Self-organization of collective escape in pigeon flocks

Bird flocks under predation demonstrate complex patterns of collective escape. These patterns may emerge by self-organization from local interactions among group-members. Computational models have been shown to be valuable for identifying what behavioral rules may govern such interactions among individuals during collective motion. However, our knowledge of such rules for collective escape is limited by the lack of quantitative data on bird flocks under predation in the field. In the present study, we analyze the first GPS trajectories of pigeons in airborne flocks attacked by a robotic falcon in order to build a species-specific model of collective escape. We use our model to examine a recently identified distance-dependent pattern of collective behavior: the closer the prey is to the predator, the higher the frequency with which flock members turn away from it. We first extract from the empirical data of pigeon flocks the characteristics of their shape and internal structure (bearing angle and distance to nearest neighbors). Combining these with information on their coordination from the literature, we build an agent-based model adjusted to pigeons’ collective escape. We show that the pattern of turning away from the predator with increased frequency when the predator is closer arises without prey prioritizing escape when the predator is near. Instead, it emerges through self-organization from a behavioral rule to avoid the predator independently of their distance to it. During this self-organization process, we show how flock members increase their consensus over which direction to escape and turn collectively as the predator gets closer. Our results suggest that coordination among flock members, combined with simple escape rules, reduces the cognitive costs of tracking the predator while flocking. Such escape rules that are independent of the distance to the predator can now be investigated in other species. Our study showcases the important role of computational models in the interpretation of empirical findings of collective behavior

Self-organized collective escape in bird flocks

Bird flocks show fascinating patterns of collective motion, particularly when escaping a predator. Little is known, however, about how these patterns come to be. This thesis aimed to fill this gap by analysing empirical data of bird flocks under attack by a robotic-predator and studying birds' collective escape in computer simulations. This approach was based on self-organization: the process with which patterns at the level of a group emerge from local interactions among individuals. We first studied the collective escape of pigeon flocks, discovering that group members avoid a predator more as the predator gets closer, even when they do not mind its position. We further investigated what patterns of collective escape arise in pigeons and studied how they emerge by self-organization. Second, we focused on the most common pattern of collective escape in bird flocks, the collective turn. We built an agent-based model in which flocks turn to escape a predator and used it to investigate how different turning types and specifics of coordination relate to a predator's confusion. Third, we studied the species demonstrating the most complex patterns of collective escape, the European starling. We identified that more than one pattern of collective escape may simultaneously co-occur in a single flock and developed a 3-dimensional agent-based model to study the emergence of this phenomenon. Finally, we presented the new framework in which our agent-based models have been developed, emphasizing on its contribution to the modelling of collective behaviour and towards a deeper understanding of collective escape

Emergence of splits and collective turns in pigeon flocks under predation

Complex patterns of collective behaviour may emerge through self-organization, from local interactions among individuals in a group. To understand what behavioural rules underlie these patterns, computational models are often necessary. These rules have not yet been systematically studied for bird flocks under predation. Here, we study airborne flocks of homing pigeons attacked by a robotic falcon, combining empirical data with a species-specific computational model of collective escape. By analysing GPS trajectories of flocking individuals, we identify two new patterns of collective escape: early splits and collective turns, occurring even at large distances from the predator. To examine their formation, we extend an agent-based model of pigeons with a ‘discrete’ escape manoeuvre by a single initiator, namely a sudden turn interrupting the continuous coordinated motion of the group. Both splits and collective turns emerge from this rule. Their relative frequency depends on the angular velocity and position of the initiator in the flock: sharp turns by individuals at the periphery lead to more splits than collective turns. We confirm this association in the empirical data. Our study highlights the importance of discrete and uncoordinated manoeuvres in the collective escape of bird flocks and advocates the systematic study of their patterns across species

Biologically inspired herding of animal groups by robots

A single sheepdog can bring together and manoeuvre hundreds of sheep from one location to another. Engineers and ecologists are fascinated by this sheepdog herding because of the potential it provides for ‘bio-herding’: a biologically inspired herding of animal groups by robots. Although many herding algorithms have been proposed, most are studied via simulation.There are a variety of ecological problems where management of wild animal groups is currently impossible, dangerous and/or costly for humans to manage directly, and which may benefit from bio-herding solutions.Unmanned aerial vehicles (UAVs) now deliver significant benefits to the economy and society. Here, we suggest the use of UAVs for bio-herding. Given their mobility and speed, UAVs can be used in a wide range of environments and interact with animal groups at sea, over the land and in the air.We present a potential roadmap for achieving bio-herding using a pair of UAVs. In our framework, one UAV performs ‘surveillance’ of animal groups, informing the movement of a second UAV that herds them. We highlight the promise and flexibility of a paired UAV approach while emphasising its practical and ethical challenges. We start by describing the types of experiments and data required to understand individual and collective responses to UAVs. Next, we describe how to develop appropriate herding algorithms. Finally, we describe the integration of bio-herding algorithms into software and hardware architecture

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

Συσχετισμός της φυσικής κατάστασης, μετρούμενης στη μέση ηλικία, με την αντοχή του οργανισμού στο stress κατά την τρίτη ηλικία στη Drosophila

ΣΚΟΠΟΣ: Η συγκεκριμένη έρευνα, είχε σαν σκοπό, την επίδραση του στρες στη διάρκεια ζωής, μέσω της φυσικής κατάστασης, στο πειραματικό μοντέλο Drosophila Melanogaster (αγρίου τύπου: Oregon-R). ΜΕΘΟΔΟΣ: Οι μύγες αναισθητοποιήθηκαν σε ηλικία 40-60 ημερών (ανάλογα με το πείραμα), με διοξείδιο του άνθρακα, για να απομονωθούν τα αρσενικά στελέχη. Μετά από 24 ώρες γινόταν έλεγχος της ταχύτητας αναρρίχησης, των αρσενικών στελεχών (negative geotaxis assay), ξεχωριστά για το κάθε ένα (single fly), με κάμερα Conrad. Η ίδια διαδικασία πραγματοποιήθηκε και στα 3 πειράματα desiccation, starvation και οξειδωτικό στρες. ΑΠΟΤΕΛΕΣΜΑΤΑ: Στο desiccation και starvation στρες το προσδόκιμο ζωής δεν συσχετίστηκε με την καλή ή όχι φυσική κατάσταση των ζώων. Όμως, στο οξειδωτικό στρες, παρατηρήθηκε ότι οι μύγες που είχαν μεγαλύτερο προσδόκιμο ζωής, είχαν και καλύτερη φυσική κατάσταση. ΣΥΜΠΕΡΑΣΜΑΤΑ: Επομένως, στο οξύ θανατογόνο οξειδωτικό στρες εμφανίστηκε το προβάδισμα της καλής φυσικής κατάστασης, στη διάρκεια ζωής, ενώ, το πολύ έντονο στρες (desiccation και starvation στρες) δεν άφησε να εξελιχθεί η συσχέτιση αυτή.OBJECTIVE: This study aimed to study the relationship between state of physical status and lifespan under stressful conditions in the experimental model Drosophila Melanogaster (type: Oregon-R). METHODS: Flies 40-60 days old were anesthetized with carbon dioxide to isolate the males. The climbing speed of every individual fly was measured by geotaxis assay with a Conrad camera 24 hours later. The same procedure was carried out in all 3 experiments on desiccation, starvation and oxidative stress. RESULTS: In desiccation and starvation stress lifespan was not correlated with the physical condition of the animals. However, in oxidative stress, it was observed that flies that had a longer lifespan were in a better physical condition. CONCLUSIONS: Thus, under oxidative stress, physical fitness predicts resistance to stress. Faster flies lived longer. In contrast, the very intense desiccation and starvation stresses did not allow this correlation to evolve

Reliability and psychometric properties of the Greek translation of the State-Trait Anxiety Inventory form Y: Preliminary data

BACKGROUND: The State-Trait Anxiety Inventory form Y is a brief self-rating scale for the assessment of state and trait anxiety. The aim of the current preliminary study was to assess the psychometric properties of its Greek translation. MATERIALS AND METHODS: 121 healthy volunteers 27.22 ± 10.61 years old, and 22 depressed patients 29.48 ± 9.28 years old entered the study. In 20 of them the instrument was re-applied 1–2 days later. Translation and Back Translation was made. The clinical diagnosis was reached with the SCAN v.2.0 and the IPDE. The Symptoms Rating Scale for Depression and Anxiety (SRSDA) and the EPQ were applied for cross-validation purposes. The Statistical Analysis included the Pearson Correlation Coefficient and the calculation of Cronbach's alpha. RESULTS: The State score for healthy subjects was 34.30 ± 10.79 and the Trait score was 36.07 ± 10.47. The respected scores for the depressed patients were 56.22 ± 8.86 and 53.83 ± 10.87. Both State and Trait scores followed the normal distribution in control subjects. Cronbach's alpha was 0.93 for the State and 0.92 for the Trait subscale. The Pearson Correlation Coefficient between State and Trait subscales was 0.79. Both subscales correlated fairly with the anxiety subscale of the SRSDA. Test-retest reliability was excellent, with Pearson coefficient being between 0.75 and 0.98 for individual items and equal to 0.96 for State and 0.98 for Trait. CONCLUSION: The current study provided preliminary evidence concerning the reliability and the validity of the Greek translation of the STAI-form Y. Its properties are generally similar to those reported in the international literature, but further research is necessary

Self-organization of collective escape in pigeon flocks

Bird flocks under predation demonstrate complex patterns of collective escape. These patterns may emerge by self-organization from local interactions among group-members. Computational models have been shown to be valuable for identifying what behavioral rules may govern such interactions among individuals during collective motion. However, our knowledge of such rules for collective escape is limited by the lack of quantitative data on bird flocks under predation in the field. In the present study, we analyze the first GPS trajectories of pigeons in airborne flocks attacked by a robotic falcon in order to build a species-specific model of collective escape. We use our model to examine a recently identified distance-dependent pattern of collective behavior: the closer the prey is to the predator, the higher the frequency with which flock members turn away from it. We first extract from the empirical data of pigeon flocks the characteristics of their shape and internal structure (bearing angle and distance to nearest neighbors). Combining these with information on their coordination from the literature, we build an agent-based model adjusted to pigeons’ collective escape. We show that the pattern of turning away from the predator with increased frequency when the predator is closer arises without prey prioritizing escape when the predator is near. Instead, it emerges through self-organization from a behavioral rule to avoid the predator independently of their distance to it. During this self-organization process, we show how flock members increase their consensus over which direction to escape and turn collectively as the predator gets closer. Our results suggest that coordination among flock members, combined with simple escape rules, reduces the cognitive costs of tracking the predator while flocking. Such escape rules that are independent of the distance to the predator can now be investigated in other species. Our study showcases the important role of computational models in the interpretation of empirical findings of collective behavior