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Methods to measure performance of Grapholitha molesta on apples of five varieties

文献类型: 外文期刊

作者: Wang, Yi 1 ; Kong, Wei-Na 2 ; Zhao, Long-Long 3 ; Xiang, Hui-Ming 1 ; Zhang, Li-Jun 1 ; Li, Jie 3 ; Ridsdill-Smith, Ja 1 ;

作者机构: 1.Shanxi Agr Univ, Coll Agr, Taigu 030801, Shanxi, Peoples R China

2.Shanxi Acad Agr Sci, Inst Plant Protect, Taiyuan 030031, Shanxi, Peoples R China

3.Shanxi Acad Agr Sci, Pomol Inst, Taigu 030815, Shanxi, Peoples R China

4.Univ Western Australia, Sch Biol Sci, Perth, WA 6009, Australia

5.Univ Western Australia, Inst Agr, Perth, WA 6009, Australia

关键词: population traits; apple varieties; Lepidoptera; Tortricidae; Oriental fruit moth; weighted fitness scores; host ranking; Rosaceae; fruitborer

期刊名称:ENTOMOLOGIA EXPERIMENTALIS ET APPLICATA ( 影响因子:2.25; 五年影响因子:2.184 )

ISSN: 0013-8703

年卷期: 2018 年 166 卷 3 期

页码:

收录情况: SCI

摘要: Host plants will affect the performance and population dynamics of the polyphagous insect pest Grapholitha molesta (Busck) (Lepidoptera: Tortricidae). Cultivar-level variability needs to be considered in assessing the performance of G.molesta. We evaluated the fitness of G.molesta on five apple varieties in the laboratory by placing eggs on apples and observing their performance including survival, development, and fecundity of the adult insect. Our results demonstrated differences between indices such as larval duration, pupal weight, egg production, length of tunnel, and exiting rate of larvae, which gave similar host rankings. Variety Danxia was less suitable than Jinqing and Shuohong, and more suitable than Empire and Jinxing. However, other indices such as pupal duration, total pre-oviposition time, and the estimated population parameters resulted in a different host ranking. We calculated weighted fitness scores to overcome the problem of inconsistent rankings. Empire changed from being the second highest ranking variety based on all population parameters to the second lowest using weighted fitness scores. The difference in ranking observed between apple varieties demonstrates the care needed in predicting population performance in the field using single traits.

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