Bird Flocks Research Articles

Synchronous and Fully Steerable Active Particle Systems for Enhanced Mimicking of Collective Motion in Nature.

The collective motion observed in living active matter, such as fish schools and bird flocks, is characterized by its dynamic and complex nature, involving various moving states and transitions. By tailoring physical interactions or incorporating information exchange capabilities, inanimate active particles can exhibit similar behavior. However, the lack of synchronous and arbitrary control over individual particles hinders their use as a test system for the study of more intricate collective motions in living species. Herein, a novel optical feedback control system that enables the mimicry of collective motion observed in living objects using active particles is proposed. This system allows for the experimental investigation of the velocity alignment, a seminal model of collective motion (known as the Vicsek model), in a microscale perturbed environment with controllable and realistic conditions. The spontaneous formation of different moving states and dynamic transitions between these states is observed. Additionally, the high robustness of the active-particle group at the critical density under the influence of different perturbations is quantitatively validated. These findings support the effectiveness of velocity alignment in real perturbed environments, thereby providing a versatile platform for fundamental studies on collective motion and the development of innovative swarm microrobotics.

Flocking and swarming in a multi-agent dynamical system.

Over the past few decades, the research community has been interested in the study of multi-agent systems and their emerging collective dynamics. These systems are all around us in nature, such as bacterial colonies, fish schools, and bird flocks, as well as in technology, such as microswimmers and robotics, to name a few. Flocking and swarming are two key components of the collective behaviors of multi-agent systems. In flocking, the agents coordinate their direction of motion, but in swarming, they congregate in space to organize their spatial position. We propose a minimal mathematical model of a locally interacting multi-agent system where the agents simultaneously swarm in space and exhibit flocking behavior. Various cluster structures are found depending on the interaction range. When the coupling strength value exceeds a crucial threshold, flocking behavior is observed. We do in-depth simulations and report the findings by changing the other parameters and with the incorporation of noise.

Effect of Flocks of Anseriform Birds on Seston and Phytoplankton in Lakes of the Taimyr Peninsula

The effect of molting anseriform birds on the structure and elemental composition of phytoplankton (seston) has been assessed in 20 Arctic lakes of the Taimyr Peninsula. In lakes (part of the lake) inhabited by ~50–700 birds of six species, the average stoichiometric ratio N : P (mol : mol) was statistically significantly lower than in lakes without anseriforms: 15.8 ± 1.4 and 22.4 ± 2.7, respectively. There was also a tendency of higher average specific electrical conductivity in the lakes with the birds, 113 ± 32 µS/cm, when compared with those without anseriforms, 60 ± 18 µS/cm. The differences could be explained with high probability by the effect of guanotrophication, namely, by a flow in water of metabolites of molting anseriforms. The total biomass of phytoplankton and proportions of algal taxa and cyanobacteria in the total biomass did not differ statistically significantly in lakes with and without molting anseriforms. Therefore, under guanotrophication, the main threat of eutrophication was absent: an increase of biomass of cyanobacteria, causing the nuisance “bloom” of water. Moreover, an opposite tendency occured: in lakes with molting anseriforms, the proportion of cyanobacteria in total biomass of phytoplankton was on average lower than that in lakes without the birds, 16.2 ± 5.3% and 30.8 ± 9.3%, respectively. Thus, a hypothesis was confirmed that artificial guanotrophication should be regarded as a suitable ecotechnology for the increase of productivity of oligotrophic Arctic lakes.

Impact of flapping trajectory and foil gap on induced thrust of a flapping foil

Wings and fins with tandem configuration are commonly observed in a flock of birds or schools of fish for enhanced performance while flying and summing. Emerging study on these subject underpinned by flapping foil is challenging. However, our study focuses on the tandem hydrofoil by varying the conventional (vertical heave and pitch motion) flapping motion to two new flapping trajectories: (i) elliptical and (ii) fishtail flapping trajectories. The hydrodynamic efficiency, such as propulsive efficiency and wake structure of the tandem hydrofoil, are examined using computational methods. The effect of Strouhal number (St) and inter-foil spacing are also discussed along with the flapping trajectories. The results show that by implementing the two new flapping trajectories, the induced thrust of the hind foil can be enhanced up to 45% compared to the simple flapping. This study shed new light on improving the biomimetic propulsion device, as it aims to enhance the elicited thrust of the hydrofoil.

Aerodynamics and bird ingestion characteristics of a bulge-adjustable turboprop engine inlet

Turboprop aircraft plays an important role in the field of regional airliners and military transport. During take-off or landing, turboprop aircrafts frequently encounter bird flocks that can be ingested into the engine inlet, thereby posing a significant threat to flight safety. Consequently, the aerodynamic characteristics and bird ingestion characteristics of turboprop inlet are investigated in this article. An unsteady computational fluid dynamics (CFD) approach has been proposed to calculate bird ingestion characteristics in a turboprop inlet with different bulge heights under propeller interference by combining a self-developed collision-rebound model with the overset mesh method and 6-degree of freedom (6-DOF) motion method. Considering the characteristics of bird strikes, a parameterized controlled bulge of turboprop inlet with a scavenge duct has been designed. Moreover, the trajectory characteristics of the bird, bird-inlet coupling flowfield characteristics and the aerodynamic characteristics are analyzed in depth. The bulge of the inlet plays a positive role in preventing the bird object from entering the engine duct at a 23° incidence angle. The bird rebounds from the upper bulge and is discharged through the scavenge duct at MaAIP=0.45. The results show that the growth of inlet bulge height has the minimal influence on the total pressure recovery coefficient σ, with only approximately 0.3% variation within the bulge height range of interest. However, it significantly affects the total pressure distortion DC60 with an increase up to 84%.

Propagation of chaos for topological interactions by a coupling technique

We consider a system of particles that interact through a jump process. The jump intensities are functions of the proximity rank of the particles, a type of interaction referred to as topological in the literature. Such interactions have been shown relevant for the modelling of bird flocks. We show that, in the large number of particles limit and under minimal smoothness assumptions on the data, the model converges to a kinetic equation which was derived in earlier works both formally and rigorously under more stringent regularity assumptions. The proof relies on the coupling method which assigns to the particle and limiting processes a joint process posed on the cartesian product of the two configuration spaces of the former processes. By appropriate estimates in a suitable Wasserstein metric, we show that the distance between the two processes tends to zero as the number of particles tends to infinity, with an error typical of the law of large numbers.

Flight order does not influence individuals’ flight initiation distance in small bird flocks

Abstract Researchers often measure the flight initiation distance (FID) of a selected individual to represent the FID of the whole groups which may vary with their flight order (the sequence to flee) and their spatial position within a group. In this study, we examined FID of all individuals in small bird flocks in urban environments when approached by an investigator. We found that individuals’ FID did not vary with their flight orders within these flocks. Moreover, the FID of the nearest individual, often used as a proxy for the entire flock, did not differ from the average FID of all flock members. This suggests that the conventional method of measuring the nearest individual’s FID to represent is a valid approximation for representing the behaviour of the entire flock in similar contexts. These results have important implications for understanding anti-predator strategies in group-living animals and can inform future studies in this field.

Island biogeography of mixed‐species bird flocks: A gregarious nuclear species influences island area effects

AbstractAimIsland biogeography theory has been extended to include various species interactions, but has yet to consider intraguild mutualisms. We compare species‐area relationships for mixed‐species flocks to those for the entire bird community and determine how ‘nuclear’ species, those important to flock formation or cohesion, which often have functional traits such as intraspecific gregariousness, influence flock species richness. We hypothesized that mixed flocks would only form on large islands where nuclear species are present. On the subset of larger islands, we expected flock species richness to rise with island area, reflecting a larger pool of species.LocationThirty‐one islands of varying area in the reservoir system of Thousand Island Lake, China.TaxonThe resident forest bird community and its subcomponent of flock participants. Similar to elsewhere in southern China, the primary nuclear species was Huet's Fulvetta (Alcippe hueti), which averaged 20 individuals per flock, and led the flocks when present.MethodsBirds were surveyed in the winter of 2017 and the autumn of 2021.ResultsThe probability for a flock to be present, and the species richness of birds per transect, increased with island area, but the species richness of flocks did not. Flocks with Huet's Fulvetta were only found on islands larger than 30 ha. Flocks on smaller islands formed around secondary nuclear species typical of northern Chinese flocks.Main ConclusionGregariousness as a functional trait may make nuclear species more susceptible to disappearing in small fragments, leading to flock collapse. The extreme gregariousness of fulvettas may also act to increase competition in the flocks they lead and limit the flocks' species richness. We encourage further comparisons of the species‐area curves of mixed‐species flocks and whole bird communities to better understand the influence of flocks on community assembly and species diversity.

Modelling flocks of birds and colonies of ants from the bottom up

This paper advocates the use of compositional specifications based on formal languages as a means of modelling and analysing sophisticated collective behaviour in natural systems. With the use of appropriate linguistic constructs, models can be developed that are both compact and intuitive, and can be easily refined and extended in small steps. Automated workflows can be implemented on top of this methodology to provide quick feedback, enabling rapid design iterations. To support our argument, we present three examples from the natural world, focusing on flocks of birds and colonies of ants, which feature well-known examples of emergent behaviour in collective adaptive systems. We use an agent-based language to develop simple models that aim at capturing these collective phenomena, and discuss the specific language constructs that we use in the process. Then, we adapt an existing verification tool for the language to simulate our models, and show that our simulations do display emergent behaviour.

Understanding Collective Human Behavior in Social Media Networks Via the Dynamical Hypothesis: Applications to Radicalization and Conspiratorial Beliefs.

The dynamical hypothesis has served to explore the ways in which cognitive agents can be understood dynamically and considered dynamical systems. Originally used to explain simple physical systems as a metaphor for cognition (i.e., the Watt governor) and eventually more complex animal systems (e.g., bird flocks), we argue that the dynamical hypothesis is among the most viable approaches to understanding pressing modern-day issues that arise from collective human behavior in online social networks. First, we discuss how the dynamical hypothesis is positioned to describe, predict, and explain the time-evolving nature of complex systems. Next, we adopt an interdisciplinary perspective to describe how online social networks are appropriately understood as dynamical systems. We introduce a dynamical modeling approach to reveal information about emergent properties in social media, where radicalized conspiratorial beliefs arise via coordination between user-level and community-level comments. Lastly, we contrast how the dynamical hypothesis differs from alternatives in explaining collective human behavior in social networks.

Measuring response functions of active materials from data

From flocks of birds to biomolecular assemblies, systems in which many individual components independently consume energy to perform mechanical work exhibit a wide array of striking behaviors. Methods to quantify the dynamics of these so-called active systems generally aim to extract important length or time scales from experimental fields. Because such methods focus on extracting scalar values, they do not wring maximal information from experimental data. We introduce a method to overcome these limitations. We extend the framework of correlation functions by taking into account the internal headings of displacement fields. The functions we construct represent the material response to specific types of active perturbation within the system. Utilizing these response functions we query the material response of disparate active systems composed of actin filaments and myosin motors, from model fluids to living cells. We show we can extract critical length scales from the turbulent flows of an active nematic, anticipate contractility in an active gel, distinguish viscous from viscoelastic dissipation, and even differentiate modes of contractility in living cells. These examples underscore the vast utility of this method which measures response functions from experimental observations of complex active systems.

Multi-scale dynamic imaging reveals that cooperative motility behaviors promote efficient predation in bacteria

Many species, such as fish schools or bird flocks, rely on collective motion to forage, prey, or escape predators. Likewise, Myxococcus xanthus forages and moves collectively to prey and feed on other bacterial species. These activities require two distinct motility machines enabling adventurous (A) and social (S) gliding, however when and how these mechanisms are used has remained elusive. Here, we address this long-standing question by applying multiscale semantic cell tracking during predation. We show that: (1) foragers and swarms can comprise A- and S-motile cells, with single cells exchanging frequently between these groups; (2) A-motility is critical to ensure the directional movement of both foragers and swarms; (3) the combined action of A- and S-motile cells within swarms leads to increased predation efficiencies. These results challenge the notion that A- and S-motilities are exclusive to foragers and swarms, and show that these machines act synergistically to enhance predation efficiency.

Enhancing sepsis detection with nature-inspired optimization integrated neural network

Sepsis, a disorder that can be fatal that is brought on by a dysregulated immunological reaction to an infection, keeps presenting complex problems for early detection and treatment. In this article, they suggest a novel (MBSO-LSTM) method for improving sepsis diagnosis that combines long short-term memory (LSTM) networks and modified bird swarm optimization (MBSO). The biases and weights of the network known as LSTM are optimized using the MBSO algorithm, which was developed in response to research on the social behavior of bird flocks. MBSO enables faster and more successful learning of the LSTM model, resulting in increased sepsis detection performance by emulating the self-organizing behavior of bird swarms. Our work uses a sizable collection of medical records from individuals who have probable sepsis, including demographic information, laboratory findings, and vital signs. The LSTM network in the suggested framework recognizes the time-dependent relationships and patterns present in the data by using these characteristics as inputs. They contrast our method's effectiveness with other cutting-edge sepsis detection strategies, such as conventional approaches. The outcomes show that concerning sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC-ROC), our proposed system performs better than alternative methods.

Interindividual distances and orientations of laying hens under 8 stocking densities measured by integrative deep learning techniques

Interindividual distances and orientations of laying hens provide quantitative measures to calculate and optimize space allocations for bird flocks. However, these metrics were often measured manually and have not been examined for different stocking densities of laying hens. The objectives of this study were to 1) integrate and develop several deep learning techniques to detect interindividual distances and orientations of laying hens; and 2) examine the 2 metrics under 8 stocking densities via the developed techniques. Laying hens (Jingfen breed, a popular hen breed in China) at 35 wk of age were raised in experimental compartments at 8 different stocking densities of 3,840, 2,880, 2,304, 1,920, 1,646, 1,440, 1,280, and 1,152 cm2•bird-1 (3-10 hens per compartment, respectively), and cameras on the top of the compartments recorded videos for further analysis. The designed deep learning image classifier achieved over 99% accuracy to classify bird's perching status and excluded frames with bird perching to ensure that all birds analyzed were on the same horizontal plane, reducing calculation errors. The YOLOv5m oriented object detection model achieved over 90% precision, recall, and F1 score in detecting birds in compartments and can output bird centroid coordinates and angles, from which interindividual distances and orientations were calculated based on pairs of birds. Laying hens maintained smaller minimum interindividual distances in higher stocking densities. They were in an intersecting relationship with conspecifics for over 90% of the time. The developed integrative deep learning techniques and behavior metrics provide animal-based measurement of space requirement for laying hens.

The Large-Scale Cultivation of Nematodes to Study Their Collective Behaviors.

Animals exhibit dynamic collective behaviors, as observed in flocks of birds, schools of fish, and crowds of humans. The collective behaviors of animals have been investigated in the fields of both biology and physics. In the laboratory, researchers have used various model animals such as the fruit fly and zebrafish for approximately a century, but it has remained a major challenge to study large-scale complex collective behavior orchestrated by these genetically tractable model animals. This paper presents a protocol to create an experimental system of collective behaviors in Caenorhabditis elegans. The propagated worms climb on the lid of the Petri plate and show collective swarming behavior. The system also controls worm interactions and behaviors by changing the humidity and light stimulation. This system allows us to examine the mechanisms underlying collective behaviors by changing environmental conditions and examining the effects of individual-level locomotion on collective behaviors using mutants. Thus, the system is useful for future research in both physics and biology.

Immunogenicity and Cross-Protective Efficacy Induced by an Inactivated Recombinant Avian Influenza A/H5N1 (Clade 2.3.4.4b) Vaccine against Co-Circulating Influenza A/H5Nx Viruses.

Controlling avian influenza viruses (AIVs) is mainly based on culling of the infected bird flocks or via the implementation of inactivated vaccines in countries where AIVs are considered to be endemic. Over the last decade, several avian influenza virus subtypes, including highly pathogenic avian influenza (HPAI) H5N1 clade 2.2.1.2, H5N8 clade 2.3.4.4b and the recent H5N1 clade 2.3.4.4b, have been reported among poultry populations in Egypt. This demanded the utilization of a nationwide routine vaccination program in the poultry sector. Antigenic differences between available avian influenza vaccines and the currently circulating H5Nx strains were reported, calling for an updated vaccine for homogenous strains. In this study, three H5Nx vaccines were generated by utilizing the reverse genetic system: rgH5N1_2.3.4.4, rgH5N8_2.3.4.4 and rgH5N1_2.2.1.2. Further, the immunogenicity and the cross-reactivity of the generated inactivated vaccines were assessed in the chicken model against a panel of homologous and heterologous H5Nx HPAIVs. Interestingly, the rgH5N1_2.3.4.4 induced high immunogenicity in specific-pathogen-free (SPF) chicken and could efficiently protect immunized chickens against challenge infection with HPAIV H5N1_2.3.4.4, H5N8_2.3.4.4 and H5N1_2.2.1.2. In parallel, the rgH5N1_2.2.1.2 could partially protect SPF chickens against infection with HPAIV H5N1_2.3.4.4 and H5N8_2.3.4.4. Conversely, the raised antibodies to rgH5N1_2.3.4.4 could provide full protection against HPAIV H5N1_2.3.4.4 and HPAIV H5N8_2.3.4.4, and partial protection (60%) against HPAIV H5N1_2.2.1.2. Compared to rgH5N8_2.3.4.4 and rgH5N1_2.2.1.2 vaccines, chickens vaccinated with rgH5N1_2.3.4.4 showed lower viral shedding following challenge infection with the predefined HPAIVs. These data emphasize the superior immunogenicity and cross-protective efficacy of the rgH5N1_2.3.4.4 in comparison to rgH5N8_2.3.4.4 and rgH5N1_2.2.1.2.

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.