High-throughput ethomics in large groups of Drosophila

Abstract

We present a camera-based method for automatically quantifying the individual and social behaviors of fruit flies, Drosophila melanogaster, interacting within a planar arena. Our system includes machine vision algorithms that accurately track many individuals without swapping identities and classification algorithms that detect behaviors. The data may be represented as an ethogram that plots the time course of behaviors exhibited by each fly, or as a vector that concisely captures the statistical properties of all behaviors displayed within a given period. We found that behavioral differences between individuals are consistent over time and are sufficient to accurately predict gender and genotype. In addition, we show that the relative positions of flies during social interactions vary according to gender, genotype, and social environment. We expect that our software, which permits high-throughput screening, will complement existing molecular methods available in Drosophila, facilitating new investigations into the genetic and cellular basis of behavior.