99 4315 4445 4565 6.99 0.003 Mudflat 64829 0.99 4372 4687 4986 6.34 0.016 Vegetated mudflat 64355 0.99 4521 4692 4926 6.70 0.005 (*Nseqs: Number of sequences, Sobs : Observed mean species richness, ACE: Abundance-based coverage estimator) Additional Declarations No competing interests reported. Supplementary Files 1.png Fig. S1Map of the study area. 2.png Fig. S2 Rarefaction curves based on the sequences of the V3-V4 regions of the 16S rRNA gene. 3.png Fig. S3 Dendrogram (based on OTUs) showing bacterial distribution among different samples (Bray-Curtis distance metric hierarchical clustering). 4.png Fig.S4 Heatmap depicting the relative abundances of the most dominant classes in each sample. 5.png Fig. S5 Heatmap depicting the relative abundances of the most dominant bacterial families in each sample. Colours red, yellow and blue represent low, medium and high abundance, respectively. 6.png Fig. S6 Venn diagram displaying the shared and unique archaeal OTUs among different samples. SupplementaryTablesS1toS8.docx Cite Share Download PDF Status: Published Journal Publication published 06 Jan, 2023 Read the published version in Archives of Microbiology → Version 1 posted Editorial decision: Major revision 09 Oct, 2022 Reviews received at journal 03 Oct, 2022 Reviewers agreed at journal 29 Sep, 2022 Reviewers invited by journal 26 Sep, 2022 Editor assigned by journal 26 Sep, 2022 Submission checks completed at journal 26 Sep, 2022 First submitted to journal 24 Sep, 2022 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. 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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-2098972","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":139585308,"identity":"ec41e301-e39a-428d-b4c9-942307f22c17","order_by":0,"name":"Doongar Chaudhary","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDCCAwxsQJI5AUgwPgCLMEMkeIjRwmxAshY2CaLcxXf78LMHP9us8/hnnzGr+PFnm7x5OwPbhw9/GGTMcWiRPJdmbtjbll4scS7H7GZv223DOYcZmGfObGPgsWzArsXgDA+bBG/b4cSGMzxmtxkbbidIMDMwM/M2MPAYHMCtRfIvUMt8oJZihj9QLX/+4NciDbJlA1ALMwMbVAswTHBqkTzDZiYtcy49ceMZtmJJkF9mMDM2M/a2SeDUwneG+ZnkmzLrxHlnmDd++PHntrwE/+HDDD/+2Njj0oIEOAygDMYGIEFUHLE/IEbVKBgFo2AUjEAAAGXRVS9HUsv4AAAAAElFTkSuQmCC","orcid":"","institution":"CSIR - Central Salt and Marine Chemicals Research Institute G. 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Marg","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Madhav","middleName":"","lastName":"Kumar","suffix":""},{"id":139585311,"identity":"2712d175-3918-49af-a263-822ca89e2e51","order_by":2,"name":"Vandana Kalla","email":"","orcid":"","institution":"Lachoo Memorial College of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Vandana","middleName":"","lastName":"Kalla","suffix":""}],"badges":[],"createdAt":"2022-09-24 10:59:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2098972/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2098972/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00203-022-03398-4","type":"published","date":"2023-01-06T18:13:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":27098626,"identity":"adfee048-f838-4942-8c2b-4f066ea48974","added_by":"auto","created_at":"2022-09-28 17:53:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":279629,"visible":true,"origin":"","legend":"\u003cp\u003eNMS representation of sediment sample distance based on molar % of PLFA extracted from coastal sediments (A) and, biplot of sediment characteristics and microbial groups (B). Error bars indicate standard error of the mean. (GM+ve: Gram-positive; GM-ve: Gram-negative; F/B: ratio of fungal to bacterial PLFAs).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2098972/v1/7a88d7bae24e8c8de86b6581.png"},{"id":27099018,"identity":"9ec0e2a2-bbf7-411d-bb95-249a4d4a76f6","added_by":"auto","created_at":"2022-09-28 18:03:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":386934,"visible":true,"origin":"","legend":"\u003cp\u003eRelative molar percent of PLFA for different microbial taxonomic groups in coastal sediments. Error bars indicate standard error of the mean. Error bars indicate standard error of the mean. (GM+ve: Gram-positive; GM-ve: Gram-negative). [Different letters denote significant differences (Tukey’s HSD test) at p ≤ 0.05 among sediments].\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2098972/v1/a67d4a77d26581c5a4ad72ec.png"},{"id":27099099,"identity":"c006b86a-6d4a-483c-b5b4-66fc28b56480","added_by":"auto","created_at":"2022-09-28 18:08:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":314153,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundances of bacteria at the phylum level in different samples.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2098972/v1/f13c3c68dc10abb02bfe4afb.png"},{"id":27098804,"identity":"26353712-df8b-4b06-bfc5-501d6b0f3898","added_by":"auto","created_at":"2022-09-28 17:58:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":132647,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram displaying the shared and unique bacterial OTUs among different samples.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2098972/v1/0b74fc3a3e9db35ee2fc3e3c.png"},{"id":27098096,"identity":"aa47e027-217b-4164-9d3a-9185fb54abc5","added_by":"auto","created_at":"2022-09-28 17:48:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":125160,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundances of archaea at the phylum (A) and class (B) levels in different samples.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2098972/v1/de15d9f6d1169690cd220ef4.png"},{"id":27098630,"identity":"26d54216-fcd4-4b4b-abd7-62741e3ff841","added_by":"auto","created_at":"2022-09-28 17:53:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":152987,"visible":true,"origin":"","legend":"\u003cp\u003eGene copy numbers (per g sediments) in sediments. [Different letters denote significant differences (Tukey’s HSD test) at p\u0026lt;0.05 among different sediment types; Error bars indicate the standard error].\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2098972/v1/7ad65c8482b758f024797f88.png"},{"id":44715792,"identity":"7d7388ce-9c0e-4b59-8487-eef1c1a9100e","added_by":"auto","created_at":"2023-10-16 18:18:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1810839,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2098972/v1/5efd598d-9d69-4eab-9858-3292b872a60d.pdf"},{"id":27098084,"identity":"a786dbdb-44ff-451a-b091-c3cbced1c376","added_by":"auto","created_at":"2022-09-28 17:48:58","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":499097,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S1\u003c/strong\u003eMap of the study area.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2098972/v1/5daa3fddf9e76c8474556086.png"},{"id":27098090,"identity":"37d0fd06-1e28-410d-b851-35e8310fbae9","added_by":"auto","created_at":"2022-09-28 17:48:58","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":204317,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S2 \u003c/strong\u003eRarefaction curves based on the sequences of the V3-V4 regions of the 16S rRNA gene.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2098972/v1/c49403134317b2fde2657efd.png"},{"id":27098087,"identity":"f9406c18-4898-41a1-9d37-4b89e89979ff","added_by":"auto","created_at":"2022-09-28 17:48:58","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":33150,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S3\u003c/strong\u003e Dendrogram (based on OTUs) showing bacterial distribution among different samples (Bray-Curtis distance metric hierarchical clustering).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2098972/v1/e56577e39d8d299b4d13e2f4.png"},{"id":27098806,"identity":"ca0d1298-210f-4a0e-83b6-d5686d4cf862","added_by":"auto","created_at":"2022-09-28 17:58:58","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":262237,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig.S4\u003c/strong\u003e Heatmap depicting the relative abundances of the most dominant classes in each sample.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2098972/v1/b18b13303b4b5088a319dad7.png"},{"id":27098085,"identity":"45de356a-4bb3-4cc5-b2e5-c565547205f2","added_by":"auto","created_at":"2022-09-28 17:48:58","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":575454,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S5 \u003c/strong\u003eHeatmap depicting the relative abundances of the most dominant bacterial families in each sample. Colours red, yellow and blue represent low, medium and high abundance, respectively.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2098972/v1/53e592d15848d8d0f7d7344c.png"},{"id":27098095,"identity":"d7084203-0485-4473-bfed-774f7ec727e8","added_by":"auto","created_at":"2022-09-28 17:48:58","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":334053,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S6\u003c/strong\u003e Venn diagram displaying the shared and unique archaeal OTUs among different samples.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2098972/v1/29e308d04a3105bee6c21390.png"},{"id":27098093,"identity":"aeaf3fe8-9573-4a88-925c-2c355465e8b6","added_by":"auto","created_at":"2022-09-28 17:48:58","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":33538,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesS1toS8.docx","url":"https://assets-eu.researchsquare.com/files/rs-2098972/v1/85d2bec59fd232f34a227dbf.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sediment microbial community structure, enzymatic activities and functional gene abundance in the coastal hypersaline habitats","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIntertidal coastal zones constitute the interface between terrestrial and aquatic ecosystems which is an important ecosystem for carbon and nutrient cycling and natural resources with higher biodiversity (Li et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The coastal ecosystem is the most productive system which provides services like climate regulation/buffering from natural hazards, nutrient cycling, carbon sequestration, shoreline stabilization, food and fodder, habitat for marine life and biodiversity (Calv\u0026atilde;o et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Thompson and Schlacher \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Coastal areas are under the influence of various pressure (natural and anthropogenic), such as mariculture, solar-salt production, coastal mining, erosion, pollution, grazing of riparian vegetation and deforestation of mangroves (Calv\u0026atilde;o et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Microbes play a fundamental role in ecological processes, including organic matter decomposition, nutrient cycling and fixation which are necessary for the functions and services of the coastal ecosystem (Rathore et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang and Wang \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Microbial activities greatly depend on the environmental conditions of sediments, including carbon substrate and nutrient availability, salt concentration, water content, temperature and pH (Li et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rathore et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, it is necessary to investigate the influence of coastal vegetation, mudflats and salt production on the microbial community composition of sediments that generally exist in a coastal ecosystem.\u003c/p\u003e \u003cp\u003eIndia ranks the third position in global salt production after China and USA (Bhat et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Gujarat state shares the longest coastline (1600 km) which has a diversity of habitats like mangroves, halophytic vegetation, mudflats, salt marshes, coral reefs, wetlands, salt pans etc. Salt ponds are shallow pools with elevated borders designed to store seawater or other brines to produce salts. The salt ponds consist of three distinct interconnected ponds, namely; reservoir, condenser and crystallizer pond (Mani et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). During the tidal influxes or mechanical pumping, the seawater is collected into the reservoir pond and then seawater is transferred into the condenser ponds, where seawater evaporates by solar radiation. The condenser feeds crystallizer ponds and the salinity of crystallizer ponds is much higher than the rest of the two. The salt concentration is a crucial component of these salt ponds. During salt production, calcium carbonate is initially precipitated when salinity is two times seawater, calcium sulfate (gypsum) precipitation occurs when salt content reaches three times seawater and sodium chloride precipitates when salinity approaches ten times to seawater (Davis \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Williams \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Salt production ponds invariably increase the concentration of salt (salinity) in the sediments as compared to coastal intertidal sediments (Song et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); however, the influence of increased salinity on sediment community composition/structure of microbes is poorly understood, particularly in the hypersaline environments. Salt pans and marshes are attractive model systems for the investigation of halophilic microorganisms and also the habitat for novel organisms (Berrada et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Higher salt concentrations generate stress for the microorganisms and only tolerant organisms survive under such circumstances (Oren \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Sfax solar saltern in Tunisia, Mediterranean coastal salterns and saltern of South China have been studied widely for microbial communities (Boujelben et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Oren \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, very few studies compared the microbial communities in sediments of coastal vegetation and salt ponds; further, these informations are scarce in Indian salterns.\u003c/p\u003e \u003cp\u003eSoil/sediments microorganisms can be characterized using a variety of methods, including phospholipid fatty acid profiling (PLFA), fluorescence in situ hybridization (FISH), denaturing gradient gel electrophoresis (DGGE), 16S rRNA gene clone libraries, and next-generation sequencing (NGS). Salinity depressed the bacterial and actinomycetes biomarker PLFA concentrations but slightly influenced the amount of fungal PLFA in salinized sediments (Wang and Wang \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In contrast, elevated salinity had increased the bacterial, fungal and total PLFAs concentrations as observed by Li et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, salinity exerts pressure on microorganisms and only tolerant microbes can survive under high salinity (Boujelben et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Bacterial diversity decrease with an increase in salinity due to osmotic stress (Baati et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Berrada et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Song et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Bacteria in a hypersaline environment are more susceptible to the shifts in salinity than archaea (Leoni et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Also, the archaeal community can withstand wide salinity fluctuations and remain stable (Mani et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Leoni et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Studies carried out in hypersaline conditions exhibited that archaea followed by bacteria were dominant halophilic members (Baati et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Baati et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Trigui et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, many studies demonstrated that the bacterial domain plays a vital role in hypersaline environments (Berrada et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Morrissey et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). \u003cem\u003eSalicola marasensis\u003c/em\u003e belonged to the ɣ-Proteobacteria subdivision and was found predominantly in the non-crystallizer pond (Boujelben et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Culturable bacteria in hypersaline environments showed the predominance of Gram-positive (\u003cem\u003eBacillus\u003c/em\u003e species, which can grow up to 25% salinity) and Gram-negative (\u003cem\u003eVibrio\u003c/em\u003e species) bacteria (Berrada et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In the salt crystals of salterns, Bacteroidetes were dominant, while ɣ-Proteobacteria and α-Proteobacteria were equally distributed (Baati et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Euryarchaeota and Crenarchaeota phyla were previously reported as the dominant phyla in solar salterns (Ahmad et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Xie et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCarbon (C) and nitrogen (N) cycling are the key processes carried out by the microorganisms which are involved in biogeochemical cycling in the coastal sediments. The RuBisCO (ribulose-1,5-bisphosphate carboxylase/ oxygenase) is a notably recognized protein for CO\u003csub\u003e2\u003c/sub\u003e assimilation through the Calvin cycle which is encoded by the \u003cem\u003ecbbL\u003c/em\u003e gene (Spiridonova et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The nitrogen assimilation process is governed by the diazotrophic bacteria and the \u003cem\u003enifH\u003c/em\u003e gene which encodes the dinitrogenase reductase enzyme involved in N-fixation (Poly et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The 16S rRNA, \u003cem\u003ecbbL\u003c/em\u003e and \u003cem\u003enifH\u003c/em\u003e were quantified using a quantitative real-time polymerase chain reaction to enumerate the abundance of bacteria, C- and N-fixer, respectively. However, how sediments from the salt pond, mudflat and coastal vegetation affect the abundance of functional genes (16S rRNA, \u003cem\u003ecbbL\u003c/em\u003e and \u003cem\u003enifH\u003c/em\u003e) in the coastal ecosystem is still indistinct. We hypothesized that the diverse microbial community exists in the hypersaline environment, and microbial community diversity and functional gene abundance would decrease at higher salinity. Hence, the objective of the present investigation was to elucidate how microbial community structure (phospholipid fatty acid (PLFA) profiling and 16S rRNA gene sequencing ), enzyme activities (β glucosidase, urease, phosphatase, and sulfatase) and functional genes (16S rRNA, \u003cem\u003enifH\u003c/em\u003e and \u003cem\u003ecbbL\u003c/em\u003e) abundance influenced by the different type of coastal sediments (salt ponds, mudflat and vegetated mudflat).\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eSites and sediment sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was carried out at the Experimental Salt Farm of CSIR-CSMCRI, located at the coastal intertidal area (N21\u0026deg; 47.521\u0026rsquo; to N21\u0026deg; 47.732\u0026rsquo;; E72\u0026deg;07.417\u0026rsquo; to E72\u0026deg;07.644\u0026rsquo;) of Bhavnagar district of Gujarat, India (Fig. S1). The study site is located in hot semi-arid, with minimum and maximum temperatures of 24- 44\u0026deg; C and 15-32\u0026deg; C in summer and winter, respectively. The annual average rainfall is 593 mm and is unevenly distributed.\u0026nbsp;The usual\u0026nbsp;onset and cessation of rain occur during the second\u0026nbsp;week of June and second week of September, respectively. Winter begins in November and extends up to February followed by the summer season. The texture of sediment ranged from clay loam to clay. Five different types of sediments were collected for the present study. Among the five sediments, three sediment samples were collected from different ponds of solar salt pans (reservoir, condenser and crystallizer ponds) and one each from mudflat (without vegetation) and vegetated mudflat (halophyte growing area) (four replicates of each; total samples: 20). Mudflat and vegetated mudflat had similar seawater flooding frequencies. Sediments from halophyte growing areas were compared\u0026nbsp;with sediments from other habitats.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the experimental salt farm, salt is produced by several interconnected ponds. First seawater is collected in reservoir ponds (N 21\u0026deg; 47.73\u0026rsquo;, E 072\u0026deg;07.64\u0026rsquo;; area: 8.29 ha) then water flows through successive ponds, condenser (N21\u0026deg; 47.66\u0026rsquo;, E 072\u0026deg;07.42\u0026rsquo;; area 1.52 ha) and crystallizer (N 21\u0026deg; 47.604\u0026rsquo;, E 072\u0026deg;07.461\u0026rsquo;; area: 1.52 ha), these salt ponds are approximately 30 years old. Mudflats (N 21\u0026deg; 47.53, E 072\u0026deg;07.52; area: 2 ha) and halophyte growing area (N 21\u0026deg; 47.53, E 072\u0026deg;07.53) were also located near the salt pans and had similar diurnal tidal amplitude. In the halophyte vegetated area, \u003cem\u003eSuaeda\u0026nbsp;\u003c/em\u003especies were predominantly growing. In each sediment type (crystallizer, condenser, reservoir, mudflat and vegetated mudflat), four replicate quadrats (1x1 m) were constructed and sampled from 0-20 cm depth in January 2018. Distance between replicate quadrats was more than 20 m and a composite sediment sample was prepared by combining three adjacent cores from each replicate quadrat. Sediment samples were immediately brought to the laboratory after collection. Sediment samples were separated into two subsets; one set was used for physicochemical analysis after air-drying and the second subset was kept at -20 \u0026deg;C for analysis of microbial assay (enzyme activities, PLFA and genomic DNA analysis).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePhysico-chemical characteristics of sediments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMoist sediment samples were oven-dried at 105 \u0026deg;C (24 hours) to measure moisture content. Sediment samples were passed through a 2 mm sieve after air drying and stored for chemical analysis. Salinity and pH of sediment samples were determined in a 1:2.5 (w:v; sediment : water) suspension using electrical conductivity and pH meters, respectively. Organic carbon (OC) content in sediments was measured by chromic acid oxidation method and back titrated with ferrous ammonium sulfate (Nelson and Sommers 1982).\u0026nbsp;Water (40 mL) was used to extract\u0026nbsp;dissolved organic carbon (DOC) from sediments (4 g) followed by determination through TOC analyzer (Liqui TOC, Elementar, Germany). Potassium chloride (2M KCl) was used to extract ammonium (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N) and nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N) and were measured colorimetrically using a\u0026nbsp;spectrophotometer\u0026nbsp;(Keeney\u0026nbsp;and\u0026nbsp;Nelson 1982). Phosphorus (P) was extracted with 0.5M NaHCO\u003csub\u003e3\u003c/sub\u003e solution (Olsen et al. 1954) and quantified by the inductively coupled plasma spectrometer (Optima 2000, PerkinElmer Inc., USA). Neutral normal ammonium acetate (1N NH\u003csub\u003e4\u003c/sub\u003eOAc, pH 7.0) was used to extract potassium (K\u003csup\u003e+\u003c/sup\u003e), sodium (Na\u003csup\u003e+\u003c/sup\u003e), calcium (Ca\u003csup\u003e2+\u003c/sup\u003e) and magnesium (Mg\u003csup\u003e2+\u003c/sup\u003e) (Hanway and Heidel 1952). \u0026nbsp;The concentrations of K\u003csup\u003e+\u003c/sup\u003e and Na\u003csup\u003e+\u003c/sup\u003e were quantified using a flame photometer. An inductively coupled plasma spectrometer was used for the estimation of Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e. Sulfate (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e) was extracted with 0.15% calcium chloride and was determined turbidimetrically using spectrophotometer (Chesnin and Yien 1950). \u0026nbsp;The chloride from the sediments was extracted with water (Lundmark and Olofsson 2007) and estimated by titration with AgNO\u003csub\u003e3\u003c/sub\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eMicrobial activities in sediments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBasal respiration and enzyme assay\u003c/em\u003e\u003c/strong\u003e:\u0026nbsp;The basal respiration rate of sediments was assessed by determining the evolved CO\u003csub\u003e2\u003c/sub\u003e in the headspace of the incubation bottle for 24 h using the alkali (NaOH) absorption method (Vogeler et al. 2008). The \u0026beta;-glucosidase (carbon cycling enzyme) enzyme activity was measured using p-nitrophenyl-\u0026beta;-D-glucopyranoside as substrate (Tabatabai 1994) with slight adaptations: toluene was not used due to the short incubation time (Lee et al. 2007). \u0026nbsp; Activity of urease (nitrogen cycling enzyme) was measured using a method of Tabatabai (1994) and the released NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N was extracted with 2M KCl and measured spectrophotometrically (Keeney and Nelson 1982). Alkaline phosphatase (phosphorus cycling enzyme) activity was analysed using p-nitrophenyl phosphate as a substrate (Tabatabai and Bremner 1969). p-nitrophenyl sulfate was used as a substrate to measure the activity of sulfatase (sulfur cycling enzyme) using the method of Tabatabai and Bremner (1970).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ePhospholipid fatty acid (PLFA)\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003ePLFAs were extracted from sediments (5g) using the procedures of Bardgett et al. (1996) and Frosteg\u0026aring;rd et al. (1993). In brief, lipids were extracted from sediments in three steps, using citrate buffer, chloroform and methanol in the ratio of 0.8:1:2 followed by separation of phospho-, neutral- and glycolipids using a silicic acid column. The esterification of phospholipids was carried out using alkaline methanol and then analysed by the gas chromatograph (GC) system (Agilent 6850, Agilent Technologies, Inc.) fitted with a capillary column and FID (flame ionization detector). The identification and quantification of PLFAs were carried out with MI System (MIDI Inc., Newark, DE) and internal standard (methyl nonadecanoate; 19:0), respectively.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The concentration of PLFAs was expressed as nmol PLFA g\u003csup\u003e-1\u003c/sup\u003e sediment which indicating the microbial biomass. Taxonomic groups of microbes such as bacteria (15:0i, 15:0a, 16:0i, 16:0a, 17:0i, 17:0a, 17:0, 17:0cy, 19:0cy, 16:1\u0026omega;7, 16:1\u0026omega;5, 18:1\u0026omega;7, 17:1\u0026omega;9), Gram-positive (GM+ve) bacteria (15:0i, 15:0a, 16:0i, 16:0a, 17:0i, 17:0a), Gram negative (GM-ve) bacteria (17:0cy, 19:0cy, 16:1\u0026omega;7, 16:1\u0026omega;5, 18:1\u0026omega;7, 17:1\u0026omega;9), fungi (18:2\u0026omega;6,9), and actinomycetes (16:0 (10Me), 17:0 (10Me), 18:0 (10Me)) were indicated by individual PLFA biomarkers (Zelles 1996; Kaur et al. 2005). \u0026nbsp;The ratios of fungal to bacterial PLFAs (F/B) and GM+ve/GM-ve were used to indicate the shift in microbial community structure (B\u0026aring;\u0026aring;th 2003; Li et al. 2017; Wang and Wang 2018). Specific microbial groups (bacterial, fungal and actinomycetes) PLFAs were computed using both absolute concentration (nmole g\u003csup\u003e-1\u003c/sup\u003e soil) as well as relative abundances (molar percent of total PLFA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eDNA extraction, quantitative real\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e‐\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003etime polymerase chain reaction (qRT-PCR) and\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;amplification of 16S rRNA gene\u003c/em\u003e\u003c/strong\u003e: Microbial genomic DNA was extracted from 0.5 g of sediment samples using FastDNA\u0026trade; SPIN Kit for soil (MP Biomedicals, USA) according to instructions of manufacturer\u0026rsquo;s protocol and using FastPrep bead beating instrument (MP Biomedicals, USA). DNA was quantified by Nanodrop (NanoDrop Technologies, USA) and quality was determined on the agarose gel (0.8% w/v). Extracted genomic DNA was stored at -20 \u0026deg;C for amplification of functional gene(s) and 16S rRNA gene.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePreviously designed primers for phylogenetic marker and functional genes (\u003cem\u003ecbbL\u003c/em\u003e and \u003cem\u003enifH\u003c/em\u003e) involved in biogeochemical cycles (C and N cycling) were used for PCR amplification with slight alteration in the PCR conditions (Table S1). Quantitative real-time PCR was conducted on a Bio-Rad thermal cycler (CFX96; Bio-Rad, USA) using QuantiFast SYBR Green PCR Kit (Qiagen, USA). The RT-PCR reactions were performed in 20 \u0026mu;L volume, containing 10\u0026mu;L of 2x qPCR-SYBR green master mix (premix of dNTPs, Taq DNA polymerase, PCR buffers and SYBR green), 0.2 \u0026mu;L of each gene-specific primers of 20 \u0026mu;M, 8.6 \u0026mu;L MQ and 1\u0026mu;L template DNA (diluted) for \u003cem\u003enifH\u003c/em\u003e and 16S rRNA genes. For \u003cem\u003ecbbL\u003c/em\u003e, the PCR reactions were achieved in 20 \u0026mu;L volume, consisting 10\u0026mu;L of 2x qPCR-SYBR green master mix, 0.3 \u0026mu;L of each gene-specific primers of 20 \u0026mu;M, 7.4 \u0026mu;L MQ and 2\u0026mu;L template DNA (diluted). Recombinant plasmids comprising one copy of respective gene fragments (16S rRNA/ \u003cem\u003ecbbL\u003c/em\u003e/ \u003cem\u003enifH\u003c/em\u003e) were 10-fold diluted and standard curves were prepared.\u003c/p\u003e\n\u003cp\u003eMicrobial metagenomics DNA from samples of the same origin was pooled and concentrated in the concentrator and around 1000 \u0026mu;g of DNA was shipped to Macrogen Ltd., South Korea, for paired-end sequencing using the Illumina MiSeq platform. For library preparation, the target-specific primers, Bakt_341F (5\u0026rsquo;-CCTACGGGNGGCWGCAG-3\u0026rsquo;) and Bakt_805R (3\u0026rsquo;-GACTACHVGGGTATCTAATCC-5\u0026rsquo;) were used to amplify the V3-V4 hypervariable regions of the bacterial 16S rRNA gene (Mizrahi-Man et al. 2013; Sinclair et al. 2015). Sequence files obtained from Macrogen Ltd. were checked for their quality using FastQC software (Galaxy version 19.01). Low-quality sequences with a Phred score lower than 25 were excluded using the Trimmomatic package listed under galaxy server (www.usegalaxy.com; Galaxy version 19.01). Further, the sequences were analyzed using Mothur software package (version 1.39.1), according to the MiSeq Standard operating procedure (SOP) (http://www.mothur.org/wiki/MiSeq_SOP). Paired-end reads were joined to form contigs using both forward and reverse sequence files (Kumar et al. 2022). Both forward and reverse primers were trimmed and sequences were further screened for the following parameters: minimum length: 403 bp, maximum length: 429 bp, maximum homopolymers: 8, maxambig: 0 and pdiffs: 3. Unique sequences from each file were screened in order to enhance the speed and avoid computational problems. Chimera detection was performed with the UCHIME algorithm (Edgar et al. 2011) and removed. The resulting sequences were aligned against SILVA bacterial reference database. Archaeal communities were analysed by aligning the processed sequences against SILVA archaeal reference database. The taxonomic assignment to the sequences was done according to SILVA taxonomy. Sequences flagged as chloroplasts, mitochondria or eukaryotes were also excluded by using the remove lineage command. Further, the remaining sequences were clustered de novo using a distance matrix algorithm at a distance cut-off of 97% similarity. A total of 12031 and 9745 OTUs were obtained for bacteria and archaea, respectively. Rarefaction curves for the observed OTUs were generated using \u0026ldquo;rarefaction.single\u0026rdquo; command in Mothur to evaluate the alpha diversity and sampling effort\u0026nbsp;(Fig. S2). Statistical analyses were performed with the web-based microbiome analyst software (Dhariwal et al. 2017). InteractiVenn software (http://www.interactivenn.net/) was used to construct the Venn diagram. The sequences were submitted in the NCBI SRA database under bio-project number PRJNA521453.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the study are presented as means (n = 4) \u0026plusmn; standard errors (SE) and ANOVA (one-way analysis of variance) was carried out to differentiate the significant differences (Tukey\u0026apos;s honest significance test; p\u0026lt;0.05) between treatment means using SPSS version 19.0 (IBM Corp, NY). Nonmetric-multidimensional scaling (NMS) analysis was carried out using PC-ORD software (McCune and Mefford 2006). Molar per cent of PLFAs was used for NMS analysis using the Sorensen distance measure and a second matrix was prepared by summed values for microbial groups and soil physicochemical properties which was used for creating the joint plot overlaid on the NMS plot. The NMS scores were analysed by one-way ANOVA. The relationships between sediment microbial community and physicochemical characteristics were examined using correlation coefficients (p\u0026lt;0.05).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSediment characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChemical characteristics and nutrient contents of sediments showed significant variability (except dissolved organic carbon) (Table S2 and S3). Moisture content was the highest in the sediments from the condenser (42.2%) and the lowest in the vegetated mudflat (27.3%). The pH and EC\u0026nbsp;varied from 8.1 (mudflat) to 8.4 (condenser) and from 12.3 (vegetated mudflat) to 49.8 dS m\u003csup\u003e-1\u003c/sup\u003e (crystallizer), respectively. Organic carbon content was the highest in sediments from the condenser (1.2%) and crystallizer (1.1%) while the lowest was in vegetated mudflat (0.7%). Salt production significantly influenced the nutrient content of the sediments from salt ponds. Sediments from crystallizer had much highest contents of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e (6.9 mg kg\u003csup\u003e-1\u003c/sup\u003e), K\u003csup\u003e+\u0026nbsp;\u003c/sup\u003e(4.1 g kg\u003csup\u003e-1\u003c/sup\u003e), SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u0026nbsp;\u003c/sup\u003e(2.5 g kg\u003csup\u003e-1\u003c/sup\u003e), Cl\u003csup\u003e‑\u003c/sup\u003e (67.5 g kg\u003csup\u003e-1\u003c/sup\u003e) and Mg\u003csup\u003e2+\u003c/sup\u003e (8.5 g kg\u003csup\u003e-1\u003c/sup\u003e) than other sediments. Sediments from mudflat and vegetated mudflat had remarkably higher contents of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e (1.6-1.92 mg kg\u003csup\u003e-1\u003c/sup\u003e) and P (19.3-20.7 mg kg\u003csup\u003e-1\u003c/sup\u003e) while concentrations of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u0026nbsp;\u003c/sup\u003e(3.6-4.4 mg kg\u003csup\u003e-1\u003c/sup\u003e), K\u003csup\u003e+\u0026nbsp;\u003c/sup\u003e(1.1-1.6 g kg\u003csup\u003e-1\u003c/sup\u003e), Na\u003csup\u003e+\u0026nbsp;\u003c/sup\u003e(8.8-21.3 g kg\u003csup\u003e-1\u003c/sup\u003e), SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u0026nbsp;\u003c/sup\u003e(1.2-1.5 g kg\u003csup\u003e-1\u003c/sup\u003e), Cl\u003csup\u003e-\u0026nbsp;\u003c/sup\u003e(11.6-32.8 g kg\u003csup\u003e-1\u003c/sup\u003e), Ca\u003csup\u003e2+\u0026nbsp;\u003c/sup\u003e(3.6-4.6 mg kg\u003csup\u003e-1\u003c/sup\u003e) and Mg\u003csup\u003e2+\u0026nbsp;\u003c/sup\u003e(1.4-2.6 mg kg\u003csup\u003e-1\u003c/sup\u003e) were the lowest in comparison with salt pan sediments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMicrobial respiration and enzyme activities in sediments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBasal respiration and enzyme activities were significantly influenced by different sediment sources (Table 1). Basal respiration was the highest in sediments from vegetated mudflat and the lowest in crystallizer and condenser. \u0026beta;-glucosidase and urease activities were noticeably lower in the sediments of saltpan and mudflat than in vegetated mudflat. Sediments from mudflat and vegetated mudflat had much higher alkaline phosphatase activity than the other sediments. Unlike other enzyme activities, the activities of the sulfatase enzyme were the highest in the reservoir sediment and the lowest in mudflats.\u003c/p\u003e\n\u003cp\u003eCorrelation analysis between sediment characteristics and enzyme activities showed that \u0026beta;-glucosidase was positively correlated with pH (Table S4). The activities of urease and alkaline phosphatase, as well as the rate of basal respiration were positively correlated with NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e and P while it was negatively correlated with sediment moisture content (except basal respiration), EC, OC, K\u003csup\u003e+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e, Cl\u003csup\u003e-\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e. Sulfatase activity showed positive correlation with pH, OC and Ca\u003csup\u003e2+\u003c/sup\u003e while negative correlation with P.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMicrobial community composition/structure of sediments (PLFA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferent sediment samples significantly affected the amount\u0026nbsp;of total PLFAs, Gram-positive, Gram-negative, total bacterial and actinomycetes biomarker PLFAs (Table 2). The highest concentrations of PLFAs microbial biomarkers were exhibited in vegetated mudflat sediments, whereas the lowest were in crystallizer sediments. There was no significant influence of different sediments on the fungi/bacterial ratio of PLFAs. The ratios of GM+ve /GM-ve PLFAs showed significant differences among the sediments and the ratio was the highest in crystallizer and mudflat while the lowest in vegetated mudflat sediments.\u003c/p\u003e\n\u003cp\u003eFurther, attempts were made to correlate the PLFA concentrations of biomarkers of microbial groups with sediment characteristics (Table S5). Amounts of total PLFAs, Gram-positive and actinomycetes biomarkers were negatively correlated with EC, K\u003csup\u003e+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e, Cl\u003csup\u003e‑\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e while positively correlated with NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e. The EC, K\u003csup\u003e+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e, Cl\u003csup\u003e‑\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003eand Mg\u003csup\u003e2+\u003c/sup\u003e were negatively correlated with the amount of Gram-negative, total bacterial and fungal PLFAs while the same microbial groups were positively correlated with NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e. Furthermore, biomarker PLFA of fungi had a positive relationship with the P concentration of sediments. Amounts of total PLFAs and biomarkers of microbial groups were positively correlated with activities of all enzymes (except sulfatase) (Table S6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMolar per cent of PLFAs was subjected to NMS (Nonmetric-multidimensional scaling) ordination analysis and resulted in a two-dimensional elucidation (Fig. 1). The ordination of both axes (2 and 3) explained 93.2% of the total variability (70.5 and 22.7% by axis two and three, respectively; Fig. 2). Gram-positive, Gram-negative, total bacteria, fungi and actinomycetes had a negative relation with axis 2. Among sediment characteristics, EC, OC, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e, Cl\u003csup\u003e-\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e were positively correlated while NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e and P were negatively correlated with axis 2. Axis 3 was positively associated with the F/B ratio while negatively associated with Gram-negative and total bacteria. The sediment microbial communities of crystallizer, mudflat and vegetated mudflat were significantly different from each other as well as distinct from the condenser and reservoir. However, the microbial community of condenser and reservoir was similar to each other.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe molar percent (relative abundance) of different microbial groups is presented in Fig. 2 and was significantly influenced by the sediment sources. The molar percent of Gram-positive was similar in the vegetated mudflat, mudflat and condenser while significantly lower in the crystallizer. Vegetated mudflat sediments had the highest molar percent of Gram-negative, total bacteria and actinomycetes while the lowest were in sediments from crystallizer. \u0026nbsp;A similar abundance of fungi was observed in crystallizer, reservoir, mudflat and vegetated mudflat. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eBacterial community structure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 492804 contigs were obtained after aligning paired-end raw sequence reads. The number of contigs per sample ranged from 161398 to 172103 (average length ~400 bp). Further, 252238 unique sequences were produced by Mothur software after aligning the sequences with SILVA reference database. Finally, 63384 sequence reads were obtained after excluding low-quality sequence reads, pre-clustering and chimera removal. Sequences were classified into 12031 OTUs at 97% similarity cutoff. The observed OTUs for bacteria were assigned to 22 Phyla, 52 classes, 98 orders and 154 families.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe rarefaction curves of the observed OTUs combined with the goods coverage values reached saturation for all samples indicating sufficient sampling depth and large library size to capture a vast majority of the bacterial diversity in all samples (Fig. S2). The rarefaction curves suggested that the highest bacterial OTUs observed were in vegetated mudflat (4521) followed by mudflat (4372), reservoir (4315), condenser (3529) and crystallizer (2250).\u003c/p\u003e\n\u003cp\u003eThe observed OTUs (after quality filtering) were used to calculate bacterial community composition.\u0026nbsp;More than 99% of the bacterial OTUs were successfully classified at the phylum level into 17 different phyla and six candidate divisions (Fig. 3). All samples showed a high percentage of unclassified bacteria (ranging from 16%-37%). The unclassified bacterial phyla clustered into 4023 OTUs. Crystallizer and reservoir harbored the highest percentage of unclassified bacterial phyla (37 and 26%) followed by Proteobacteria (22 and 17%) and Bacteroidetes (17 and 14%, respectively). \u0026nbsp;Similarly, a high percentage of unclassified bacterial phyla (28%) were also observed in condenser followed by Cyanobacteria (20%), Bacteroidetes (19%) and Proteobacteria (15%). Vegetated mudflat showed a similar percentage of Actinobacteria (19%) and unclassified bacteria (19%) followed by Proteobacteria (15%), Bacteroidetes (15%) and Firmicutes (14%). Bacteroidetes (18%), unclassified Bactria (17%), Cyanobacteria (16%), Actinobacteria (14%) and Proteobacteria (10%) were dominant phyla in mudflat samples.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the samples were clustered into two groups based on\u0026nbsp;16S rRNA gene sequence dendrogram (OTU level) (Fig. S3). Samples from the reservoir, condenser and crystallizer were clustered in the same group while samples from vegetated mudflat and mudflat were clustered in another group. In the both groups, unclassified bacteria (30 and 18%) were predominantly observed. First group (reservoir, condenser and crystallizer) was dominated by Proteobacteria (18%), Bacteroidetes (17%), Cyanobacteria (9%) and Firmicutes (5%) phyla, while another group (vegetated mudflat and mudflat) was dominated by Actinobacteria (16%), Bacteroidetes (16%), Proteobacteria (13%), Firmicutes (11%), Cyanobacteria (8%), Chloroflexi (6%) and Planctomycetes (5%).\u003c/p\u003e\n\u003cp\u003eClustering of top bacterial classes also showed that a number of the bacteria belong to unclassified groups (Fig. S4). In the crystallizer, Bacteroidia, Deltaproteobacteria, Lentisphaeria were mainly distributed while Sphingobacteria, Chlorobia and Cyanobacteria SubsectionIII dominated in the condenser. \u0026nbsp;Similarly, mudflat had more bacteria belonging to Phycisphaerae, Optitutae, Flavobacteria and Verrucomicrobia OPB35 classes. The reservoir was dominated by Gemmantimonadetes, Firmicutes RF3, Planctomycetes MBMPE71, Acidobacteria, Alphaproteobacteria and Anaerolineae while vegetated mudflat showed a dominance of Clostridia, Betaproteobacteria, Actinobacteria, Caldilineae, Bacilli, Holophagae and Thermomicrobia. At class level, condenser showed a similar distribution of bacterial classes with crystallizer while reservoir showed similarity with vegetative mudflat (Fig. S4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFamily level analysis showed a wide range of distribution among the samples (Fig. S5). Acidobacteriaceae and Opitutaceae were found to be present in the reservoir, mudflat and vegetated mudflat, while the same families were absent in crystallizer and condenser. Rhodothermaceae, Bacillaceae, Paenibacillaceae, Peptococcaceae, Rhodobacteriaceae, Bdellovibrionaceae, Bacteriovoraceae, Halomonadaceae, Alteromonadaceae, Oceanospirillaceae, Punniceicoccaceae and Sinobacteraceae were present in all the samples with higher abundance in vegetated mudflat and mudflat. Members of Desulfobacteriaceae, Desulfovibrionaceae, Desulfobulbaceae, Geobacteriaceae, Spirochaetaceae, Victivallaceae and Halanaerobiaceae were relatively found in abundance in crystallizer, condenser and reservoir. At the genera level majority of the bacterial OTUs were unclassified.\u003c/p\u003e\n\u003cp\u003eAltogether 18,987 bacterial OTUs were detected across all libraries, with 169 OTUs common to all (Fig. 4). The commonly shared OTUs represented unclassified Bacteria (27%), Proteobacteria (24%), Firmicutes (17%), Bacteroidetes (11%), Planctomycetes (7%), Actinobacteria (5%) and other Bacteria (9%) at phylum level. The numbers of OTUs exclusive to the reservoir, condenser, crystallizer, mudflat and vegetated mudflat samples were 2157, 1272, 680, 1306 and 1963, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe bacterial community richness and diversity values are shown in Table 3. The reads obtained from different samples were compared for alpha diversity measures such as richness (Chao1), abundance-based coverage estimator (ACE) and species diversity (Shannon index). The values of Chao1 and ACE were the highest in mudflat and vegetated mudflat, whereas the lowest in the crystallizer. The highest Shannon index was observed in the reservoir and lowest was in condenser and crystallizer. Simpson index (calculates a measure of diversity) was highest in condenser followed by mudflat, crystallizer, vegetated mudflat and reservoir.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cs\u003e\u0026nbsp;\u003c/s\u003e\u003cstrong\u003eArchaeal community structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter aligning the sequences with the archaeal silva reference database, taxonomic affiliation with 97% similarity threshold yielded 9745 OTUs. From the observed OTUs, archaeal community composition was calculated. More than 97% of archaeal OTUs were unclassified and the remaining OTUs were classified into two different phyla (Fig. 5).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eIn classified archaeal phyla, Euryarchaeota was the dominant followed by Crenarchaeota in all the samples. The highest percentage of Euryarchaeota phyla was observed in the condenser followed by crystallizer and mudflat, while the lowest percentage was observed in the reservoir. For the Crenarchaeota phyla, the highest percentage was observed in the reservoir followed by crystallizer and condenser while the lowest percentage was observed in mudflat samples. Further on the class level, the phyla Euryarchaeota was classified into Halobacteria and Methanomicrobia while a large fraction of the same phyla was unclassified (Fig. 5). This showed that the saltpan archaeal community has been poorly studied using the high-throughput NGS technique and needs to be studied. The OTUs from the Crenarchaeota phyla were further classified into Thermoprotei class (18%) and the rest OTUs remain unclassified (Fig. 5). Thermoprotei class was most abundant in crystallizer and condenser while it was absent in mudflat samples. The highest percentage of Halobacteria was observed in the condenser followed by mudflat and crystallizer while the same class was absent in the reservoir and vegetated mudflat. Methanomicrobia class was detected in mudflat, vegetated mudflat and crystallizer while it was absent in condenser and reservoir samples.\u003c/p\u003e\n\u003cp\u003eVenn diagram for archaeal OTUs showed 91 common among the samples. The OTUs exclusive to the reservoir, condenser, crystallizer, mudflat and vegetated mudflat samples were 1758, 1143, 975, 1073 and 1414, respectively (Fig. S6).\u003c/p\u003e\n\u003cp\u003eThe community richness and diversity index values for archaeal communities were calculated (Table S7). Chao1 and ACE values were observed highest in the reservoir followed by condenser and mudflat. The highest Shannon index value was observed in the reservoir, mudflat and vegetated mudflat, while the lowest in the crystallizer. Simpson index values were highest in condenser and crystallizer.\u003c/p\u003e\n\u003cp\u003e\u003cs\u003e\u0026nbsp;\u003c/s\u003e\u003cstrong\u003eFunctional g\u003c/strong\u003e\u003cstrong\u003eene abundance\u003c/strong\u003e\u003cstrong\u003es in sediments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;genomic DNA concentration varied from 7.4 to 20.4 ng \u0026micro;l\u003csup\u003e-1\u003c/sup\u003e and the highest concentration was observed in the condenser followed by vegetated mudflat, reservoir, mudflat and crystallizer sediments. \u0026nbsp;The copies of the bacterial 16S rRNA gene varied from 1.8x10\u003csup\u003e7\u003c/sup\u003e (crystallizer) to 153.0x10\u003csup\u003e7\u003c/sup\u003e (vegetated mudflat) per g of sediment. The abundance of \u003cem\u003ecbbL\u003c/em\u003e gene was markedly different in sediments and the highest copy number was exhibited in vegetated mudflat, whereas the lowest in crystallizer sediments. Sediments significantly affected the abundance of \u003cem\u003enifH\u003c/em\u003e gene and the highest abundance was observed in the condenser followed by vegetated mudflat, mudflat, reservoir and crystallizer sediments (Fig. 6). The copies of the bacterial 16S rRNA and \u0026nbsp;\u003cem\u003ecbbL\u003c/em\u003e genes showed a positive correlation with all enzyme activities and microbial biomarker PLFA concentrations (Table S8).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eMicrobial respiration and enzyme activities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBasal respiration is extensively used to assess the microbial activity in the sediments/soils and measures the overall decomposition rate of the organic carbon (Vogeler et al. 2008). The basal respiration rate was higher in vegetated mudflat sediments than any other sediments, even though other sediments (crystallizer and condenser) had higher organic carbon which is burial and preserved C in the precipitated salts (Table 1). This indicates that the higher sediment salinity significantly reduced the decomposition rate of organic matter\u0026nbsp;which is confirmed by\u0026nbsp;negative relationship between basal respiration rate and salinity (Table S4). Previous studies have also shown that basal respiration was reduced considerably with increased salinity (Mahajan et al. 2015). Enzyme activities are sensitive indicators of the quality of sediments and were closely related to physico-chemical characteristics of sediments (Mahajan et al. 2015; Yang et al. 2017). The enzymes activities (\u0026beta;-glucosidase, urease and alkaline phosphatase) considerably decreased in sediments of salt ponds while vegetated mudflat sediment had the highest enzyme activities. The decreased enzyme activities in salt ponds (with higher salinity) might be due to increased osmotic stress on microbes (Frankenberger and Bingham 1982; Wei et al. 2022)\u0026nbsp;which shows the reduced capability of sediments to mineralize nutrients (C, N and P) as evident in table (S2 and S3) as well as negative correlation with salinity. Enzyme activities under vegetated mudflats could be stimulated by the higher microbial activities sustained by root exudation and litter decomposition (Yang et al. 2017). The salinity did not affect the sulfatase activity; similar results were also observed by Oshrain and Wiebe (1979).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Sediment microbial community composition/structure (PLFA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe biomass of microbes and their activities show the size and magnitude of the microbial population associated with nutrient cycling and biogeochemical processes occurring in the sediments. Sediment microbial activities can be enumerated by the culture-dependent as well as culture-independent approaches. In the present study, culture-independent (PLFA analysis) approach was used for estimation of microbial biomass and microbial community structure which is extensively used for sediment-microbial interaction studies (Li\u0026nbsp;et al. 2017; Rathore\u0026nbsp;et al.\u0026nbsp;2017; Wang and Wang 2018). The values of total PLFAs, Gram-positive, Gram-negative, total bacterial and actinomycetes biomarker PLFAs were the lowest in crystallizer and the highest in vegetated mudflat sediments (Table 2). The increased content of PLFA in vegetated mudflat sediments was mainly attributed to higher contents of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e and lower salinity (salinity-associated ions) as revealed by the correlation study (Table S6). These results are supported by earlier studies (Wang and Wang 2018) which described that the rise in salinity would depress microbial activity and biomass. The crystallizer and condenser had four times; and the reservoir and mudflat had two times higher salinity than the sediments from the vegetated mudflat (Table S2). Furthermore, crystallizer and condenser had a significantly higher amount of organic carbon than vegetated mudflat sediments; however, there was no relationship between organic carbons on microbial biomass, as observed by Li et al. (2017) in coastal sediments. It shows a more dominant effect of salt content than organic carbon on microbial biomass. Further, fungal biomass was positively correlated to the P concentration of sediments which implying that adequate availability of P is necessary to sustain fungal biomass (Teste et al. 2016). Different sediments did not influence the ratio of F/B. Similar results were observed in earlier studies (Li et al. 2017; Wang and Wang 2018) which might be due to the adaptation of fungus to the high salt concentration of the coastal wetlands. In all studied sediments, the biomass of Gram-negative bacteria was higher than Gram-positive bacteria and the ratio of Gram-positive /Gram-negative was significantly affected by different sediments (Table 2). Berrada et al. (2012) also observed that Gram-positive bacteria were widely represented in higher salinity which agreed with the highest Gram-positive /Gram-negative ratio in crystallizer sediments.\u003c/p\u003e\n\u003cp\u003eSediment microbial communities of crystallizer, mudflat and vegetated mudflat have varied from each other and also differed from condenser and reservoir (Fig. 1) due to the changes in the abundance of bacterial, fungal and actinomycetes PLFA biomarkers (Fig. 2). \u0026nbsp;The increased salinity in sediments from mudflat and salt ponds had shifted the bacterial abundance (dominance of Gram-positive) (Morrissey et al. 2014). There was also a marginal reduction in the abundance of fungus at higher salinity. The Gram-positive bacteria are slow-growing as compared to Gram-negative bacteria and adopt k-strategists (low growth rate with high resource use efficiency) which is related to the resistance of the bacterial community to salinity and increased the ratio of Gram-positive /Gram-negative with a rise in salinity (de Vries and Shade 2013). In earlier studies, it was observed that the genera and species numbers decreased from marsh to salterns at Lower Loukkos (Morocco) (Berrada et al. 2012) and Wendeng salterns of China (Song et al. 2022). Similarly, elevated salinity reduced the abundance of actinomycetes. Actinomycetes were higher in mangrove sediments followed by mudflat and saltpan sediments (Vijayakumar et al. 2007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eBacterial community structure (16S rRNA sequencing)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBacterial 16S rRNA gene OTUs rarefaction curve reached saturation depicting sufficient sampling depth and large library size to capture a vast majority of the diversity in all samples (Fig. S2). Proteobacteria were detected as the most dominant phyla in crystallizer and reservoir after unclassified bacteria (Fig. 3). \u0026nbsp;Core microbiome analysis of the selected phyla showed that most of the bacterial OTUs belonged to unclassified phyla, indicating that the diversity of the hypersaline ecosystem is poorly studied (Najjari et al. 2015; Zhong et al. 2016). This finding is similar to previous studies on coastal wetlands and intertidal soils (Li et al. 2017; Wang and Wang 2018). Further, Hu et al. (2014) reported that the abundance of Proteobacteria is influenced by a change in salinity and is directly proportional to salinity. Apart from Proteobacteria phyla, Bacteroidetes, Firmicutes, Chloroflexi, Actinobacteria, Cyanobacteria and Planctomycetes were also observed in all the samples; this finding is supported by Trigui et al. (2011), Wang et al. (2012) and Wei et al. (2022). Firmicutes produce spores under extreme conditions for their survival (Yu et al. 2012). In mudflat and vegetated mudflat samples, Bacteroidetes (in mudflat) and Actinobacteria (in vegetated mudflat) were the most abundant phyla. Actinobacteria can withstand harsh environmental conditions in the dormant stage or sporulation, or in an inactive but viable form. When the condition becomes favourable the Actinobacterial cells start dividing again (Jones and Lennon 2010; Crits-Christoph et al. 2013). Gammaproteobacteria, the most predominant Proteobacterial class observed in this study (Fig. S4) which is phylogenetically and physiologically diverse and involved in the nutrients cycling (Evans et al. 2008). The absence of Acidobacteriaceae and Opitutaceae family in the crystallizer and condenser showed that these bacterial families could not tolerate higher salinity (Fig. S5). Desulfovibrionaceae and Desulfobulbaceae represent incomplete oxidizers while Desulfobacteriaceae represents members of complete oxidizers in a hypersaline environment (Foti et al. 2007) which were detected abundantly in crystallizer, condenser and reservoir. Sorokin et al. (2004) demonstrated the sulfur-reducing activity in the Siberian soda lakes with saturated salinity. Many sulfur-reducing bacteria have previously been reported in hypersaline environments (Foti et al. 2007; Song et al. 2022; Wei et al. 2022). Mudflat and vegetated mudflat shared the highest OTUs between them while crystallizer and condenser shared the second-highest OTUs (Fig. 4). This indicated that the bacterial communities in mudflat and vegetated mudflat were similar while the bacterial communities in crystallizer showed similarity with condenser.\u003c/p\u003e\n\u003cp\u003eIn the present study, bacterial diversity was decreased with an increase in salinity (Table 3) which is also supported by previous studies (Baldwin et al. 2006; Song et al. 2022). Based on the OTU analysis and the different diversity indices (Shannon), the highest diversity was observed in the reservoir and vegetated mudflat samples while the lowest diversity was observed in crystallizer and condenser which is due to the higher concentration of salt (Baldwin et al. 2006; Song et al. 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eArchaeal community structure (16S rRNA sequencing)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArchaeal 16S rRNA gene sequencing results depicted a higher percentage of unclassified archaeal sequences at the phylum level which shows that the diversity in the salt ponds is poorly studied using the NGS technique (Fig. 5). We found that the Euryarchaeota was the most dominant phyla followed by Crenararchaeta in all the samples (Fig. S5). The abundance of Euryarchaeota has been reported by many researchers in saline soils (Xie et al. 2017;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWalsh et al. 2005). The presence of halophilic archaea such as Halobacteria (Fig. 5) in this study is in accordance with other reported studies in hypersaline environments (Maturrano et al. 2006; Youssef et al. 2012; Weigold et al. 2016). The halophilic archaea (Halobacteria) adopt a salt-in strategy for osmoregulation which requires less metabolic energy compared with the synthesis of compatible solutes (Kulp et al. 2007; Genderjahn et al. 2018), therefore, detected only in condenser, crysytallizer and mudflat. The Crenarchaeota phyla observed in the present study is very diverse, ranging from chemolithoautotrophs to chemoorganotrophs which also vary from aerobes to facultative anaerobes to anaerobes (Ahmad et al. 2011). The majority of the Crenarchaeota phyla were unclassified at the class level while classified sequences showed the abundance of Thermoprotei class in all the samples except for mudflat which showed that the class Thermoprotei could also withstand a wide salinity range (Yan et al. 2018). Venn diagram analysis of the archaeal community showed that the mudflat shared maximum OTUs with vegetated mudflat and reservoir (Fig. S6). This may be because of the lower salinity in both vegetated mudflat and reservoir. Archaeal diversity was the highest in the reservoir followed by mudflat, vegetated mudflat and condenser while the lowest diversity was observed in crystallizer (Table. S6). The diversity pattern indicated that the archaeal communities did not follow the same pattern as bacteria and did not show a reduced diversity pattern with respect to salinity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaxonomic assignment and community composition depend on the choice of variable regions. In the present study, we have targeted V3-V4 regions for the amplification of microbial 16S rRNA gene analysis, while studies suggest that targeting V4-V5 regions gives superior recognition of Archaea (Willis et al. 2019; Parada et al. 2016; Satari et al. 2021). A large number of unclassified archaeal communities on the phylum level may be overcome by targeting the archaeal-specific V4-V5 regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eFunctional gene abundances in sediments \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuantification of key functional genes offers an exceptional tool to study sediment microbial communities \u003cem\u003ein-situ\u003c/em\u003e without cultivation biases for environmental samples (Spring et al. 2000). Abundances of bacterial 16S rRNA gene and two functional biomarker genes (\u003cem\u003ecbbL\u003c/em\u003e and \u003cem\u003enifH\u003c/em\u003e) involved in C and N cycling were quantified (Fig. 6). The relative abundance of gene copy numbers (per gram sediment) occurring in different coastal sediments (crystallizer, condenser, mudflat and vegetated mudflat) was significantly affected by the sediment types. Relatively low copy numbers of the 16S rRNA gene in the crystalline sediment indicates hostile habitat condition due to the high salt concentration and nutrient-deficient environment in comparison to other sediments which imposed additional stress conditions on microbes so that the consumption of C substrate will not be efficient (Marinari et al. 2012; Keshri et al. 2015). Sediments from vegetated mudflats appeared to have a higher abundance of bacterial flora which can be helpful in improving stressed environmental conditions. The carbon-fixing bacterial communities (abundance of \u003cem\u003ecbbL\u003c/em\u003e gene) were most abundant in vegetated mudflat sediments and its copy number was inversely proportional to the sediment salinity (lowest abundance in crystalline sediments). Similar results have also been reported by Keshri et al. (2015) in sediments of the Arabian Sea. The abundance of \u003cem\u003enifH\u0026nbsp;\u003c/em\u003egene was the lowest in crystallizer sediments while remaining sediments had a similar abundance. These results align with other reports observed in coastal sediments (Sorokin et al. 2008; Keshri et al. 2013).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study found notable differences in the chemical and microbial characteristics of sediments collected from salt ponds (crystallizer, condenser and reservoir), mudflat and vegetated mudflat. These changes advocate that salt production processes strongly affect the biogeochemical processes and nutrient cycling in the coastal ecosystem. It was established from the present study that the key controller of the microbial community structure and enzyme activities in sediments are sediment salinity and ionic concentration. Vegetation (halophyte) created the most conducive environment for microbial activities in the sediments. The majority of phyla belonged to the unclassified bacteria and archaea which shows that the diversity of hypersaline ecosystems is poorly studied. The bacterial population was dominated by Proteobacteria, Bacteroidetes, Firmicutes and Chloroflexi while Euryarchaeota and Crenarchaeota dominated archaeal communities. \u0026nbsp;The abundance of genes in sediments enhances our information and understanding of the role of microbes in the nutrients biogeochemical cycling. \u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003eConflict of interest: The authors declare that there is no conflict of interest (financial or non-financial).\u003c/p\u003e\n\u003cp\u003eThis MS did not include any published work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors gratefully acknowledge the financial assistance GAP2012 and GAP2125/CRG/2020/000542 rendered by the Ministry of Earth Sciences (MoES) and Science and Engineering Research Board (SERB), New Delhi, respectively.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors are thankful to Mr. S. C. Upadhyay and Aditya P. Rathore for the help received during sample collection and analysis.\u003c/p\u003e\n\u003cp\u003eCSIR-CSMCRI communication No.: 115/2018. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funding was received from the Ministry of Earth Sciences (MoES) (GAP2012) and the Science and Engineering Research Board (SERB), (GAP2125/CRG/2020/000542), New Delhi to carry out the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData Availability Statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMK and VK did analytical work, and MK and DRC wrote the manuscript, prepared the figures and tables. Conception and study designed by DRC. Acquisition of data and statistical analysis carried out by the MK, VK and DRC. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad N, Johri S, Sultan P, Abdin MZ, Qazi GN (2011) Phylogenetic characterization of archaea in saltpan sediments. Indian J Microbiol 51:132-137. https://doi.org/10.1007/s12088-011-0125-2\u003c/li\u003e\n\u003cli\u003eB\u0026aring;\u0026aring;th E (2003) The use of neutral lipid fatty acids to indicate the physiological conditions of soil fungi. Microb Ecol 45:373-383. https://doi.org/10.1007/s00248-003-2002-y\u003c/li\u003e\n\u003cli\u003eBaati H, Guermazi S, Amdouni R et al (2008) Prokaryotic diversity of a Tunisian multipond solar saltern. 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FEMS Microb Ecol 65:425-433. https://doi.org/10.1111/j.1574-6941.2008.00542.x\u003c/li\u003e\n\u003cli\u003eSpiridonova EM, Kuznetsov BB, Pimenov NV et al (2006) Phylogenetic characterization of endosymbionts of the hydrothermal vent mussel \u003cem\u003eBathymodiolus\u003c/em\u003e \u003cem\u003eazoricus\u003c/em\u003e by analysis of the 16S \u003cem\u003erRNA\u003c/em\u003e, \u003cem\u003ecbbL\u003c/em\u003e and \u003cem\u003epmoA\u003c/em\u003e genes. Microbiology 75:694-701. https://doi.org/10.1134/S0026261706060129\u003c/li\u003e\n\u003cli\u003eSpring S, Schulze R, Overmann J et al (2000) Identification and characterization of ecologically significant prokaryotes in the sediment of freshwater lakes: molecular and cultivation studies. FEMS Microb Rev 24:573-590. https://doi.org/10.1111/j.1574-6976.2000.tb00559.x\u003c/li\u003e\n\u003cli\u003eTabatabai MA, Bremner JM (1969) Use of p-nitrophenyl phosphate for assay of soil phosphatase activity. Soil Biol Biochem 1:301-307. https://doi.org/10.1016/0038-0717(69)90012-1\u003c/li\u003e\n\u003cli\u003eTabatabai MA, Bremner JM (1970) Arylsulfatase activity of soils. Soil Sci Soc Am J 34:225-229. https://doi.org/10.2136/sssaj1970.03615995003400020016x\u003c/li\u003e\n\u003cli\u003eTabatabai MA (1994) Soil enzymes. In: Bottomley PS, Angle JS, Weaver RW. (eds) Methods of soil analysis. Part, 2, Microbioogical and biochemical properties. Soil Science Society of America, Madison, WI, pp 775-883. https://doi.org/10.2136/sssabookser5.2.c37\u003c/li\u003e\n\u003cli\u003eTeste FP, Lalibert\u0026eacute; E, Lambers H et al (2016) Mycorrhizal fungal biomass and scavenging declines in phosphorus impoverished soils during ecosystem retrogression. Soil Biol Biochem 92:119-132. https://doi.org/10.1016/j.soilbio.2015.09.021\u003c/li\u003e\n\u003cli\u003eThompson L, Schlacher TA (2008) Physical damage to coastal dunes and ecological impacts caused by vehicle tracks associated with beach camping on sandy shores: A case study from Fraser Island, Australia. J Coast Conserv 12:67-82. https://doi.org/10.1007/s11852-008-0032-9\u003c/li\u003e\n\u003cli\u003eTrigui H, Masmoudi S, Brochier-Armanet C et al (2011) Characterization of heterotrophic prokaryote subgroups in the Sfax coastal solar salterns by combining flow cytometry cell sorting and phylogenetic analysis. Extremophiles 15:347-358. https://doi.org/10.1007/s00792-011-0364-5\u003c/li\u003e\n\u003cli\u003eVijayakumar R, Muthukumar C, Thajuddin N et al (2007) Studies on the diversity of actinomycetes in the Palk Strait region of Bay of Bengal, India. Actinomycetologica 21:59-65. https://doi.org/10.3209/saj.SAJ210203\u003c/li\u003e\n\u003cli\u003eVogeler I, Vachey A, Deurer M et al (2008) Impact of plants on the microbial activity in soils with high and low levels of copper. Eur J Soil Biol 44:92-100. https://doi.org/10.1016/j.ejsobi.2007.12.001\u003c/li\u003e\n\u003cli\u003eWalsh DA, Papke RT, Doolittle WF (2005) Archaeal diversity along a soil salinity gradient prone to disturbance. Environ Microbiol 7:1655-1666. https://doi.org/10.1111/j.1462-2920.2005.00864.x\u003c/li\u003e\n\u003cli\u003eWang Y, Sheng HF, He Y et al (2012) Comparison of the levels of bacterial diversity in freshwater, intertidal wetland, and marine sediments by using millions of illumine tags. Appl Environ Microbiol 78:8264-8271. https://doi.org/10.1128/AEM.01821-12\u003c/li\u003e\n\u003cli\u003eWang Y, Wang ZL (2018) Shifts of sediment microbial community structure along a salinized and degraded river continuum. J Coast Res 34:443-450. https://doi.org/10.2112/JCOASTRES-D-16-00216.1\u003c/li\u003e\n\u003cli\u003eWei YL, Long ZJ, Ren MX (2022) Microbial community and functional prediction during the processing of salt production in a 1000-year-old marine solar saltern of South China. Sci Tot Environ 819:152014. https://doi.org/10.1016/j.scitotenv.2021.152014\u003c/li\u003e\n\u003cli\u003eWeigold P, Ruecker A, Loesekann-Behrens T et al (2016) Ribosomal tag pyrosequencing of DNA and RNA reveals \u0026ldquo;rare\u0026rdquo; taxa with high protein synthesis potential in the sediment of a hypersaline lake in western Australia. Geomicrobiol J 33:426\u0026ndash;440. https://doi.org/10.1080/01490451.2015.1049304\u003c/li\u003e\n\u003cli\u003eWilliams WD (1998) Guidelines of lake management, Volume 6: Management of inland saline lakes. International Lake Environment Committee Foundation and the United Nations Environment Programme, Kusatsu, Japan.\u003c/li\u003e\n\u003cli\u003eWillis C, Desai D, LaRoche J (2019) Influence of 16S rRNA variable region on perceived diversity of marine microbial communities of the Northern North Atlantic. FEMS Microbiol Lett 366:fnz152. https://doi.org/10.1093/femsle/fnz152\u003c/li\u003e\n\u003cli\u003eXie K, Deng Y, Zhang S et al (2017) Prokaryotic community distribution along an ecological gradient of salinity in surface and subsurface saline soils. Sci Rep 7:13332. https://doi.org/10.1038/s41598-017-13608-5\u003c/li\u003e\n\u003cli\u003eYan L, Yu D, Hui N et al (2018) Distribution of archaeal communities along the coast of the Gulf of Finland and their response to oil contamination. Front Microbiol 9:15. https://doi.org/10.3389/fmicb.2018.00015\u003c/li\u003e\n\u003cli\u003eYang W, Li P, Rensing C et al (2017) Biomass, activity and structure of rhizosphere soil microbial community under different metallophytes in a mining site. Plant soil 434:245-262. https://doi.org/10.1007/s11104-017-3546-9\u003c/li\u003e\n\u003cli\u003eYoussef NH, Ashlock-Savage KN, Elshahed MS (2012) Phylogenetic diversities and community structure of members of the extremely halophilic Archaea (order Halobacteriales) in multiple saline sediment habitats. Appl Environ Microbiol 78:1332-1344. https://doi.org/10.1128/AEM.07420-11\u003c/li\u003e\n\u003cli\u003eYu Y, Wang H, Liu J et al (2012) Shifts in microbial community function and structure along the successional gradient of coastal wetlands in Yellow River Estuary. Eur J Soil Biol 49:12-21. https://doi.org/10.1016/j.ejsobi.2011.08.006\u003c/li\u003e\n\u003cli\u003eZelles L (1996) Fatty acid patterns of microbial phospholipids and lipopolysaccharides. In: Schinner F, Ohlinger R, Kandeler E, Margesin R. (eds) Methods in soil biology. Springer-Verlag, Berlin, pp 80-92\u003c/li\u003e\n\u003cli\u003eZhong ZP, Liu Y, Miao LL et al (2016) Prokaryotic community structure driven by salinity and ionic concentrations in plateau lakes of the Tibetan plateau. Appl Environ Microbiol 82:1846-1858. https://doi.org/10.1128/AEM.03332 -15\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u0026nbsp;\u003c/strong\u003eBasal respiration and enzyme activities in the sediments\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.129032258064516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.240266963292548%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasal respiration (mg CO\u003csub\u003e2\u003c/sub\u003e kg\u003csup\u003e-1\u003c/sup\u003e hr\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.24137931034483%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;-glucosidase (\u0026micro;g PNP g\u003csup\u003e-1\u003c/sup\u003e hr\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.684093437152391%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrease (\u0026micro;g N g\u003csup\u003e-1\u003c/sup\u003e hr\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.798665183537263%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhosphatase (\u0026micro;g PNP g\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003ehr\u003csup\u003e-1\u003c/sup\u003e)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.906562847608454%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSulfatase (\u0026micro;g PNP g\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003ehr\u003csup\u003e-1\u003c/sup\u003e) \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.129032258064516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrystallizer\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.240266963292548%\"\u003e\n \u003cp\u003e5.28\u0026plusmn;0.46b*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.24137931034483%\"\u003e\n \u003cp\u003e1.67\u0026plusmn;0.29b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.684093437152391%\"\u003e\n \u003cp\u003e3.87\u0026plusmn;0.30b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.798665183537263%\"\u003e\n \u003cp\u003e40.29\u0026plusmn;2.76b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.906562847608454%\"\u003e\n \u003cp\u003e6.98\u0026plusmn;0.52ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.129032258064516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCondenser\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.240266963292548%\"\u003e\n \u003cp\u003e5.75\u0026plusmn;0.32b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.24137931034483%\"\u003e\n \u003cp\u003e3.87\u0026plusmn;0.41b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.684093437152391%\"\u003e\n \u003cp\u003e7.48\u0026plusmn;0.70b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.798665183537263%\"\u003e\n \u003cp\u003e42.30\u0026plusmn;5.66b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.906562847608454%\"\u003e\n \u003cp\u003e10.26\u0026plusmn;0.93ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.129032258064516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReservoir\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.240266963292548%\"\u003e\n \u003cp\u003e6.05\u0026plusmn;0.35ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.24137931034483%\"\u003e\n \u003cp\u003e2.00\u0026plusmn;0.33b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.684093437152391%\"\u003e\n \u003cp\u003e4.28\u0026plusmn;0.15b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.798665183537263%\"\u003e\n \u003cp\u003e49.97\u0026plusmn;8.01b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.906562847608454%\"\u003e\n \u003cp\u003e12.21\u0026plusmn;2.71a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.129032258064516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMudflat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.240266963292548%\"\u003e\n \u003cp\u003e6.13\u0026plusmn;0.62ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.24137931034483%\"\u003e\n \u003cp\u003e1.72\u0026plusmn;0.26b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.684093437152391%\"\u003e\n \u003cp\u003e11.49\u0026plusmn;1.79b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.798665183537263%\"\u003e\n \u003cp\u003e77.73\u0026plusmn;6.63a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.906562847608454%\"\u003e\n \u003cp\u003e4.98\u0026plusmn;1.34b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.129032258064516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVegetated mudflat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.240266963292548%\"\u003e\n \u003cp\u003e7.62\u0026plusmn;0.14a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.24137931034483%\"\u003e\n \u003cp\u003e8.80\u0026plusmn;0.98a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.684093437152391%\"\u003e\n \u003cp\u003e23.11\u0026plusmn;4.49a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.798665183537263%\"\u003e\n \u003cp\u003e95.29\u0026plusmn;3.17a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.906562847608454%\"\u003e\n \u003cp\u003e9.42\u0026plusmn;0.55ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Different letters denote statistical significant differences (Tukey\u0026apos;s test) between treatments (within column) at p\u0026lt;0.05 level; mean\u003cu\u003e+\u003c/u\u003estandard error (n=4).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003ePLFA (nmol g\u003csup\u003e-1\u003c/sup\u003e soil) concentration and F/B ratio in the sediments\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"14.59403905447071%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.408016443987666%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal PLFA (nmol g\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.483042137718396%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGM+ve (nmol g\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.483042137718396%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGM-ve (nmol g\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.483042137718396%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Bacteria (nmol g\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.071942446043165%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Fungi (nmol g\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.127440904419322%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Actinomycetes (nmol g\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.660842754367934%\"\u003e\n \u003cp\u003e\u003cstrong\u003eF/B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.688591983556012%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGM+ve/GM-ve\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"14.59403905447071%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrystallizer\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.408016443987666%\"\u003e\n \u003cp\u003e11.63\u0026plusmn;0.56d*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.483042137718396%\"\u003e\n \u003cp\u003e0.76\u0026plusmn;0.10d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.483042137718396%\"\u003e\n \u003cp\u003e1.76\u0026plusmn;0.19d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.483042137718396%\"\u003e\n \u003cp\u003e2.52\u0026plusmn;0.44d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.071942446043165%\"\u003e\n \u003cp\u003e0.42\u0026plusmn;0.05b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.127440904419322%\"\u003e\n \u003cp\u003e0.49\u0026plusmn;0.03d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.660842754367934%\"\u003e\n \u003cp\u003e0.15\u0026plusmn;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.688591983556012%\"\u003e\n \u003cp\u003e0.76\u0026plusmn;0.08a\u003c/p\u003e\n \u003c/td\u003e\n 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\u003ctd valign=\"top\" width=\"9.660842754367934%\"\u003e\n \u003cp\u003e0.08\u0026plusmn;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.688591983556012%\"\u003e\n \u003cp\u003e0.61\u0026plusmn;0.05ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"14.59403905447071%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReservoir\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.408016443987666%\"\u003e\n \u003cp\u003e18.24\u0026plusmn;1.11c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.483042137718396%\"\u003e\n \u003cp\u003e2.08\u0026plusmn;0.28cd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.483042137718396%\"\u003e\n \u003cp\u003e4.01\u0026plusmn;0.38c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.483042137718396%\"\u003e\n \u003cp\u003e6.09\u0026plusmn;0.65c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.071942446043165%\"\u003e\n \u003cp\u003e0.60\u0026plusmn;0.14b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.127440904419322%\"\u003e\n \u003cp\u003e1.74\u0026plusmn;0.18bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.660842754367934%\"\u003e\n \u003cp\u003e0.10\u0026plusmn;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.688591983556012%\"\u003e\n \u003cp\u003e0.48\u0026plusmn;0.06ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"14.59403905447071%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMudflat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.408016443987666%\"\u003e\n \u003cp\u003e20.28\u0026plusmn;1.92c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.483042137718396%\"\u003e\n \u003cp\u003e2.90\u0026plusmn;0.28bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.483042137718396%\"\u003e\n \u003cp\u003e4.59\u0026plusmn;0.50bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.483042137718396%\"\u003e\n \u003cp\u003e7.49\u0026plusmn;0.68bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.071942446043165%\"\u003e\n \u003cp\u003e0.70\u0026plusmn;0.07b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.127440904419322%\"\u003e\n \u003cp\u003e1.36\u0026plusmn;0.19cd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.660842754367934%\"\u003e\n \u003cp\u003e0.10\u0026plusmn;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.688591983556012%\"\u003e\n \u003cp\u003e0.75\u0026plusmn;0.07a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"14.59403905447071%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVegetated mudflat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.408016443987666%\"\u003e\n \u003cp\u003e49.33\u0026plusmn;2.08a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.483042137718396%\"\u003e\n \u003cp\u003e6.40\u0026plusmn;0.40a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.483042137718396%\"\u003e\n \u003cp\u003e14.75\u0026plusmn;0.72a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.483042137718396%\"\u003e\n \u003cp\u003e21.66\u0026plusmn;0.77a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.071942446043165%\"\u003e\n \u003cp\u003e2.51\u0026plusmn;0.37a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.127440904419322%\"\u003e\n \u003cp\u003e4.65\u0026plusmn;0.36a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.660842754367934%\"\u003e\n \u003cp\u003e0.12\u0026plusmn;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.688591983556012%\"\u003e\n \u003cp\u003e0.43\u0026plusmn;0.09b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Different letters denote statistical significant differences (Tukey\u0026apos;s test) between treatments (within column) at p\u0026lt;0.05 level; mean+standard error (n=4).\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eOperational taxonomic unit (OTU), richness and diversity indices of bacteria in different samples with a 97% similarity cut-off\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.450800915331808%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNseqs*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.382151029748284%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoverage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.496567505720824%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSobs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.988558352402746%\"\u003e\n \u003cp\u003e\u003cstrong\u003eChao 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.958810068649885%\"\u003e\n \u003cp\u003e\u003cstrong\u003eACE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.098398169336384%\"\u003e\n \u003cp\u003e\u003cstrong\u003eShannon\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.929061784897025%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSimpson\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.450800915331808%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrystallizer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e83129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.382151029748284%\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.496567505720824%\"\u003e\n \u003cp\u003e2250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.988558352402746%\"\u003e\n \u003cp\u003e2322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.958810068649885%\"\u003e\n \u003cp\u003e2395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.098398169336384%\"\u003e\n \u003cp\u003e5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.929061784897025%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.450800915331808%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCondenser\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e66589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.382151029748284%\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.496567505720824%\"\u003e\n \u003cp\u003e3529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.988558352402746%\"\u003e\n \u003cp\u003e3714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.958810068649885%\"\u003e\n \u003cp\u003e3936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.098398169336384%\"\u003e\n \u003cp\u003e5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.929061784897025%\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.450800915331808%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReservoir\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e77146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.382151029748284%\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.496567505720824%\"\u003e\n \u003cp\u003e4315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.988558352402746%\"\u003e\n \u003cp\u003e4445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.958810068649885%\"\u003e\n \u003cp\u003e4565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.098398169336384%\"\u003e\n \u003cp\u003e6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.929061784897025%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.450800915331808%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMudflat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e64829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.382151029748284%\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.496567505720824%\"\u003e\n \u003cp\u003e4372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.988558352402746%\"\u003e\n \u003cp\u003e4687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.958810068649885%\"\u003e\n \u003cp\u003e4986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.098398169336384%\"\u003e\n \u003cp\u003e6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.929061784897025%\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.450800915331808%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVegetated mudflat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e64355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.382151029748284%\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.496567505720824%\"\u003e\n \u003cp\u003e4521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.988558352402746%\"\u003e\n \u003cp\u003e4692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.958810068649885%\"\u003e\n \u003cp\u003e4926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.098398169336384%\"\u003e\n \u003cp\u003e6.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.929061784897025%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(*Nseqs: Number of sequences, Sobs : Observed mean species richness, ACE: Abundance-based coverage estimator)\u003c/p\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":"archives-of-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aomi","sideBox":"Learn more about [Archives of Microbiology](https://www.springer.com/journal/203)","snPcode":"203","submissionUrl":"https://submission.nature.com/new-submission/203/3","title":"Archives of Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Biogeochemical Cycling, Coastal Sediments, Enzymes, Functional Gene, Next-Generation Sequencing, PLFA","lastPublishedDoi":"10.21203/rs.3.rs-2098972/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2098972/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSalt marsh vegetation, mudflat and salt production are common features in worldwide coastal areas; however, their influence on microbial community composition and structure has been poorly studied and rarely compared. In the present study, microbial community composition (phospholipid fatty acid (PLFA) profiling and 16S rRNA gene sequencing (bacterial and archaeal)), enzymatic activities and abundance of functional genes in the sediments of salt ponds (crystallizer, condenser and reservoir), mudflat and vegetated mudflat were determined. Physicochemical characteristics of the sediments were also studied. Enzyme activities (β-glucosidase, urease and alkaline phosphatase) were considerably decreased in saltpan sediments because of elevated salinity while sediment of vegetated mudflat showed the highest enzyme activities. Concentrations of total and microbial biomarker PLFAs (total bacterial, Gram-positive, Gram-negative, fungal and actinomycetes) were the highest in vegetated mudflat sediments and the lowest in crystallizer sediments. Nonmetric-multidimensional scaling (NMS) analysis of PLFA data revealed that the microbial community of crystallizer, mudflat and vegetated mudflat was significantly different from each other as well as different from condenser and reservoir. The most predominant phyla within the classified bacterial fractions were Proteobacteria followed by Firmicutes, Bacteroidetes and Planctomycetes, while Euryarchaeota and Crenarchaeota phyla dominated the classified archaeal fraction. Cyanobacterial genotypes were the most dominant in the condenser. Mudflat and vegetated mudflat supported a greater abundance of Bacteroidetes and Actinobacteria, respectively. The results of the present study suggest that salt ponds had significantly decreased the microbial and enzyme activities in comparison to mudflat and vegetated mudflat sediments due to very high salinity, ionic concentrations and devoid of vegetation.\u003c/p\u003e","manuscriptTitle":"Sediment microbial community structure, enzymatic activities and functional gene abundance in the coastal hypersaline habitats","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-28 17:48:56","doi":"10.21203/rs.3.rs-2098972/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-10-09T12:57:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-10-03T15:01:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"adb3907c-41cf-4b46-9920-dddb58b88195","date":"2022-09-30T03:03:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-09-27T01:52:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-09-26T09:36:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-09-26T09:12:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Microbiology","date":"2022-09-24T10:45:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aomi","sideBox":"Learn more about [Archives of Microbiology](https://www.springer.com/journal/203)","snPcode":"203","submissionUrl":"https://submission.nature.com/new-submission/203/3","title":"Archives of Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"63aedc52-f415-4dad-b526-c9a1138e8771","owner":[],"postedDate":"September 28th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T18:15:42+00:00","versionOfRecord":{"articleIdentity":"rs-2098972","link":"https://doi.org/10.1007/s00203-022-03398-4","journal":{"identity":"archives-of-microbiology","isVorOnly":false,"title":"Archives of Microbiology"},"publishedOn":"2023-01-06 18:13:48","publishedOnDateReadable":"January 6th, 2023"},"versionCreatedAt":"2022-09-28 17:48:56","video":"","vorDoi":"10.1007/s00203-022-03398-4","vorDoiUrl":"https://doi.org/10.1007/s00203-022-03398-4","workflowStages":[]},"version":"v1","identity":"rs-2098972","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2098972","identity":"rs-2098972","version":["v1"]},"buildId":"ApUGefWb6u5IBVtyqm6d5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}