Bird Flocks Research Articles

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.

IMPROVED QoE IN 5G BASE STATION USING PSO BASED DSA

In the 5G era, base stations can offer multiple services in various scenarios, enhancing spectrum efficiency by utilizing idle frequency bands for low-demand services while ensuring high-priority services are prioritized. This creates a distributed architecture for spectrum allocation. The proposed dynamic spectrum allocation scheme for base stations, optimizes spectrum rearrangement based on service priority, energy consumption, and renting cost. We employ the Particle Swarm Optimization (PSO) algorithm, inspired by bird flocking or fish schooling behavior, to solve the problem. Moreover, provide detailed information on our approach, demonstrating the effectiveness of PSO in optimizing spectrum allocation. By using PSO, we find the optimal solution considering service priority, energy consumption, and renting cost, supported by mathematical proofs of PSO algorithm performance. MATLAB is used as the simulation tool to carry out these techniques. Simulation experiments validate the approach, showing the stability and efficacy of PSO-based algorithm, which aligns with the theoretical analysis. . Key Words: Spectrum efficiency; 5G; dynamic spectrum allocation; service priority; PSO.

Diagnostic Detection of H7N3 Low Pathogenicity Avian Influenza in a Commercial Game Bird Flock.

Low pathogenicity avian influenza (LPAI) H7N3 was diagnosed in a flock of commercially raised Japanese quail (Coturnix japonica) breeder hens. The birds were submitted with an initial complaint of a drop in egg production and watery droppings. Initial PCR testing of the flock for avian influenza (AI) was negative; however, mortality started increasing drastically, prompting further examination and the submission of more birds to the diagnostic laboratory. On antemortem examination, the birds appeared lethargic, with ruffled feathers, and had labored breathing. Gross examination revealed poor body condition, moderate dehydration, splenomegaly, ovarian regression, and airsacculitis. Several hens produced thin-shelled and shell-less eggs. Microscopically, lymphoplasmacytic encephalitis with neuronal cell necrosis, fibrinoheterophilic pneumonia, and splenic lymphoid depletion with amyloid deposition were seen. AI virus was detected in a follow-up submission and characterized as LPAI H7N3. Quail are a species of interest in the pathobiology of avian influenza viruses, underlined by the ability to serve as an amplification host.

Human Crowds as Social Networks: Collective Dynamics of Consensus and Polarization

A ubiquitous type of collective behavior and decision-making is the coordinated motion of bird flocks, fish schools, and human crowds. Collective decisions to move in the same direction, turn right or left, or split into subgroups arise in a self-organized fashion from local interactions between individuals without central plans or designated leaders. Strikingly similar phenomena of consensus (collective motion), clustering (subgroup formation), and bipolarization (splitting into extreme groups) are also observed in opinion formation. As we developed models of crowd dynamics and analyzed crowd networks, we found ourselves going down the same path as models of opinion dynamics in social networks. In this article, we draw out the parallels between human crowds and social networks. We show that models of crowd dynamics and opinion dynamics have a similar mathematical form and generate analogous phenomena in multiagent simulations. We suggest that they can be unified by a common collective dynamics, which may be extended to other psychological collectives. Models of collective dynamics thus offer a means to account for collective behavior and collective decisions without appealing to a priori mental structures.

Entropy by Neighbor Distance as a New Measure for Characterizing Spatiotemporal Orders in Microscopic Collective Systems.

Collective systems self-organize to form globally ordered spatiotemporal patterns. Finding appropriate measures to characterize the order in these patterns will contribute to our understanding of the principles of self-organization in all collective systems. Here we examine a new measure based on the entropy of the neighbor distance distributions in the characterization of collective patterns. We study three types of systems: a simulated self-propelled boid system, two active colloidal systems, and one centimeter-scale robotic swarm system. In all these systems, the new measure proves sensitive in revealing active phase transitions and in distinguishing steady states. We envision that the entropy by neighbor distance could be useful for characterizing biological swarms such as bird flocks and for designing robotic swarms.

First isolation and genomic characterization of avian reovirus from black swans (Cygnus atratus) in China

Identification and analysis of the avian reovirus from black swan. Isolation of the strain through the chorioallantoic membrane route of duck embryos, identified through transmission electron microscopy and RT-PCR based on the ARV S2 gene. The complete genome of the ARV strain was obtained using next-generation sequencing technology. The isolated strain of ARV was named CD200801 and was identified through transmission electron microscopy and RT-PCR based on the ARV S2 gene. Experimental infection with CD200801 resulted in the death of ducklings with serious spleen and liver focal necrosis. BLAST analysis of CD200801 sequences showed a 35.5 to 98.6% similarity to a novel duck reovirus that was isolated in recent years. Phylogenetic analysis revealed that CD200801 was closely related to ARV isolates YL, GX-Y7, and XT-18. We report the first avian reovirus infection in the black swan. This study provides important new insights into the evolutionary relationships among different ARV strains and highlights the need for continued surveillance and monitoring of these viruses in both domestic and wild bird flocks. These findings have significant implications for the development of effective strategies for disease prevention and control in the poultry industry.

Diffusion during collective turns in bird flocks under predation

Moving in groups offers animals protection against predation. When under attack, grouped individuals often turn collectively to evade a predator, which sometimes makes them rapidly change their relative positions in the group. In bird flocks in particular, the quick reshuffling of flock members confuses the predator, challenging its targeting of a single individual. This confusion is considered to be greater when the internal structure of the group changes faster (i.e. the ‘diffusion’ of the group is higher). Diffusion may increase when individual birds turn collectively with equal radii (same angular velocity) but not when individuals keep their paths parallel (by adjusting their speed). However, how diffusion depends on individual behaviour is not well known. When under attack, grouping individuals change the way they interact with each other, referred to as ‘alarmed coordination’ (e.g., increase their reaction frequency or their cohesion tendency), but the effect of such changes on collective turning is unknown. Here, we aimed to gain an understanding of the dynamics of collective turning in bird flocks. First, to investigate the relation between alarmed coordination and flock diffusion, we developed an agent-based model of bird flocks. Second, to test how diffusion relates to collective turns with equal-radii and parallel-paths, we developed a metric of the deviation from these two types. Third, we studied collective turning under predation empirically, by analysing the GPS trajectories of pigeons in small flocks pursued by a RobotFalcon. As a measure of diffusion, we used the instability of neighbours: the rate with which the closest neighbours of a flock member are changing. In our simulations, we showed that this instability increases with group size, reaction frequency, topological range, and cohesion tendency and that the relation between instability of neighbours and the deviation from the two turning types depends in often counter-intuitive ways on these coordination specifics. Empirically, we showed that pigeons turn collectively with less diffusion than starlings and that their collective turns are in between those with equal-radii and parallel-paths. Overall, our work provides a framework for studying collective turning across species.

Arbitrage Equilibrium, Invariance, and the Emergence of Spontaneous Order in the Dynamics of Bird-like Agents.

The physics of active biological matter, such as bacterial colonies and bird flocks, exhibiting interesting self-organizing dynamical behavior has gained considerable importance in recent years. Current theoretical advances use techniques from hydrodynamics, kinetic theory, and non-equilibrium statistical physics. However, for biological agents, these approaches do not seem to recognize explicitly their critical feature: namely, the role of survival-driven purpose and the attendant pursuit of maximum utility. Here, we propose a game-theoretic framework, statistical teleodynamics, that demonstrates that the bird-like agents self-organize dynamically into flocks to approach a stable arbitrage equilibriumof equal effective utilities. This is essentially the invisible handmechanism of Adam Smith's in an ecological context. What we demonstrate is for ideal systems, similar to the ideal gas or Ising model in thermodynamics. The next steps would involve examining and learning how real swarms behave compared to their ideal versions. Our theory is not limited to just birds flocking but can be adapted for the self-organizing dynamics of other active matter systems.

Scale-free correlations and potential criticality in weakly ordered populations of brain cancer cells.

Collective behavior spans several orders of magnitude of biological organization, from cell colonies to flocks of birds. We used time-resolved tracking of individual glioblastoma cells to investigate collective motion in an ex vivo model of glioblastoma. At the population level, glioblastoma cells display weakly polarized motion in the (directional) velocities of single cells. Unexpectedly, fluctuations in velocities are correlated over distances many times the size of a cell. Correlation lengths scale linearly with the maximum end-to-end length of the population, indicating that they are scale-free and lack a characteristic decay scale other than the size of the system. Last, a data-driven maximum entropy model captures statistical features of the experimental data with only two free parameters: the effective length scale (nc) and strength (J) of local pairwise interactions between tumor cells. These results show that glioblastoma assemblies exhibit scale-free correlations in the absence of polarization, suggesting that they may be poised near a critical point.

Automatic detection of migrating soaring bird flocks using weather radars by deep learning

Abstract The use of weather radars to detect and distinguish between different biological patterns greatly improves our understanding of aeroecology and its consequences for our lives. Importantly, it allows us to quantify passerine bird migration at different scales. Yet, no algorithm to detect soaring bird flocks in weather radar is available, precluding our ability to study this type of migration over large spatial scales. We developed the first automatic algorithm for detecting the migration of flocks of soaring birds, an important bio‐flow phenomenon involving many millions of birds that travel across large spatial extents, with implications for risk of bird‐aircraft collisions. The algorithm was developed with a deep learning network for semantic segmentation using U‐Net architecture. We tested several models with different weather radar products and with image sequences for flock movement identification. The best model includes the radial velocity product and a sequence of two previous images. It identifies 93% of soaring bird flocks that were tagged by a human on the radar image, with a false discovery of less than 20%. Large birds such as those detected by the algorithm pose a serious risk for flight safety of civilian and military transportation and therefore the application of this algorithm can substantially reduce bird‐strikes, leading to reduced financial losses and threats to human lives. In addition, it can help overcome one of the main challenges in the study of bird migration by automatically and continuously detecting flocks of large birds over wide spatial scales without the need to equip the birds with tracking devices, unravelling the abundance, timing, spatial flyways, seasonal trends and influences of environmental conditions on the migration of bird flocks.

Feature selection for domain adaptation using complexity measures and swarm intelligence

Particle Swarm Optimization is an optimization algorithm that mimics the behaviour of a flock of birds, setting multiple particles that explore the search space guided by a fitness function in order to find the best possible solution. We apply the Sticky Binary Particle Swarm Optimization algorithm to perform feature selection for domain adaptation, a specific type of transfer learning in which the source and the target domain have a common feature space, a common task, but different distributions. When applying Particle Swarm Optimization, classification error is usually employed in the fitness function to evaluate the goodness of subsets of features. In this paper, we aim to compare this approach with using complexity metrics instead, under the assumption that reducing the complexity of the problem will lead to results that are independent from the classifier used for testing while being less computationally demanding. Therefore, we carried out experiments to compare the performance of both approaches in terms of classification accuracy, speed and number of features selected. We found out that our proposal, although in some cases incurs in a slight degradation of classification performance, it is indeed faster and selects fewer features, making it a feasible trade-off.

Global review of the H5N8 avian influenza virus subtype.

Orthomyxoviruses are negative-sense, RNA viruses with segmented genomes that are highly unstable due to reassortment. The highly pathogenic avian influenza (HPAI) subtype H5N8 emerged in wild birds in China. Since its emergence, it has posed a significant threat to poultry and human health. Poultry meat is considered an inexpensive source of protein, but due to outbreaks of HPAI H5N8 from migratory birds in commercial flocks, the poultry meat industry has been facing severe financial crises. This review focuses on occasional epidemics that have damaged food security and poultry production across Europe, Eurasia, the Middle East, Africa, and America. HPAI H5N8 viral sequences have been retrieved from GISAID and analyzed. Virulent HPAI H5N8 belongs to clade 2.3.4.4b, Gs/GD lineage, and has been a threat to the poultry industry and the public in several countries since its first introduction. Continent-wide outbreaks have revealed that this virus is spreading globally. Thus, continuous sero- and viro-surveillance both in commercial and wild birds, and strict biosecurity reduces the risk of the HPAI virus appearing. Furthermore, homologous vaccination practices in commercial poultry need to be introduced to overcome the introduction of emergent strains. This review clearly indicates that HPAI H5N8 is a continuous threat to poultry and people and that further regional epidemiological studies are needed.

Mechanistic signal transmission modulates collective migration

Living systems coordinate their motion for survival at all scales. Although bio-chemical signals and environmental gradients are known to influence the networks at a collective level, mechanistic signal transmission through the moving medium is not clear. Here, we use aerodynamics to identify these signals at an individual organismic level. We elucidate a V-shaped flapping bird flock as a response to fluidic gradients in a modeled dipolar double gyre wake. Since wakes are universal in bird flight, our method applies to complex formations. The present work has implications in bio-inspired design, solution of dilemma in cooperative flight and collective cellular migration.

An assessment of the highly pathogenic avian influenza resurgence at human-poultry-environment interface in North-central Nigeria: Sociocultural determinants and One Health implications

Highly pathogenic avian influenza H5N1 resurgence has occurred in Nigerian domestic bird flocks with public health concern. This study assessed poultry farmers' knowledge, perceptions, and biosecurity and biosafety practices regarding H5N1 resurgence, explore risk pathways for viral dissemination and associated socio-cultural and economic drivers in poultry flocks in Nigeria. A cross-sectional survey was carried out in randomly selected two poultry production systems, commercial intensive poultry production system and the backyard traditional free-range poultry production system. A One Health framework model was conceptualized to assess inter-links of biophysical, environmental, and sociocultural activities that interface to drive resurgence for better interventions. Descriptive and analytical statistical analyses were performed at 95% confidence level. Of the 422 recruited poultry farmers, 98.6% (n = 416) participated. Majorities of smallholder commercial farmers (93.5%) and backyard poultry keepers (97.7%) engaged in intensive and extensive management, respectively. Identified significant zoonotic risk pathways for H5N1 virus spread were through consumption of undercooked poultry meat and products, and contacts with infected birds and contaminated fomites. Separation of sick birds from apparently healthy ones, frequent cleaning and disinfection of equipment and premises, movement control of birds to nearby water bodies, use of personal protective equipment, and movement control of persons and vehicles into the flock settlements were significantly practiced biosecurity measures. Presence of nearby water bodies (ponds) close to flock settlements (p < 0.001), frequent contact of wild and domestic birds (p < 0.001), cultural practice of bird exchange between flocks (p < 0.001), and wild waterfowls' seasonal migrations (p < 0.001) significantly influenced resurgence. Understanding determinants interactions in the ‘Conceptual One Health framework model’ is required for better intercontinental intervention against HPAI H5N1. Reform of socio-cultural and economic activities using One Health approach will not only assure food safety and food security, but also guarantee public and environmental health.

Optimum k-Nearest Neighbors for Heading Synchronization on a Swarm of UAVs under a Time-Evolving Communication Network.

Heading synchronization is fundamental in flocking behaviors. If a swarm of unmanned aerial vehicles (UAVs) can exhibit this behavior, the group can establish a common navigation route. Inspired by flocks in nature, the k-nearest neighbors algorithm modifies the behavior of a group member based on the k closest teammates. This algorithm produces a time-evolving communication network, due to the continuous displacement of the drones. Nevertheless, this is a computationally expensive algorithm, especially for large groups. This paper contains a statistical analysis to determine an optimal neighborhood size for a swarm of up to 100 UAVs, that seeks heading synchronization using a simple P-like control algorithm, in order to reduce the calculations on every UAV, this is especially important if it is intended to be implemented in drones with limited capabilities, as in swarm robotics. Based on the literature of bird flocks, that establishes that the neighborhood of every bird is fixed around seven teammates, two approaches are treated in this work: (i) the analysis of the optimum percentage of neighbors from a 100-UAV swarm, that is necessary to achieve heading synchronization, and (ii) the analysis to determine if the problem is solved in swarms of different sizes, up to 100 UAVs, while maintaining seven nearest neighbors among the members of the group. Simulation results and a statistical analysis, support the idea that the simple control algorithm behaves like a flock of starlings.

Data for: "Diffusion during collective turns in bird flocks under predation"

Data for: "Diffusion during collective turns in bird flocks under predation"

Collective evolution learning model for vision-based collective motion with collision avoidance.

Collective motion (CM) takes many forms in nature; schools of fish, flocks of birds, and swarms of locusts to name a few. Commonly, during CM the individuals of the group avoid collisions. These CM and collision avoidance (CA) behaviors are based on input from the environment such as smell, air pressure, and vision, all of which are processed by the individual and defined action. In this work, a novel vision-based CM with CA model (i.e., VCMCA) simulating the collective evolution learning process is proposed. In this setting, a learning agent obtains a visual signal about its environment, and throughout trial-and-error over multiple attempts, the individual learns to perform a local CM with CA which emerges into a global CM with CA dynamics. The proposed algorithm was evaluated in the case of locusts' swarms, showing the evolution of these behaviors in a swarm from the learning process of the individual in the swarm. Thus, this work proposes a biologically-inspired learning process to obtain multi-agent multi-objective dynamics.

Is the neighborhood of interaction in human crowds metric, topological, or visual?

Global patterns of collective motion in bird flocks, fish schools, and human crowds are thought to emerge from local interactions within a neighborhood of interaction, the zone in which an individual is influenced by their neighbors. Both metric and topological neighborhoods have been reported in animal groups, but this question has not been addressed for human crowds. The answer has important implications for modeling crowd behavior and predicting crowd disasters such as jams, crushes, and stampedes. In a metric neighborhood, an individual is influenced by all neighbors within a fixed radius, whereas in a topological neighborhood, an individual is influenced by a fixed number of nearest neighbors, regardless of their physical distance. A recently proposed alternative is a visual neighborhood, in which an individual is influenced by the optical motions of all visible neighbors. We test these hypotheses experimentally by asking participants to walk in real and virtual crowds and manipulating the crowd's density. Our results rule out a topological neighborhood, are approximated by a metric neighborhood, but are best explained by a visual neighborhood that has elements of both. We conclude that the neighborhood of interaction in human crowds follows naturally from the laws of optics and suggest that previously observed "topological" and "metric" interactions might be a consequence of the visual neighborhood.

Revisiting the 'nuclear species' concept: do we really know what we think we know?

The idea of 'nuclear species' has received a lot of attention in mixed-species flock research. Our impression of this literature is that referenced statements tend to cite the same papers in support of a small set of ideas, and often there is a mismatch between what papers contain and what they're cited for. Motivated by these impressions, we built and quantitatively examined a database of referenced statements about nuclearity in flocks. This confirmed our impression quantitatively, but more strikingly, a single paper stood out in its influence on ideas around nuclearity in flocks. Moynihan's 1962 monograph on mixed-species flocks in Panama, 'The organization and probable evolution of some mixed-species flocks of neotropical birds' published in Smithsonian Miscellaneous Collections, was cited twice as much as the next most-cited paper and was the most-cited paper for 10 out of 15 most-discussed ideas related to nuclearity. Further, a number of other highly cited papers are strongly influenced by Moynihan's ideas, i.e. its influence is much greater than what a count of citations conveys. We also found that Moynihan was mis-cited frequently. We juxtapose what we found from the citation analysis with what the paper actually contains to better understand the nature of support that Moynihan provides, and discuss the implications of our findings for what we know about and how we research nuclearity in flocks. This article is part of the theme issue 'Mixed-species groups and aggregations: shaping ecological and behavioural patterns and processes'.

Can convergence in mixed-species flocks lead to evolutionary divergence? Evidence for and methods to test this hypothesis.

One of the most fundamental goals of modern biology is to achieve a deep understanding of the origin and maintenance of biodiversity. It has been observed that in some mixed-species animal societies, there appears to be a drive towards some degree of phenotypic trait matching, such as similar coloration or patterning. Here we build on these observations and hypothesize that selection in mixed-species animal societies, such as mixed-species bird flocks, may drive diversification, potentially leading to speciation. We review evidence for possible convergent evolution and even outright mimicry in flocks from southwestern China, where we have observed several cases in which species and subspecies differ from their closest relatives in traits that match particular flock types. However, understanding whether this is phenotypic matching driven by convergence, and whether this divergence has promoted biodiversity, requires testing multiple facets of this hypothesis. We propose a series of steps that can be used to tease apart alternative hypotheses to build our understanding of the potential role of convergence in diversification in participants of mixed-species societies. Even if our social convergence/divergence hypothesis is not supported, the testing at each step should help highlight alternative processes that may affect mixed-species flocks, trait evolution and possible convergence. This article is part of the theme issue 'Mixed-species groups and aggregations: shaping ecological and behavioural patterns and processes'.