Behavior Of Animal Groups Research Articles

Multidimensional social influence drives leadership and composition-dependent success in octopus-fish hunting groups.

Collective behaviour, social interactions and leadership in animal groups are often driven by individual differences. However, most studies focus on same-species groups, in which individual variation is relatively low. Multispecies groups, however, entail interactions among highly divergent phenotypes, ranging from simple exploitative actions to complex coordinated networks. Here we studied hunting groups of otherwise-solitary Octopus cyanea and multiple fish species, to unravel hidden mechanisms of leadership and associated dynamics in functional nature and complexity, when divergence is maximized. Using three-dimensional field-based tracking and field experiments, we found that these groups exhibit complex functional dynamics and composition-dependent properties. Social influence is hierarchically distributed over multiscale dimensions representing role specializations: fish (particularly goatfish) drive environmental exploration, deciding where, while the octopus decides if, and when, the group moves. Thus, 'classical leadership' can be insufficient to describe complex heterogeneous systems, in which leadership instead can be driven by both stimulating and inhibiting movement. Furthermore, group composition altered individual investment and collective action, triggering partner control mechanisms (that is, punching) and benefits for the de facto leader, the octopus. This seemingly non-social invertebrate flexibly adapts to heterospecific actions, showing hallmarks of social competence and cognition. These findings expand our current understanding of what leadership is and what sociality is.

Avalanche dynamics of zebrafish schools: Unveiling self-organization and phase transitions

Collective behavior in animal groups exhibits intriguing dynamics that can be analyzed through the lens of self-organized criticality. In this context, we analyze behavioral cascades in zebrafish (Danio rerio) groups of varying sizes within controlled tank environments. Through experimental observations and data analysis, we unveil scale-free signatures reminiscent of self-organized critical processes in the collective movement of zebrafish. Notably, as fish density varies, we observe a dynamic phase transition: at low densities, coordinated and highly polarized movement dominates, while at high densities, the group fractures into uncorrelated domains. These findings shed light on the complex dynamics of collective behavior in fish groups and provide valuable insights into the responses of individuals to environmental stimuli. Moreover, the observed phase transition highlights the sensitivity of zebrafish behavior to changes in population density, which has implications for understanding collective behavior in various contexts, from ecological systems to preclinical studies. Finally, we compare our findings with the known results of avalanche analyses of collective motion and neuronal activity. All follow the same power law, indicating a possible universality in one parameter of avalanche processes.

Development of neural circuits for social motion perception in schooling fish

The collective behavior of animal groups emerges from the interactions among individuals. These social interactions produce the coordinated movements of bird flocks and fish schools, but little is known about their developmental emergence and neurobiological foundations. By characterizing the visually based schooling behavior of the micro glassfish Danionella cerebrum, we found that social development progresses sequentially, with animals first acquiring the ability to aggregate, followed by postural alignment with social partners. This social maturation was accompanied by the development of neural populations in the midbrain that were preferentially driven by visual stimuli that resemble the shape and movements of schooling fish. Furthermore, social isolation over the course of development impaired both schooling behavior and the neural encoding of social motion in adults. This work demonstrates that neural populations selective for the form and motion of conspecifics emerge with the experience-dependent development of collective movement.

Phenomenon of self-oscillation in bubble dynamics: Bouncing acoustic bubbles

Self-oscillations underlie many natural phenomena, such as heartbeat, ocean waves, and the pulsation of variable stars. From pendulum clocks to the behavior of animal groups, self-oscillation is one of the keys to the understanding of synchronization phenomena and hence to the collective behavior of interacting systems. In this study, we consider two closely spaced bubbles pulsating in the kHz range in response to ultrasonic excitation. A translational bouncing motion emerges from their interaction with a much lower frequency than the bubble pulsation frequency. Our analysis reveals that the observed bubble bouncing exhibits the main features of self-oscillation, such as negative damping and the emergence of a limit cycle. These results highlight unexpected nonlinear effects in the field of microbubbles and give insights into the understanding of synchronization in large bubble clouds.

IoT-based simulation of flocking algorithm

The study of animal groups’ flocking behavior is of great significance to the study of multi-agent system. Flocking algorithm is a typical algorithm for group control based on animal group behavior. The research on flocking algorithm is mature, but there is little research on the implementation of Flocking algorithm using digital twin. Digital twins, as a key technology in the direction of IoT, is potentially valuable for improving the efficiency of cloud and devices. Therefore, the main research goal of this paper is to simulate the flocking phenomenon in nature by using digital twins and flocking algorithm to realize the cooperative control of multi-agent systems under the scenarios of obstacle-free and obstacle. In this paper, the author uses digital twins and flocking algorithm to achieve simulations of animal flocking Behaviors. The final simulation results show that the Multi-agent system can converge agents and reach consensus, and finally cross the obstacles to reach the target node, which verifies the rationality and effectiveness of the algorithm. Through the way of digital twins, the burden of the intelligence body’s computation on obstacle identification can be reduced, and the synchronization efficiency of the cloud and the device can be effectively elevated, thus in a practical sense, favoring the deployment implementation.

Toward Comparative Collective Behavior to Discover Fundamental Mechanisms Underlying Behavior in Human Crowds and Nonhuman Animal Groups

This article provides comparative perspectives on collective behaviors that are widely found throughout the animal kingdom, ranging from insect and crustacea swarms, fish schools, bird flocks, and mammal herds to human crowds. Studies of nonhuman animal and human collective behaviors have progressed almost separately even though they have a similar history. Theoretical studies have investigated the reproduction of collective phenomena from simple inter-individual rules, and subsequent empirical and experimental studies have found diverse and complex collective behaviors that are difficult to explain with classical theoretical models. As a consequence, a wide variety of interaction rules have been proposed. To determine models to be implemented in nature and find fundamental mechanisms of collective behaviors, this paper argues that we should compare collective behaviors among various species while adopting Tinbergen’s four questions regarding mechanism, function, development, and evolution as a methodological basis. As an example of a comparative collective behavior paradigm, we introduce our studies in which a mutual anticipation mechanism inspired by nonhuman animal collective behaviors can be linked to a self-organization function in human collective behaviors. We expect that the study of comparative collective behaviors will expand, the methodology will become more sophisticated, and new perspectives regarding the multitemporal features of collective behaviors will emerge.

Skeletonizing Caenorhabditis elegans Based on U-Net Architectures Trained with a Multi-worm Low-Resolution Synthetic Dataset

AbstractSkeletonization algorithms are used as basic methods to solve tracking problems, pose estimation, or predict animal group behavior. Traditional skeletonization techniques, based on image processing algorithms, are very sensitive to the shapes of the connected components in the initial segmented image, especially when these are low-resolution images. Currently, neural networks are an alternative providing more robust results in the presence of image-based noise. However, training a deep neural network requires a very large and balanced dataset, which is sometimes too expensive or impossible to obtain. This work proposes a new training method based on a custom-generated dataset with a synthetic image simulator. This training method was applied to different U-Net neural networks architectures to solve the problem of skeletonization using low-resolution images of multiple Caenorhabditis elegans contained in Petri dishes measuring 55 mm in diameter. These U-Net models had only been trained and validated with a synthetic image; however, they were successfully tested with a dataset of real images. All the U-Net models presented a good generalization of the real dataset, endorsing the proposed learning method, and also gave good skeletonization results in the presence of image-based noise. The best U-Net model presented a significant improvement of 3.32% with respect to previous work using traditional image processing techniques.

A guide to sampling design for GPS‐based studies of animal societies

Abstract GPS‐based tracking is widely used for studying wild social animals. Much like traditional observational methods, using GPS devices requires making a number of decisions about sampling that can affect the robustness of a study's conclusions. For example, sampling fewer individuals per group across more distinct social groups may not be sufficient to infer group‐ or subgroup‐level behaviours, while sampling more individuals per group across fewer groups limits the ability to draw conclusions about populations. Here, we provide quantitative recommendations when designing GPS‐based tracking studies of animal societies. We focus on the trade‐offs between three fundamental axes of sampling effort: (1) sampling coverage—the number and allocation of GPS devices among individuals in one or more social groups; (2) sampling duration—the total amount of time over which devices collect data and (3) sampling frequency—the temporal resolution at which GPS devices record data. We first test GPS tags under field conditions to quantify how these aspects of sampling design can affect both GPS accuracy (error in absolute positional estimates) and GPS precision (error in the estimate relative position of two individuals), demonstrating that GPS error can have profound effects when inferring distances between individuals. We then use data from whole‐group tracked vulturine guineafowl Acryllium vulturinum to demonstrate how the trade‐off between sampling frequency and sampling duration can impact inferences of social interactions and to quantify how sampling coverage can affect common measures of social behaviour in animal groups, identifying which types of measures are more or less robust to lower coverage of individuals. Finally, we use data‐informed simulations to extend insights across groups of different sizes and cohesiveness. Based on our results, we are able to offer a range of recommendations on GPS sampling strategies to address research questions across social organizational scales and social systems—from group movement to social network structure and collective decision‐making. Our study provides practical advice for empiricists to navigate their decision‐making processes when designing GPS‐based field studies of animal social behaviours, and highlights the importance of identifying the optimal deployment decisions for drawing informative and robust conclusions.

A global analysis of aerial displays in passerines revealed an effect of habitat, mating system and migratory traits.

Aerial displaying is a flamboyant part of the sexual behaviour of several volant animal groups, including birds. Nevertheless, little attention has been focused on identifying correlates of large-scale diversity in this trait. In this study, we scored the presence and absence of aerial displays in males of 1732 species of passerine birds (Passeriformes) and employed Bayesian phylogenetically informed mixed models to test for associations between aerial displays and a set of life-history and environmental predictors. Our multi-variate models revealed that species with males that perform aerial displays inhabited open rather than closed (forested) habitats. These species also exhibited higher levels of polygyny, had more elongated wings, migrated over longer distances and bred at higher latitudes. When we included species where the sexual function of displays has not been explicitly described but is likely to occur, we found that aerial displaying was also associated with smaller body size and increased male plumage coloration. Our results suggest that both sexual selection and natural selection have been important sources of selection on aerial displays in passerines.

Coordination of a Semi-Informed Flocking System via Model Predictive Control

Coordination of mobile multi-agent systems has been studied intensively for many different purposes. These include swarm robotics, transportation, control of human crowds, and even understanding of collective behavior of animal groups. Mainly, the flocking behavior of multiple agents has been investigated under usually idealized conditions regarding information availability. This paper proposes a model predictive control framework to control flocking agents with limited information. The flocking system is composed of a leader agent and agents that have no information about the group objective and cannot perceive the unknown operation area. The leader agent is a part of the group. However, it has prior information about the group objective and can sense the environment. With the proposed control scheme, the leader steers the flocking agents like a shepherd dog to accomplish the group goal. The presented control scheme is validated in simulation examples.

Evolution of heterogeneous perceptual limits and indifference in competitive foraging.

The collective behaviour of animal and human groups emerges from the individual decisions and actions of their constituent members. Recent research has revealed many ways in which the behaviour of groups can be influenced by differences amongst their constituent individuals. The existence of individual differences that have implications for collective behaviour raises important questions. How are these differences generated and maintained? Are individual differences driven by exogenous factors, or are they a response to the social dilemmas these groups face? Here I consider the classic case of patch selection by foraging agents under conditions of social competition. I introduce a multilevel model wherein the perceptual sensitivities of agents evolve in response to their foraging success or failure over repeated patch selections. This model reveals a bifurcation in the population, creating a class of agents with no perceptual sensitivity. These agents exploit the social environment to avoid the costs of accurate perception, relying on other agents to make fitness rewards insensitive to the choice of foraging patch. This provides a individual-based evolutionary basis for models incorporating perceptual limits that have been proposed to explain observed deviations from the Ideal Free Distribution (IFD) in empirical studies, while showing that the common assumption in such models that agents share identical sensory limits is likely false. Further analysis of the model shows how agents develop perceptual strategic niches in response to environmental variability. The emergence of agents insensitive to reward differences also has implications for societal resource allocation problems, including the use of financial and prediction markets as mechanisms for aggregating collective wisdom.

Performance Analysis of Cost and Accuracy for Whale Swarm and RMSprop Optimizer

The scientific fields’ deals with day to day problems, like economic planning and engineering design, mostly are disconnected, high dimensional, multimodal and oscillated optimization problems. These complex problems cannot be solved well within reasonable time using conventional process based on gradient. The natural phenomenon motivates and animal group behavior characteristics, the investigators have anticipated many efficient natural heuristic algorithms for high-dimensional complex optimization problems in real-world. This manuscript deals with one of the meta-heuristic algorithm, Whale Swarm Optimization algorithm (WSO) and compares the performance with RMS prop optimization techniques.

Two extensions of heterogeneous boid model to avoid metastable patterns

A boid is a simple multiagent model of animal group behavior. Boid agents communicate locally. I studied a heterogeneous boid model comprised of many agents that are divided into several types. While varying interaction among types of agents, this model generates stable patterns with a symmetric interface among different types of agents. As the number of agents increases and as agent clusters grow larger, this model forms a metastable pattern (i.e., a complex of such stable patterns) that is caused by conflict among the local growths of stable patterns. To avoid metastable patterns, I designed two extended heterogeneous boid models: a two-component boid with noise control and a three-component boid with a type transition of agents. In this paper, I examined how these extended models rearrange agents from metastable patterns into stable patterns. These extended models generate stable patterns regardless of the agent number in a shorter time than the original heterogeneous boid model.

Group-level patterns emerge from individual speed as revealed by an extremely social robotic fish.

Understanding the emergence of collective behaviour has long been a key research focus in the natural sciences. Besides the fundamental role of social interaction rules, a combination of theoretical and empirical work indicates individual speed may be a key process that drives the collective behaviour of animal groups. Socially induced changes in speed by interacting animals make it difficult to isolate the effects of individual speed on group-level behaviours. Here, we tackled this issue by pairing guppies with a biomimetic robot. We used a closed-loop tracking and feedback system to let a robotic fish naturally interact with a live partner in real time, and programmed it to strongly copy and follow its partner's movements while lacking any preferred movement speed or directionality of its own. We show that individual differences in guppies' movement speed were highly repeatable and in turn shaped key collective patterns: a higher individual speed resulted in stronger leadership, lower cohesion, higher alignment and better temporal coordination of the pairs. By combining the strengths of individual-based models and observational work with state-of-the-art robotics, we provide novel evidence that individual speed is a key, fundamental process in the emergence of collective behaviour.

Maximum Markovian order detection for collective behavior.

Many advances have been achieved in the study of collective behavior of animal groups and human beings. Markovian order is a significant property in collective behavior, which reveals the inter-agent interaction strategy of the system. In this study, we propose a method using the time-series data of collective behavior to determine the optimal maximum Markov order of time-series motion data so as to reflect the maximum memory capacity of the interacting network. Our method combines a time-delayed causal inference algorithm and a multi-order graphical model. We apply the method to the data of pigeon flocks, dogs, and a group of midges to determine their optimal maximum order for validation and construct high-order De Bruijn graphs as a stochastic model to describe their interacting relationships. Most temporal network data of animal movements can be effectively analyzed by our method, which may provide a practical and promising solution to detection of the optimal maximum Markovian order of collective behavior.

Who goes there? Social surveillance as a response to intergroup conflict in a primitive termite

Intergroup conflict has been suggested as a major force shaping the evolution of social behaviour in animal groups. A long-standing hypothesis is that groups at risk of attack by rivals should become more socially cohesive, to increase resilience or protect against future attack. However, it is usually unclear how cohesive behaviours (such as grooming or social contacts) function in intergroup conflict. We performed an experiment in which we exposed young colonies of the dampwood termite, Zootermopsis angusticollis, to a rival colony while preventing physical combat with a permeable barrier. We measured social contacts, allogrooming and trophallaxis before, during and after exposure. Termites showed elevated rates of social contacts during exposure to a rival compared to the pre-exposure phase, but rates returned to pre-exposure levels after colonies were separated for 9 days. There was evidence of a delayed effect of conflict on worker trophallaxis. We suggest that social contacts during intergroup conflict function as a form of social surveillance, to check individual identity and assess colony resource holding potential. Intergroup conflict may increase social cohesion in both the short and the long term, improving the effectiveness of groups in competition.

Critical mass effect in evolutionary games triggered by zealots

Tiny perturbations may trigger large responses in systems near criticality, shifting them across equilibria. Committed minorities are suggested to be responsible for the emergence of collective behaviors in many physical, social, and biological systems. Using evolutionary game theory, we address the question whether a finite fraction of zealots can drive the system to large-scale coordination. We find that a tipping point exists in coordination games, whereas the same phenomenon depends on the selection pressure, update rule, and network structure in other types of games. Our study paves the way to understand social systems driven by the individuals' benefit in presence of zealots, such as human vaccination behavior or cooperative transports in animal groups.

Nonmutual torques and the unimportance of motility for long-range order in two-dimensional flocks.

As the constituent particles of a flock are polar and in a driven state, their interactions must, in general, be fore-aft asymmetric and nonreciprocal. Within a model that explicitly retains the classical spin angular momentum field of the particles we show that the resulting asymmetric contribution to interparticle torques, if large enough, leads to a buckling instability of the flock. More precisely, this asymmetry also yields a natural mechanism for a difference between the speed of advection of polarization information along the flock and the speed of the flock itself, concretely establishing that the absence of detailed balance, and not merely the breaking of Galilean invariance, is crucial for this distinction. To highlight this we construct a model of asymmetrically interacting spins fixed to lattice points and demonstrate that the speed of advection of polarization remains nonzero. We delineate the conditions on parameters and wave number for the existence of the buckling instability. Our theory should be consequential for interpreting the behavior of real animal groups as well as experimental studies of artificial flocks composed of polar motile rods on substrates.

Mathematical modeling for flocking flight of autonomous multi-UAV system, including environmental factors

In this study, we propose a decentralized mathematical model for predictive control of a system of multi-autonomous unmanned aerial vehicles (UAVs), also known as drones. Being decentralized and autonomous implies that all members make their own decisions and fly depending on the dynamic information received from other unmanned aircraft in the area. We consider a variety of realistic characteristics, including time delay and communication locality. For this flocking flight, we do not possess control for central data processing or control over each UAV, as each UAV runs its collision avoidance algorithm by itself. The main contribution of this work is a mathematical model for stable group flight even in adverse weather conditions (e.g., heavy wind, rain, etc.) by adding Gaussian noise. Two of our proposed variance control algorithms are presented in this work. One is based on a simple biological imitation from statistical physical modeling, which mimics animal group behavior; the other is an algorithm for cooperatively tracking an object, which aligns the velocities of neighboring agents corresponding to each other. We demonstrate the stability of the control algorithm and its applicability in autonomous multi-drone systems using numerical simulations.

Social Spider Optimization Algorithm: Modifications, Applications, and Perspectives

Swarm intelligence (SI) is a research field which has recently attracted the attention of several scientific communities. An SI approach tries to characterize the collective behavior of animal or insect groups to build a search strategy. These methods consider biological systems, which can be modeled as optimization processes to a certain extent. The Social Spider Optimization (SSO) is a novel swarm algorithm that is based on the cooperative characteristics of the social spider. In SSO, search agents represent a set of spiders which collectively move according to the biological behavior of the colony. In most of SI algorithms, all individuals are modeled considering the same properties and behavior. In contrast, SSO defines two different search agents: male and female. Therefore, according to the gender, each individual is conducted by using a different evolutionary operation which emulates its biological role in the colony. This individual categorization allows reducing critical flaws present in several SI approaches such as incorrect exploration-exploitation balance and premature convergence. After its introduction, SSO has been modified and applied in several engineering domains. In this paper, the state of the art, improvements, and applications of the SSO are reviewed.