海洋生态系统极易受到环境扰动影响，系统稳态转换往往因稳定性丧失而被触发。然而，全球尺度下海洋生态系统稳定性的驱动机制目前尚不明确。本数据集采用全球大洋 171 个海洋生态系统的 EwE 食物网模型数据，量化了稳定性的多维度特征（抵抗力、恢复力、变异性），并借助机器学习方法探究海洋生态系统稳定性的关键驱动因子。\n
不同生境下各稳定性指标的差异及其相关关系，揭示了海洋生态系统稳定性具有多维属性。随机森林回归结果表明，物种丰富度与渔业捕捞是影响海洋生态系统稳定性的两大主导因子。具体而言，物种丰富度可通过增强抵抗力、恢复力与时间稳定性（即降低变异性），支撑海洋生态系统结构稳态；而渔业开发活动则会削弱生态系统的抵抗力与时间稳定性。\n
本数据集从成因机制层面深化了对海洋食物网稳定性提升机理的认知，可为未来人类活动压力背景下海洋生态系统的修复与管理提供理论支撑。", "ds_source": "", "ds_process_way": "", "ds_quality": "", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "全球", "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": 26868575, "ds_files_count": 0, "ds_format": "Excel、R", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "04036eb1-514a-4227-82bb-2bc563070a40.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a4dd5849-78f2-44c5-b0f1-3450e952b2a2", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.6040" ], "quality_level": 0, "publish_time": "2026-05-11 14:49:01", "last_updated": "2026-05-11 15:13:18", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.ecology.db7318.2026", "i18n": { "en": { "title": "Ecosystem stability, complexity and function in real food webs", "ds_format": "Excel、R", "ds_source": "", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "
Marine ecosystems are vulnerable to environmental perturbations and regime shifts are often detected due to the loss of stability. Yet, the driving factors for the marine ecosystem stability in a global scale are still unknown. Here, using EwE food web models of 171 marine ecosystem data across the globe ocean, we quantified different dimensions of stability (resistance, resilience, and variability) and investigated the key drivers of marine ecosystem stability with machine learning method. Difference for stability metrics in habitats and their correlations revealed the multidimensionality of marine ecosystem stability. Random forest regression models showed that species richness and fishing stand out as the two most important factors for marine ecosystem stability. Specifically, we found that species richness underpins marine ecosystem configurations by strengthening resistance, resilience, and temporal stability (i.e. weakening variability), while fish exploitation weakens resistance and temporal stability. This study helps improve the understanding of what enhances stability in marine food webs from a causal perspective and, thus, helpful for restoration and management of marine ecosystems under future anthropogenic pressures.", "ds_time_res": "", "ds_acq_place": "global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "", "ds_ref_instruction": "" } }, "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": [ "生态系统稳定性", "生态路径", "生物多样性", "抵抗力", "韧性" ], "ds_subject_tags": [ "海洋生物学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [], "ds_contributors": [ { "true_name": "冯剑丰", "email": "fengjf@nankai.edu.cn", "work_for": "南开大学环境科学与工程学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "冯剑丰", "email": "fengjf@nankai.edu.cn", "work_for": "南开大学环境科学与工程学院", "country": "中国" } ], "ds_managers": [ { "true_name": "冯剑丰", "email": "fengjf@nankai.edu.cn", "work_for": "南开大学环境科学与工程学院", "country": "中国" } ], "category": "生态" }