基于性状的方法在预测植被变化以及将大尺度生态系统结构与功能联系起来方面越来越受到关注。然而，这类方法面临的一个关键挑战是获取空间连续的植物功能性状图。本文选择了六种关键的植物功能性状，包括比叶面积（SLA）、叶干物质含量（LDMC）、叶氮浓度（LNC）、叶磷浓度（LPC）、叶面积（LA）和木质密度（WD），因为它们可以反映植物资源获取策略和生态系统功能。在中国的 1430 个采样点共采集了 3447 种种子植物的 34589 个原位性状测量值，并利用两种机器学习模型（随机森林和提升回归树）生成了空间植物功能性状图（1 km）以及环境变量和植被指数。为获得最佳估计值，进一步应用加权平均算法合并两个模型的预测结果，得出最终的空间植物功能性状图。这些模型在估计 WD、LPC 和 SLA 方面表现出良好的准确性，平均R2值在 0.48 到 0.68 之间。相比之下，这两个模型在估计 LDMC 方面表现较弱，平均R2值小于 0.30。同时，LA 在某些地区显示出两种模型之间的巨大差异。在预测植物功能性状的空间分布方面，气候影响比土壤因素的影响更重要。由于取样稀少，中国东北和青藏高原植物功能性状的估计值具有相对较高的不确定性，这意味着未来需要在这些地区进行更多的观测。我们的空间性状图可为基于性状的植被模型提供重要支持，并可在大尺度上探索植被特征与生态系统功能之间的关系。
", "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。
", "ds_process_way": "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。八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_quality": "数据质量良好。
", "ds_acq_start_time": null, "ds_acq_end_time": null, "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": "open-access", "ds_total_size": 772839315, "ds_files_count": 8, "ds_format": "tif", "ds_space_res": "1000", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "9643cca9-0e22-4c96-98aa-d2e607da37e0.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": "2024-10-29 09:39:44", "last_updated": "2026-05-15 11:57:02", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB6658.2024", "i18n": { "en": { "title": "Maps of plant functional traits with 1-km spatial resolution in terrestrial ecosystems across China", "ds_format": "tif", "ds_source": "Plant trait data were obtained and collected via two main sources. The first source was public trait databases, including the TRY database and the China Plant Trait Database. The second source was from the literature.
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "Trait-based approaches are of increasing concern in predicting vegetation changes and linking ecosystem structures to functions at large scales. However, a critical challenge for such approaches is acquiring spatially continuous plant functional trait maps. Here, six key plant functional traits were selected as they can reflect plant resource acquisition strategies and ecosystem functions, including specific leaf area (SLA), leaf dry matter content (LDMC), leaf N concentration (LNC), leaf P concentration (LPC), leaf area (LA) and wood density (WD). A total of 34 589 in situ trait measurements of 3447 seed plant species were collected from 1430 sampling sites in China and were used to generate spatial plant functional trait maps (∼1 km), together with environmental variables and vegetation indices based on two machine learning models (random forest and boosted regression trees). To obtain the optimal estimates, a weighted average algorithm was further applied to merge the predictions of the two models to derive the final spatial plant functional trait maps. The models showed good accuracy in estimating WD, LPC and SLA, with average R2 values ranging from 0.48 to 0.68. In contrast, both the models had weak performance in estimating LDMC, with average R2 values less than 0.30. Meanwhile, LA showed considerable differences between the two models in some regions. Climatic effects were more important than those of edaphic factors in predicting the spatial distributions of plant functional traits. Estimates of plant functional traits in northeastern China and the Qinghai–Tibetan Plateau had relatively high uncertainties due to sparse samplings, implying a need for more observations in these regions in the future. Our spatial trait maps could provide critical support for trait-based vegetation models and allow exploration of the relationships between vegetation characteristics and ecosystem functions at large scales.
", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "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_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, "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, "ds_topic_tags": [ "陆地生态系统", "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": [], "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" }