Using Networks to Connect Individual-Level Reproductive Behavior to Population Patterns

Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning

Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social interactions. Traditional tracking methods (e.g., marking each animal with dye or surgically implanting microchips) can be invasive and may have an impact on the social behavior being measured. To overcome these shortcomings, video-based methods for tracking unmarked animals, such as fruit flies and zebrafish, have been developed. However, tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction patterns. In this study, we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network (R-CNN) deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion technology. The system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing behavior. The proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.

The impact of social structure on breeding strategies in an island bird

The social environment is a key factor determining fitness by influencing multiple stages of reproduction, including pair formation, mating behavior and parenting. However, the influence of social structure across different aspects of breeding is rarely examined simultaneously in wild populations. We therefore lack a consolidation of the mechanisms by which sociality impacts reproduction. Here we investigate the implications of the social environment before and during breeding on multiple stages of reproduction in an island population of the ground nesting shorebird, the Kentish plover (Charadrius alexandrinus). We utilise information on mating decisions, nest locations and nesting success across multiple years in combination with social network analysis. Sociality before breeding was connected with patterns of pair formation. In addition, site fidelity and personal breeding experience was associated with the spatial organisation of breeding pairs. Our results provide evidence that, while differential social interactions at localised scales influence patterns of reproductive pairing, site fidelity and personal breeding experience influence the structure of populations at the landscape scale. Our results underline the tight link between the social structure of populations and patterns of mating, while revealing that the relative influence of sociality, breeding experience and local ecology are dynamic across different facets of reproduction.