Dataset of Microbial Community Structure and Metabolic Characteristics in Litopenaeus vannamei ponds, Xiqing District, Tianjin,China

1.    Water and sediment samplesThe ponds located at the breeding base of Animal Husbandry and Aquaculture Bureau, Xiqing District, Tianjin, China. The stocking density of shrimp larvae was 600,000 per hectare, and commercial shrimp feed was fed during the cultivation period. The cultivation process followed the standard protocol for aquaculture bases. Shrimp were successfully cultured in the three ponds, with no major disease outbreak.Water samples WS1, WS2, and WS3 and sediment samples SD1, SD2, and SD3 from three adjacent shrimp culture ponds (0.5 ha) in the later stage of shrimp farming. Surface water samples (0.2–0.5 m below the water surface) were collected using an organic glass water sampler. Surface sediment samples (0–5 cm depth) were collected using a cylindrical sampler. All samples were collected from the center and surrounding areas of each pond and mixed to form a composite biological replicate sample, with five replicates established for each sample.2.    CLPP analysis using Biolog EcoPlatesAn eight-channel pipette was used to add the sample solution to the preheated Biolog EcoPlates at the rate of 150 μL per well. The sediment suspension used for inoculating microplate wells was prepared as follows: (1) fresh sediment equivalent to 10 g of dry sediment was accurately weighed and added to sterile physiological saline containing glass beads to prepare a 10% sediment suspension, (2) the suspension was shaken at 250 rpm for 30 min, and 30 mL of the sediment suspension was transferred to a 50 mL centrifuge tube, (3) the suspension was centrifuged at 600 rpm for 10 min, and the supernatant was directly transferred to Biolog EcoPlates, (4) the plates were covered and placed in a freshness preservation box, and the box was incubated at 25°C, and (5) the absorbance value of each well was read at 590 and 750 nm wavelengths every 24 h by using a Biotek ELx808 microplate reader. Three water samples from each pond were added to the Biolog EcoPlates. Each plate had three replicates, with a total of nine replicates. Data were collected from the sediment samples after incubation for 216 h and from water samples after incubation for 48 h.The 31 distinct carbon sources present in the Biolog EcoPlate are categorized into six groups: 6 amino acids, 12 carbohydrates, 5 carboxylic acids, 4 polymers, 2 amines, and 2 phenolic acids. The average OD590–OD750 value for each carbon source category was calculated to determine the utilization of these sources by the bacterial community.3.    DNA extraction, bacterial 16S rRNA gene amplification, and Illumina sequencing analysisTo analyze microbial community structure, WS1-WS3 were initially filtered through a sterilized 5 μm filter membrane, and the filtrate is transferred into a sterile bottle. Subsequently, the bacterial cells in the filtrate are decanted onto a 50 mm diameter filter membrane with a 0.22 μm pore size using a filtration apparatus, and the membrane is placed into a 5 mL cryogenic vial. The sediment and filter membrane samples collected are stored at -80°C in a refrigerator for future use.The pretreated water and sediment samples are transported to Tianjin Novogene Biological Information Technology Co., Ltd. at -20℃ for microbial community analysis. Select primer 515F (5 ′ - GTGCCAGCGGTAA-3 ′) -806R (5 ′ - GACTACHVGGGTWTCTAAT-3 ′) for amplicon sequencing in the 16S V4 region. The raw sequencing data obtained from this study were submitted to the National Center for Biotechnology Information Sequence Read Archive (SRA) and BioProject under ID: PRJNA544379.4.    Network analysis and identification of potential keystone taxa and core taxaWe used the iNAP online analysis platform (https://inap.denglab.org.cn/) to construct and analyze the bacterial network. We then used Gephi software to visualize the bacterial network. Data on the absolute abundances of bacteria at the genus level in the water and sediment samples were used as the input files for these analyses. First, the bacterial genera in the water and sediment samples were filtered such that only genera that were present in more than half of all samples in each category were retained in the analysis. Next, the SparCC method was used to calculate the correlation coefficient and P-value. The correlation threshold of the bacterial communities in the water and sediment was determined to be 0.69 using the Random Matrix theory (cutoff) method. The significance of the difference was determined to be P<0.05.We classified the nodes of each network into different roles according to their within-module connectivity (Zi) and among-module connectivity (Pi) values, as follows: module hubs (Zi ≥ 2.5, Pi < 0.62), network hubs (Zi ≥ 2.5, Pi ≥ 0.62), connectors (Zi < 2.5, Pi ≥ 0.62) and peripherals (Zi < 2.5, Pi < 0.62). We considered taxa that were module hubs, network hubs and connectors as potential keystone taxa due to their important roles in network topology. The calculations of Zi and Pi were performed on the iNAP. We used the K-core decomposition method in Gephi software to identify core taxa in the largest core subset of each network.5. Statistical analysisStatistical analyses were primarily performed using SPSS version 19.0 for Windows (IBM Corp., Armonk, NY, USA, 2010). Data on keystone taxa abundance, physicochemical indicators, and carbon source utilization by bacterial community were subjected to outlier removal by using the local outlier factor method available at the Majorbio Cloud Platform (https://cloud.majorbio.com/page/tools/). After the outliers were removed from the physicochemical factors, the mean value was used for imputation. Subsequently, the physicochemical factors for redundancy analysis were determined by SPSS collinearity diagnosis and the Akaike information criterion.