Abstract:Abstract. Models of collective animal motion can greatly aid in the design and interpretation of behavioural experiments that seek to unravel, isolate, and manipulate the determinants of leader-follower relationships. Here, we develop an initial model of zebrafish social behaviour, which accounts for both speed and angular velocity regulatory interactions among conspecifics. Using this model, we analyse the macroscopic observables of small shoals influenced by an "informed" agent, showing that leaders which ac… Show more
“…3A). These changes in speed, based on neighbours' positions, have also been observed in juvenile blacksmith ( Chromis punctipinnis ) and zebrafish ( Danio rerio ) (Parrish and Turchin, 1997; Zienkiewicz et al, 2015). Fig.…”
Section: Differences and Similarities Between Speciesmentioning
confidence: 57%
“…Individuals can also adjust their direction. Indeed, in addition to changes in speed, golden shiners and zebrafish adjust their direction depending on the position of their neighbours (Katz et al, 2011; Zienkiewicz et al, 2015). An individual will turn away from a neighbour that is positioned <1 body length and directly to the side of itself, acting to maintain a minimum distance between pairs.…”
Section: Differences and Similarities Between Speciesmentioning
confidence: 99%
“…When golden shiners or zebrafish are >1 body length apart, they turn towards their neighbours (Katz et al, 2011; Zienkiewicz et al, 2015). The turning responses of mosquitofish are similarly dependent on the position of their neighbours.…”
Section: Differences and Similarities Between Speciesmentioning
Moving animal groups display remarkable feats of coordination. This coordination is largely achieved when individuals adjust their movement in response to their neighbours' movements and positions. Recent advancements in automated tracking technologies, including computer vision and GPS, now allow researchers to gather large amounts of data on the movements and positions of individuals in groups. Furthermore, analytical techniques from fields such as statistical physics now allow us to identify the precise interaction rules used by animals on the move. These interaction rules differ not only between species, but also between individuals in the same group. These differences have wide-ranging implications, affecting how groups make collective decisions and driving the evolution of collective motion. Here, I describe how trajectory data can be used to infer how animals interact in moving groups. I give examples of the similarities and differences in the spatial and directional organisations of animal groups between species, and discuss the rules that animals use to achieve this organisation. I then explore how groups of the same species can exhibit different structures, and ask whether this results from individuals adapting their interaction rules. I then examine how the interaction rules between individuals in the same groups can also differ, and discuss how this can affect ecological and evolutionary processes. Finally, I suggest areas of future research.
“…3A). These changes in speed, based on neighbours' positions, have also been observed in juvenile blacksmith ( Chromis punctipinnis ) and zebrafish ( Danio rerio ) (Parrish and Turchin, 1997; Zienkiewicz et al, 2015). Fig.…”
Section: Differences and Similarities Between Speciesmentioning
confidence: 57%
“…Individuals can also adjust their direction. Indeed, in addition to changes in speed, golden shiners and zebrafish adjust their direction depending on the position of their neighbours (Katz et al, 2011; Zienkiewicz et al, 2015). An individual will turn away from a neighbour that is positioned <1 body length and directly to the side of itself, acting to maintain a minimum distance between pairs.…”
Section: Differences and Similarities Between Speciesmentioning
confidence: 99%
“…When golden shiners or zebrafish are >1 body length apart, they turn towards their neighbours (Katz et al, 2011; Zienkiewicz et al, 2015). The turning responses of mosquitofish are similarly dependent on the position of their neighbours.…”
Section: Differences and Similarities Between Speciesmentioning
Moving animal groups display remarkable feats of coordination. This coordination is largely achieved when individuals adjust their movement in response to their neighbours' movements and positions. Recent advancements in automated tracking technologies, including computer vision and GPS, now allow researchers to gather large amounts of data on the movements and positions of individuals in groups. Furthermore, analytical techniques from fields such as statistical physics now allow us to identify the precise interaction rules used by animals on the move. These interaction rules differ not only between species, but also between individuals in the same group. These differences have wide-ranging implications, affecting how groups make collective decisions and driving the evolution of collective motion. Here, I describe how trajectory data can be used to infer how animals interact in moving groups. I give examples of the similarities and differences in the spatial and directional organisations of animal groups between species, and discuss the rules that animals use to achieve this organisation. I then explore how groups of the same species can exhibit different structures, and ask whether this results from individuals adapting their interaction rules. I then examine how the interaction rules between individuals in the same groups can also differ, and discuss how this can affect ecological and evolutionary processes. Finally, I suggest areas of future research.
“…. , 5, to favor coordinated motion, based on results in Zienkiewicz et al (2015b) and Butail et al (2016). For all fish, the wall avoidance parameters is set to k…”
Collective motion in animal groups manifests itself in the form of highly coordinated maneuvers determined by local interactions among individuals. A particularly critical question in understanding the mechanisms behind such interactions is to detect and classify leader-follower relationships within the group. In the technical literature of coupled dynamical systems, several methods have been proposed to reconstruct interaction networks, including linear correlation analysis, transfer entropy, and event synchronization. While these analyses have been helpful in reconstructing network models from neuroscience to public health, rules on the most appropriate method to use for a specific dataset are lacking. Here, we demonstrate the possibility of detecting leaders in a group from raw positional data in a model-free approach that combines multiple methods in a maximum likelihood sense. We test our framework on synthetic data of groups of selfpropelled Vicsek particles, where a single agent acts as a leader and both the size of the interaction region and the level of inherent noise are systematically varied. To assess the feasibility of detecting leaders in real-world applications, we study a synthetic dataset of fish shoaling, generated by using a recent data-driven model for social behavior, and an experimental dataset of pharmacologically treated zebrafish. Not only does our approach offer a robust strategy to detect leaders in synthetic data but it also allows for exploring the role of psychoactive compounds on leader-follower relationships.
“…The first computational model of zebrafish swimming has been proposed by our group in refs 43, 44, 45 and 46. In this series of studies, we have proposed a data-driven modelling framework to predict the turn rate dynamics and speed modulation of zebrafish swimming in a shallow water tank.…”
Zebrafish is fast becoming a species of choice in biomedical research for the investigation of functional and dysfunctional processes coupled with their genetic and pharmacological modulation. As with mammals, experimentation with zebrafish constitutes a complicated ethical issue that calls for the exploration of alternative testing methods to reduce the number of subjects, refine experimental designs, and replace live animals. Inspired by the demonstrated advantages of computational studies in other life science domains, we establish an authentic data-driven modelling framework to simulate zebrafish swimming in three dimensions. The model encapsulates burst-and-coast swimming style, speed modulation, and wall interaction, laying the foundations for in-silico experiments of zebrafish behaviour. Through computational studies, we demonstrate the ability of the model to replicate common ethological observables such as speed and spatial preference, and anticipate experimental observations on the correlation between tank dimensions on zebrafish behaviour. Reaching to other experimental paradigms, our framework is expected to contribute to a reduction in animal use and suffering.
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