2026-10-14T20:05:00Z https://www.scidb.cn/oai

10.57760/sciencedb.cjae.00257 2026-07-29T16:59:11Z

2026-07-29 Dataset of Dissolved Organic Matter Characteristics and Associated Environmental Factors in Surface Soils from 57 Typical Freshwater Wetlands in Eastern China doi:10.57760/sciencedb.cjae.00257 en This dataset was generated from a study on the spatial patterns and driving factors of soil dissolved organic matter in 57 typical freshwater wetlands across eastern China. It contains site geographic information, climatic and vegetation variables, soil physicochemical properties, bacterial community indicators, and data on the quantity, optical characteristics, and fluorescent component composition of soil dissolved organic matter (DOM).The study covers five types of freshwater wetlands—peatlands, forested swamp wetlands, herbaceous marsh wetlands, lakeshore wetlands, and riparian wetlands—distributed across four climatic zones: the cold and middle temperate zone, warm temperate zone, northern and middle subtropical zone, and southern subtropical and tropical zone. The dataset was developed to reveal the spatial variation in wetland soil DOM along a large-scale climatic gradient in eastern China and its relationships with climate, vegetation, soil properties, and bacterial communities. It provides fundamental data for research on wetland active soil carbon pools, carbon cycling assessment, and wetland conservation and management.The dataset is stored in CSV (.csv) format and contains one data file.1. Data generation and processing methods(1) Sample collectionSoil samples were collected from 57 typical freshwater wetland sites in eastern China between June and August 2023. The sites were distributed between 18.7° and 52.7° N and between 100.2° and 134.6° E, spanning approximately 4,000 km from south to north.Four sampling positions were randomly established at each site, with a minimum distance of 20 m between adjacent positions. After removing surface litter, soil samples were collected from the 0–10 cm layer using a soil auger approximately 8 cm in diameter. At each sampling position, three to five soil cores were randomly collected and combined to form one composite sample. Four spatially independent replicate samples were obtained from each site, resulting in a total of 228 soil samples.(2) Extraction and measurement of soil dissolved organic matterAfter stones and plant residues were removed, the soil samples were passed through a 20-mesh sieve. Eight grams of fresh soil were mixed with 40 mL of deionized water at a soil-to-water ratio of 1:5 (m/v) in a 50 mL centrifuge tube. Glass beads were added, and the mixture was vortexed for approximately 10 s and then shaken in an 80 °C water bath at 40 r·min⁻¹ for 16 h. After shaking, the samples were vortexed again and centrifuged at 3,500 r·min⁻¹ for 20 min. The supernatant was filtered through a 0.45 μm cellulose membrane filter. The filtrate was stored in the dark at 4 °C and analyzed within 14 days.DOM quantity was represented by dissolved organic carbon (DOC) content, which was measured using an automatic total organic carbon analyzer (Analytik Jena Multi N/C 2100, Germany). DOM absorbance spectra were measured using a UV–visible spectrophotometer (Shimadzu UV-2600, Japan) over a wavelength range of 200–800 nm at 1 nm intervals with a 1 cm quartz cuvette.To minimize the inner-filter effect, the samples were appropriately diluted according to their absorbance at 254 nm so that the absorbance after dilution was below 0.2. Excitation–emission matrix fluorescence spectra were measured using a fluorescence spectrophotometer (Hitachi F-7000, Japan). Excitation wavelengths ranged from 200 to 480 nm at 5 nm intervals, and emission wavelengths ranged from 250 to 580 nm at 2 nm intervals. Both excitation and emission slit widths were set to 5 nm, the scanning speed was 2,400 nm·min⁻¹, and the photomultiplier tube voltage was 700 V.Fluorescence data were processed using MATLAB R2024a and the drEEM v0.6.6 toolbox. Processing procedures included blank subtraction, normalization to Raman units, removal and interpolation of Rayleigh scattering regions, and correction of negative values. Four fluorescent components were identified using parallel factor analysis (PARAFAC): C1, a humic acid-like component; C2, a fulvic acid-like component; C3, a tryptophan-like protein component; and C4, a tyrosine-like protein component. The relative abundance of each component was calculated from its maximum fluorescence intensity (Fmax).(3) Measurement of soil physicochemical propertiesThe measured soil properties included total carbon (TC), total nitrogen (TN), ammonium nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), pH, electrical conductivity (EC), and the combined clay and silt content (Clay&Silt).Soil TC was measured using the high-temperature combustion method, and soil TN was measured using the Kjeldahl method. Ammonium and nitrate nitrogen were extracted using a 2 mol·L⁻¹ KCl solution and measured using a continuous-flow analyzer (SEAL AA3, Germany). Soil pH and EC were measured using a potentiometric method at a soil-to-water ratio of 1:2.5 (m/v). Soil particle-size distribution was determined using laser particle-size analysis, and the sum of clay and silt contents was used to represent the proportion of fine soil particles.(4) Climate, vegetation, and bacterial community dataThe climatic variables included mean annual temperature (MAT) and mean annual precipitation (MAP). These data were obtained from the 1970–2000 long-term mean climate dataset provided by the WorldClim database. The corresponding raster values were extracted according to the geographic coordinates of each sampling site.Vegetation conditions were represented by the annual maximum normalized difference vegetation index (NDVI). NDVI data were obtained from the 2022 China annual maximum NDVI dataset with a spatial resolution of 30 m, available through the Google Earth Engine platform. The original pixel value corresponding to each sampling site was extracted and multiplied by a scale factor of 0.0001 to obtain the actual NDVI value.Bacterial community structure was characterized through high-throughput sequencing of the 16S rRNA gene. Genomic DNA was extracted from fresh soil samples, and the V3–V4 region of the bacterial 16S rRNA gene was amplified by PCR using the 338F/806R primer pair. Paired-end sequencing was performed on the Illumina MiSeq platform. Before sequencing, the four spatial replicate samples from each site were combined into one composite sample.An amplicon sequence variant (ASV) table was constructed from the processed sequencing data. Low-abundance ASVs with relative abundances below 0.01% were removed. Bacterial richness (BRichness) was calculated, and non-metric multidimensional scaling (NMDS) based on Bray–Curtis distances was conducted. BNMDS1 and BNMDS2 were extracted to represent differences in bacterial community structure.(5) Data organizationThe original data were organized using Excel 2019. DOM fluorescence spectra and PARAFAC analyses were processed using MATLAB R2024a and drEEM v0.6.6, while other statistical analyses were conducted using R 4.3.0.Except for bacterial sequencing data, for which one composite sample per site was used as the statistical unit, all other indicators were represented by the mean of four spatial replicate samples at each site. Fields with the suffix “_meanSD” record within-site variation in the form of “mean ± standard deviation.”2. Description of the dataset contentsThe dataset contains one CSV file named “2026-0387.csv.” The file is UTF-8 encoded and contains 50 fields and 57 valid site records. Each row represents one freshwater wetland site. The final row provides the names and abbreviations of the climatic zones and is not an observational record.The basic site information fields include Site, Site Name, Climatic Zone, Wetland Type, Thermalzones, Type, LAT, and LON. Site represents the site identifier, and Site Name represents the Chinese name of the sampling site. Climatic Zone and Thermalzones provide the Chinese and English climatic-zone classifications, respectively, while Wetland Type and Type provide the Chinese and English wetland-type classifications, respectively. LAT and LON represent latitude and longitude in decimal degrees (°N and °E).The climate, vegetation, and bacterial community fields include MAT, MAP, NDVI, BRichness, BNMDS1, and BNMDS2. MAT is the mean annual temperature in °C, and MAP is the mean annual precipitation in mm. NDVI is the dimensionless normalized difference vegetation index. BRichness represents bacterial community richness, while BNMDS1 and BNMDS2 are dimensionless NMDS ordination-axis scores representing bacterial community structure.The soil physicochemical fields include TC, TN, NH4N, NO3N, pH, EC, and claysilt. TC and TN represent soil total carbon and total nitrogen, respectively, in g·kg⁻¹. NH4N and NO3N represent soil ammonium nitrogen and nitrate nitrogen, respectively, in mg·kg⁻¹. Soil pH is dimensionless. EC represents soil electrical conductivity in μS·cm⁻¹, and claysilt represents the combined clay and silt content as a percentage (%).The DOM-related fields include DOC, A300, SUVA254, E2_E3, BIX, HIX, and C1–C4. DOC represents dissolved organic carbon content in g·kg⁻¹. A300 represents the absorption coefficient at 300 nm in m⁻¹. SUVA254 represents specific ultraviolet absorbance at 254 nm in L·mg⁻¹·m⁻¹. E2_E3 is the ratio of the absorption coefficients at 254 and 365 nm and is used to indicate the relative molecular weight of DOM. BIX is the biological index and indicates the biological or autochthonous characteristics of DOM. HIX is the humification index and indicates the degree of DOM humification. C1–C4 represent the relative abundances of the four DOM fluorescent components.Fields from TC_meanSD to C4_meanSD record the corresponding indicators in the form of “mean ± standard deviation.” The “_meanSD” fields correspond to the means and standard deviations of soil physicochemical properties, DOM optical indices, and fluorescent components.No blank or missing values were identified among the 57 valid site records. CSV is a commonly used data format and can be opened using Microsoft Excel, WPS Office, LibreOffice Calc, R, or Python without requiring specialized software.3. Description of data uncertaintiesPotential sources of data uncertainty include the spatial heterogeneity of wetland soils; sample collection and compositing procedures; DOM extraction; instrumental errors during the measurement of soil physicochemical properties and optical indices; errors introduced during fluorescence-spectrum preprocessing and PARAFAC modeling; experimental variation associated with DNA extraction and high-throughput sequencing; and spatial mismatches between sampling-site coordinates and the corresponding climatic or remote-sensing raster pixels.To reduce these uncertainties, four spatial replicates were established at each sampling site, and consistent procedures were applied for sampling depth, sample preservation, DOM extraction, instrumental measurements, and data processing. Fluorescence-data reliability was further improved by controlling absorbance at 254 nm and applying blank subtraction, Raman-unit normalization, and scattering-region correction.The dataset does not provide a single standardized range of instrumental error. The “mean ± standard deviation” fields can be used to characterize variation among spatial replicates within the same site, but these standard deviations should not be interpreted as instrumental measurement errors. dissolved organic matter; freshwater wetlands; soil characteristics Guo Muxi Jiang Baizhi Wei Zhenyu Tan Zhoujing Wang Xinxin Chen Hongyang RESTRICTED dataset Science Data Bank