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青海地区番茄潜叶蛾的发生动态及幼虫空间分布
赵原1, 邵海南1, 宋继昌2, 王康3, 刘雲祥1, 咸文荣1
1.青海大学 青海省农业有害生物综合治理重点实验室,青海 西宁 810016;2.青海省互助县高原特色现代农业示范园区管委会,青海 西宁 810016;3.西北农林科技大学 植物保护学院 植物资源与病虫害治理教育部重点实验室,陕西 杨凌 712100
摘要:
【 目的】明确青海地区设施茄果类蔬菜重大入侵害虫番茄潜叶蛾(Tuta absoluta)的发生动态及其幼虫空间分布,为制定番茄潜叶蛾绿色精准防控策略提供科学依据。【 方法】在青海省海东市平安区和黄南藏族自治州尖 扎县共选取8个设施番茄大棚,采用性诱监测法对番茄潜叶蛾的种群动态进行监测,并运用Z型抽样法调查棚内番茄 潜叶蛾幼虫的虫口密度。基于6种聚集度指标及Iwao回归模型分析番茄潜叶蛾幼虫种群的空间分布特征。【 结果】番茄潜叶蛾在青海省海东市平安区和黄南藏族自治州尖扎县设施番茄大棚中均有发生,且其种群动态均呈现“早期潜 伏-快速增殖-长期高位”三阶段特征。番茄潜叶蛾幼虫种群密度主要分布在0~4头/叶之间,其中0和1头/叶的频次占比最高,呈现出典型的“低密高频”特征。空间分布型适合度检验结果表明,青海省番茄潜叶蛾分布不符合二项分布 及泊松分布等随机模型(P≤0.001),更符合奈曼分布、奈曼A型分布及负二项分布等聚集性分布模型,多数样地模型 拟合度良好(P>0.05)。丛生指数、聚块指数、久野指数、扩散系数、负二项分布参数均呈现聚集分布;Iwao回归方程 的α与β均大于0,进一步表明番茄潜叶蛾个体间存在相互吸引关系。基于理论抽样模型,当番茄潜叶蛾幼虫密度为2头/叶时,建议抽样数为72片样叶;序贯抽样模型可判断种群密度是否超过经济阈值,指导适时防控。【 结论】青海省设施番茄中番茄潜叶蛾种群呈现明显的聚集性分布,该种群于10月初进入暴发期并长期维持高位,应在关键发生阶 段加强监测与分区治理。
关键词:  番茄潜叶蛾  种群动态  空间分布  抽样模型
DOI:10. 13207/j. jnwafu. 2026. 07. 014
分类号:
基金项目:青海省科技成果转化项目(2024-NK-107)
Population monitoring and larval spatial distribution patterns of Tuta absoluta in Qinghai Province
ZHAO Yuan1, SHAO Hainan1, SONG Jichang2, WANG Kang3, LIU Yunxiang1, XIAN Wenrong1
1.Key Laboratory of Agricultural Integrated Pest Management,Qinghai Province,Qinghai University,Xining,Qinghai 810016,China;2.Management Committee of the Plateau Characteristic Modern Agricultural Demonstration Park,Huzhu County,Qinghai Province,Xining,Qinghai 810016,China;3.Key Laboratory of Plant Protection Resources and Pest Management,Ministry of Education,College of Plant Protection,Northwest A&F University,Yangling,Shaanxi 712100,China
Abstract:
Objective】This study aims to systematically investigate the occurrence patterns of Tuta absoluta,a major invasive pest in facility-grown solanaceous vegetables in the Qinghai Province and clarify the dy?namics of the pest population and larval spatial distribution,thereby providing a scientific basis for green manage?ment strategies.【Method】A total of eight tomato greenhouses were selected in Ping’an District of Haidong City and Jianzha County of Huangnan Tibetan Autonomous Prefecture,Qinghai Province. Sex pheromone traps were used to monitor population dynamics of adult T. absoluta,and a Z-type sampling method was employed to determine larval density. Based on six aggregation indices and Iwao regression model,the spatial distribution patterns of the larvae were analyzed.【Result】T. absoluta was observed in all study sites,with its population dynamics exhibiting a three-phase pattern:early latency,rapid proliferation and sustained high abundance. Larval densities ranged from 0 to 4 larvae per leaf,with 0 and 1 larva per leaf showing the highest frequencies,indicating a characteristic pattern of“ high frequency for low density”. Spatial distribution analysis showed that the distribu?tion did not fit for random models such as the binomial or Poisson distributions(P≤0. 001).Instead,it fitted for aggregated distribution models,including the Neyman distribution,Neyman A-type distribution,and negative binomial distribution,with good model fitting for most sites(P>0.05). Aggregation indices-including clumping indices,aggregation indices,Hisano indices,diffusion coefficients,and negative binomial distribution parametersall indicated aggregated distribution patterns. In the Iwao regression,both α and β values were greater than 0,suggesting positive interactions among individuals within the population. Based on the theoretical sampling model,a sample size of 72 leaves was recommended when larval density reaches 2 larvae per leaf. The sequential sampling model can be used to determine whether the population density exceeds the economic threshold, thereby guiding timely control measures.【Conclusion】The larval population of T. absoluta in greenhousegrown tomatoes in Qinghai Province exhibits a distinctly aggregated distribution pattern. The outbreak phase of the population starts in early October and remains at elevated levels for an extended period. It is recommended to enhance monitoring and implement zonal management during critical occurrence stages.
Key words:  Tuta absoluta  population dynamics  spatial distribution  sampling model

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