A semi-automated design for high-throughput Lepidoptera larvae feeding bioassays

Abstract

Lepidopteran pests cause considerable damage to all crops over the world. As larvae are directly responsible for these damages, many research efforts are devoted to find plant cultivars which are resistant against them. However, such studies take time, efforts and are costly, especially when one wants to not only find resistance traits but also evaluate their heritability. We present here a high throughput approach to screen plants for resistance or chemicals for their deterrence, using a leaf-disk consumption assay, which is both suitable for large scale tests and economically affordable. To monitor larvae feeding on leaf disks placed over a layer of agar, we designed 3D models of 50 cages arrays. One webcam can sample simultaneously 3 of such arrays at a rate of 1 image/min, and follow individual feeding activities in each cage as the movements of 150 larvae. The resulting image stacks are first processed with a custom program running under an open-source image analysis package (Icy) to measure the surface of each leaf disk over time. We further developed statistical procedures running under the R package, to analyze the time course of the feeding activities of the larvae and to compare them between treatments. As a test case, we compared how European corn borer larvae respond to quinine, considered as a bitter alkaloid for many organisms, and to Neemazal containing azadirachtin, which is a common antifeedant against pest insects. We found that increasing doses of azadirachtin reduce and delay feeding. However, contrary to our expectation, quinine was found poorly effective at the range of concentrations tested. The 3D printed model of the cage, of the camera holder, the plugins running under Icy, and the R procedures are freely available, and can be modified according to the particular needs of the users.