中国新疆阿尔泰山脉南部典型草地生态系统绣线菊叶片和土壤碳氮磷化学计量特征数据集主要包括温带草原荒漠、温带荒漠草原、温带草原、温带草甸草原、山地草甸5种样地类型的绣线菊灌木叶片碳氮磷含量、非结构性碳水化合物(NSCs)浓度、土壤容重和土壤含水量等数据，采样时间为2023年7月。
", "ds_source": "2023年7月，在阿尔泰山依次在温带荒漠草原（TSD）；温带沙漠草原（TDS）；温带草原（TS）； 温带草甸草原（TMS），山地草甸（ MM）分别设置样地。每种草原类型中设置3个100米 × 100 米的大样方，每个大样方内沿对角线及中心位置设立3个10米 × 10米的重复小样方，在每个小样方内采集植物叶片和土壤样品。
", "ds_process_way": "在植物叶片样本采集之前，先测量了样方中灌木的盖度（%）、冠幅（cm）、和植株高度（cm），并根据冠幅测量数据计算出斑块面积（m2）。数据形式为平均值±标准差。 植物叶片采集采用5点（S形）采样法，在每个小样方内，在11:00至16:00时间内随机采集当年生、位于冠层中部且阳光充足的5株健康绣线菊的叶片，混合后作为一个样本，共计5个重复样本。将样本带回实验室，用105°C杀青10分钟，随后在65°C下烘干至恒重，最后用球磨机研磨并通过0.2毫米筛网过筛，用于测定植物养分（C、N、P）。 土壤样品采集，在选定灌木茎基周围按东西南北四个方位等距使用土钻采集0 40cm土层的土壤样品4份，混合后成一个样本，每个样点设3个重复，共获得45份土壤样本。采集的土壤样品通过2mm筛网过筛以去除大颗粒残渣及根系，存放在无菌袋中。同时使用全球定位系统（GPS）记录每个采样点的海高度和地理坐标。 土壤容重（BD）通过环刀法在田间测定。土壤黏粒（<2μμm）、粉粒（2–20 μμm）和砂粒（20–2000 μμm）的占比使用激光粒度分析仪（Mastersizer 2000，马尔文帕纳科有限公司，英国马尔文）测定。土壤含水量（SWC）采用烘干法测定 （Almási等，2025），土壤pH值使用pH计测定，电导率（EC）则通过EC计（SevenExcellence S470，梅特勒 托利多，瑞士格赖芬塞）评估。土壤有机碳（SOC）和植物叶片全碳采用元素分析仪（Enviro TOC cube，Elementar Analysensysteme G mbH，德国朗根费尔德）的高温燃烧法测定。此外，土壤全氮（STN）和植物叶片全氮采用凯氏定氮法测定，而土壤全磷（STP）和植物叶片全磷则通过HClO4 H2SO4 消解后钼锑抗分光光度法测量。土壤速效氮（SAN）采用碱解扩散法评估，土壤速效磷（SAP）通过 NaHCO3 浸提 钼锑抗比色法测定。 非结构性碳水化合物NSCs浓度按可溶性糖与淀粉之和计算，二者均通过传统蒽酮-硫酸法测定。操作方法如下：准确称取0.15g 干燥植物叶片粉末，加入10mL80%无水乙醇，沸水浴提取10分钟后，以4,000转/分钟离心10分钟，收集上清液作为可溶性糖提取液。随后向离心沉淀物中加入10mL30%(v/v)高氯酸，静置过夜。80°C水浴精确提取10分钟确保淀粉完全水解后，冷却并再次以4,000转/分钟离心10分钟，最终取上清液测定淀粉浓度。
", "ds_quality": "数据生产过程中的具体质量控制有： (1)样品采集过程中的质量控制。在研究区域内，根据样地调查内容沿山体等高线依次从低到高选择具有代表性的5个温带草原带, 在每个样带内随机设置3个重复样地, 在每个样地内随机设置3个取样样方, 同时详细记录样地编号、样方编号、样方数、样方面积、地理经纬度、海拔高度、植被盖度、植株高度等属地信息。样地设置时遵循植物群落结构一致、物种组成均匀、覆盖度一致的原则, 避免植物群落结构、物种组成不一致导致的取样误差。绣线菊叶片取样时, 只采集符合实验要求的叶片，避免对整个植株的破坏。土壤样品采集时仔细去除土壤表面腐殖质层, 对所取得的土壤样品分别标记, 特别注意各样方内不同土层土壤样品的标记。取样结束时, 仔细核实样品数量、种类等相关信息。 (2)数据生产及整理过程中的质量控制。叶片和土壤样品带回实验室后, 尽快测定相关要素指标。数据记录时再次核对样品信息, 包括样地编号、样方编号、样品种类、样品数量等。数据检测时严格按照相关技术规范指导操作, 认真记录原始检测数据, 并备份，做到数据源可复查。数据整理过程为按每个样地每个小样方整理生成数据产品。当数据出现异常值时, 应严格核实, 对测定仪器进行必要检修校准, 以确保数据真实准确, 必要时对相应样品再次重复取样测定。所有数据的原始记录不得涂抹、删改, 以备数据后续数据核查和录入时溯源。 (3)数据核查和录入过程中的质量控制。在进行数据核查时, 再次核实样品基本信息, 包括样地编号、样方编号、样品种类、样品数量等, 如发现数据数值有偏差, 立即溯源原始数据,同时在原数据旁使用不同颜色的笔迹更正, 备注产生错误的原因。在数据录入时, 做到认真仔细检查每一个数据, 减少录入时发生数据错误, 以确保录入数据的准确性。
", "ds_acq_start_time": "2023-07-01 00:00:00", "ds_acq_end_time": "2023-08-01 00:00:00", "ds_acq_place": "新疆阿尔泰山南麓", "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": 34767, "ds_files_count": 1, "ds_format": ".xlsx格式", "ds_space_res": "无", "ds_time_res": "无", "ds_coordinate": "无", "ds_projection": "无", "ds_thumbnail": "9a2313dd-9d8e-4e3f-a05d-e8de09c62be3.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "马学喜,李耀明. 中国新疆阿尔泰山南麓典型草地生态系统绣线菊灌木叶片和土壤碳氮磷化学计量特征数据集. 国家冰川冻土沙漠科学数据中心(http://www.ncdc.ac.cn), 2025. https://cstr.cn/CSTR:11738.11.NCDC.ECOLOGY.DB7025.2025.", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 1, "publish_time": "2025-12-05 13:25:55", "first_publish_time": null, "last_updated": "2026-01-22 18:16:37", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ECOLOGY.DB7025.2025", "license": null, "extra": null, "files_shape": [ { "name": "Spiraea Leaves and Soil_XJAltai.xlsx", "size": 34767, "is_dir": false } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "Data set of carbon, nitrogen and phosphorus stoichiometry characteristics of Spiraea shrub leaves and soil in a typical grassland ecosystem at the southern foot of the Altai Mountains, Xinjiang, China", "ds_abstract": "The data set of carbon, nitrogen and phosphorus stoichiometry characteristics of Spiraea chinensis leaves and soil in a typical grassland ecosystem in the southern Altai Mountains of Xinjiang, China mainly includes data on carbon, nitrogen and phosphorus content in the leaves of Spiraea chinensis shrubs, non-structural carbohydrates (NSCs) concentrations, soil bulk density and soil water content in five sample plot types: temperate grassland desert, temperate desert grassland, temperate grassland, and mountainous grassland. The sampling time is July 2023.
", "ds_source": "In July 2023, sample plots will be set up in the Altai Mountains in order of temperate desert grassland (TSD); temperate desert grassland (TDS); temperate grassland (TS); temperate meadow grassland (TMS), and mountain meadow (MM). Three 100-meter × 100-meter large sample squares are set up for each grassland type, and three 10-meter × 10-meter repeating small sample squares are set up along the diagonal and center position in each large sample square. Plant leaf and soil samples are collected in each small sample square.
", "ds_process_way": "Before collecting plant leaf samples, the coverage ( %), crown width (cm), and plant height (cm) of shrubs in the sample were measured, and the patch area (m2) was calculated based on the crown width measurement data. Data are in the form of mean ± standard deviation. Plant leaves were collected using a 5-point (S-shaped) sampling method. In each small sample square, the leaves of 5 healthy spiraea plants that were old and located in the middle of the canopy and had sufficient sunshine were randomly collected from 11:00 to 16:00. After mixing, they were used as one sample, with a total of 5 replicates. The samples were taken back to the laboratory, inactivated at 105°C for 10 minutes, then dried at 65°C to constant weight, and finally ground with a ball mill and sifted through a 0.2 mm screen for determination of plant nutrients (C, N, P). Soil sample collection: Collect 4 soil samples with a 0.40 cm soil layer around the stem base of the selected shrub in four directions equidistant from east, west, north and south using soil drills. After mixing, they form a sample, and set 3 replicates for each sample point. A total of 45 soil samples were obtained. The collected soil samples were sieved through a 2mm screen to remove large particle residue and root systems, and stored in sterile bags. The global positioning system (GPS) is also used to record the sea altitude and geographical coordinates of each sampling point. Soil bulk density (BD) was determined in the field by ring knife method. The proportions of soil clay (2μμm), powder (2 - 20 μm) and sand (20 - 2000 μm) were determined using a laser particle size analyzer (Mastersizer 2000, Malvern Panaco Ltd., Malvern, UK). Soil moisture content (SWC) is determined using the drying method (Almási et al., 2025), soil pH is determined using a pH meter, and electrical conductivity (EC) is evaluated using an EC meter (SevenExcellence S470, Mettler Toledo, Greifensee, Switzerland). Soil organic carbon (SOC) and plant leaf total carbon were measured using the high temperature combustion method using an elemental analyzer (Enviro TOC cube, Elementar Analysensysteme GmbH, Langenfeld, Germany). In addition, soil total nitrogen (STN) and plant leaf total nitrogen are measured using the Kjeldahl method, while soil total phosphorus (STP) and plant leaf total phosphorus are measured using molybdenum antimony resistant spectrophotometry after digestion with HClO4H2SO4. Soil available nitrogen (SAN) was assessed using alkali dissolution diffusion method, and soil available phosphorus (SAP) was determined using NaHCO3 extracted molybdenum and antimony resistance colorimetric method. The concentration of non-structural carbohydrates NSCs is calculated as the sum of soluble sugar and starch, both of which are determined by the traditional anthrone-sulfuric acid method. The operation method is as follows: Accurately weigh 0.15g of dry plant leaf powder, add 10mL of 80% absolute ethanol, extract in a boiling water bath for 10 minutes, centrifuge at 4,000 rpm for 10 minutes, and collect the supernatant as soluble sugar extract. Subsequently, 10 mL of 30%(v/v) perchloric acid was added to the centrifuged precipitate and allowed to stand overnight. After precise extraction in a water bath at 80°C for 10 minutes to ensure complete hydrolysis of the starch, it was cooled and centrifuged again at 4,000 revolutions per minute for 10 minutes. Finally, the supernatant was taken to measure the starch concentration.
", "ds_quality": "Specific quality controls during the data production process include: (1) Quality control during sample collection. In the study area, five representative temperate grassland zones were selected from low to high along the mountain contour line according to the plot survey content. Three duplicate plots were randomly set up in each plot, and three sampling plots were randomly set up in each plot. At the same time, territorial information such as plot number, plot number, plot area, geographical latitude and longitude, altitude, vegetation coverage, and plant height was recorded in detail. The principles of consistent plant community structure, uniform species composition, and uniform coverage are followed when setting sample plots to avoid sampling errors caused by inconsistent plant community structure and species composition. When sampling Spiraea leaves, only the leaves that meet the experimental requirements are collected to avoid damage to the entire plant. When collecting soil samples, carefully remove the humus layer on the soil surface, and label the obtained soil samples separately, paying special attention to the labeling of soil samples from different soil layers in each area. At the end of sampling, carefully verify relevant information such as the number and type of samples. (2)Quality control during data production and collation. After the leaf and soil samples are brought back to the laboratory, relevant element indicators are measured as soon as possible. Check the sample information again during data recording, including sample site number, sample square number, sample type, sample quantity, etc. During data testing, the operation should be strictly guided in accordance with relevant technical specifications, and the original test data should be carefully recorded and backed up so that the data source can be reviewed. The data collation process is to organize and generate data products according to each sample site and each sample square. When abnormal values appear in the data, strict verification should be carried out and necessary maintenance and calibration should be carried out on the measuring instrument to ensure that the data is true and accurate. If necessary, the corresponding samples should be sampled and measured again. The original records of all data shall not be smeared or deleted in order to be traced back to the data during subsequent data verification and entry. (3)Quality control during data verification and entry. When performing data verification, verify the basic information of the sample again, including the sample site number, sample square number, sample type, sample quantity, etc. If any deviation is found in the data value, trace the original data immediately, and use different colored handwriting next to the original data. Correct and note the reason for the error. When entering data, carefully and carefully check every data to reduce data errors during entry to ensure the accuracy of entered data.
", "ds_acq_place": "Southern foot of Altai Mountains, Xinjiang", "ds_ref_instruction": "Ma Xuexi, Li Yaoming. Data set of carbon, nitrogen and phosphorus stoichiometry characteristics of Spiraea shrub leaves and soil in a typical grassland ecosystem at the southern foot of the Altai Mountains, Xinjiang, China. National Glacier, Frozen Soil and Desert Scientific Data Center (www.ncdc.ac.cn), 2025. https://cstr.cn/CSTR:11738.11.NCDC.ECOLOGY.DB7025.2025.", "ds_format": ".xlsx format", "ds_projection": "no", "ds_space_res": "no", "ds_time_res": "no" } }, "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": [ "绣线菊灌木", "非结构性碳水化合物", "阿尔泰山", "土壤容重", "土壤含水量", "叶片碳氮磷含量" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆", "阿尔泰山" ], "ds_time_tags": [ 2023 ], "ds_contributors": [ "马学喜", "李耀明" ], "ds_meta_authors": [ "李锦" ], "ds_managers": [ "马学喜" ], "category": "生态环境" }