昆仑山中段北坡土壤理化性质数据，包括总氮、总磷、总钾、速效氮、速效磷、有效钾、矿物质结合有机碳、颗粒有机碳、pH、电导率、总盐、土壤含水量。
", "ds_source": "在昆仑山中段北坡进行取样，分析测试了土壤理化性质。
", "ds_process_way": "1、实验设计：\n本研究在昆仑山中段北坡（36°10′–36°50′N，80°10′–80°50′E）海拔1707米至3548米之间选择七个样带进行取样，包括荒漠（1707 m）、山地半灌木荒漠（1960 m、2448 m和2746 m）、山地荒漠草原（2905 m）以及高寒草原（3248 m和3548 m）（表1）。在采样期间，1707 m海拔处未发现植物。在1960-2746 m海拔处，植被主要由灌木和半灌木组成，如沙拐枣 (Calligonum roborovskii)、枇杷柴 (Reaumuria soongonica) 和合头草 (Sympegma regelii)。2905 m海拔处的植被属于山地荒漠草原，主要包括针茅 (Stipa capillata)、绢蒿 (Seriphidium rhodanthum) 和芨芨草 (Achnatherum splendens)。3548 m海拔处的植被属于高寒草原，优势植物为紫花针茅 (Stipa purpurea)、细叶嵩草 (Kobresia filifolia) 和委陵菜 (Potentilla)。\n2、采样方法：\n根据植被类型分类，在海拔3548 m、3248 m和2905 m处，分别设置四个独立的1 m×1 m样方，样方之间水平间隔10 m。在海拔2746 m、2448 m、1960 m和1707 m处，设置四个独立的10 m×10 m大样方，样方之间水平间隔100 m。在每个样方内，调查了植物多样性和植被覆盖度，并采集了植被的地上部分。随后，在移除凋落物后，沿每个样方的对角线随机采集五个表层土芯（0-10 cm）。将这些表层土芯混合形成一个混合样品。总计从七个不同海拔水平的样方中采集了28份土壤样品。在土壤采样过程中，始终佩戴一次性手套，并在采集每个样品前用75%酒精对土钻进行消毒。将土壤样品带回实验室后，为避免交叉污染，使用经紫外线消毒30 min的2 mm筛网对不同海拔的土壤进行过筛。之后，将土壤样品分成两份：一份用于风干以分析理化性质，另一份则储存于-20℃下，用于后续的土壤微生物DNA提取。\n3、分析过程：\n采用E.Z.N.A.® Soil DNA Kit提取土壤DNA，利用TBS-380荧光计测定DNA浓度，NanoDrop 2000检测纯度，1%琼脂糖凝胶电泳评估完整性。DNA经Covaris M220打断至约400 bp，构建双端测序文库，并在Illumina NovaSeq PE150平台进行宏基因组测序。测序数据已上传至国家基因组科学数据中心GSA数据库（PRJCA017392）。\n原始序列经Fastp进行质量控制，采用MEGAHIT完成序列组装，利用Prodigal预测开放阅读框（ORF），筛选长度≥100 bp基因并翻译为氨基酸序列。通过KEGG数据库注释碳循环相关功能基因，并采用SOAPaligner聚类构建非冗余基因集（95%序列一致性）。利用HMMER进行CAZyme功能注释，将其划分为GHs、GTs、CEs、CBMs、PLs和AAs六类酶家族；采用RPKM标准化基因丰度，计算功能基因α和β多样性，并结合Diamond和NR数据库开展优势类群物种注释。\n所有统计分析在R 4.2.1中完成。采用单因素方差分析和Duncan检验比较不同海拔间功能基因、微生物及环境因子的差异；利用Shannon指数评估α多样性，线性回归分析海拔、多样性及功能基因丰度间关系；通过PCoA和PERMANOVA分析群落组成及酶组成差异。进一步采用RDA、VPA、Pearson相关分析和Mantel检验揭示环境因子、微生物群落与碳循环功能基因的关系，并利用非线性回归分析MAOC/POC比值与环境因子的响应关系。
", "ds_quality": "", "ds_acq_start_time": "2022-07-01 00:00:00", "ds_acq_end_time": "2022-07-31 00:00:00", "ds_acq_place": "新疆维吾尔自治区和田地区策勒县昆仑山中段北坡（36°10′–36°50′N，80°10′–80°50′E）", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 17384, "ds_files_count": 1, "ds_format": "xlsx格式", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "712aa86c-07df-4087-b38d-b84887592961.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "210.50" ], "quality_level": 1, "publish_time": "2026-10-13 18:37:39", "first_publish_time": null, "last_updated": "2026-10-13 18:37:39", "protected": false, "protected_to": null, "lang": "zh", "cstr": "33110.11.ariddc.02517", "license": null, "extra": null, "files_shape": [ { "name": "昆仑山中段北坡土壤理化性质数据.xlsx", "size": 17384, "is_dir": false } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "Soil physical and chemical properties data on the northern slope of the middle Kunlun Mountains", "ds_abstract": "Soil physical and chemical properties on the northern slope of the middle section of Kunlun Mountains, including total nitrogen, total phosphorus, total potassium, available nitrogen, available phosphorus, available potassium, mineral-bound organic carbon, particulate organic carbon, pH, conductivity, total salt, soil water content", "ds_source": "Samples were taken on the northern slope of the middle Kunlun Mountains, and soil physical and chemical properties were analyzed and tested.", "ds_process_way": "1. Experimental Design\r\n\r\nThis study was conducted on the northern slope of the central Kunlun Mountains (36°10′-36°50′N, 80°10′-80°50′E), where seven sampling transects were established along an elevation gradient ranging from 1,707 m to 3,548 m. These included desert (1,707 m), montane semi-shrub desert (1,960 m, 2,448 m, and 2,746 m), montane desert steppe (2,905 m), and alpine steppe (3,248 m and 3,548 m) ecosystems (Table 1). During the sampling period, no vegetation was observed at the 1,707 m site. At elevations between 1,960 m and 2,746 m, vegetation was predominantly composed of shrubs and semi-shrubs, including Calligonum roborovskii, Reaumuria soongonica, and Sympegma regelii. The vegetation at 2,905 m belonged to montane desert steppe and was mainly composed of Stipa capillata, Seriphidium rhodanthum, and Achnatherum splendens. At 3,548 m, the vegetation was classified as alpine steppe, with Stipa purpurea, Kobresia filifolia, and Potentilla species as the dominant plants.\r\n\r\n2. Sampling Methods\r\n\r\nAccording to vegetation type, four independent quadrats (1 m × 1 m) were established at elevations of 3,548 m, 3,248 m, and 2,905 m, with a horizontal distance of 10 m between adjacent quadrats. At elevations of 2,746 m, 2,448 m, 1,960 m, and 1,707 m, four independent large quadrats (10 m × 10 m) were established, with a horizontal spacing of 100 m between quadrats. Within each quadrat, plant diversity and vegetation cover were surveyed, and aboveground vegetation biomass was collected.\r\n\r\nAfter removing surface litter, five topsoil cores (0-10 cm) were randomly collected along the diagonal line of each quadrat. These soil cores were thoroughly mixed to form one composite sample. In total, 28 soil samples were collected from quadrats distributed across the seven elevation levels.\r\n\r\nDuring soil sampling, disposable gloves were worn at all times, and the soil auger was sterilized with 75% ethanol before collecting each sample. After transportation to the laboratory, soil samples from different elevations were sieved through a 2 mm mesh that had been sterilized under ultraviolet light for 30 min to prevent cross-contamination. Subsequently, each soil sample was divided into two subsamples: one portion was air-dried for physicochemical analyses, while the other was stored at −20°C for subsequent extraction of soil microbial DNA.\r\n\r\n3. Analytical Procedures\r\n\r\nSoil DNA was extracted using the E.Z.N.A.® Soil DNA Kit. DNA concentration was measured with a TBS-380 fluorometer, DNA purity was assessed using a NanoDrop 2000 spectrophotometer, and DNA integrity was evaluated by 1% agarose gel electrophoresis.\r\n\r\nThe extracted DNA was fragmented to approximately 400 bp using a Covaris M220 ultrasonicator. Paired-end sequencing libraries were then constructed and subjected to metagenomic sequencing on the Illumina NovaSeq PE150 platform. The sequencing data have been deposited in the Genome Sequence Archive (GSA) of the National Genomics Data Center under accession number PRJCA017392.\r\n\r\nRaw sequencing reads were quality-filtered using Fastp and assembled with MEGAHIT. Open reading frames (ORFs) were predicted using Prodigal, and genes with lengths ≥100 bp were retained and translated into amino acid sequences. Carbon cycle-related functional genes were annotated against the KEGG database. A non-redundant gene catalog was constructed using SOAPaligner clustering at 95% sequence identity.\r\n\r\nCAZyme functional annotation was performed using HMMER, and the identified carbohydrate-active enzymes were classified into six enzyme families: glycoside hydrolases (GHs), glycosyltransferases (GTs), carbohydrate esterases (CEs), carbohydrate-binding modules (CBMs), polysaccharide lyases (PLs), and auxiliary activities (AAs). Gene abundance was normalized using the RPKM method. The α-diversity and β-diversity of functional genes were calculated, and dominant microbial taxa were taxonomically annotated using Diamond searches against the NR database.\r\n\r\nAll statistical analyses were conducted in R version 4.2.1. One-way analysis of variance (ANOVA) followed by Duncan's multiple range test was used to compare differences in functional genes, microbial communities, and environmental factors among elevations. The Shannon index was used to evaluate α-diversity, while linear regression analysis was applied to examine relationships among elevation, diversity, and functional gene abundance. Principal Coordinates Analysis (PCoA) and Permutational Multivariate Analysis of Variance (PERMANOVA) were used to assess differences in community composition and enzyme composition.\r\n\r\nFurthermore, redundancy analysis (RDA), variance partitioning analysis (VPA), Pearson correlation analysis, and Mantel tests were employed to reveal the relationships among environmental factors, microbial communities, and carbon cycle functional genes. Nonlinear regression analysis was also conducted to evaluate the response of the MAOC/POC ratio to environmental factors.", "ds_quality": "", "ds_acq_place": "The northern slope of the middle section of Kunlun Mountains in Celle County, Hotan Prefecture, Xinjiang Uygur Autonomous Region (36°10 '-36°50' N, 80°10 '-80°50' E)", "ds_ref_instruction": "", "ds_ref_way": "", "ds_format": "xlsx format", "ds_projection": "", "ds_space_res": "", "ds_time_res": "" } }, "license_type": null, "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "土壤理化性质", "土壤含水量" ], "ds_subject_tags": [ "土壤学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆维吾尔自治区和田地区策勒县昆仑山中段北坡" ], "ds_time_tags": [ 2022 ], "ds_contributors": [ "张志浩", "鲁艳" ], "ds_meta_authors": [ "张志浩" ], "ds_managers": [ "鲁艳" ], "category": "土壤" }