本数据集包含人工草地生态系统中植物空间配置方式、杂草入侵响应及土壤生态过程相关数据。研究通过构建不同本地植物斑块大小梯度（混播、0.0625 m²、0.25 m²和1 m²斑块），模拟不同空间异质性条件，探究植物空间结构对杂草入侵抗性的调控作用。实验连续两年监测本地植物和入侵植物地上生物量，并同步测定土壤理化性质，包括土壤有机碳（SOC）、铵态氮（NH₄⁺-N）、硝态氮（NO₃⁻-N）、有效磷（AP）、pH及土壤磷组分等指标。\n
本数据集包含2024—2025年人工草地物种空间配置与杂草入侵控制试验获得的植物群落和土壤理化性质数据。主要观测指标包括本地植物地上生物量、入侵植物地上生物量、群落总地上生物量，以及土壤有机碳（SOC）、硝态氮（NO₃⁻-N）、铵态氮（NH₄⁺-N）和有效磷（AP）等。数据涵盖4种本地植物空间配置和3种杂草入侵处理，可用于分析植物空间配置和杂草入侵对人工草地生产力、入侵抗性及土壤养分特征的影响。", "ds_source": "
本数据集来源于江苏省南京市溧水区白马农场试验基地（32°32′56.0″N，118°12′39.0″E）的人工草地长期试验平台。该平台建立于2016年，2024年在原有物种空间配置试验基础上开展杂草入侵试验。试验采用随机区组设计，每个试验小区面积为4 m × 4 m。本地植物群落由白三叶（Trifolium repens）、紫花苜蓿（Medicago sativa）、紫羊茅（Festuca rubra）和鸭茅（Dactylis glomerata）4种多年生牧草组成。\n
设置4种本地植物空间配置，包括完全混播（Mixed）、0.0625 m²小斑块（SP）、0.25 m²中斑块（MP）和1.0 m²大斑块（LP），并设置无目标入侵植物对照（Control）、一年生飞燕草（Consolida ajacis）入侵和多年生双穗雀稗（Paspalum distichum）入侵3种处理。入侵处理每种空间配置设置3个重复，对照处理每种空间配置设置4个重复，共40个独立试验小区。目标入侵植物于2024和2025年分别播入相应处理小区，播种密度为100粒·m⁻²。\n
植物地上生物量分别于2024年7月下旬和2025年7月下旬群落生物量高峰期采集。植物于距地表3 cm处刈割，70 ℃烘干48 h至恒重后称量，并分别记录本地植物和目标入侵植物地上生物量。土壤样品采集深度为0–15 cm，每个试验小区随机设置4个取样点，取样位置距小区边缘至少1 m，将4个土芯混合形成1个代表该小区的复合土壤样品。土壤无机氮采用2 mol·L⁻¹ KCl提取并使用Auto Analyzer 3 High Resolution连续流动分析仪测定，有效磷采用Olsen法提取测定，土壤有机碳采用重铬酸钾氧化-外加热法（Walkley-Black法）测定。", "ds_process_way": "
原始数据经过整理、质量检查和标准化处理后形成数据集。数据分析过程中进行了异常值检查、变量整理和统计分析，包括线性混合效应模型、主成分分析（PCA）以及相关分析等方法。原始观测数据按照“观测年份—入侵处理—本地植物空间配置—区组—试验小区”的层级进行整编，并对不同年份的数据字段名称、变量缩写、计量单位和数据格式进行统一和标准化。", "ds_quality": "
本数据集来源于人工草地田间控制实验，采用随机区组设计设置不同物种空间配置和杂草入侵处理，并设置独立重复。实验以完整样地作为独立实验单元，避免伪重复。植物和土壤样品均按照统一时间、采样方法和实验室分析流程进行采集与测定，植物生物量烘干至恒重后称量，土壤样品采用多点混合采样。实验开始前对土壤背景性质进行调查，各处理间初始土壤性质无显著差异。数据经完整性和异常值检查，同时检验模型基本假设。整体数据质量稳定，具有较好的完整性、一致性和可靠性，可满足后续统计分析和科学研究需求。", "ds_acq_start_time": "2024-07-25 00:00:00", "ds_acq_end_time": "2025-07-25 00:00:00", "ds_acq_place": "中国江苏省南京市溧水区白马农场人工草地实验基地", "ds_acq_lon_east": 118.21083333333334, "ds_acq_lat_south": 32.54888888888889, "ds_acq_lon_west": 118.21083333333334, "ds_acq_lat_north": 32.54888888888889, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 18151, "ds_files_count": 0, "ds_format": "*.xlsx", "ds_space_res": "", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "87e66c4b-a7ed-4e47-8ce0-2f55a2aa75b1.jpeg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "c49c8ab1-d3df-4dd2-b84b-8709ba45d418", "ds_serv_man": null, "ds_serv_phone": null, "ds_serv_mail": null, "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 0, "publish_time": "2026-09-03 11:41:03", "last_updated": "2026-09-03 11:41:03", "protected": false, "protected_to": "2027-08-27 00:00:00", "lang": "zh", "cstr": "11738.11.ncdc.db7774.2026", "i18n": { "en": { "title": "Dataset of plant patch structure effects on weed invasion resistance and soil ecological processes in artificial grasslands", "ds_format": "*.xlsx", "ds_source": "
This dataset comes from the artificial grassland long-term test platform at the Baima Farm Test Base in Lishui District, Nanjing City, Jiangsu Province (32°32 '56.0 \"N, 118°12' 39.0\" E). The platform was established in 2016 and will carry out weed invasion tests based on the original species spatial configuration tests in 2024. The trial adopted a randomized block design, with an area of 4 m × 4 m per test plot. The local plant community consists of four perennial forages: white clover (Trifolium repens), alfalfa (Medicago sativa), purple fescue (Festuca rubra) and Dactylis glomerata.\r\n
Four local plant spatial configurations were set up, including fully mixed (Mixed), 0.0625 m2 small patch (SP), 0.25 m2 medium patch (MP), and 1.0 m2 large patch (LP), and three treatments were set up: no target invasive plant control (Control), annual Consolida ajacis invasion, and perennial double stachum distichum invasion. There are 3 replicates for each spatial configuration for intrusion processing, and 4 replicates for each spatial configuration for control processing, for a total of 40 independent test cells. The target invasive plants will be sown into the corresponding treatment areas in 2024 and 2025 respectively, with a sowing density of 100 grains·m ².\r\n
Plant aboveground biomass was collected during the peak period of community biomass in late July 2024 and late July 2025 respectively. Plants were cut 3 cm away from the surface, dried at 70 ℃ for 48 hours to constant weight, and then weighed, and the above-ground biomass of local plants and target invasive plants was recorded respectively. Soil samples were collected at a depth of 0 - 15 cm. Four sampling points were randomly set for each test plot, and the sampling locations were at least 1 m away from the edge of the plot. Four soil cores were mixed to form a composite soil sample representing the plot. Soil inorganic nitrogen was extracted with 2 mol·L KCl and measured with an Auto Analyzer 3 High Resolution continuous flow analyzer, available phosphorus was extracted and measured with Olsen method, and soil organic carbon was measured with potassium dichromate oxidation-external heating method (Walkley-Black method).", "ds_quality": "
This dataset was derived from an artificial grassland field control experiment. A randomized block design was used to set up different species spatial configurations and weed invasion treatments, and independent replicates were set up. The experiment uses the complete sample plot as an independent experimental unit to avoid false duplication. Plant and soil samples are collected and determined according to a unified time, sampling method and laboratory analysis process. Plant biomass is dried to constant weight and then weighed. Soil samples are sampled at multiple points. Soil background properties were investigated before the start of the experiment, and there were no significant differences in initial soil properties between treatments. The data were checked for integrity and outliers, and the basic assumptions of the model were also tested. The overall data quality is stable, with good integrity, consistency and reliability, and can meet the needs of subsequent statistical analysis and scientific research.", "ds_ref_way": "", "ds_abstract": "
This dataset contains data on the spatial allocation of plants, weed invasion responses and soil ecological processes in artificial grassland ecosystems. The study explored the regulatory effect of plant spatial structure on weed invasion resistance by constructing different local plant patch size gradients (mixed sowing, 0.0625 m², 0.25 m² and 1 m² patches) and simulating different spatial heterogeneity conditions. The experiment monitored the aboveground biomass of local plants and invasive plants for two consecutive years, and simultaneously measured soil physical and chemical properties, including soil organic carbon (SOC), ammonium nitrogen (NH-N), nitrate nitrogen (NO-N), available phosphorus (AP), pH and soil phosphorus composition and other indicators.\r\n
This dataset contains plant community and soil physical and chemical properties data obtained from the spatial allocation of artificial grassland species and weed invasion control experiments from 2024 to 2025. The main observation indicators include aboveground biomass of local plants, aboveground biomass of invasive plants, total aboveground biomass of communities, as well as soil organic carbon (SOC), nitrate nitrogen (NO-N), ammonium nitrogen (NH-N) and available phosphorus (AP), etc. The data covers the spatial configuration of 4 local plants and the treatment of 3 weed invasion, which can be used to analyze the impact of plant spatial configuration and weed invasion on artificial grassland productivity, invasion resistance and soil nutrient characteristics.", "ds_time_res": "", "ds_acq_place": "Baima Farm Artificial Grassland Experimental Base, Lishui District, Nanjing City, Jiangsu Province, China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
The raw data is sorted, quality checked and standardized to form a data set. During the data analysis process, outlier checking, variable sorting and statistical analysis were carried out, including linear mixed effects models, principal component analysis (PCA), and correlation analysis. The original observation data are organized according to the level of \"observation year-intrusion processing-local plant spatial configuration-block-experimental plot\", and the data field names, variable abbreviations, measurement units and data formats for different years are unified and standardized.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "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, "belong_to_nieer": false, "allow_update_data": false, "ds_topic_tags": [ "人工草地", "植物斑块结构", "生物抗性", "杂草入侵", "土壤养分循环", "生态恢复" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国", "江苏省", "南京市", "溧水区", "白马农场" ], "ds_time_tags": [ 2024, 2025 ], "ds_contributors": [ { "true_name": "Erola Fenollosa", "email": "erola.fenollosaromani@biology.ox.ac.uk", "work_for": "Nanjing Agricultural University; University of Oxford", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "Haiyan Ren", "email": "hren@njau.edu.cn", "work_for": "南京农业大学", "country": "中国" } ], "ds_managers": [ { "true_name": "Haiyan Ren", "email": "hren@njau.edu.cn", "work_for": "南京农业大学", "country": "中国" } ], "category": "生态" }