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Article(id=1276175729847046492, tenantId=1146029695717560320, journalId=1235980609244409860, issueId=1276175380184695804, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-2561.2024.09.024, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1695657600000, receivedDateStr=2023-09-26, revisedDate=1715184000000, revisedDateStr=2024-05-09, acceptedDate=null, acceptedDateStr=null, onlineDate=1782193604193, onlineDateStr=2026-06-23, pubDate=1727193600000, pubDateStr=2024-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1782193604193, onlineIssueDateStr=2026-06-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1782193604193, creator=13701087609, updateTime=1782193604193, updator=13701087609, issue=Issue{id=1276175380184695804, tenantId=1146029695717560320, journalId=1235980609244409860, year='2024', volume='45', issue='9', pageStart='1761', pageEnd='1997', issueExtLink='null', onlineDate='null', pubDate='1727193600000', pubDateStr='2024-09-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1782193520816, creator='13701087609', updateTime=1782193908264, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1276177005326504448, tenantId=1146029695717560320, journalId=1235980609244409860, issueId=1276175380184695804, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1276177005326504449, tenantId=1146029695717560320, journalId=1235980609244409860, issueId=1276175380184695804, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1989, endPage=1997, ext={EN=ArticleExt(id=1276175730383917406, articleId=1276175729847046492, tenantId=1146029695717560320, journalId=1235980609244409860, language=EN, title=MaxEnt Modeling Prediction of the Potential Distribution of Acmella uliginosa, a Newly Recorded Invasive Plant in Hainan, China, columnId=1236318328365577171, journalTitle=Chinese Journal of Tropical Crops, columnName=Agricultural Ecology & Environmental Protection, runingTitle=null, highlight=null, articleAbstract=

As global temperature rises and international trade becomes more frequent, the world is facing increasing pressure from invasive species. Therefore, it is crucial to have a deep understanding of the types of invasive species and the possible distribution ranges to take effective prevention and control measures. This paper reported a new record invasive plant species in Hainan, Acmella uliginosa, and MaxEnt maximum entropy ecological niche modeling combined with Geographic Information Systems (GIS) was used to predict the primary potential suitable areas for A. uliginosa under current and future climatic conditions in China, ROC curve analysis method was used to validate, knife-edge method to was used to analyze the major environmental variables influencing the distribution of the A. uliginosa. A. uliginosa had a large distribution space and invasion potential in China, with the main distribution areas concentrated in southern, central and southwestern regions such as Guangxi, Guangdong, Fujian, Hainan and Taiwan. The low suitable area was mainly concentrated in Jiangxi, Hunan, Chongqing, Guizhou, Hunan and Zhejiang. By 2070, the overall suitable area of A. uliginosa in China is expected to decrease from 20.64% to 11.98%, with a decrease of 5.38% in low suitable areas and 3.28% in medium and high suitable areas. The area of low suitable areas in Sichuan, Yunnan, Guizhou, Hunan and Zhejiang has significantly reduced. The ROC curve analysis method showed that the average area under the curve (AUC) value is 0.968 with a standard deviation of 0.004, indicating that the prediction results are reliable. The knife-cutting method analysis showed that the four key environmental variables that have the greatest impact on the potential geographical distribution of A. uliginosa in China are Bio13 (precipitation of the wettest month), Bio16 (precipitation of the wettest season), Bio2 (mean diurnal temperature range), and Bio11 (mean temperature of the coldest season) are 27.6%, 22.5%, 11.1% and 8.0% respectively, indicating that precipitation and temperature range have a significant impact on the distribution of Viola palustris. The four key environmental variables that have the least impact on the potential geographical distribution of Viola palustris in China are Sq2 (soil nutrient retention capacity), Sq6 (soil toxicity), Bio9 (mean temperature of the driest season), and Sq5 (soil salinity). The four key environmental variables with the least impact on the potential geographical distribution of aquatic golden buttons in China are Sq6 toxicity, Sq7 operability, Bio1 annual average temperature, and Sq5 soil salt content. The potential suitable area for A. uliginosa is large in southern China and needs to be monitored to prevent further spread.

, authors=null, authorsList=Xiaoxia LI, Kuanyi HU, Anyi ZENG, Dingchao DONG, authorCompany=null, correspAuthors=Dingchao DONG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1276175733173129580, articleId=1276175729847046492, tenantId=1146029695717560320, journalId=1235980609244409860, language=CN, title=基于MaxEnt模型预测海南外来入侵植物新记录种—沼生金纽扣在中国的潜在适生区, columnId=1236292523270918153, journalTitle=热带作物学报, columnName=农业生态与环境保护, runingTitle=null, highlight=null, articleAbstract=

随着全球气温升高及国际贸易往来日益频繁，全球正面临着外来入侵持续上升的压力，因此深入了解外来入侵物种的种类及其可能分布范围对于采取有效防控措施至关重要。本研究报道了海南入侵植物新纪录种—沼生金纽扣，并采用最大熵（MaxEnt）模型结合地理信息系统（ArcGis），预测沼生金纽扣在中国的主要潜在适生区域，运用ROC曲线分析法进行验证，并运用刀切法分析影响沼生金纽扣分布的主要环境变量。结果表明：沼生金纽扣在中国有较大的分布空间和入侵潜力，主要集中在华南、华中和西南等地区，其中高、中适生区主要集中在广西、广东、福建、海南和台湾，低适生区主要集中在江西、湖南、重庆、贵州、湖南和浙江。到2070年，沼生金纽扣在中国的适生区总体呈减少趋势，总体适生面积由当前的20.64%减少到11.98%，其中低适生区减少5.38%，中、高适生区减少3.28%，主要表现在四川、云南、贵州、湖南和浙江的低度适生区面积明显缩小，中、高度适生区仍然主要集中在海南、广西、广东、福建和台湾。ROC曲线分析法得出平均面积AUC值为0.98，标准差为0.004，表明预测结果可靠。刀切法分析显示，影响沼生金纽扣在中国潜在地理分布的4个关键环境变量分别是Bio13（最湿月降水量）、Bio16（最湿季节降水量）、Bio2（月平均昼夜温差）和Bio11（最冷季节平均温度），其相对贡献率分别为27.6%、22.5%、11.1%和8.0%，说明降水量和温差对沼生金纽扣的分布影响较大；而对沼生金纽扣在中国潜在地理分布影响最小的4个关键环境变量分别是Sq2（土壤营养保持能力）、Sq6（土壤毒性）、Bio9（最干季节平均温度）和Sq5（土壤含盐量）。沼生金纽扣在华南地区潜在适生区分布面积较大，需要加强防范和监测，防止蔓延危害。

, authors=

李晓霞（1983—），女，硕士，副研究员，研究方向：入侵植物生态学及科学普及。

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* 董定超（DONG Dingchao），E-mail：。
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李晓霞（1983—），女，硕士，副研究员，研究方向：入侵植物生态学及科学普及。

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李晓霞（1983—），女，硕士，副研究员，研究方向：入侵植物生态学及科学普及。

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DC, refAbstract=null)], funds=[Fund(id=1276465402741920289, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276175729847046492, awardId=721RC631, language=CN, fundingSource=海南省自然科学基金高层次人才项目(721RC631), fundOrder=null, country=null), Fund(id=1276465402804834850, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276175729847046492, awardId=323QN299, language=CN, fundingSource=海南省自然科学基金青年基金项目(323QN299), fundOrder=null, country=null), Fund(id=1276465402867749411, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276175729847046492, awardId=HNSK（YB）23-75, language=CN, fundingSource=海南省哲学社会科学规划项目(HNSK（YB）23-75), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1276465394672079339, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276175729847046492, xref=1., ext=[AuthorCompanyExt(id=1276465394680467948, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276175729847046492, companyId=1276465394672079339, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.Institute of Scientific and Technology Information, Chinese Academy of Tropical Agricultural Sciences, Haikou, Hainan 571101, China), AuthorCompanyExt(id=1276465394688856557, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276175729847046492, companyId=1276465394672079339, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.中国热带农业科学院科技信息研究所，海南海口　571101)]), AuthorCompany(id=1276465394747576814, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276175729847046492, xref=2., ext=[AuthorCompanyExt(id=1276465394755965423, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276175729847046492, companyId=1276465394747576814, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.Yunnan Innovation Institute, Beihang University, Kunming, Yunnan 650051, China), AuthorCompanyExt(id=1276465394764354032, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276175729847046492, companyId=1276465394747576814, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.北京航空航天大学云南创新研究院，云南昆明　650051)])], figs=[ArticleFig(id=1276465401303274001, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276175729847046492, language=EN, label=Fig. 1, caption=The characteristic figure of A. uliginosa

A: Plant and habitat; B: Above-ground portion of the plant; C: Capitula; D: Root; E: Leaf.

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A：植株及生境；B：植株地上部分；C：头状花序；D：根；E：叶。

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Evaluation indicators for the potential geographic distribution of A. uliginosa

, figureFileSmall=null, figureFileBig=null, tableContent=
编号No.变量/单位Variables/Unit指标意义Significance of variable
GloElev海拔/m反映海拔高度
Sq1营养的可用性/%土壤质地、土壤有机碳、土壤pH、总可交换碱
Sq2营养保持能力/%土壤有机碳、土壤质地、碱饱和度、土壤与黏土组分的阳离子交换能力
Sq3支持条件/%土壤质地、容重、粗碎片、顶点土壤性质和土壤相对根系渗透、土壤深度和土壤体积的影响
Sq4根部的氧气利用率/%土壤排水和影响土壤排水的土壤阶段
Sq5土壤盐含量/%土壤盐度、土壤溶度和土壤相对盐分条件的影响
Sq6毒性/%碳酸钙和石膏
Sq7可操作性/%土壤质地、有效土层深度/体积，以及制约土壤管理的土相
Bio1年平均气温/℃反映平均温度及其变异幅度
Bio2月平均昼夜温差/℃反映温差特点
Bio3等温性/%反映温差特点
Bio4温度季节性变化标准差/℃反映平均温度及其变异幅度
Bio5最暖月最高温度/℃反映极端温度的影响
Bio6最冷月最低温度/℃反映极端温度的影响
Bio7年均温变化范围/℃反映温差特点
Bio8最湿季节平均温度/℃反映水热是否同步
Bio9最干季节平均温度/℃反映水热是否同步
Bio10最暖季节平均温度/℃反映极端温度的影响
Bio11最冷季节平均温度/℃反映极端温度的影响
Bio12年降水量/mm反映降雨量及季节性分布
Bio13最湿月降水量/mm反映极端水分条件
Bio14最干月降水量/mm反映极端水分条件
Bio15降水量变异系数/mm反映降雨量及季节性分布
Bio16最湿季节降水量/mm反映极端水分条件
Bio17最干季节降水量/mm反映极端水分条件
Bio18最暖季节降水量/mm反映水热是否同步
Bio19最冷季节降水量/mm反映水热是否同步
), ArticleFig(id=1276465402473484830, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276175729847046492, language=CN, label=表1, caption=

沼生金纽扣潜在地理分布的评价指标

, figureFileSmall=null, figureFileBig=null, tableContent=
编号No.变量/单位Variables/Unit指标意义Significance of variable
GloElev海拔/m反映海拔高度
Sq1营养的可用性/%土壤质地、土壤有机碳、土壤pH、总可交换碱
Sq2营养保持能力/%土壤有机碳、土壤质地、碱饱和度、土壤与黏土组分的阳离子交换能力
Sq3支持条件/%土壤质地、容重、粗碎片、顶点土壤性质和土壤相对根系渗透、土壤深度和土壤体积的影响
Sq4根部的氧气利用率/%土壤排水和影响土壤排水的土壤阶段
Sq5土壤盐含量/%土壤盐度、土壤溶度和土壤相对盐分条件的影响
Sq6毒性/%碳酸钙和石膏
Sq7可操作性/%土壤质地、有效土层深度/体积，以及制约土壤管理的土相
Bio1年平均气温/℃反映平均温度及其变异幅度
Bio2月平均昼夜温差/℃反映温差特点
Bio3等温性/%反映温差特点
Bio4温度季节性变化标准差/℃反映平均温度及其变异幅度
Bio5最暖月最高温度/℃反映极端温度的影响
Bio6最冷月最低温度/℃反映极端温度的影响
Bio7年均温变化范围/℃反映温差特点
Bio8最湿季节平均温度/℃反映水热是否同步
Bio9最干季节平均温度/℃反映水热是否同步
Bio10最暖季节平均温度/℃反映极端温度的影响
Bio11最冷季节平均温度/℃反映极端温度的影响
Bio12年降水量/mm反映降雨量及季节性分布
Bio13最湿月降水量/mm反映极端水分条件
Bio14最干月降水量/mm反映极端水分条件
Bio15降水量变异系数/mm反映降雨量及季节性分布
Bio16最湿季节降水量/mm反映极端水分条件
Bio17最干季节降水量/mm反映极端水分条件
Bio18最暖季节降水量/mm反映水热是否同步
Bio19最冷季节降水量/mm反映水热是否同步
), ArticleFig(id=1276465402544787999, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276175729847046492, language=EN, label=Tab. 2, caption=

Estimates of relative contributions of the environmental variables to the MaxEnt model

, figureFileSmall=null, figureFileBig=null, tableContent=
编号No.贡献百分比Percent contribution/%重要性排序Importance ranking
Bio1327.64.9
Bio1622.54.3
Bio211.110.2
Bio118.029.5
Bio125.93.6
Bio65.17.8
Gloland3.01.1
Bio32.911.4
Bio182.11.0
Bio151.41.1
Bio41.48.7
Bio51.31.3
Bio81.22.2
Bio191.00.7
Bio100.82.5
Bio70.80.6
Sq10.71.5
Bio10.70.5
Bio170.71.1
Bio140.61.0
Sq30.41.9
Sq70.20.8
Sq40.20.5
Sq20.20.4
Sq60.11.1
Bio90.10.1
Sq500.1
), ArticleFig(id=1276465402611896864, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276175729847046492, language=CN, label=表2, caption=

环境变量对MaxEnt模型的相对贡献估计值

, figureFileSmall=null, figureFileBig=null, tableContent=
编号No.贡献百分比Percent contribution/%重要性排序Importance ranking
Bio1327.64.9
Bio1622.54.3
Bio211.110.2
Bio118.029.5
Bio125.93.6
Bio65.17.8
Gloland3.01.1
Bio32.911.4
Bio182.11.0
Bio151.41.1
Bio41.48.7
Bio51.31.3
Bio81.22.2
Bio191.00.7
Bio100.82.5
Bio70.80.6
Sq10.71.5
Bio10.70.5
Bio170.71.1
Bio140.61.0
Sq30.41.9
Sq70.20.8
Sq40.20.5
Sq20.20.4
Sq60.11.1
Bio90.10.1
Sq500.1
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基于MaxEnt模型预测海南外来入侵植物新记录种—沼生金纽扣在中国的潜在适生区
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李晓霞 1 , 胡宽义 2 , 曾安逸 1 , 董定超 1, *
热带作物学报 | 农业生态与环境保护 2024,45(9): 1989-1997
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热带作物学报 |农业生态与环境保护 2024 , 45 (9) : 1989 -1997
基于MaxEnt模型预测海南外来入侵植物新记录种—沼生金纽扣在中国的潜在适生区
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李晓霞1, 胡宽义2, 曾安逸1, 董定超1, *
作者信息
  • 1.中国热带农业科学院科技信息研究所，海南海口　571101
  • 2.北京航空航天大学云南创新研究院，云南昆明　650051
通讯作者:
* 董定超（DONG Dingchao），E-mail：。
MaxEnt Modeling Prediction of the Potential Distribution of Acmella uliginosa, a Newly Recorded Invasive Plant in Hainan, China
Xiaoxia LI1, Kuanyi HU2, Anyi ZENG1, Dingchao DONG1, *
Affiliations
  • 1.Institute of Scientific and Technology Information, Chinese Academy of Tropical Agricultural Sciences, Haikou, Hainan 571101, China
  • 2.Yunnan Innovation Institute, Beihang University, Kunming, Yunnan 650051, China
出版时间: 2024-09-25 doi: 10.3969/j.issn.1000-2561.2024.09.024
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摘要
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随着全球气温升高及国际贸易往来日益频繁，全球正面临着外来入侵持续上升的压力，因此深入了解外来入侵物种的种类及其可能分布范围对于采取有效防控措施至关重要。本研究报道了海南入侵植物新纪录种—沼生金纽扣，并采用最大熵（MaxEnt）模型结合地理信息系统（ArcGis），预测沼生金纽扣在中国的主要潜在适生区域，运用ROC曲线分析法进行验证，并运用刀切法分析影响沼生金纽扣分布的主要环境变量。结果表明：沼生金纽扣在中国有较大的分布空间和入侵潜力，主要集中在华南、华中和西南等地区，其中高、中适生区主要集中在广西、广东、福建、海南和台湾，低适生区主要集中在江西、湖南、重庆、贵州、湖南和浙江。到2070年，沼生金纽扣在中国的适生区总体呈减少趋势，总体适生面积由当前的20.64%减少到11.98%，其中低适生区减少5.38%，中、高适生区减少3.28%，主要表现在四川、云南、贵州、湖南和浙江的低度适生区面积明显缩小，中、高度适生区仍然主要集中在海南、广西、广东、福建和台湾。ROC曲线分析法得出平均面积AUC值为0.98，标准差为0.004，表明预测结果可靠。刀切法分析显示，影响沼生金纽扣在中国潜在地理分布的4个关键环境变量分别是Bio13（最湿月降水量）、Bio16（最湿季节降水量）、Bio2（月平均昼夜温差）和Bio11（最冷季节平均温度），其相对贡献率分别为27.6%、22.5%、11.1%和8.0%，说明降水量和温差对沼生金纽扣的分布影响较大；而对沼生金纽扣在中国潜在地理分布影响最小的4个关键环境变量分别是Sq2（土壤营养保持能力）、Sq6（土壤毒性）、Bio9（最干季节平均温度）和Sq5（土壤含盐量）。沼生金纽扣在华南地区潜在适生区分布面积较大，需要加强防范和监测，防止蔓延危害。

入侵植物  /  沼生金纽扣  /  潜在适生区  /  MaxEnt  /  ArcGis

As global temperature rises and international trade becomes more frequent, the world is facing increasing pressure from invasive species. Therefore, it is crucial to have a deep understanding of the types of invasive species and the possible distribution ranges to take effective prevention and control measures. This paper reported a new record invasive plant species in Hainan, Acmella uliginosa, and MaxEnt maximum entropy ecological niche modeling combined with Geographic Information Systems (GIS) was used to predict the primary potential suitable areas for A. uliginosa under current and future climatic conditions in China, ROC curve analysis method was used to validate, knife-edge method to was used to analyze the major environmental variables influencing the distribution of the A. uliginosa. A. uliginosa had a large distribution space and invasion potential in China, with the main distribution areas concentrated in southern, central and southwestern regions such as Guangxi, Guangdong, Fujian, Hainan and Taiwan. The low suitable area was mainly concentrated in Jiangxi, Hunan, Chongqing, Guizhou, Hunan and Zhejiang. By 2070, the overall suitable area of A. uliginosa in China is expected to decrease from 20.64% to 11.98%, with a decrease of 5.38% in low suitable areas and 3.28% in medium and high suitable areas. The area of low suitable areas in Sichuan, Yunnan, Guizhou, Hunan and Zhejiang has significantly reduced. The ROC curve analysis method showed that the average area under the curve (AUC) value is 0.968 with a standard deviation of 0.004, indicating that the prediction results are reliable. The knife-cutting method analysis showed that the four key environmental variables that have the greatest impact on the potential geographical distribution of A. uliginosa in China are Bio13 (precipitation of the wettest month), Bio16 (precipitation of the wettest season), Bio2 (mean diurnal temperature range), and Bio11 (mean temperature of the coldest season) are 27.6%, 22.5%, 11.1% and 8.0% respectively, indicating that precipitation and temperature range have a significant impact on the distribution of Viola palustris. The four key environmental variables that have the least impact on the potential geographical distribution of Viola palustris in China are Sq2 (soil nutrient retention capacity), Sq6 (soil toxicity), Bio9 (mean temperature of the driest season), and Sq5 (soil salinity). The four key environmental variables with the least impact on the potential geographical distribution of aquatic golden buttons in China are Sq6 toxicity, Sq7 operability, Bio1 annual average temperature, and Sq5 soil salt content. The potential suitable area for A. uliginosa is large in southern China and needs to be monitored to prevent further spread.

invasive plant  /  Acmella uliginosa  /  potential suitable area  /  MaxEnt  /  ArcGis
李晓霞, 胡宽义, 曾安逸, 董定超. 基于MaxEnt模型预测海南外来入侵植物新记录种—沼生金纽扣在中国的潜在适生区. 热带作物学报, 2024 , 45 (9) : 1989 -1997 . DOI: 10.3969/j.issn.1000-2561.2024.09.024
Xiaoxia LI, Kuanyi HU, Anyi ZENG, Dingchao DONG. MaxEnt Modeling Prediction of the Potential Distribution of Acmella uliginosa, a Newly Recorded Invasive Plant in Hainan, China[J]. Chinese Journal of Tropical Crops, 2024 , 45 (9) : 1989 -1997 . DOI: 10.3969/j.issn.1000-2561.2024.09.024
正文
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随着全球经济深度融合，各国之间的交通、旅游、经贸等领域的联系日益紧密，以及近年来引种活动日益频繁，导致外来植物的引入频率增加[1-2]。外来物种的入侵已经对国家的生态安全和粮食安全构成了重大威胁，被认为是21世纪五大全球性环境问题之一。因此，开展外来生物入侵的防控已是全球各国政府关注的主要环境问题[3-5]。海南是我国面向太平洋和印度洋的重要对外开放门户，独特的地理位置和优越的自然气候条件有利于物种的生长，也因此成为外来有害生物入侵压力较重的区域。目前，海南外来入侵生物的新纪录也不断增加，以海南为跳板，扩散至我国其他省（区），将给生态安全和国民经济安全造成威胁[6-7]。为了摸清底数，研判风险，促进防控，农业农村部于2022年启动全国外来入侵物种普查工作，海南是重点普查省份之一。本团队在海南开展普查中，发现外来入侵植物新纪录种—沼生金纽扣，现予以报道，并附以照片及其描述。
预测外来入侵生物潜在分布是制定物种防控策略以阻止其种群蔓延的重要途径之一[8]。近年来生态位模型（species distribution models）越来越多地被应用于入侵物种、物种引种等适生区的预测及早期预警中，并起到了良好的效果[9-10]。其中最大熵（maxi-mum entropy，MaxEnt）模型具有模拟精度高、软件运行时间短、操作简便、运行结果稳定、所需样本量小、预测覆盖面积更大、与实际更为相符合等特点在业内得到广泛认可，是目前最常用的物种分布模型之一，已应用于多种病虫草害适生性的模拟预测[11-14]。本研究利用采集的沼生金纽扣分布数据，结合气候、高程、土壤因子等环境数据，将MaxEnt模型与地理信息系统（ArcGis）相结合，确定影响沼生金纽扣潜在分布的环境因子的潜在适生区变化，预测主要潜在适生区域，以期为预防和制定合理的入侵控制措施提供科学依据。
1　海南外来入侵植物新纪录种—沼生金纽扣[15]
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Acmella uliginosa（Sw.）Cass. in F. Cuvier Dict. Sci. Nat. 24: 331. 1822. Flora of China 20-21:324. 2011.
Hainan（海南）：海口，棠甘村，2023-04-01，on limestone valley，alt. 24 m，X. X. Li（李晓霞）（图1）；保亭，七仙岭，2016-12-23，中国植物图像库，康瑞华（http://ppbc.iplant.cn/tu/6247186）。
一年生草本，高10~30 cm。茎直立或偶匍匐，绿色至紫色，疏被毛。叶柄长约1 cm；叶片披针形，狭卵形至卵形，长1~5 cm，宽3~25 mm，两面疏被毛，基部渐狭至楔形，边缘深波状，疏生纤毛，先端锐尖至渐尖。头状花序辐射状，单生或2~3个顶生，卵圆形，约6 mm×5 mm；花序梗长1~3 cm，疏生柔毛；花托约4 mm×1 mm。舌状花4~7枚，花冠黄色至橙黄色，约3 mm。管状花，多数，黄色至橙黄色，具4裂片；花冠约1.5 mm，裂片三角形。瘦果黑色，长约1.5 mm，具缘毛；冠毛近等长。花期全年。
生境：喜温暖湿润环境，耐旱、耐盐、耐水淹，可在沼泽、湿地、水田、路边、荒地等多种生境中生长。
分布：归化于香港和台湾[16]，福建、云南、广东、广西、四川有记录。原产于非洲、美洲和亚洲的整个热带地区，归化于太平洋岛屿；现分布于非洲、亚洲西南部、印度洋岛屿、马达加斯加等。
2　沼生金纽扣在中国的潜在适生区预测
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2.1　材料与方法
2.1.1　材料
研究对象为海南新发现的入侵归化新记录种—沼生金纽扣。2022年，项目组在海南调查入侵物种中发现沼生金纽扣在儋州、海口等地的农田、湿地和荒地中相对常见，同时记录其分布信息，为风险预测提供材料和数据支持。
2.1.2　方法
采用MaxEnt模型结合地理信息系统（ArcGis），预测沼生金纽扣在中国的主要潜在适生区域。沼生金纽扣的分布数据通过以下方式获得：（1）实地考察。海南的采样点多为实地考察获得，调查点主要位于海口市和儋州市，发现沼生金纽扣即记为“存在”，GPS记录经纬度。（2）查询物种分布数据库。查询的数据库包括全球生物多样性信息网络数据库（Global Biodiversity Information Facility，GBIF，http://www.Gbif.org/）、国际农业与生物科学中心数据库（CABI，http://www.Cabi.org/）、Inaturalist自然爱好者社区数据库（http://www.inaturalist.org/）。（3）检索国内外公开发表的沼生金纽扣相关的期刊论文。
所用的环境因子来自联合国粮农组织世界土壤数据库（https://www.fao.org/）与Worldclim气候数据库（https://www.worldclim.org/），数据以全球的气象记录信息为基础数据，整合插值生成的全球气候数据[11]。利用ArcGis软件整合研究区的海拔、营养可用性、营养保持能力等7个土壤因子以及月平均降水量、月平均最高及最低气温等19个生物气候因子（表1）。
软件来源：从官网（https://www.r-project.org/）下载R4.3.0软件并载入Rinat和Coordnate Clieaner程序包；从GitHub下载最新版的最大熵分析软件MaxEnt；从官网（http://www.sdmtoolbox.org/）下载ArcGIS插件SDMtoolbox；ArcGIS 10.8软件由中国热带农业科学院科技信息研究所智慧农业研究中心购买。
2.1.3　数据处理
在inaturalisth和GBIF数据库中获取并整合沼生金纽扣分布数据，利用Coordnate Clieaner软件包删除错误分布点，利用ArcGis软件SDMtoolbox工具箱减少空间自相关和采样偏差，并消除分析区域外的数据记录，最终获得345个有效分布点，并将数据存储为.csv格式，用于MaxEnt模型分析。使用ArcGis软件SDMtoolbox工具箱对27个变量（GloElev、Sq1~Sq7、Bio1~Bio19）进行裁切和预处理，重新编码为.asc格式，用于MaxEnt模型分析。
3　结果与分析
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3.1　研究方法验证与运算
3.1.1　MaxEnt模型预测能力验证
MaxEnt模型预测的准确度和可靠性可采用训练集和测试集数据的受试者工作特征（receiver operating characteristic，ROC）曲线进行检验（图2）。ROC曲线是以真阳性率为纵坐标，假阳性率为横坐标所形成的曲线。曲线下面积（area undercurve，AUC）是ROC曲线与横坐标围成的面积，AUC值越大表示与随机分布相距越远，环境变量与预测的物种地理分布模型之间相关性越大，即模型预测效果越好[17]。根据AUC的ROC评估模型的预测效果，当AUC≥0.9，表示模型预测结果优秀；当0.7≤AUC<0.9，表示模型预测结果良好；当0.5≤AUC<0.7，表示模型预测结果可接受；当AUC<0.5，表示模型预测无效[12]。在历史气候条件下，重复10次运行模型后，平均AUC值为0.98标准差为0.004，表明结合现有沼生金纽扣分布数据和筛选后的环境变量，MaxEnt模型能较好预测沼生金纽扣在中国的潜在分布，该模型具有较高的可靠性，可用于预测沼生金纽扣在中国的潜在地理分布。
3.1.2　模型运算
将整理好的全国沼生金纽扣分布点数据、历史气候和未来气候变量、研究区的海拔和7个土壤因子数据分别导入MaxEnt模型中，分别预测历史气候条件下及未来气候条件下沼生金纽扣在中国以及全球的潜在地理分布。沼生金纽扣分布点共345个，应用随机种子掉落，千分之五的患病概率，随机选择25%的分布点作为测试集，75%的分布点作为训练集，最大迭代次数设置为1000，其他为软件默认参数，输出文件为ASCⅡ栅格图层。使用最大训练敏感度加特异性逻辑阈值将连续的MaxEnt预测结果转换为沼生金纽扣的适生区与非适生区等级划分图，同时参考自然点段法将模型运算结果分为4级：SI<0.08为非适生区；0.08≤SI<0.2为低适生区；0.2≤SI<0.45为中适生区；SI≥0.45为高适生。
3.1.3　沼生金纽扣适生区与环境变量的关系
Maxent运行的结果表明（表2），Bio13（最湿月降水量）、Bio16（最湿季节降水量）、Bio2（月平均昼夜温差）和Bio11（最冷季节平均温度）等4个环境变量对运行的结果贡献最大，其相对贡献率分别为27.6%、22.5%、11.1%和8%；Sq2（土壤营养保持能力）、Sq6（土壤毒性）、Bio9（最干季节平均温度）和Sq5（土壤含盐量）等4个环境因素对运行的结果贡献最小，其相对贡献率分别为0.2%、0.1%、0.1%和0%。
变量重要性的jackknife检验结果（图3）表明，单独使用时增益最大的环境变量是Bio13（最湿月降水量），因此该变量对沼生金钮扣的生长而言，似乎是最有价值的变量。当省略变量时，收益降低最多的环境变量是Bio3（等温性），因此，该变量对于结果而言也起到一定的作用。
3.1.4　影响MaxEnt预测的主要环境变量分析
MaxEnt的结果显示，对沼生金纽扣在中国潜在地理分布影响最大的4个关键环境变量（图4）分别是Bio13（最湿月降水量）、Bio16（最湿季节降水量）、Bio2（月平均昼夜温差）和Bio11（最冷季节平均温度）。Bio13（最湿月降水量）的单变量正规化训练增益最高、Bio3（等温性）的省略变量正规化训练增益变化最大，表明降水量和温度是影响模型预测结果的主要因素。
3.2　沼生金纽扣在中国的适生区预测分析
3.2.1　历史环境条件下诏生金纽扣在中国的适生区预测
历史气候条件下，在全国34个省级行政区域中，沼生金纽扣的适生区有17个省（区）。其中，海南、广西、广东、湖南、重庆、贵州、湖南、江西、福建、浙江、台湾等省（区）全范围适生；西藏、云南、四川、湖北、江苏、安徽等省（区）部分范围适生；广西、广东、福建、海南、台湾以中、高适生区为主，其余省（区）以低适生区为主。沼生金纽扣在中国的适生区总面积为224.02×104 km2，占中国陆地总面积的23.29%，其中高、中和低适宜区面积分别为2.00×104、37.05×104、184.97×104 km2，分别占中国陆地总面积的0.21%、3.85%和19.23%（图5）。
3.2.2　未来环境条件下沼生金纽扣在中国的潜在适生区预测
未来环境条件下，沼生金纽扣在我国个别省（区）的适生区略有缩小趋势，在我国的适生区总体减少（图6），总体适生面积由当前的20.64%减少到11.98%，其中低适生区减少5.38%；中、高适生区减少3.28%。主要表现在四川、云南、贵州、湖南、浙江的低适生区面积明显缩小，中、高度适生区仍然主要集中在海南、广西、广东、福建和台湾。
4　讨论
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生态位模型是利用物种已知的分布数据和相关的环境变量，如气候、地形、土壤类型等，来建立一个数学或统计模型推算物种的生态需求，预测物种的实际和潜在分布，以揭示物种与环境之间的关系[18]。在构建生态位模型时，常见的方法包括最大熵模型、生态位分布模型、物种分布模型等，这些模型可以通过比较物种分布数据和环境因素数据，确定哪些环境因素对物种分布具有重要影响；模型还可用于预测物种在不同地理区域的适宜性和分布情况，为生态保护和管理提供决策支持。近年来，生态位模型在入侵生物学、保护生物学、全球气候变化等多个领域得到开拓和应用[11-14]。MaxEnt模型的模拟结果可揭示物种与环境的关系以及模型中各变量的贡献率，是目前经过许多研究证明对物种（大小样本数据）分布预测精度最好的模型[19]。
目前，对模型精度评价应用最广泛的方法为ROC曲线法，即AUC法[20]。通常情况，MaxEnt模拟结果准确性选用ROC曲线法评价。ROC曲线与横坐标围成的面积大小是评估模型预测是否准确的度量。利用MaxEnt绘制ROC曲线，并计算AUC值。AUC值的范围为0~1，数值越接近1越达理想状态，表明模型的精准性越高[21]。本研究基于世界范围沼生金纽扣的分布数据，利用MaxEnt模型和ArcGIS 10.8软件对气候因子（历史和未来）、研究区的海拔和7个土壤因子在我国及世界范围的适生区进行预测，其结果经ROC曲线分析验证，得到的模型ROC值均大于0.95。一般认为AUC≥0.9时，具有较高精确度。因此，本研究可信度较高。
MaxEnt模型检验表明，对沼生金纽扣分布预测影响最大的因子是Bio13（最湿月降水量），其次是最湿季节降水量、月平均昼夜温差和最冷季节平均温度。因此，降水量在沼生金纽扣分布特征中扮演着重要角色，但其相关机制尚不明确。此外，最湿月降水量的单变量正规化训练增益最高，等温性的省略变量正规化训练增益变化最大，表明降水量和温度是影响模型预测结果的主要因素。对沼生金纽扣在中国潜在地理分布影响最小的4个关键环境变量分别是Sq2（土壤营养保持能力）、Sq6（土壤毒性）、Bio9（最干季节平均温度）和Sq5（土壤含盐量），其相对贡献率分别为0.2%、0.1%、0.1%和0%。
本研究表明，在未来气候变化的背景下，沼生金纽扣在中国的低适生区面积有明显变化，但中高适生区以及适生区的核心区域、大体分布均无明显变化。这与其他研究中的白栎（Quercus fabri）[22]、白花鬼针草（Bidens alba）[23]等的分布预测结果相似。
本研究采用MaxEnt模型利用19种气候因子（历史和未来）、研究区的海拔和7个土壤因子预测沼生金纽扣在中国的潜在地理分布，并评价了MaxEnt模型的效果和准确性。结果表明，MaxEnt模型具有很高的预测能力和泛化能力，未出现过拟合或欠拟合现象，也能够很好地反映沼生金纽扣对环境变量的响应关系和影响因素。但是，已有研究表明，外来植物的入侵、扩散受环境因素、地形因素、土壤植被类型、人类交通干扰和物种的相互作用等多种因素的影响[11]。本研究仅采用与降水量、温度等生物气候因素相关的数据，未考虑植被类型、人类交通、气候变化以及全球变暖与生物相互作用等其他因素，这可能导致对适生区的预测存在一定的偏差。未来研究可以综合考虑这些因素，以提高预测结果的准确性。
综上，本研究报道了海南新发现的入侵植物沼生金纽扣，并利用MaxEnt模型预测了沼生金纽扣在中国的潜在地理分布和入侵风险，结果表明，沼生金纽扣在中国有较大的分布空间和入侵潜力，尤其是在华南、华中和西南等地区，这些地区是我国的重要农业区和生物多样性热点区，如果沼生金纽扣在这些地区大规模扩散和入侵，可能会对当地的生态系统和人类社会造成严重的危害。因此，需要加强对沼生金纽扣的防范和监测，防止其蔓延为害。
基金
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  • 海南省自然科学基金高层次人才项目(721RC631)
  • 海南省自然科学基金青年基金项目(323QN299)
  • 海南省哲学社会科学规划项目(HNSK（YB）23-75)
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2024年第45卷第9期
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doi: 10.3969/j.issn.1000-2561.2024.09.024
  • 接收时间：2023-09-26
  • 首发时间：2026-06-23
  • 出版时间：2024-09-25
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  • 收稿日期：2023-09-26
  • 修回日期：2024-05-09
基金
海南省自然科学基金高层次人才项目(721RC631)
海南省自然科学基金青年基金项目(323QN299)
海南省哲学社会科学规划项目(HNSK（YB）23-75)
作者信息
    1.中国热带农业科学院科技信息研究所，海南海口　571101
    2.北京航空航天大学云南创新研究院，云南昆明　650051

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* 董定超（DONG Dingchao），E-mail：。
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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