The software and documentation for the analysis pipeline is available online ( https://github.com/nsotta/feeding-mark-analysis ).
Open resource ↗nsotta/feeding-mark-analysis · nsotta/feeding-mark-analysis · lines:91-152Unverified paper record
Time-course analysis system for leaf feeding marks reveals effects of Arabidopsis trichomes on insect herbivore feeding behavior.
Journal of experimental botany · 1 Sept 2024 · 10.1093/jxb/erae184
Abstract
Bioassay with an insect herbivore is a common approach to studying plant defense. While measuring insect growth rate as a negative indicator of plant defense levels is simple and straightforward, analysing more detailed feeding behavior parameters of insects, such as feeding rates, leaf area consumed per feeding event, intervals between feeding events, and spatio-temporal patterns of feeding sites on leaves, is more informative. However, such observations are generally time consuming and labor-intensive. Here, we provide a semi-automated system for quantifying feeding behavior parameters of insects feeding on plant leaves. Automated photo scanners record the time-course development of feeding marks on leaves. An image analysis pipeline processes the scanned images and extracts leaf area. By analysing changes in leaf area over time, it detects insect feeding events and calculates the leaf area consumed during each feeding event, providing quantitative parameters of the feeding behavior of insects. In addition, it visualizes spatio-temporal changes in feeding sites, providing a measure of the complex behavior of insects on leaves. Using this analysis pipeline, we demonstrate that Arabidopsis trichomes reduce insect feeding rate, but not feeding duration or intervals between feeding events. Our image acquisition system requires only a photo scanner and a laptop computer and does not require any specialized equipment. The analysis software is provided as an ImageJ macro and R package and is available at no cost. Taken together, our work provides a scalable method for quantitative assessment of the feeding behavior of insects on leaves, facilitating understanding of plant defense mechanisms.
Plant phenotyping relevance
葉の摂食痕をスキャン画像と解析パイプラインで定量し、摂食イベントごとの消費葉面積や時空間的な摂食部位を抽出する方法が研究の中心であるため、植物の損傷状態を測定するフェノタイピング手法として採用する。
abstractHere, we provide a semi-automated system for quantifying feeding behavior parameters of insects feeding on plant leaves.
abstractAn image analysis pipeline processes the scanned images and extracts leaf area.
abstractThe analysis software is provided as an ImageJ macro and R package and is available at no cost.
Code and data availability
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