• 首页关于本刊投稿须知订阅指南广告合作投稿指南旧版入口联系我们
期刊订阅

植物保护淘宝

植物保护微店
李志文#, 赵 瑞#, 李有志*.油茶象种群密度及空间格局[J].植物保护,2019,45(5):163-170.
油茶象种群密度及空间格局
Population density and spatial distribution pattern of Curculio chinensis(Coleoptera: Curculionidae) in Hunan, China
投稿时间:2018-09-03  修订日期:2018-10-14
DOI:DOI: 10.16688/j.zwbh.2018379
中文关键词:  油茶象  普通油茶  小果油茶  种群密度  空间格局  最适抽样数
英文关键词:Curculio chinensis  Camellia oleifera  Camellia meiocarpa  population density  spatial pattern  optimum sample size
基金项目:湖南省自然科学基金(2016JJ2067)
作者单位
李志文#, 赵 瑞#, 李有志* 湖南农业大学昆虫研究所, 植物病虫害生物学与防控湖南省重点实验室, 长沙 410128 
摘要点击次数: 69
全文下载次数: 77
中文摘要:
      油茶象Curculio chinensis Chevrolat是我国特有木本油料树种——油茶Camellia spp.的专性蛀果害虫, 常导致其大量落果。本文旨在明确油茶象幼体(卵+幼虫)种群动态、空间格局及其关键影响因子, 以便为调查取样、长期监测和科学防控等提供理论依据。2017年在湖南常宁普通油茶林, 2016年-2017年在湖南益阳小果油茶林于雌虫产卵季节进行了调查, 解剖收集的果实并记录其所含幼体数。用6种聚集度指标(m*/m、Iδ、C、I、Ca、k)和Taylor幂法则、Iwao回归模型分析油茶象幼体的空间格局, 并探讨其关键影响因子。调查和分析表明, 雌虫于6月上、中旬开始产卵, 幼体种群密度呈单峰变化, 2016年和2017年高峰期分别在7月25日和7月18日。小果油茶林2016年、2017年高峰期种群密度分别为17.95±1.53和16.27±1.06幼体/样株, 高于普通油茶林, 分别是普通油茶林种群密度(8.16±1.23幼体/样株)的2.20倍和1.99倍, 差异极显著(P<0.001)。聚集度指标分析显示, 油茶象在普通油茶林均呈聚集分布, 在小果油茶林呈聚集分布或随机分布。回归模型表明, 该虫在两种油茶林均呈聚集分布。Blackith种群聚集均数λ>2, 表明油茶象幼体聚集是由亲代雌虫本身的产卵行为与寄主果实特征综合作用的结果。给出了最适理论抽样数公式并绘制了3种抽样精度下最适抽样数随种群密度变化的曲线图。综上, 初步明确了油茶象幼体在普通油茶林和小果油茶林的空间分布格局和种群动态, 并探讨了其关键影响因子。试验结果可为该虫调查取样、长期监测和科学防控等提供理论依据。
英文摘要:
      The camellia weevil, Curculio chinensis Chevrolat, is an important pest attacking fruits of Camellia spp., an endemic genus in China, and causes fruit drop. To provide theoretical basis for survey sampling, long-term monitoring and scientific prevention and control of the weevil pest, its spatial distribution pattern and population fluctuation were investigated. During the egg-laying season of the weevil, systematic investigations were conducted on the Camellia oleifera farm in Changning city, Hunan province in 2017 and C.meiocarpa farm in Yiyang city, Hunan province in 2016 and 2017, respectively. The fruits collected were dissected and the weevil immatures contained in them were recorded; then the spatial distribution pattern of the immatures was analyzed using 6 aggregation indices (m*/m, Iδ, C, I, Ca and k) and 2 regression models (Taylor’s Power Law and Iwao’s Patchiness Regression), and finally key factors affecting spatial pattern were identified. The egg-laying of the weevil females initiated in early to mid-June. The population density of the immatures showed a single peak change and the highest density was recorded on 18th July and 25th July. The density on C.meiocarpa farm was obviously higher than that on C.oleifera farm in the same season; the peak density of the former was 17.95±1.53 immatures per tree in 2016 and 16.27±1.06 immatures per tree in 2017, 2.20 and 1.99 times higher than that of the latter (8.16±1.23 immatures per tree) in 2017, respectively. The analysis of aggregation indices showed that it was aggregated on C.oleifera farm for each sampling of the immatures, but random or aggregated distribution on C.meiocarpa farm. The regression models indicated that the weevil immature’s spatial distribution pattern was clustered on each farm. It was proposed that the combined action of the oviposition behavior of the parent females and its host fruit traits led to the aggregation distribution pattern of the weevil immatures, as the Blackith λ value was more than 2. The formula of optimal sample size and the curve charts of optimum sample size changing with population density of the weevil immature were presented. In conclusion, the spatial pattern, population density and fluctuation of the weevil immatures were clarified, and the key influencing factors were also discussed. The experimental results can provide a theoretical basis for survey sampling, long-term monitoring and scientific prevention and control of the weevil pest.
查看全文  查看/发表评论  下载PDF阅读器
关闭