Biology subjects

Lecheval, V.

Publications and source records attributed to Lecheval, V..

5 recordsLinked to original sources

bioRxiv2024

Random walks with spatial and temporal resets may underlie searching movements in ants

AO_SCPLOWBSTRACTC_SCPLOWMany ant species are central place foragers, living in a nest and exploiting the surrounding environment. It is however unclear how their exploration behaviour relates to the emerging exploited area. Ants provide a great opportunity to study the emergence of foraging range from individual movements, given the potentially large number of scouting workers involved. Here, we introduce a random walk model with stochastic resetting to depict the movements of searching ants. Stochastic resetting refers to spatially reset at random times the position of agents to a given location, here the nest of searching ants. We investigate the effect of a range of resetting mechanisms by changing how the probability of returning to the nest depends on the duration of unsuccessful foraging trips. We compare the macroscopic predictions of our model to laboratory and field data. We find that the probability for searching ants to return to their nest decreases as the number of foraging trips increases, resulting in scouts going further away from the nest as the number of foraging trips increases. Our findings highlight the importance of resetting random walk models to depict the movements of central place foragers and nurtures novel questions regarding the searching behaviour of ants.

animal behavior and cognition↗

bioRxiv2023

Smart self-propelled particles: a framework to investigate the cognitive bases of movement

AO_SCPLOWBSTRACTC_SCPLOWDecision-making and movement of single animals or group of animals are often treated and investigated as separate processes. However, many decisions are taken while moving in a given space. In other words, both processes are optimised at the same time and optimal decision-making processes are only understood in the light of movement constraints. To fully understand the rational of decisions embedded in an environment (and therefore the underlying evolutionary processes), it is instrumental to develop theories of spatial decision-making. Here, we present a framework specifically developed to address this issue by the means of artificial neural networks and genetic algorithms. Specifically, we investigate a simple task in which single agents need to learn to explore their square arena without leaving its boundaries. We show that agents evolve by developing increasingly optimal strategies to solve a spatially-embedded learning task while not having an initial arbitrary model of movements. The process allows the agents to learn how to move (i.e. by avoiding the arena walls) in order to make increasingly optimal decisions (improving their exploration of the arena). Ultimately, this framework makes predictions of possibly optimal behavioural strategies for tasks combining learning and movement.

animal behavior and cognition↗

bioRxiv2019

Conditioning a collective avoidance response in rummy-nose tetra

We develop an experimental method to induce controlled and local perturbations in a group of fish. Using the paradigm of the shuttle box we condition aversive escape reactions in groups of rummy-nose tetra (Hemigrammus rhodostomus) in response to a green light. Our experiments suggest that aversive conditioning can (i) be used successfully in this schooling species, (ii) trigger collective escape reactions and (iii) be transferred from the training set-up to a new environment. These results are discussed in the context of propagation of information among group members in reaction to external stimuli such as perturbations mimicking sudden changes in the environment, e.g. predator attack.

animal behavior and cognition↗

bioRxiv2019

A data-driven method for reconstructing and modelling social interactions in moving animal groups

Group-living organisms that collectively migrate range from cells and bacteria to human crowds, and include swarms of insects, schools of fish and flocks of birds or ungulates. Unveiling the behavioural and cognitive mechanisms by which these groups coordinate their movements is a challenging task. These mechanisms take place at the individual scale and they can be described as a combination of pairwise interactions between individuals and interactions between these individuals and the physical obstacles in the environment. Thanks to the development of novel tracking techniques that provide large and accurate data sets, the main characteristics of individual and collective behavioural patterns can be quantified with an unprecedented level of precision. However, in a large number of works, social interactions are usually described by force map methods that only have a limited capacity of explanation and prediction, being rarely suitable for a direct implementation in a concise and explicit mathematical model. Here, we present a general method to extract the interactions between individuals that are involved in the coordination of collective movements in groups of organisms. We then apply this method to characterize social interactions in two species of shoaling fish, the rummynose tetra (Hemigrammus rhodostomus) and the zebrafish (Danio rerio), which both present a burst-and-coast motion. The detailed quantitative description of microscopic individual-level interactions thus provides predictive models of the emergent dynamics observed at the macroscopic group-level. This method can be applied to a wide range of biological and social systems.

animal behavior and cognition↗

bioRxiv2017

Domino-Like Propagation Of Collective U-Turns In Fish Schools

Moving animal groups such as schools of fish or flocks of birds often undergo sudden collective changes of their travelling direction as a consequence of stochastic fluctuations in heading of the individuals. However, the mechanisms by which these behavioural fluctuations arise at the individual level and propagate within a group are still unclear. In the present study, we combine an experimental and theoretical approach to investigate spontaneous collective U-turns in groups of rummy-nose tetra (Hemigrammus rhodostomus) swimming in a ring-shaped tank. U-turns imply that fish switch their heading between the clockwise and anticlockwise direction. We reconstruct trajectories of individuals moving alone and in groups of different sizes. We show that the group decreases its swimming speed before a collective U-turn. This is in agreement with previous theoretical predictions showing that speed decrease facilitates an amplification of fluctuations in heading in the group, which can trigger U-turns. These collective U-turns are mostly initiated by individuals at the front of the group. Once an individual has initiated a U-turn, the new direction propagates through the group from front to back without amplification or dampening, resembling the dynamics of falling dominoes. The mean time between collective U-turns sharply increases as the size of the group increases. We develop an Ising spin model integrating anisotropic and asymmetrical interactions between fish and their tendency to follow the majority of their neighbours nonlinearly (social conformity). The model quantitatively reproduces key features of the dynamics and the frequency of collective U-turns observed in experiments.

animal behavior and cognition↗