Simultaneous measurements of three-dimensional trajectories and wingbeat frequencies of birds in the field

This is the author accepted manuscript. The final version is available from the Royal Society via the DOI in this recordData accessibility: We provide data including images recorded by four cameras, camera parameters, videos showing the time variation of the bird 3D positions, and plain text files that include bird id number, positions, times, velocities, accelerations, and wingbeat frequencies at every time step. We also provide the Matlab codes that were used to: (i) detect birds on images; (ii) reconstruct birds' 3D locations using the new stereo-matching algorithm; (iii) track individual's 3D motions; and (iv) calculate wing motion and wingbeat frequency from tracking results. The code and data are available at: https://github.com/linghj/3DTracking.git and https://figshare.com/s/3c572f91b07b06ed30aa.Tracking the movements of birds in three dimensions is integral to a wide range of problems in animal ecology, behaviour and cognition. Multi-camera stereo-imaging has been used to track the three-dimensional (3D) motion of birds in dense flocks, but precise localization of birds remains a challenge due to imaging resolution in the depth direction and optical occlusion. This paper introduces a portable stereo-imaging system with improved accuracy and a simple stereo-matching algorithm that can resolve optical occlusion. This system allows us to decouple body and wing motion, and thus measure not only velocities and accelerations but also wingbeat frequencies along the 3D trajectories of birds. We demonstrate these new methods by analysing six flocking events consisting of 50 to 360 jackdaws (Corvus monedula) and rooks (Corvus frugilegus) as well as 32 jackdaws and 6 rooks flying in isolated pairs or alone. Our method allows us to (i) measure flight speed and wingbeat frequency in different flying modes; (ii) characterize the U-shaped flight performance curve of birds in the wild, showing that wingbeat frequency reaches its minimum at moderate flight speeds; (iii) examine group effects on individual flight performance, showing that birds have a higher wingbeat frequency when flying in a group than when flying alone and when flying in dense regions than when flying in sparse regions; and (iv) provide a potential avenue for automated discrimination of bird species. We argue that the experimental method developed in this paper opens new opportunities for understanding flight kinematics and collective behaviour in natural environments.Human Frontier Science Progra

Swarm Intelligence

Swarm Intelligence has emerged as one of the most studied artificial intelligence branches during the last decade, constituting the fastest growing stream in the bio-inspired computation community. A clear trend can be deduced analyzing some of the most renowned scientific databases available, showing that the interest aroused by this branch has increased at a notable pace in the last years. This book describes the prominent theories and recent developments of Swarm Intelligence methods, and their application in all fields covered by engineering. This book unleashes a great opportunity for researchers, lecturers, and practitioners interested in Swarm Intelligence, optimization problems, and artificial intelligence

Simulating collective motion from particles to birds

The main work of this thesis is the construction of a 3D computer model of animal flocking based on vision. The model took an additional input, to those usually considered in tradition models: the projection of all other flock members on to an individual's field of view. Making 2D models is easy (in fact 4 new ones are included in this thesis), but we should be drawing parallels with experimental data for behaviour in animal systems and we should be cautious indeed when drawing conclusions, based on those models. It is common in the literature not to compare model behaviours with measurable quantities of natural flocks. However this work makes a concerted effort to do so in the case of the 3D model. A direct comparison was made in this work between the simulations and an empirical study of starling flocks, of the scaling behaviour of the maximum distance through the flock and the number of flock members, for which the agreement was very good. Other flock properties were compared with the natural flocks, but with less satisfactory results. A careful literature survey was made to investigate and ultimately support the biological plausibility of the 3D projection model. Biological and physiological plausibility is a factor not often considered by computational modellers. A series of novel and related 2D computer flocking models were investigated with hopes to find a single flocking rule that could manifest the most important features of collective motion and thereby be highly parsimonious. The final part of this thesis concerns a 2D computer model of photothermophoresis based on langevin dynamics, which it may be possible to use to find evidence of a density transition found in the continuum model. There was some evidence that a transition from a transparent diffuse state to an opaque compact one may exist for the discrete particle simulation

Formation and organisation in robot swarms.

A swarm is defined as a large and independent collection of heterogeneous or homogeneous agents operating in a common environment and seemingly acting in a coherent and coordinated manner. Swarm architectures promote decentralisation and self-organisation which often leads to emergent behaviour. The emergent behaviour of the swarm results from the interactions of the swarm with its environment (or fellow agents), but not as a direct result of design. The creation of artificially simulated swarms or practical robot swarms has become an interesting topic of research in the last decade. Even though many studies have been undertaken using a practical approach to swarm construction, there are still many problems need to be addressed. Such problems include the problem of how to control very simple agents to form patterns; the problem of how an attractor will affect flocking behaviour; and the problem of bridging formation of multiple agents in connecting multiple locations. The central goal of this thesis is to develop early novel theories and algorithms to support swarm robots in. pattern formation tasks. To achieve this, appropriate tools for understanding how to model, design and control individual units have to be developed. This thesis consists of three independent pieces of research work that address the problem of pattern formation of robot swarms in both a centralised and a decentralised way.The first research contribution proposes algorithms of line formation and cluster formation in a decentralised way for relatively simple homogenous agents with very little memory, limited sensing capabilities and processing power. This research utilises the Finite State Machine approach.In the second research contribution, by extending Wilensky's (1999) work on flocking, three different movement models are modelled by changing the maximum viewing angle each agent possesses during the course of changing its direction. An object which releases an artificial potential field is then introduced in the centre of the arena and the behaviours of the collective movement model are studied.The third research contribution studies the complex formation of agents in a task that requires a formation of agents between two locations. This novel research proposes the use Of L-Systems that are evolved using genetic algorithms so that more complex pattern formations can be represented and achieved. Agents will need to have the ability to interpret short strings of rules that form the basic DNA of the formation

Partitioning Method for Emergent Behavior Systems Modeled by Agent-Based Simulations

Used to describe some interesting and usually unanticipated pattern or behavior, the term emergence is often associated with time-evolutionary systems comprised of relatively large numbers of interacting yet simple entities. A significant amount of previous research has recognized the emergence phenomena in many real-world applications such as collaborative robotics, supply chain analysis, social science, economics and ecology. As improvements in computational technologies combined with new modeling paradigms allow the simulation of ever more dynamic and complex systems, the generation of data from simulations of these systems can provide data to explore the phenomena of emergence. To explore some of the modeling implications of systems where emergent phenomena tend to dominate, this research examines three simulations based on familiar natural systems where each is readily recognized as exhibiting emergent phenomena. To facilitate this exploration, a taxonomy of Emergent Behavior Systems (EBS) is developed and a modeling formalism consisting of an EBS lexicon and a formal specification for models of EBS is synthesized from the long history of theories and observations concerning emergence. This modeling formalism is applied to each of the systems and then each is simulated using an agent-based modeling framework. To develop quantifiable measures, associations are asserted: 1) between agent-based models of EBS and graph-theoretical methods, 2) with respect to the formation of relationships between entities comprising a system and 3) concerning the change in uncertainty of organization as the system evolves. These associations form the basis for three measurements related to the information flow, entity complexity, and spatial entropy of the simulated systems. These measurements are used to: 1) detect the existence of emergence and 2) differentiate amongst the three systems. The results suggest that the taxonomy and formal specification developed provide a workable, simulation-centric definition of emergent behavior systems consistent with both historical concepts concerning the emergence phenomena and modern ideas in complexity science. Furthermore, the results support a structured approach to modeling these systems using agent-based methods and offers quantitative measures useful for characterizing the emergence phenomena in the simulations

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

Fine-scale changes in flight effort revealed by animal-borne loggers

The movements of the air are central to the life of flying birds, because they can determine whether the costs of flight are closer to resting or sprinting, and whether birds are able to reach their destination. Yet for species relying mainly on flapping flight, studies about the effects of weather on flight effort have mainly focussed on wind, with other atmospheric factors receiving less attention. In addition, with the development of new technologies to measure flight effort, it has become clear that some methods need standardisation and further verification. The goal of this PhD is to provide insight into how atmospheric conditions affect flight costs more broadly and study the extent to which birds prioritise energy expenditure over other currencies, such as time and risk. I used high-frequency data-loggers to explore the combined effects of wind and thermals, as well as air density, on flight effort over fine scales, as well as how birds adjust their behaviour to these factors. Results showed that pigeons (Columba livia), which are not limited by energy expenditure, prioritise speed over energy savings, and use a very costly flight style which could serve as a predator-avoidance strategy. I also found that wind support was a strong predictor of whether chick-rearing tropicbirds (Phaethon rubricauda) use thermal soaring to save energy during foraging trips, suggesting that birds were weighing up the trade-off between energy and time, and chose to save energy only when this would not cost them too much time. Comparison of air density between seasons also revealed that the flapping flight of tropicbirds was more costly during summer, when air density was lower. This finding shows that the effect of seasonal changes in air density on flight costs is significant, outweighing the influence of both wind and thermal availability. It also sheds new light on how flight costs (particularly those in tropical birds) might be affected by global change. Finally, the analysis of the accelerometer data showed that the type of tag used, as well as differences in the longitudinal position and attachment method, affected the amplitude of the signal, which has implications for the robustness of acceleration-based proxies for flight effort. Nonetheless, the adoption of standardized calibrations should facilitate the comparison of these metrics between study sites and through time, improving the prospect that they can be used to study the effect of a changing climate on flight costs and avian ecology