Abstract:Motivated by the theoretical interest in reconstructing long 3D trajectories of individual birds in large flocks, we developed CoMo, a co-moving camera system of two synchronized cameras coupled with rotational stages, which allow us to dynamically follow the motion of a target flock. With the rotation of the cameras we overcome the limitations of standard static systems that restrict the duration of the collected data to the short interval of time in which targets are in the cameras common field of view, but … Show more
“…However, performing such an experiment with a large group of mutant fish is challenging, as mutants display a relatively high mortality rate. In addition, tracking larger groups requires better camera systems (see [65] for example), more optimised algorithms (see [66,67] for examples), and a very large space to avoid the interruption from the boundary. These requirements are beyond our current experimental capability.…”
Zebrafish collective behaviour is widely used to assess their physical and mental state, serving as a valuable tool to assess the impact of ageing, disease genetics, and the effect of drugs. The essence of these macroscopic phenomena can be represented by active matter models, where the individuals are abstracted as interactive self-propelling agents. The behaviour of these agents depends on a set of parameters in a manner reminiscent of those between the constituents of physical systems. In a few cases, the system may be controlled at the level of the individual constituents such as the interactions between colloidal particles, or the enzymatic behaviour of de novo proteins. Usually, however, while the collective behaviour may be influenced by environmental factors, it typically cannot be changed at will. Here, we challenge this scenario in a biological context by genetically modifying zebrafish. We thus demonstrate the potential of genetic modification in the context of controlling the collective behaviour of biological active matter systems at the level of the constituents, rather than externally. In particular, we probe the effect of the lack of col11a2 gene in zebrafish, which causes the early onset of osteoarthritis. The resulting col11a2 -/- zebrafish exhibited compromised vertebral column properties, bent their body less while swimming, and took longer to change their orientations. Surprisingly, a group of 25 mutant fish exhibited more orderly collective motion than the wildtype. We show that the collective behaviour of wildtype and col11a2 zebrafish are captured with a simple active matter model, in which the mutant fish are modelled by self–propelling agents with a higher orientational noise on average. In this way, we demonstrate the possibility of tuning a biological system, changing the state space it occupies when interpreted with a simple active matter model.
“…However, performing such an experiment with a large group of mutant fish is challenging, as mutants display a relatively high mortality rate. In addition, tracking larger groups requires better camera systems (see [65] for example), more optimised algorithms (see [66,67] for examples), and a very large space to avoid the interruption from the boundary. These requirements are beyond our current experimental capability.…”
Zebrafish collective behaviour is widely used to assess their physical and mental state, serving as a valuable tool to assess the impact of ageing, disease genetics, and the effect of drugs. The essence of these macroscopic phenomena can be represented by active matter models, where the individuals are abstracted as interactive self-propelling agents. The behaviour of these agents depends on a set of parameters in a manner reminiscent of those between the constituents of physical systems. In a few cases, the system may be controlled at the level of the individual constituents such as the interactions between colloidal particles, or the enzymatic behaviour of de novo proteins. Usually, however, while the collective behaviour may be influenced by environmental factors, it typically cannot be changed at will. Here, we challenge this scenario in a biological context by genetically modifying zebrafish. We thus demonstrate the potential of genetic modification in the context of controlling the collective behaviour of biological active matter systems at the level of the constituents, rather than externally. In particular, we probe the effect of the lack of col11a2 gene in zebrafish, which causes the early onset of osteoarthritis. The resulting col11a2 -/- zebrafish exhibited compromised vertebral column properties, bent their body less while swimming, and took longer to change their orientations. Surprisingly, a group of 25 mutant fish exhibited more orderly collective motion than the wildtype. We show that the collective behaviour of wildtype and col11a2 zebrafish are captured with a simple active matter model, in which the mutant fish are modelled by self–propelling agents with a higher orientational noise on average. In this way, we demonstrate the possibility of tuning a biological system, changing the state space it occupies when interpreted with a simple active matter model.
Bird flocking is a paradigmatic case of self‐organised collective behaviours in biology. Stereo camera systems are employed to observe flocks of starlings, jackdaws, and chimney swifts, mainly on a spot‐fixed basis. A portable non‐fixed stereo vision‐based flocking observation system, namely FlockSeer, is developed by the authors for observing more species of bird flocks within field scenarios. The portable flocking observer, FlockSeer, responds to the challenges in extrinsic calibration, camera synchronisation and field movability compared to existing spot‐fixed observing systems. A measurement and sensor fusion approach is utilised for rapid calibration, and a light‐based synchronisation approach is used to simplify hardware configuration. FlockSeer has been implemented and tested across six cities in three provinces and has accomplished diverse flock‐tracking tasks, accumulating behavioural data of four species, including egrets, with up to 300 resolvable trajectories. The authors reconstructed the trajectories of a flock of egrets under disturbed conditions to verify the practicality and reliability. In addition, we analysed the accuracy of identifying nearest neighbours, and then examined the similarity between the trajectories and the Couzin model. Experimental results demonstrate that the developed flocking observing system is highly portable, more convenient and swift to deploy in wetland‐like or coast‐like fields. Its observation process is reliable and practical and can effectively support the study of understanding and modelling of bird flocking behaviours.
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