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.

The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers. Measuring behaviorIndividual behavior (see Glossary) underlies almost all aspects of ecology [1][2][3][4][5]. Accurate and highly resolved behavioral data are therefore critical for obtaining a mechanistic and predictive understanding of ecological systems [5]. Historically, direct observation by trained biologists was used to quantify behavior [6,7]. However, the extent and resolution to which direct observations can be made is highly constrained [8] and the number of individuals that can be observed simultaneously is small. In addition, an exact record of events is not preserved, only the biologist's subjective account of them.Recent technological advances in tracking now make it possible to collect large amounts of highly precise and accurate behavioral data. For many organisms equipment can be attached that provide information about the Glossary Background subtraction: a method used by software to compare the current video frame with a stored picture of the background; any pixel of the current frame that is significantly different from the corresponding pixel in the background is likely to be associated with the body of an animal. Useful in situations where the background is unchanging, for example, when the surface of the background is rigid and lighting does not change. Behavior: the actions of individuals, often in response to stimuli. Behavior can involve movement of the individual's body through space, such as walking or chasing, or can occur while the animal is stationary, such as grooming or eating. Bio-logging: attachment or implantation of equipment to organisms to provide information about their identity, location, behavior, or physiology (e.g., global positioning systems, accelerometers, video cameras, telemetry tags). Ecological interaction: any interaction between an organism and its environment, or between two organisms (i.e., including interactions between conspecifics). Fingerprinting: a method used to identify unmarked individuals using natural variation in their physical and/or behavioral appearance. The method works by transforming the images of each individual ...

turns averaging 35°in 80·ms, similar to the kinematics of free flight saccades. Our results indicate that tethered and free flight saccades share a common neural basis, but that the lack of appropriate feedback signals distorts the behavior performed by rigidly fixed flies. Using our new paradigm, we also investigated the features of visual stimuli that elicit saccades. Our data suggest that saccades are triggered when expanding objects reach a critical threshold size, but that their timing depends little on the precise time course of expansion. These results are consistent with expansion detection circuits studied in other insects, but do not exclude other models based on the integration of local movement detectors.

Animals negotiating complex natural terrain must consider cues around them and alter movement parameters accordingly. In the arthropod brain, the central complex (CC) receives bilateral sensory relays and sits immediately upstream of premotor areas, suggesting that it may be involved in the context-dependent control of behavior. In previous studies, CC neurons in various insects responded to visual, chemical, and mechanical stimuli, and genetic or physical lesions affected locomotor behaviors. Additionally, electrical stimulation of the CC led to malformed chirping movements by crickets, and pharmacological stimulation evoked stridulation in grasshoppers, but no more precise relationship has been documented between neural activity in the CC and movements in a behaving animal. We performed tetrode recordings from the CC of cockroaches walking in place on a slippery surface. Neural activity in the CC was strongly correlated with, and in some cases predictive of, stepping frequency. Electrical stimulation of these areas also evoked or modified walking. Many of the same neural units responded to tactile antennal stimulation while the animal was standing still but became unresponsive during walking. Therefore, these CC units are unlikely to be reporting only sensory signals, but their activity may be directing changes in locomotion based on sensory inputs.

SUMMARY The flight trajectories of fruit flies consist of straight flight segments interspersed with rapid turns called body saccades. Although the saccades are stereotyped, it is not known whether their brief time course is due to a feed-forward (predetermined) motor program or due to feedback from sensory systems that are reflexively activated by the rapid rotation. Two sensory modalities, the visual system and the mechanosensory halteres, are likely sources of such feedback because they are sensitive to angular velocities within the range experienced during saccades. Utilizing a magnetic tether in which flies are fixed in space but free to rotate about their yaw axis, we systematically manipulated the feedback from the visual and haltere systems to test their role in determining the time course of body saccades. We found that altering visual feedback had no significant effect on the dynamics of saccades, whereas increasing and decreasing the amount of haltere-mediated feedback decreased and increased saccade amplitude, respectively. In other experiments, we altered the aerodynamic surface of the wings such that the flies had to actively modify their wing-stroke kinematics to maintain straight flight on the magnetic tether. Flies exhibit such modification, but the control is compromised in the dark, indicating that the visual system does provide feedback for flight stability at lower angular velocities, to which the haltere system is less sensitive. Cutting the wing surface disrupted the time course of the saccades, indicating that although flies employ sensory feedback to modulate saccade dynamics, it is not precise or fast enough to compensate for large changes in wing efficacy.