Latest Cover

Online Office

Contact Us

Issue:ISSN 1000-7083
          CN 51-1193/Q
Director:Sichuan Association for Science and Technology
Sponsored by:Sichuan Society of Zoologists; Chengdu Giant Panda Breeding Research Foundation; Sichuan Association of Wildlife Conservation; Sichuan University
Address:College of Life Sciences, Sichuan University, No.29, Wangjiang Road, Chengdu, Sichuan Province, 610064, China
Tel:+86-28-85410485
Fax:+86-28-85410485
Email:scdwzz@vip.163.com & scdwzz001@163.com
Your Position :Home->Past Journals Catalog->2019 Vol.38 No.1

Prediction of the Potential Distribution of Bursaphelenchus xylophilus
Author of the article:WEI Shuting1, LI Tao2, LIN Yucheng1*
Author's Workplace:1. College of Life Sciences, Sichuan University, Chengdu 610065, China;
2. General Station of Forest Plant Quarantine and Pest Control of Sichuan Province, Chengdu 610081, China
Key Words:Bursaphelenchus xylophilus; Monochamus alternatus; MaxEnt model; potential distribution
Abstract:Pine wood nematode (Bursaphelenchus xylophilus) is one of the hazardous forestry quarantine pests in China, and the pine wilt disease caused by this species has brought great economic loss and hindered the development of forestry health seriously. To monitor and control pine wilt disease in Sichuan province, we survey the potential distribution area of B. xylophilus based on the geographic distribution data (B. xylophilus: n=208, Monochamus alternatus: n=803) and 19 bioclimatic data accessed to the Sichuan Forestry Department, and the potential distribution of B. xylophilus in Sichuan province was predicted by MaxEnt and ArcGIS. Meanwhile, the receiver operating characteristic was used to test the simulation precision, and the "Jackknife" method was conducted to determine the importance of the environmental variables. The results showed that the highly suitable areas for B. xylophilus were Yibin, Guang'an, Dazhou, Zigong, Xichang, and the ecotone of Leshan and Meishan cities, and the area was 36 541 km2. The important environmental variables affecting the distribution of B. xylophilus were the mean temperature of the driest season (the range is 1.5-8.0 ℃, the optimal is 6.4 ℃), the seasonal precipitation coefficient of variation (the range is 22.5%-34.0%, the optimal is 34.0%), the lowest temperature of the coldest month (the range is 0.4-2.5 ℃, the optimal is 1.9 ℃), the elevation (the range is 250-5 500 m, the optimal is 450 m), the annual temperature range (the range is 5.9-9.1 ℃, the optimal is 5.9 ℃), and the annual precipitation (the range is 64-135 mm, the optimal is 68 mm).
2019,38(1): 37-46 收稿日期:2018-07-25
分类号:Q959.17
作者简介:魏淑婷(1992—),硕士研究生,主要从事森林病虫害调查及监测等研究
*通信作者:林玉成,副教授,主要从事动物分类学、森林资源保护与病虫害生物防治等研究工作,E-mail:linyucheng@scu.edu.cn
参考文献:
柴希民, 蒋平. 2003. 松材线虫病的发生和防治[M]. 北京: 中国农业出版社.
陈凤毛, 汤坚, 叶建仁. 2005. 松材线虫病鉴定方法与评价[J]. 安徽农业大学学报, 32(1): 22-25.
陈守常. 2010. 松材线虫病病原与致病机理研究进展[J]. 四川林业科技, 31(1): 18-25.
国家林业局. 2018. 国家林业局公告(2018年第1号)[EB/OL]. [2018-02-07]. http://www.forestry.gov.cn/portal/main/s/4461/content-1074329.html.
黄麟, 叶建仁, 刘雪莲. 2009. 松材线虫病病原种群分化研究现状[J]. 南京林业大学学报(自然科学版), 33(4): 135-139.
孔维娜, 王慧, 李捷, 等. 2006. 温湿度对松墨天牛越冬幼虫寿命的影响[J]. 山西农业大学学报(自然科学版), 26(3): 294-295.
刘会香, 吕全, 马跃, 等. 2012. 山东省松材线虫病的发生和研究进展[C]. 南京: 中国林业青年学术论坛.
宋玉双. 2013. 中国松材线虫防控 [M]. 哈尔滨: 东北林业大学出版社.
王茹琳, 李庆, 封传红, 等. 2017. 基于MaxEnt的西藏飞蝗在中国的适生区预测[J]. 生态学报, 37(24): 8556-8566.
王运生, 谢丙炎, 万方浩, 等. 2007. 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, 15(4): 365-372.
谢辉. 2005. 植物线虫分类学[M]. 北京: 高等教育出版社.
杨宝君. 1995. 中国松材线虫病的流行与治理[M]. 北京:中国林业出版社.

杨希. 2009. 利用松墨天牛早期监测松材线虫病的研究[J]. 武夷科学, 25(1): 36-43.
于治军, 李硕, 周艳涛, 等. 2018. 不同增温模式下我国松材线虫适生分布模拟与预测[J]. 东北林业大学学报, 46(1): 85-91.
张田. 2010. 福建省三明市松材线虫病遥感监测预测研究[D]. 北京: 北京林业大学.
erevková A, Mota M, Vieira P. 2014.Bursaphelenchus xylophilus (Steiner & Buhrer, 1934) Nickle 1970-pinewood nematode: a threat to European forests[J]. Forestry Journal, 60(2): 125-129.
Jiménez-Valverde A, Lobo JM. 2007. Threshold criteria for conversion of probability of species presence to either-or presence-absence[J]. Acta Oecologica, 31(3): 361-369.
Kishi Y. 1995. The pine wood nematode and Japanese pine sawyer[M]. Tokyo: Forest Pests in Japan.
Lemke D, Hulme PE, Brown JA, et al. 2011. Distribution modelling of Japanese honeysuckle (Lonicera japonica) invasion in the Cumberland Plateau and Mountain Region, USA[J]. Forest Ecology and Management, 262(2): 139-149.
Li BN, Wei W, Ma J, et al. 2009. Maximum entropy niche-based modeling (Maxent) of potential geographical distributions of fruit flies Dacus bivittatus, D. ciliatus and D. vertebrates (Diptera: Tephritidae)[J]. Acta Entomologica Sinica, 52(10): 1122-1131.
Li R, Xu M, Wong MHG, et al. 2015. Climate change threatens giant panda protection in the 21st century[J]. Biological Conservation, 182(182): 93-101.
Linit MJ. 1988. Nemtaode-vector relationships in the pine wilt disease system[J]. Journal of Nematology, 20(2): 227-235.
Mamiya Y, Enda N. 1972. Transmission of Bursaphelenchus lignicolus (Nematoda: Aphelenchoididae) by Monochamus alternatus (Coleoptera: Cerambycidae)[J]. Nematologica, 18(2): 159-162.
Mamiya Y. 1983. Pathology of the pine wilt disease caused by Bursaphelenchus xylophilus[J]. Annual Review of Phytopathology, 21(21): 201-220.
Phillips SJ, Anderson RP, Schapire RE. 2006. Maximum entropy modeling of species geographic distributions[J]. Ecological Modelling, 190(3): 231-259.
Swets JA. 1988. Measuring the accuracy of diagnostic systems[J]. Science, 240(4857): 1285-1293.
Yang XQ, Kushwaha SPS, Saran S, et al. 2013. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills[J]. Ecological Engineering, 51(1): 83-87.

CopyRight©2019 Editorial Office of Sichuan Journal of Zoology