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James Crall

Publications and source records attributed to James Crall.

2 recordsLinked to original sources

Social context modulates idiosyncrasy of behaviour in the gregarious cockroach Blaberus discoidalis

Individuals are different, but they can work together to perform adaptive collective behaviours. Despite emerging evidence that individual variation strongly affects group performance, it is less clear to what extent individual variation is modulated by participation in collective behaviour. We examined light avoidance (negative phototaxis) in the gregarious cockroach Blaberus discoidalis, in both solitary and group contexts. Cockroaches in groups exhibit idiosyncratic light-avoidance performance that persists across days, with some individual cockroaches avoiding a light stimulus 75% of the time, and others avoiding the light just above chance (i.e. ~50% of the time). These individual differences are robust to group composition. Surprisingly, these differences do not persist when individuals are tested in isolation, but return when testing is once again done with groups. During the solo testing phase cockroaches exhibited individually consistent light-avoidance tendencies, but these differences were uncorrelated with performance in any group context. Therefore, we have observed not only that individual variation affects group-level performance, but also that whether or not a task is performed collectively can have a significant, predictable effect on how an individual behaves. That individual behavioural variation is modulated by whether a task is performed collectively has major implications for understanding variation in behaviours that are facultatively social, and it is essential that ethologists consider social context when evaluating individual behavioural differences.\n\nAbbreviations: DLP: digital light processing; MP: megapixels; ANOVA: analysis of variance, ICC: intraclass correlation coefficient, CI: confidence interval.

Animal Behavior and Cognition

BEEtag : a low-cost, image-based tracking system for the study of animal behavior and locomotion

A fundamental challenge common to studies of animal movement, behavior, and ecology is the collection of high-quality datasets on spatial positions of animals as they change through space and time. Recent innovations in tracking technology have allowed researchers to collect large and highly accurate datasets on animal spatiotemporal position while vastly decreasing the time and cost of collecting such data. One technique that is of particular relevance to the study of behavioral ecology involves tracking visual tags that can be uniquely identified in separate images or movie frames. These tags can be located within images that are visually complex, making them particularly well suited for longitudinal studies of animal behavior and movement in naturalistic environments. While several software packages have been developed that use computer vision to identify visual tags, these software packages are either (a) not optimized for identification of single tags, which is generally of the most interest for biologist, or (b) suffer from licensing issues, and therefore their use in the study of animal behavior has been limited. Here, we present BEEtag, an open-source, image-based tracking system in Matlab that allows for unique identification of individual animals or anatomical markers. The primary advantages of this system are that it (a) independently identifies animals or marked points in each frame of a video, limiting error propagation, (b) performs well in images with complex background, and (c) is low-cost. To validate the use of this tracking system in animal behavior, we mark and track individual bumblebees (Bombus impatiens) and recover individual patterns of space use and activity within the hive. Finally, we discuss the advantages and limitations of this software package and its application to the study of animal movement, behavior, and ecology.

Animal Behavior and Cognition