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Fatty Acid Profile as an Indicator of Larval Host for Adult Drosophila suzukii

Author:
Wiman, Nik G., Andrews, Heather, Rudolph, Erica, Lee, Jana, Choi, Man-Yeon
Source:
Insects 2020 v.11 no.11
ISSN:
2075-4450
Subject:
Drosophila suzukii, algorithms, artificial intelligence, blackberries, blueberries, diet, fatty acid composition, fatty acids, females, fruit crops, fruits, host plants, imagos, insecticides, landscapes, larvae, larval development, oviposition, pests, raspberries, rearing, strawberries, trophic levels
Abstract:
Drosophila suzukii is a severe economic invasive pest of soft-skinned fruit crops. Management typically requires killing gravid adult female flies with insecticides to prevent damage resulting from oviposition and larval development. Fruits from cultivated and uncultivated host plants are used by the flies for reproduction at different times of the year, and knowledge of D. suzukii seasonal host plant use and movement patterns could be better exploited to protect vulnerable crops. Rearing and various marking methodologies for tracking movement patterns of D. suzukii across different landscapes have been used to better understand host use and movement of the pest. In this study, we report on potential to determine larval host for adult D. suzukii using their fatty acid profile or signature, and to use larval diet as an internal marker for adult flies in release-recapture experiments. Fatty acids can pass efficiently through trophic levels unmodified, and insects are constrained in the ability to synthesize fatty acids and may acquire them through diet. In many holometabolous insects, lipids acquired in the larval stage carry over to the adult stage. We tested the ability of a machine learning algorithm to discriminate adult D. suzukii reared from susceptible small fruit crops (blueberry, strawberry, blackberry and raspberry) and laboratory diet based on the fatty acid profile of adult flies. We found that fatty acid components in adult flies were significantly different when flies were reared on different hosts, and the machine learning algorithm was highly successful in correctly classifying flies according to their larval host based on fatty acid profile.
Agid:
7189283