Objective To study the association of the spatial distribution of Nosopsyllus laeviceps kuzenkovi with climate and environmental factors, to effectively predict the suitable distribution area for N. laeviceps kuzenkovi, and to provide a scientific basis for the prevention and control of animal plague. Methods From 2005 to 2014, the information on N. laeviceps kuzenkovi was collected from the survey of rat flea and nest flea in routine plague surveillance. The information source was the plague prevention and management information system, which was a subsystem of the Chinese Disease Prevention and Control System. Logistic regression was used to explore the climate and environmental factors for N. laeviceps kuzenkovi. The genetic algorithm for rule-set prediction (GARP) ecological niche model was used to predict the suitable distribution area for N. laeviceps kuzenkovi. The receiver operating characteristic (ROC) curve was used to validate the model. Results After screening, 21 climate and environmental factors were enrolled in the model. The area under ROC curve was 0.978, suggesting a strong prediction capability of the GARP model. The suitable distribution area for N. laeviceps kuzenkovi was located in the central part of Inner Mongolia Autonomous Region, northwest of Zhangjiakou in Hebei province, west of Yulin in Shaanxi province, north of Ningxia Hui Autonomous Region, northern of Shanxi province, and northeast of Gansu province. Conclusion The GARP ecological niche model is accurate and reliable in the prediction of the suitable distribution area for N. laeviceps kuzenkovi.
YAN Dong, SHI Xian-ming, DU Guo-yi, LIU Guan-chun, CUI Yao-ren, CHEN Yong-ming, KANG Dong-mei, LAN Xiao-yu, REN Xing-yu, HOU Zhi-lin
. Prediction of the suitable distribution area for Nosopsyllus laeviceps kuzenkovi by the genetic algorithm for rule-set prediction ecological niche model[J]. Chinese Journal of Vector Biology and Control, 2019
, 30(1)
: 43
-46
.
DOI: 10.11853/j.issn.1003.8280.2019.01.009
[1] 俞东征. 鼠疫动物流行病学[M]. 北京:科学出版社,2009:256.
[2] 白林庆,司晓艳,涛波. 内蒙古主要鼠疫传播媒介蚤类的分布特征及其流行病学意义[J]. 实用预防医学,2015,22(5):639-640. DOI:10.3969/j.issn.1006-3110.2015.05.044.
[3] Stockwell D. The GARP modelling system:problems and solutions to automated spatial prediction[J]. Int J Geogr Inf Sci,1999,13(2):143-158. DOI:10.1080/136588199241391.
[4] 赵文娟. 玉米霜霉病在中国的适生性分析[D]. 合肥:安徽农业大学,2009.
[5] 高孟绪,王卷乐,曹春香,等. 基于地理信息系统和生态位模型的青海省喜马拉雅旱獭空间分布预测[J]. 中华地方病学杂志,2015,34(5):318-321. DOI:10.3760/cma.j.issn.2095-4255.2015.05.003.
[6] 刘欣. 基于GARP和MAXENT的空心莲子草在中国的入侵风险预测[D]. 济南:山东师范大学,2012.
[7] Williams RAJ,Fasina FO,Peterson AT. Predictable ecology and geography of avian influenza (H5Nl) transmission in Nigeria and West Africa[J]. Trans Roy Soc Trop Med Hyg,2008,102(5):471-479. DOI:10.1016/j.trstmh.2008.01.016.
[8] 毛志远,张兆金,周坚. 基于生态位模型的石蒜适生区预测[J]. 林业科技开发,2014,28(6):50-53. DOI:10.13360/j.issn.1000-8101.2014.06.012.
[9] 吴淇铭. 6种重要果实蝇的适生区预测和风险分析[D]. 福州:福建农林大学,2014.
[10] 闫东. 基于生态位模型预测长爪沙鼠鼠疫动物间疫情的潜在风险[D]. 北京:中国疾病预防控制中心,2016.
[11] 陈璐,孙希华,林泽民. 基于GARP的大薸潜在适生区预测[J]. 安徽农业科学,2015,43(2):243-245. DOI:10.3969/j.issn. 0517-6611.2015.02.086.
[12] 吴厚永. 中国动物志.昆虫纲.蚤目[M]. 2版. 北京:科学出版社,2007:1868-1874.