Praxelis clematidea is now causing serious damage to the plant of south of China. By predicting suitable habits in China, scientific basis can be provided for the agricultural and forestry departments to strengthen the prevention and control of P. clematidea. Based on 296 distribution record sites of P. clematidea distributed in China, domestic climate factors and topographic factors, the random forest model and geographic information system (GIS) technology were used to predict its potential habitat areas in China. The predicted results from the random forest model were achievable. The primary environmental factor influencing the distribution of P. clematidea was the minimum temperature in the coldest month, followed by the average annual precipitation, while topographic factors and land use type were less influential. P. clematidea mainly locate in the southern provinces of China, Guangdong, Guangxi, Hainan, Fujian, southern Jiangxi, southwestern Yunnan belonged to the high habitat of P. clematidea, accounting for 8.08% of the national land area; coastal Zhejiang, central Jiangxi, Sichuan, Chongqing junction and other areas belonged to the moderate or general habitat, accounting for 2.32% and 3.37% of the national land area, respectively. Low-suitability and non-suitability zones accounted for 9.09% and 77.14% of the country's land area, respectively. There was no significant change in the total area of P. clematidea suitable zone under the future climate scenario compared with the current stage, but the area of moderate suitable zone increased significantly from 2.32% to 4.33%. In the context of global warming, P. clematidea is not yet at its maximum reproduction range in China, and there is a possibility of its continuous spread.
[1] Hanno S, Blackburn M T, Dyer E E. No saturation in the accumulation of alien species worldwide [J]. Nature communications, 2017, 8(1): 1–9.
[2] 宫璐, 李俊生, 柳晓燕, 赵相健, 赵彩云. 我国部分国家级自然保护区外来入侵物种的分布概况[J]. 生态科学, 2017, 36(4): 210–216.
[3] Bellard C, Cassey P, Blackburn T M. Alien species as a driver of recent extinctions [J]. Biology Letters, 2016, 12(2): 20150623.
[4] 张顺合, 张志华, 陈冬东. 外来有害植物入侵对我国生态安全的影响[J]. 植物检疫, 2015, 29(4): 4–7.
[5] 刘云慧, 王诗皓, 陈宝雄, 范顺祥, 宇振荣. 中国农业生物多样性保护主要政策、措施回顾及2020年后展望[J]. 生态与农村环境学报, 2021, 37(10): 1225–1233.
[6] 陈圣宾, 李振基. 外来植物入侵的化感作用机制探讨[J]. 生态科学, 2005, 24(1): 69–74.
[7] 中华人民共和国生态环境部.关于发布中国外来入侵物种名单(第三批)的公告[EB/OL]. [2014–08–20] https://www.mee. gov.cn/gkml/hbb/bgg/201408/t20140828_288367.htm
[8] 陈伟, 兰国玉, 安锋. 海南外来杂草——假臭草群落生态位特征研究[J]. 西北林学院学报, 2007, 22(2): 24–27.
[9] 李振宇, 解焱. 中国外来入侵种[M]. 中国林业出版社, 2002: 164.
[10] 吴海荣, 胡学难, 钟国强, 王定国, 强胜, 左然玲. 外来杂草假臭草的特征特性[J]. 杂草科学, 2008(3): 69–71.
[11] 邓文爽, 张玉宁, 赖洁莹, 袁俏玲. 假臭草化感作用的研究进展[J]. 广东化工, 2018, 45(2): 106–107.
[12] 邱宠华, 王奇志, 余岩. 外来入侵假臭草在中国分布区的预测[J]. 应用与环境生物学报, 2011, 17(6): 774–781.
[13] 郭燕青, 史梦竹, 李建宇, 傅建炜, 吴梅香. 基于Maxent模型的假臭草潜在分布区预测[J]. 热带亚热带植物学报, 2019, 27(3): 250–260.
[14] Rodrigo S R, Lalit K, Farzin S. Climate model for seasonal variation in Bemisia tabaci using CLIMEX in tomato crops [J]. International Journal of Biometeorology, 2019, 63(3): 281–291.
[15] Maywald G F, Sutherst R W. User's Guide to CLIMEX. A Computer Program for Comparing Climates in Ecology [M]. Canberra: CSIRO Division of Entomology, 1985.
[16] Ray D, Behera M D, Jacob J. Evaluating ecological niche models: A comparison between maxent and GARP for predicting distribution of Hevea brasiliensis in India [J]. Proceedings of the National Academy of Sciences India, 2017(3): 1–7.
[17] 杨芙蓉, 张琴, 孙成忠, 谢彩香, 宋经元. 蒙古黄芪潜在分布区预测的多模型比较[J]. 植物科学学报, 2019, 37(2): 136–143.
[18] Phillips S J, Anderson R P, Schapire R E. Maximum entropy modeling of species geographic distributions [J]. Ecological Modelling, 2006, 190(3–4): 231–259.
[19] 王聪, 蔡普默, 仪传冬, 陈家骅. 2007—2017年外来入侵害虫风险评估的文献分析及常用风险评估模型介绍[J]. 中国农业大学学报, 2018, 23(8): 225–238.
[20] 张华纬, 赵健, 阎波杰, 邹杰, 李志鹏. 基于Maxent模型和GIS的马缨丹在中国的适生区预测[J]. 生态与农村环境学报, 2020, 36(11): 1420–1427.
[21] Barbet M M, Jiguet F, Albert C H, Thuiller W. Selecting pseudo-absences for species distribution models: how, where and how many? [J]. Methods in Ecology and Evolution, 2012, 3(2): 327–338.
[22] Rinnhofer L J, Roura P N, Arthofer W, Dejaco T, Thaler K B, Wachter G A, Christian E, Steiner F M, Schlick S B C. Iterative species distribution modelling and ground validation in endemism research: an Alpine jumping bristletail example [J]. Biodiversity and Conservation, 2012, 21(11): 2845–2863.
[23] Yang X Q, Kushwaha S P S, Saran S, Xu J, Roy P S. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills [J]. Ecological Engineering, 2013, 51: 83–87.
[24] Aranda S C, Lobo J M. How well does presence-onlybased species distribution modelling predict assemblage diversity? A case study of the Tenerife flora [J]. Ecography, 2011, 34(1): 31–38.
[25] 李嘉昊, 李东海, 赵瑞白, 杨小波, 罗文启, 张凯, 吴庭天. 海南省主要陆生入侵植物在不同生境类型的适生性[J]. 热带生物学报, 2018, 9(2): 225–233.
[26] 罗炘武, 刘吉峰, 刘强, 阮长林. 海南及粤西热带地区外来入侵植物假臭草的群落及生境调查[J]. 草地学报, 2015, 23(6): 1167–1177.
[27] 刘小文, 齐成媚, 曾丽亚, 刘永昌, 廖海兵, 袁志辉, 何福林, 胡克坚. 不同情景增温对假臭草生长竞争的影响[J]. 湖南农业大学学报(自然科学版), 2017, 43(6): 604–609.
[28] 李欣海. 随机森林模型在分类与回归分析中的应用[J]. 应用昆虫学报, 2013, 50(4): 1190—1197.
[29] Verhelst P, Boets P, Van Thuyne G, Verreycken H, Goethals P , Mouton A M. The distribution of an invasive fish species is highly affected by the presence of native fish species: evidence based on species distribution modeling [J]. Biological Invasions, 2016, 18(2): 427–444.
[30] 李文增, 王智红. 郑州地区外来植物假臭草入侵的风险性评估[J]. 浙江农业科学, 2017, 58(1): 98–101.
[31] 赵浩宇, 朱建义, 刘胜男, 周小刚. 四川省归化植物新记录[J]. 杂草学报, 2017, 35(4): 13–16.
[32] Intanon S, Wiengmoon B, Mallory-Smith C A. Seed morphology and allelopathy of invasive Praxelis clematidea [J]. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 2020, 48(1): 261–272.
[33] 钟军弟, 徐意媚, 曾富华, 刘晚苟, 陈燕, 刘金祥. 不同生境下假臭草生长特征分析[J]. 广西植物, 2014, 34(1): 68–73, 76.
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