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Variable resistance to spinetoram in populations of Thrips palmi across a small area unconnected to genetic similarity.

Pan ShiShao-Kun GuoYong-Fu GaoLi-Jun CaoYa-Jun GongJin-Cui ChenLei YueHu LiAry Anthony HoffmannShu-Jun Wei
Published in: Evolutionary applications (2020)
The melon thrips, Thrips palmi, is an increasingly important pest of vegetables in northern China. Some populations have developed resistance in the field to the insecticide spinetoram. Understanding the origin and dispersal of insecticide-resistant populations can shed light on resistance management strategies. In this study, we tested susceptibility of seven greenhouse populations of T. palmi to spinetoram collected from a small area of about 300 km2 in Shandong Province and examined population genetic structure across the area based on a segment of mitochondrial cox1 gene and 22 microsatellite loci to infer the possible origin and dispersal of insecticide resistance. Levels of resistance to spinetoram differed among seven populations, which included one population with high resistance (LC50 = 759.34 mg/L), three populations with medium resistance (LC50 ranged from 28.69 to 34.79 mg/L), and three populations with low resistance (LC50 ranged from 7.61 to 8.97 mg/L). The populations were genetically differentiated into two groups unrelated to both levels of resistance and geographic distance. The molecular data indicated high levels of gene flow between populations with different levels of resistance to spinetoram and low gene flow among populations with the same level of resistance, pointing to a likely separate history of resistance evolution. Resistance levels of two tested populations to spinetoram decreased 23 and 4.6 times after five generations without any exposure to the pesticide. We therefore suspect that resistance of T. palmi most likely evolved in response to local applications of the insecticide. Our study suggests that the development of resistance could be avoided or resistance even reversed by reducing usage of spinetoram.
Keyphrases
  • genome wide
  • machine learning
  • high resolution
  • oxidative stress
  • heavy metals
  • transcription factor
  • electronic health record
  • data analysis
  • sewage sludge
  • genome wide analysis