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Biology subjects

Lauder, G.

Publications and source records attributed to Lauder, G..

2 recordsLinked to original sources

In-line swimming dynamics revealed by fish interacting with a robotic mechanism

Schooling in fish is linked to a number of factors such as increased foraging success, predator avoidance, and social interactions. In addition, a prevailing hypothesis is that swimming in groups provides energetic benefits through hydrodynamic interactions. Thrust wakes are frequently occurring flow structures in fish schools as they are shed behind swimming fish. Despite increased flow speeds in these wakes, recent modelling work has suggested that swimming directly in-line behind an individual may lead to increased efficiency. However, no data are available on live fish interacting with thrust wakes. Here we designed a controlled experiment in which brook trout, Salvelinus fontinalis, interact with thrust wakes generated by a robotic mechanism that produces a fish-like wake. We show that trout swim in thrust wakes, reduce their tail-beat frequencies, and synchronize with the robotic flapping mechanism. Our flow and pressure field analysis revealed that the trout are interacting with oncoming vortices and that they exhibit reduced pressure drag at the head compared to swimming in isolation. Together, these experiments suggest that trout swim energetically more efficiently in thrust wakes and support the hypothesis that swimming in the wake of one another is an advantageous strategy to save energy in a school.

animal behavior and cognition↗

Multi-animal pose estimation and tracking with DeepLabCut

Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having extremely similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, a popular open source pose estimation toolbox, and provide high-performance animal assembly and tracking--features required for robust multi-animal scenarios. Furthermore, we integrate the ability to predict an animals identity directly to assist tracking (in case of occlusions). We illustrate the power of this framework with four datasets varying in complexity, which we release to serve as a benchmark for future algorithm development.

animal behavior and cognition↗