该数据集包括两个方面。一个是本研究收集的用于机器学习模型的原始植物功能性状数据集，命名为Data file used for machine learning models.csv。另一个是中国的功能性状图。6张功能性状图的空间分辨率为 1 公里，格式为 GeoTIFF。这些性状数据集是结合实地观测、环境变量和植被指数数据生成的，用于基于两种机器学习方法（即随机森林和提升回归树）的集合建模方法。
", "ds_source": "植物性状数据主要通过两个来源获得和收集。第一个来源是公共性状数据库，包括 TRY 数据库和中国植物性状数据库。第二个来源是文献。为确保数据质量和可比性，我们只纳入符合以下五个标准的性状观测数据。(1) 必须从天然陆地田间获得测量数据，以尽量减少管理干扰的影响，因此不包括来自耕地、水生生境、对照实验和花园的观测数据。(2）根据质量比假说，植物物种对生态系统功能的影响在很大程度上取决于优势物种的性状和功能多样性，而对从属物种的丰富程度相对不敏感。因此，我们只纳入了对群落中所有物种或优势物种的植物性状观测进行测量的研究。(3）为了考虑种内性状变异，当不同研究的同一采样点出现相同物种时，我们纳入了不同研究的所有原始观测数据，而不是物种水平的平均值。(4) 植物性状观测必须针对成熟健康的植物个体，因此排除了一些特定的生长阶段（如幼苗）和大小等级（如树苗），以减少本体发育的混杂效应。(5) 我们只纳入了具有明确地理坐标以匹配预测变量的研究。数据集中还包括采样地点和采样时间。取样时间大多集中在一年中的生长季节（即5月至10月），这可以确保取样时间的相对一致性，从而将季节性的影响降至最低。
", "ds_process_way": "中国植物功能性状空间分布图的绘制基于机器学习方法，该方法由大量野外实测数据、环境变量和植被指数训练而成。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "2001-01-01 00:00:00", "ds_acq_end_time": "2018-12-31 00:00:00", "ds_acq_place": "中国", "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": "login-access", "ds_total_size": 772839315, "ds_files_count": 8, "ds_format": "tif，csv", "ds_space_res": "1000", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "ddd987fc-b282-4e30-844d-342a9e08078b.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "a4dd5849-78f2-44c5-b0f1-3450e952b2a2", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2024-08-29 09:02:45", "last_updated": "2025-05-29 11:33:10", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB6662.2024", "i18n": { "en": { "title": "Maps of plant functional traits with 1-km spatial resolution in terrestrial ecosystems across China", "ds_format": "tif，csv", "ds_source": "Plant trait data is mainly obtained and collected through two sources. The first source is the public trait database, including the TRX database and the Chinese plant trait database. The second source is literature. To ensure data quality and comparability, we only include trait observation data that meet the following five criteria. (1) Measurement data must be obtained from natural land fields to minimize the impact of management interference, therefore observation data from cultivated land, aquatic habitats, control experiments, and gardens are not included. (2) According to the quality ratio hypothesis, the impact of plant species on ecosystem function largely depends on the traits and functional diversity of dominant species, while being relatively insensitive to the richness of dependent species. Therefore, we only included studies that measured plant trait observations of all species or dominant species in the community. (3) In order to consider intraspecific trait variation, when the same species appears at the same sampling point in different studies, we included all raw observation data from different studies, rather than the average value at the species level. (4) Observation of plant traits must be targeted at mature and healthy plant individuals, thus excluding certain growth stages (such as seedlings) and size grades (such as saplings) to reduce the confounding effects of ontology development. (5) We only included studies with clear geographic coordinates to match predictor variables. The dataset also includes sampling locations and sampling times. The sampling time is mostly concentrated during the growing season of the year (i.e. May to October), which ensures relative consistency in sampling time and minimizes the impact of seasonality.
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "This dataset consists of two aspects. One is the original plant functional traits dataset collected in this study that were used for machine learning models, which was named by Data file used for machine learning models.csv. The other is functional trait maps for China. The spatial resolution of eight functional trait maps is 1 km in a GeoTIFF format. These trait datasets were generated in combination with field observations, environmental variables, and vegetation indices data for use in an ensemble modelling approach based on two machine learning methods (i.e., random forest and boosted regression trees).
", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "1000", "ds_projection": "", "ds_process_way": "The drawing of the spatial distribution map of plant functional traits in China is based on machine learning methods, which are trained on a large amount of field measured data, environmental variables, and vegetation indices.
", "ds_ref_instruction": "When using data, users should clearly declare the source of the data in the main text and cite the citation method provided by this metadata in the reference section." } }, "submit_center_id": "ncdc", "data_level": 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, "ds_topic_tags": [ "中国陆地生态系统", "关键植物", "叶面积（SLA）", "叶片干物质含量（LDMC）", "叶片氮浓度（LNC）", "叶片磷浓度（LPC）", "叶面积（LA）", "木材密度（WD）" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018 ], "ds_contributors": [ { "true_name": "吕楠", "email": "nanlv@rcees.ac.cn", "work_for": "中国科学院生态环境研究中心", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "吕楠", "email": "nanlv@rcees.ac.cn", "work_for": "中国科学院生态环境研究中心", "country": "中国" } ], "ds_managers": [ { "true_name": "吕楠", "email": "nanlv@rcees.ac.cn", "work_for": "中国科学院生态环境研究中心", "country": "中国" } ], "category": "生态" }