Bird Flocks Research Articles

Plantation forestry of Alder-leaf Birch (Betula alnoides) affects composition but not interactions of mixed-species bird flocks in southwestern China

Human disturbance impacts mixed-species bird flocks (“flocks”). Unfortunately, the impact on flocks by one large-scale disturbance, plantation forestry, has remained little explored. We examined how plantation forestry of a widespread yet understudied timber species, Alder-leaf Birch (Betula alnoides, “birch”), affects the composition and interactions of flocks in the Indo-Burma biodiversity hotspot in southwestern China. We conducted transect surveys to sample flocks in birch plantations and natural forests of two age classes (mature and young). While flock size and species richness per flock were similar across land-use types, rarefied species richness accounting for unequal sampling effort was noticeably higher in mature forests. Furthermore, flock composition differed across land-use types, with differences related to species' morphological characteristics, dietary preferences, and foraging substrates. Specifically, mature forests supported flock participants with large bodies, poor dispersal ability, and a fruit-eating diet. Birch plantations offered equal support to invertivores as both natural forests, and additional support to bark foragers. Lastly, interactions among flocking species quantified by social network metrics were similar across land-use types, suggesting that birch plantations perserved the flocking behavior itself. Our study reveals the conservation potential of birch plantations in supporting invertivorous birds and preserving interactions in flocks. More importantly, it highlights the irreplaceability of mature forests because of their unique species composition. We recommend promoting birch planting without compromising local economies and protecting remnant mature forests through education programs and continued research.

Exotic swarming dynamics of high-dimensional swarmalators.

Swarmalators are oscillators that can swarm as well as sync via a dynamic balance between their spatial proximity and phase similarity. Swarmalator models employed so far in the literature comprise only one-dimensional phase variables to represent the intrinsic dynamics of the natural collectives. Nevertheless, the latter can indeed be represented more realistically by high-dimensional phase variables. For instance, the alignment of velocity vectors in a school of fish or a flock of birds can be more realistically set up in three-dimensional space, while the alignment of opinion formation in population dynamics could be multidimensional, in general. We present a generalized D-dimensional swarmalator model, which more accurately captures self-organizing behaviors of a plethora of real-world collectives by self-adaptation of high-dimensional spatial and phase variables. For a more sensible visualization and interpretation of the results, we restrict our simulations to three-dimensional spatial and phase variables. Our model provides a framework for modeling complicated processes such as flocking, schooling of fish, cell sorting during embryonic development, residential segregation, and opinion dynamics in social groups. We demonstrate its versatility by capturing the maneuvers of a school of fish, qualitatively and quantitatively, by a suitable extension of the original model to incorporate appropriate features besides a gallery of its intrinsic self-organizations for various interactions. We expect the proposed high-dimensional swarmalator model to be potentially useful in describing swarming systems and programmable and reconfigurable collectives in a wide range of disciplines, including the physics of active matter, developmental biology, sociology, and engineering.

Swarm Guidance Based on Mean Field Game Concepts

The concept of swarm comes from the biological world. Drones gather in groups of 100 or even 1000 to fly like a flock of birds, called swarms. Swarm systems satisfy several assumptions such as decentralized controls, local information and simple platforms. Swarm systems have attractive properties such as resilience, scalability, and ease of development and implementation. Swarm techniques can perform simple tasks such as moving in a coordinated direction. The flocking behavior of a group of animals that converge by local interactions toward the same heading is an example of simple consensus for decentralized dynamic systems. However, the notion of decentralized control based on local information suffers from taking into account the overall behavior of the group. For example, in a complex environment, a swarm will adapt to the presence of obstacles and congestion reactively, whereas we would like more anticipatory control. The objective of this paper is to propose a solution based on Mean Field Game (MFG) concepts to integrate macro-level knowledge at the micro-level in decentralized flocking.

Flow interactions lead to self-organized flight formations disrupted by self-amplifying waves

Collectively locomoting animals are often viewed as analogous to states of matter in that group-level phenomena emerge from individual-level interactions. Applying this framework to fish schools and bird flocks must account for visco-inertial flows as mediators of the physical interactions. Motivated by linear flight formations, here we show that pairwise flow interactions tend to promote crystalline or lattice-like arrangements, but such order is disrupted by unstably growing positional waves. Using robotic experiments on “mock flocks” of flapping wings in forward flight, we find that followers tend to lock into position behind a leader, but larger groups display flow-induced oscillatory modes – “flonons” – that grow in amplitude down the group and cause collisions. Force measurements and applied perturbations inform a wake interaction model that explains the self-ordering as mediated by spring-like forces and the self-amplification of disturbances as a resonance cascade. We further show that larger groups may be stabilized by introducing variability among individuals, which induces positional disorder while suppressing flonon amplification. These results derive from generic features including locomotor-flow phasing and nonreciprocal interactions with memory, and hence these phenomena may arise more generally in macroscale, flow-mediated collectives.

ATI: Assemble topological interaction overcomes consistency–cohesion trade‐off in bird flocking

AbstractIn nature, various animal groups like bird flocks display proficient collective navigation achieved by maintaining high consistency and cohesion simultaneously. Both metric and topological interactions have been explored to ensure high consistency among groups. The topological interactions found in bird flocks are more cohesive than metric interactions against external perturbations, especially the spatially balanced topological interaction (SBTI). However, it is revealed that in complex environments, pursuing cohesion via existing interactions compromises consistency. The authors introduce an innovative solution, assemble topological interaction, to address this challenge. Contrasting with static interaction rules, the new interaction empowers individuals with self‐awareness to adapt to the complex environment by switching between interactions through visual cues. Most individuals employ high‐consistency k‐nearest topological interaction when not facing splitting threats. In the presence of such threats, some switch to the high‐cohesion SBTI to avert splitting. The assemble topological interaction thus transcends the limit of the trade‐off between consistency and cohesion. In addition, by comparing groups with varying degrees of these two features, the authors demonstrate that group effects are vital for efficient navigation led by a minority of informed agents. Finally, the real‐world drone‐swarm experiments validate the applicability of the proposed interaction to artificial robotic collectives.

BirdCollect: A Comprehensive Benchmark for Analyzing Dense Bird Flock Attributes

Automatic recognition of bird behavior from long-term, un controlled outdoor imagery can contribute to conservation efforts by enabling large-scale monitoring of bird populations. Current techniques in AI-based wildlife monitoring have focused on short-term tracking and monitoring birds individually rather than in species-rich flocks. We present Bird-Collect, a comprehensive benchmark dataset for monitoring dense bird flock attributes. It includes a unique collection of more than 6,000 high-resolution images of Demoiselle Cranes (Anthropoides virgo) feeding and nesting in the vicinity of Khichan region of Rajasthan. Particularly, each image contains an average of 190 individual birds, illustrating the complex dynamics of densely populated bird flocks on a scale that has not previously been studied. In addition, a total of 433 distinct pictures captured at Keoladeo National Park, Bharatpur provide a comprehensive representation of 34 distinct bird species belonging to various taxonomic groups. These images offer details into the diversity and the behaviour of birds in vital natural ecosystem along the migratory flyways. Additionally, we provide a set of 2,500 point-annotated samples which serve as ground truth for benchmarking various computer vision tasks like crowd counting, density estimation, segmentation, and species classification. The benchmark performance for these tasks highlight the need for tailored approaches for specific wildlife applications, which include varied conditions including views, illumination, and resolutions. With around 46.2 GBs in size encompassing data collected from two distinct nesting ground sets, it is the largest birds dataset containing detailed annotations, showcasing a substantial leap in bird research possibilities. We intend to publicly release the dataset to the research community. The database is available at: https://iab-rubric.org/resources/wildlife-dataset/birdcollect

Проблема приватности в цифровом обществе роевого интеллекта

Introduction. The development of digital technologies requires the specification of the type of the emerging digital society. One of the development scenarios is the formation of a digital society of swarm intelligence. Theoretical analysis. The concept of swarm intelligence means an optimization algorithm that imitates the behavior of swarms or colonies of insects and bird flocks. Human society is characterized by the manifestation of swarm intelligence as a condition of joint organized activity, but the development of digital technologies brings swarm intelligence to a higher level. Creation of private swarms causes risks in the form of “Byzantine robot” – a hacked particle of the swarm, transmitting false information and jeopardizing the whole swarm. The solution is to incorporate elements of the economic game that support the values of honesty, cooperation and solidarity into the particle interactions. This leads to making privacy a “process” and changing its value status. Protecting privacy in swarm intelligence society has the potential to cause labor to lose its public status and acquire a private, hidden status and anonymize employment. Conclusion. The possible scenario of human society development towards the digital society of swarm intelligence can become a source of profound social changes and, above all, a rethinking of the social stratification based on the division of labor and social hierarchy in general.

Effect of Strouhal number on propulsion of tandem flapping foils

Motivated by the energy efficient locomotion by flock of birds and school of fish to develop biomimetic systems, the current study studies the propulsive performance of the tandem configuration of two-foil system. The effects of the kinematic motion parameters such as heave and pitch amplitudes and flapping frequency on the propulsive performance of the tandem two-foil system is yet to be explored comprehensively due to the complex nature of the wake-foil interaction. Here, we numerically investigate these effects by utilizing a finite element based moving mesh framework to model the flapping foil motion subjected to fluid flow. The performance of the upstream foil is found to be similar to that of an isolated single foil. However, for the downstream foil, a beating phenomenon is observed in the thrust response when the flapping frequency is different for both the foils. This feature can help tune propulsive performance of the foil system. Furthermore, through a control volume analysis, we show that sole reliance on the time-averaged streamwise velocity may not yield correct trends for the mean thrust forces, which has been typically followed in the literature. Variation in the time-averaged pressure in the wake is significant in predicting correct thrust forces.

Differences in mixed‐species bird flocks across forest succession: Combining network analysis and trait‐based ecology related to the fast‐slow continuum

Abstract Secondary forests are increasingly important for biodiversity conservation and ecosystem functioning. Yet, we know little on how species interactions shift across the wide range of life history strategies in the context of forest succession, especially for non‐trophic animal social systems. Using data from mixed‐species flocks (hereafter MSFs) and life history and morphological traits from the ‘fast‐slow’ continuum, we utilized a novel combination of network theory and trait‐based ecology to examine the assembly of associations of MSFs among species with different fast‐slow strategies across stages of forest succession that exhibit different degrees of forest complexity. MSFs formed the most connected and cohesive network in the intermediate successional stage. In contrast, we observed the least connected and cohesive network in the early stage, and a strongly connected but intermediately cohesive network in the late stage. Fast species with smaller body size and mass, and larger clutch size, were gradually replaced by slow species during succession. Interspecific associations generally shifted from those among similarly fast species (early stage) to associations between fast and slow species (intermediate stage), and finally to those among similarly slow species (late stage). We suggest that vegetation complexity (e.g. tree size, height, diversity and canopy cover), related to the availability of resources and number of niches, shaped the network structure and functional composition of MSFs. Although the late successional forests play an irreplaceable role in protecting forest specialists (i.e. slow species), intermediate stage forests should be also protected, because the large‐sized networks formed by MSFs at intermediate stages can simultaneously conserve more species with varying ecological strategies. Read the free Plain Language Summary for this article on the Journal blog.

Using birds of prey to manage pest bird flocks under lethal and non-lethal conditions – A review

Abstract Raptors have been successfully used to disperse and often control flocks of pest birds. However, the question that has not been resolved is, “If the raptor kills a small number of the target flock, does this improve the efficiency of control?” This mini-review examines the few research reports that have been published that can shed light on this question.

Newcastle Disease Outbreak in Backyard Poultry: Herbal Remediation Using Phyllanthus Polygonoides and Carica Papaya Leaves

Newcastle disease (ND) is a devastating disease of backyard poultry and causes great economic losses to rural poor. The study was undertaken to assess the effectiveness of herbal extracts of Phyllanthus polygonoides and Carica papaya leaves for control of Newcastle outbreak in backyard poultry. A total of 60 representative desi fowl (chicken 50, turkey 7 and duck 3) from 40 flocks were presented to VCC hospital, VC&RI, Orathanadu for treatment. The samples like oropharyngeal fluid/ clocal swab was collected from 40 birds (flocks) and performed haemagglutination test. Out of 40 birds screened, 18 birds (Chicken-13, turkey- 4 and Duck- 1) were positive for haemagglutination test and showed the presence of Newcastle disease virus and confirmed by PCR. The infected birds were treated with aqueous extract of Carica papaya and Phyllanthus polygonoides leaves @ 0.5ml/ kg b.wt orally for 3 days. Most of the birds recovered in single dose, the moderately infected birds recovered after 3 days of treatment. The herbal extract was also used prophylactically other than the infected birds in their respective flocks to prevent the infection. None of the adult birds in their flocks become infected and protected from infection. The plant juice of Phyllanthus polygonoides and Carica papaya leaves were highly effective for control of ND in backyard poultry, it could be used prophylactically every 30 days to prevent Newcastle disease outbreak in backyard poultry to minimize economic losses to the farmers.

Optimising peak energy reduction in networks of buildings

Buildings are amongst the world’s largest energy consumers and simultaneous peaks in demand from networks of buildings can decrease electricity system stability. Current mitigation measures either entail wasteful supply-side over-specification or complex centralised demand-side control. Hence, a simple schema is developed for decentralised, self-organising building-to-building load coordination that requires very little information exchange and no top-down management—analogous to other complex systems with short range interactions, such as coordination between flocks of birds or synchronisation in fireflies. Numerical and experimental results reveal that a high degree of peak flattening can be achieved using surprisingly small load-coordination networks. The optimum reductions achieved by the simple schema can outperform existing techniques, giving substantial peak-reductions as well as being remarkably robust to changes in other system parameters such as the interaction network topology. This not only demonstrates that significant reductions in network peaks are achievable using remarkably simple control systems but also reveals interesting theoretical results and new insights which will be of great interest to the complexity and network science communities.

Research on Ground Object Echo Simulation of Avian Lidar

The clutter suppression effect of ground objects significantly impacts the detection and tracking performance of avian lidar on low-altitude bird flock targets. It is imperative to simulate the point cloud data of ground objects in lidar to explore effective methods for suppressing clutter caused by ground objects in avian lidar. The traditional ray-tracing method is enhanced in this paper to efficiently obtain the point cloud simulation results of ground objects. By incorporating a beam constraint and a light-energy constraint, the screening efficiency of effective rays is improved, making them more suitable for simulating large scenes with narrow lidar beams. In this paper, a collision detection scheme is proposed based on beam constraints, aiming to significantly enhance the efficiency of ray-tracing collision detection. The simulation and experimental results demonstrate that, in comparison with other conventional simulation methods, the proposed method yields the point cloud results of ground objects that exhibit greater conformity to the actual lidar-collected point cloud results in terms of shape characteristics and intensity features. Additionally, the simulation speed is significantly enhanced.

Directional synchrony among self-propelled particles under spatial influence.

Synchronization is one of the emerging collective phenomena in interacting particle systems. Its ubiquitous presence in nature, science, and technology has fascinated the scientific community over the decades. Moreover, a great deal of research has been, and is still being, devoted to understand various physical aspects of the subject. In particular, the study of interacting active particles has led to exotic phase transitions in such systems which have opened up a new research front-line. Motivated by this line of work, in this paper, we study the directional synchrony among self-propelled particles. These particles move inside a bounded region, and crucially their directions are also coupled with spatial degrees of freedom. We assume that the directional coupling between two particles is influenced by the relative spatial distance which changes over time. Furthermore, the nature of the influence is considered to be both short and long-ranged. We explore the phase transition scenario in both the cases and propose an approximation technique which enables us to analytically find the critical transition point. The results are further supported with numerical simulations. Our results have potential importance in the study of active systems like bird flocks, fish schools, and swarming robots where spatial influence plays a pertinent role.

Bird Flocking Inspired Methods for Multi-UAV Cooperative Target Search

Bird Flocking Inspired Methods for Multi-UAV Cooperative Target Search

Zebra finches increase social behavior in traffic noise: Implications for urban songbirds

Traffic noise is a pervasive pollutant that affects wildlife at individual and group levels through mechanisms such as disrupting communication, affecting antipredator strategy, and/or changing how they use space within a habitat. Urbanization is expanding rapidly—few places remain untouched by anthropogenic noise disturbance—so understanding the implications of noise on wildlife behavior is paramount to conservation efforts. We asked whether traffic noise could change space use and social network metrics in flocks of captive birds. Specifically, we quantified the effects of playbacks of traffic noise on individual sociality (weighted degree, number of social partners weighted by the frequency of interactions with those social partners) and flock clustering (global clustering coefficient, connectivity of neighbors). In this study, we recorded social interactions and space use of flocks of captive zebra finches (Taeniopygia guttata) before, during, and after an experimental traffic noise introduction in two treatments: high- and lower-amplitude noise. Our results demonstrated that individual sociality and flock clustering increased in response to the noise introduction in both high-amplitude and low-amplitude treatments. Additionally, birds in the high-amplitude treatment spent more time in the room with active playback during noise playback whereas birds in the lower-amplitude treatment decreased time spent in the room closest to the high-amplitude treatment. Increased social behavior in response to traffic noise could influence disease transmission, social learning, and mating dynamics. We suggest future studies explore the mechanisms driving increased social behavior in traffic noise, such as perceived predation risk, vigilance, and cross-sensory interference.

Swarm Robotics in a Sustainable Warehouse Automation: Opportunities, Challenges and Solutions

The Over 90% of the jobs in the logistics industry between 2010 and 2014 came from freight transport and storage services, according to statistics from the Census and Statistics Department. With the advent of Industry 4.0 and its associated emerging technologies (such as cloud computing, Internet of Things, autonomous robots, etc.), a smart robotic warehouse management system is recommended. These innovations transform picking and put-away procedures in warehouses by enabling autonomous mobile robots to transition from man-to-goods to goods-to-man. A group of robots collaborate to solve problems in swarm robotics by putting together practical structures and behaviours that are similar to those found in flocks of birds, schools of fish, or swarms of bees. However, the transition to industrial applications has not yet been completed to a satisfactory level. There is a dearth of knowledge in the literature regarding real-world swarm applications that make use of swarm algorithms. Typically, swarm algorithm components—or what we call basic swarm behaviours —are used. This paper therefore discusses the opportunities available for this technology as well as the challenges that may come with their use. Finally, some possible solutions have been proposed to help tackle the identified challenges in sustainable warehouse automation.

Density Control of Interacting Agent Systems

We consider the problem of controlling the group behavior of a large number of dynamic systems that are constantly interacting with each other. These systems are assumed to have identical dynamics (e.g., flocks of birds, UAV swarms) and their group behavior can be modeled by a distribution. Thus, this problem can be viewed as an optimal control problem over the space of distributions. We propose a novel algorithm to compute a feedback control strategy so that, when adopted by the agents, the distribution of them would be transformed from an initial one to a target one over a finite time window. Our method is built on the optimal transport theory but differs significantly from existing work in this area in that our method models the interactions among agents explicitly. From an algorithmic point of view, our algorithm is based on the generalized proximal gradient descent algorithm and has a convergence guarantee with a sublinear rate. We further extend our framework to account for the scenarios where the agents are from multiple species. In the linear quadratic setting, the solution is characterized by a system of coupled Riccati equations which can be solved in closed form. Finally, several numerical examples are presented to illustrate our framework.

Multi-population dissolution in confined active fluids.

Autonomous out-of-equilibrium agents or cells in suspension are ubiquitous in biology and engineering. Turning chemical energy into mechanical stress, they generate activity in their environment, which may trigger spontaneous large-scale dynamics. Often, these systems are composed of multiple populations that may reflect the coexistence of multiple species, differing phenotypes, or chemically varying agents in engineered settings. Here, we present a new method for modeling such multi-population active fluids subject to confinement. We use a continuum multi-scale mean-field approach to represent each phase by its first three orientational moments and couple their evolution with those of the suspending fluid. The resulting coupled system is solved using a parallel adaptive level-set-based solver for high computational efficiency and maximal flexibility in the confinement geometry. Motivated by recent experimental work, we employ our method to study the spatiotemporal dynamics of confined bacterial suspensions and swarms dominated by fluid hydrodynamic effects. Our in silico explorations reproduce observed emergent collective patterns, including features of active dissolution in two-population active-passive swarms, with results clearly suggesting that hydrodynamic effects dominate dissolution dynamics. Our work lays the foundation for a systematic characterization and study of collective phenomena in natural or synthetic multi-population systems such as bacteria colonies, bird flocks, fish schools, colloid swimmers, or programmable active matter.

Employing Collective Intelligence at the IoT Edge for Spatial Decisions

This paper explores the synergies between collective intelligence and edge computing, presenting a novel paradigm that harnesses decentralized processing power for collaborative problem-solving. Edge devices, such as sensors and IoT devices, collect spatial data in real time from their local environments. They can incorporate machine learning algorithms to analyze spatial data, enabling quicker and more context-aware decision-making. Spatial clustering, a pivotal strategy in IoT edge computing, is examined to optimize localized data processing, enhance resource efficiency, and enable real-time analytics in decentralized environments. By leveraging the physical proximity of devices, spatial clustering contributes to the effectiveness and sustainability of IoT deployments at the edge. The paper introduces an innovative approach to adaptive spatial clustering by adopting swarm intelligence, drawing inspiration from the collective behavior of a flock of birds. Building upon the classical flock model of Reynolds, our extended model incorporates movement in a multi-dimensional space and introduces different types of birds. In this context, the birds serve as agents for discovering points with specific characteristics in a multidimensional space. The integration of swarm intelligence into spatial clustering presents a promising avenue for addressing the challenges of decentralized processing in edge computing environments, paving the way for more efficient and responsive IoT deployments.