基于MaxEnt的非洲橘硬蓟马在全球及中国的潜在分布区预测

王茹琳,高晓清,王闫利,姜淦,沈沾红

中国农学通报. 2014, 30(28): 315-320

中国农学通报 ›› 2014, Vol. 30 ›› Issue (28) : 315-320. DOI: 10.11924/j.issn.1000-6850.2014-1016

基于MaxEnt的非洲橘硬蓟马在全球及中国的潜在分布区预测

  • 王茹琳,高晓清,王闫利,姜淦,沈沾红
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Potential distribution of Scirtothrips aurantii in china and the world as predicted by MaxEnt

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摘要

非洲橘硬蓟马是柑橘等多种植物的重要检疫性害虫，明确其在全球及中国的潜在分布区域，对于有效控制此虫害在全球的扩散蔓延具有重要的意义。基于最大熵算法的生态位模型MaxEnt 和地理信息系统软件Arc-Gis 对非洲橘硬蓟马进行适生区分析及预测，用受试者工作特征曲线对预测模型和结果进行评估，用Jackknife 法分析影响非洲橘硬蓟马的重要因子。结果表明，非洲橘硬蓟马在全球的适生区为非洲南部、南美洲中东部、墨西哥中部、澳大利亚东海岸、印度南部、东南亚中部和西亚的也门；该虫在中国的高风险区为海南大部和云南中部，中风险区为贵州大部、广西大部、广东大部、福建东南沿海和四川西南部。对非洲橘硬蓟马发生具有重要影响的是温度季节性变化标准差、等温性和年均温变化范围。

Abstract

Scirtothrips aurantii is an important pest of citrus and object of cosmopolitan plants quarantine. Determination of the potential geographical distribution of Scirtothrips aurantii is an important factor for effectively controlling the spreading of this pest. In this study, niche model MaxEnt and Arc-Gis were applied to analyze and predict the suitable distribution area of Scirtothrips aurantii, ROC was used to evaluate the prediction model and the prediction results, and Jackknife analysis was made to analyze the most important environmental factors affecting the occurrence of Scirtothrips aurantii. The results showed that, south of Africa, mid-east of southern America, mid of Mexico, east coast of Australia, south of India, mid of southeastern Asia and Yemen were the main suitable areas for Scirtothrips aurantii in the world. In China, the high risk areas were in Hainan and the mid of Yunnan, the secondary risk areas were in Guizhou, Guangxi, Guangdong, southeast of Fujian and south of Sichuan. The important environmental factors affecting the occurrence of Scirtothrips aurantii were temperature seasonality, isothermally and temperature annual range.

关键词

非洲橘硬蓟马;MaxEnt;适生性分析;影响因子

Key words

Scirtothrips aurantii; MaxEnt; suitability analysis; environmental factors

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王茹琳,高晓清,王闫利,姜淦,沈沾红. 基于MaxEnt的非洲橘硬蓟马在全球及中国的潜在分布区预测. 中国农学通报. 2014, 30(28): 315-320 https://doi.org/10.11924/j.issn.1000-6850.2014-1016
Potential distribution of Scirtothrips aurantii in china and the world as predicted by MaxEnt. Chinese Agricultural Science Bulletin. 2014, 30(28): 315-320 https://doi.org/10.11924/j.issn.1000-6850.2014-1016

参考文献

[1] Rafter M A, Walter G H. Mate Recognition in the South African Citrus Thrips Scirtothrips aurantii (Faure) and Cross-Mating Tests with Populations from Australia and South Africa[J]. Journal of Insect Behavior,2013,26(6):780-795.
[2] Brian W Garms, Laurence A Mound, Nancy A Schellhorn. Polyphagy in the Australian population of South African citrus thrips (Scirtothrips aurantii Faure) [J].Australian Journal of Entomology,2013,52(3):282-289.
[3] Grout T G, Richards G I. Value of pheromone traps for predicting infestations of red scale, Aonidiella aurantii (Maskell) (Hom., Diaspididae), limited by natural enemy activity and insecticides used to control citrus thrips, Scirtothrips aurantii Faure (Thys., Thripidae)[J]. Journal of Applied Entomology,1991,11(1/5):20-27.
[4] Samways M J. Spatial distribution of Scirtothrips aurantii Faure (Thysanoptera: Thripidae) and threshold level for one per cent damage on citrus fruit based on trapping with fluorescent yellow sticky traps[J]. Bulletin of Entomological Research,1986,76(4):649- 659.
[5] 黄丽莉,薛芳森,黄河,等.非洲橘硬蓟马的检疫特征与风险分析[J].植物检疫,2011,25(5):62-65.
[6] 邢丁亮,郝占庆.最大熵原理及其在生态学研究中的应用[J].生物多样性,2011,19(3):295-302.
[7] Berger A L, Della Pietra S A, Della Pietra V J. A maximum entropy approach to natural language processing[J]. Computational Linguistics,1996,22(1):39-71.
[8] Phillips Steven J, Dudík Miroslav, Schapire Robert E. A maximum entropy approach to species distribution modeling[A].Proceedings of the Twenty-First International Conference on Machine Learning [C].2004.
[9] 段居琦,周广胜.中国水稻潜在分布及其气象特征[J].生态学报, 2011,31(22):6659-6668.
[10] 孙敬松,周广胜.利用最大熵法(MaxEnt)模拟中国冬小麦分布区的年代际动态变化[J].中国农业气象,2012,33(4):481-487.
[11] 何奇瑾.中国玉米种植分布与气候关系研究[D].南京:南京信息工程大学,2012.
[12] 殷晓洁,周广胜,隋兴华,等.蒙古栎地理分布的主导气候因子及其阈值[J].生态学报,2013,33(1):103-109.
[13] 郭乃嘉.基于生态位模型的中间香型烤烟生态适宜区潜在分布预测[D].重庆:西南大学,2013.
[14] 齐增湘,徐卫华,熊兴耀,等.基于 MAXENT模型的秦岭山系黑熊潜在生境评价[J].生物多样性,2011,19(3):343-352.
[15] 李明阳,席庆,徐海根.基于 WEB数据库和 Maxent模型的白头叶猴潜在生境评价研究[J].林业资源管理,2010,(04):31-57.
[16] 雷军成,徐海根.基于 MaxEnt的加拿大一枝黄花在中国的潜在分布区预测[J].生态与农村环境学报,2010,26(2):137-141.
[17] 曹学仁,陈林,周益林,等.基于 MaxEnt的麦瘟病在全球及中国的潜在分布区预测[J].植物保护,2011,37(3):80-83.
[18] 常志隆,周益林,赵遵田.基于 MaxEnt模型的小麦印度黑穗病在中国的适生性分析[J].植物保护,2010,36(3):110-112.
[19] 冯益明,刘洪霞.基于 Maxent与 GIS 的锈色棕榈象甲在中国潜在的适生性分析[J].华中农业大学学报,2010,29(5):552-556.
[20] 曾辉,黄冠胜,林伟,等.利用 MaxEnt预测橡胶南美叶疫病菌在全球的潜在地理分布[J].植物保护,2008,34(3):88-92.
[21] 曲伟伟,李志红,黄贵修,等.利用 MAXENT预测橡胶树棒孢霉落叶病在中国的适生区[J].植物保护,2011,37(4):52-57.
[22] 房锋,张朝贤,黄红娟,等.基于 Maxent的麦田恶性杂草节节麦的潜在分布区预测[J].草业学报,2013,229(2):62-70.
[23] Brotons L, Thuiller W, Araujo M B, et al. Presence-absence versus presence- only modeling methods for predicting bird habitat suitability[J]. Ecography,2004,27(4):437-448.
[24] Elith J, Graham H C, Anderson R P, et al. Novel methods improve prediction of species’ distributions from occurrence data[J]. Ecography,2006,29(2):129-151.
[25] Phillips S J, Anderson R P, Schapire R E. Maximum entropy modeling of species geographic distributions. Ecological Modeling, 2006, 190(3/4):231-259.
[26] 陈新美,雷渊才,张雄清,等.样本量对 MaxEnt模型预测物种分布精度和稳定性的影响[J].林业科学,2012,48(1):53-59.
[27] Swets J A. Measuring the accuracy of diagnostic systems[J]. Science,1988,240(4857):1285-1293.
[28] 陈晨,龚伟荣,胡白石,等.基于地理信息系统的红火蚁在中国适生区的预测[J].应用生态学报,2006,17(11):2093-2097.
[29] Manners A G, Dhileepan K. Australian Scirtothrips aurantii Faure (Thysanoptera: Thripidae) only survived on mother- of- millions (Bryophyllum delagoense) in a no- choice trial[J]. Plant protection quarterly, 2005,20(1):33-35.
[30] Grove T, Giliomee J H, Pringle K L. The relationship between citrus thrips, Scirtothrips aurantii (Thysanoptera: Thripidae), abundance and fruit size in mango orchards[J]. African entomology, 2003,11(1):39-48.
[31] 董鹏,包改丽,冉志伟,等.云南柑橘黄龙病病原检测及其 16S rDNA序列分析[J].云南农业大学学报,2011,26(5):607-611.
[32] 刘美见,程立龙,张晓娟,等.云南永善橘园中蜜柑大实蝇的日活动规律及空间分布型[J].江西农业学报,2013,25(6):87-90.
[33] Phillips S J, Dudik M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation[J]. Ecography, 2008,31(2):161-175.
[34] 王运生,谢丙炎,万方浩,等.ROC曲线分析在评价入侵物种分布模型中的应用[J].生物多样性,2007,15(4):365-372.
[35] 王颖,章桂明,杨伟东,等.基于 MaxEnt的大豆南北方茎溃疡病菌在中国适生区的预测[J].植物检疫,2009,23(4):14-17.
[36] 赵文娟,陈林,丁克坚,等.利用 MaxEnt预测玉米霜霉病在中国的适生区[J].植物保护,2009,35(2):32-38.
[37] 徐烨,王金龙,刘雅婷,等.凤仙花坏死斑病毒在中国的适生性风险评估[J].植物保护,2013,39(6):26-31.
[38] 卢辉,钟义海,陈青,等.基于 MaxEnt模型的对粒材小蠹的适生性分析[J].热带作物学报,2013,34(11):2239-2245.
[39] 李明阳,席庆,徐海根.基于 WEB 数据库和 MaxEnt 模型的白头叶猴潜在生境评价研究[J].林业资源管理,2010,4:32-36,57.

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