MaxEnt 模型预测了当前（1970-2000）和未来（2021~2400，2041~2060）四种SSP（SSP1-2.6、SSP2-4.5、SSP3-7.0 和 SSP5-8.5）下软紫草在中国的潜在适生区。
", "ds_source": "软紫草51个分布点位数据和平均昼夜温差（bio2）、温度季节性变化（bio4）、最暖月最高温度（bio5）、最湿季度平均温度（bio8）、最干季度平均温度（bio9）、最干月份降水量（bio14）、降水季节性（bio15）、最暖季度降水量（bio18）、海拔（elev）、坡度（slope）、坡向（aspect）11 个环境因子。
", "ds_process_way": "软紫草在国内的51个分布点位数据和平均昼夜温差（bio2）、温度季节性变化（bio4）、最暖月最高温度（bio5）、最湿季度平均温度（bio8）、最干季度平均温度（bio9）、最干月份降水量（bio14）、降水季节性（bio15）、最暖季度降水量（bio18）、海拔（elev）、坡度（slope）、坡向（aspect）11 个环境因子导入MaxEnt version 3.4.4 中建立模型。参数设置如下：随机选取 25%的软紫草分布点用作模型测试，75%的分布点用于模型训练，要素类型为二次型（Quadratic features）、阈值型（Threshold features）、片段化型（（Hinge features），重复运行次数为10次，其余参数保持默认，模拟结果以ASC文件格式输出。选用刀切法（Jack-knife）评估各环境变量的贡献，使用受试者工作特征曲线（ROC）下面积（AUC 值）评估MaxEnt 模型预测结果的精度，AUC值越接近1，说明模型的精确度越高，越具可信度。当AUC值在0.9~1.0 时，模型预测结果最好，0.8~0.9 时，模型预测结果较好，0.7~0.8 时，模型预测结果一般，小于 0.6 时，模型预测失败。软紫草的 ROC 曲线下面积（AUC）平均值为 0.960，标准差为 0.022，这表明预测模型精度极好，软紫草的潜在适生区预测结果具有极高的可靠性。 \n将 MaxEnt 模型预测出的不同时期和情景下软紫草潜在适生区结果导入 ArcGIS 软件，利用重分类（Reclass）工具，将软紫草的适宜分布区划分 4 个等级：不适生区（0-0.07）、低适生区（0.07-0.3）、中适生区（0.3-0.6）、高适生区（0.6-1.0），并分别统计各适生区的面积。
", "ds_quality": "", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "", "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": 121602183, "ds_files_count": 20, "ds_format": "ASC文件", "ds_space_res": "2.5’", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "053c58b5-94d7-4c9c-8412-c8ab35eed47c.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 1, "publish_time": "2026-06-15 16:44:46", "first_publish_time": null, "last_updated": "2026-06-15 16:50:22", "protected": false, "protected_to": null, "lang": "zh", "cstr": null, "license": null, "extra": null, "files_shape": [ { "name": "PD2504N001C03-软紫草在中国潜在适生区的MaxEnt模型结果", "size": null, "is_dir": true } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "MaxEnt model results for potential suitable areas of Lithospermum japonica in China", "ds_abstract": "The MaxEnt model predicts the potential suitable areas for soft lithospermum in China under four SSPs (SSP1 -2.6, SSP2 -4.5, SSP3 -7.0 and SSP5 -8.5) at present (1970-2000) and in the future (2021 - 2400, 2041 - 2060).
", "ds_source": "Data of 51 distribution points of soft combia and 11 environmental factors: average day and night temperature difference (bio2), seasonal change in temperature (bio4), maximum temperature in the warmest month (bio5), average temperature in the wettest season (bio8), average temperature in the driest season (bio9), precipitation in the driest month (bio14), precipitation seasonality (bio15), precipitation in the warmest season (bio18), altitude (elev), slope), and aspect.
", "ds_process_way": "Data of 51 distribution points in the country and average day and night temperature difference (bio2), seasonal change of temperature (bio4), maximum temperature in the warmest month (bio5), average temperature in the wettest season (bio8), average temperature in the driest season (bio9), precipitation in the driest month (bio14), precipitation seasonality (bio15), precipitation in the warmest season (bio18), altitude (elev), slope (), Eleven environmental factors of aspect were imported into MaxEnt version 3.4.4 to establish a model. The parameters are set as follows: 25% of the distribution points of soft comfrey were randomly selected for model testing, and 75% of the distribution points were used for model training. The element types were Quadratic features, Threshold features, and Hinge features. The number of repeated runs was 10 times. Other parameters were kept at default. The simulation results were output in ASC file format. The Jack-knife method was used to evaluate the contribution of various environmental variables, and the area under the receiver operating characteristic curve (ROC)(AUC value) was used to evaluate the accuracy of the prediction results of the MaxEnt model. The closer the AUC value is to 1, the higher the accuracy of the model, the more reliable it is. When the AUC value is between 0.9 and 1.0, the model prediction result is the best, when the AUC value is between 0.8 and 0.9, the model prediction result is better, when the AUC value is between 0.7 and 0.8, the model prediction result is average, and when the AUC value is less than 0.6, the model prediction fails. The mean area under the ROC curve (AUC) of Arnebia rugosa was 0.960 and the standard deviation was 0.022, which indicated that the prediction model had excellent accuracy and the prediction results of potential suitable areas of Arnebia rugosa were extremely reliable. \nThe results of the potential suitable areas of Arnebia japonica predicted by the MaxEnt model under different periods and scenarios were imported into ArcGIS software, and the suitable distribution areas of Arnebia japonica were divided into four levels using the Reclass tool: unsuitable area (0-0.07), low suitable area (0.07-0.3), medium suitable area (0.3-0.6), and high suitable area (0.6-1.0), and the area of each suitable area was counted respectively.
", "ds_format": "ASC file", "ds_space_res": "2.5’" } }, "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": [ "中国", "软紫草", "MaxEnt模型" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [], "ds_time_tags": [ 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060 ], "ds_contributors": [ "商淑静" ], "ds_meta_authors": [ "商淑静" ], "ds_managers": [ "刘丹辉" ], "category": "生物多样性" }