Commencement Program, June (1939)

https://red.mnstate.edu/commencement/1056/thumbnail.jp

The intermittent nature of movement.

<p>Individual motion can be discretized into a series of move, blue, and pause, red, lengths. The black lines indicate switches between these states. The pattern of movement is shown for an individual with Brownian motion (<b>A</b>) and for individual locusts observed in experiments (<b>B–E</b>) for 40 s. We calculated a total of 44,710 move lengths and 60,103 pause lengths for all individuals. Since our measurements of locusts' movements were recorded per frame, we treated move and pause length durations as pre-binned (discrete) data, rather than continuous (following Edwards <i>et al. </i><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002498#pcbi.1002498-Edwards1" target="_blank">[68]</a>).</p

Individual behaviour after and within a pause.

<p>(<b>A</b>) showing our calculation of locust turning behaviour within moves or pauses, or after a pause. We define a turn as a change from CW to ACW movement or vice versa. Arrows indicate the time steps for which the switch between CW to ACW was considered. Within a move or pause only consecutive time steps were examined (dotted arrows). For turning after a pause, the time steps immediately before and after the pause were considered (solid arrow). (<b>B</b>) shows the mean probability of changing direction after a pause for observed pause lengths (s), using log-binned averages. The left and right dashed lines show 6 s and 100 s, respectively. (<b>C</b>) shows the mean probability of changing direction after a pause for pause lengths of up to 20 s on a normal scale. (<b>D</b>) shows the mean probability of turning within a pause for different pause lengths. We have presented pause lengths up to 6 s as pause lengths greater than 6 s show a probability of one. For (<b>B–D</b>) error bars show 95% confidence intervals of the mean. (<b>E</b>) shows the relationship between the mean proportion of turns within a pause and the probability of changing direction after a pause for pause lengths of: less than 6 s (blue squares); between 6 s and 100 s (red triangles); and greater than 100 s (black circles). Each data point is a mean calculated from data within logged bin classes for pause length.</p

SI Movie 3: Visualisation of the storks’ GPS tracks together with the estimated movement of the air. from Synchronization, coordination and collective sensing during thermalling flight of freely migrating white storks

Black curves show 10 s trajectories of each bird; the width of the path is smaller for earlier points. The moving dots indicate the velocity of the air in the thermal; they colour coding represents the vertical speed. The vertical component of the air velocity was estimated for a 5 x 5 x 4 m (x, y, z) sized grid using the average vertical speed of the individuals. While rising in thermals, storks sink in the air that rises around them at a faster speed. Sink rate during gliding has a peak value of 1.2 m/s, and defines a minimum sinking speed. We estimated the air's vertical speed to be 1.2 m/s larger than the vertical speed of the storks. The horizontal speed of the air is estimated from the path of the center of the thermal. The tracks of the storks were not modified, they show the original GPS locations, and the viewpoint of the visualisation is moving with the center of mass of the flock. The video plays 10x the normal speed. Note the dynamics of the synchronised subgroups

SI Movie 2: The vertical speed profile of the thermal at different altitudes. from Synchronization, coordination and collective sensing during thermalling flight of freely migrating white storks

The plot is similar to Fig. 7b and SI Movie 1, but it additionally shows the vertical speed distributions at different altitudes ranging from [z-50m; z+50m]. Altitude (z) is shown on the top, and can also be seen in the inset (at the bottom left). The inset shows the full trajectories of all birds (same data as on Fig. 7a) in the relative coordinate system (x, y, z) centered around the centre of the thermal

Collective Learning and Optimal Consensus Decisions in Social Animal Groups

<div><p>Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.</p></div

SI Movie 1: The vertical speed profile of the thermal resulting from the collective movements of the entire flock. from Synchronization, coordination and collective sensing during thermalling flight of freely migrating white storks

The plot shows the vertical speed of all individuals in the flock (on the z axis) represented in relative coordinate system x, y with an origin at the estimated center of the thermal at every altitude (see also Fig. 7b). The video shows that the combination of all individuals add up to a full representation of the vertical speed distribution of the thermal. The viewing angle is rotated along the z axis. Lift is maximal in the central region and decreases with the distance to the centre

Supplementary Figures S1-S5 and Legends from Synchronization, coordination and collective sensing during thermalling flight of freely migrating white storks

Exploring how flocks of soaring migrants manage to achieve and maintain coordination while exploiting thermal updrafts is important for understanding how collective movements can enhance the sensing of the surrounding environment. Here we examined the structural organization of a group of circling white storks (<i>Ciconia ciconia</i>) throughout their migratory journey from Germany to Spain. We analysed individual high-resolution GPS trajectories of storks during circling events, and evaluated each bird's flight behaviour in relation to its flock members. Within the flock, we identified subgroups that synchronize their movements and coordinate switches in their circling direction within thermals. These switches in direction can be initiated by any individual of the subgroup, irrespective of how advanced its relative vertical position is, and occur at specific horizontal locations within the thermal allowing the storks to remain within the thermal. Using the motion of all flock members, we were able to examine the dynamic variation of airflow within the thermals, and to determine the specific environmental conditions surrounding the flock. With an increasing number of high-resolution GPS-tracking, we may soon be able to use these animals as distributed sensors providing us with a new means to obtain a detailed knowledge of our environment.This article is part of the theme issue ‘Collective movement in ecology: from emerging technologies to conservation and management’

The observational correlation of cues.

<p>(a) Observational correlation describes the degree to which observations made by different group members are independent of each other. A low correlation cue provides group members with independent observations, while a high correlation cue provides just one observation to all group members on a given trial. (b) Exclusive use of a low correlation cue results in a monotonic increase in collective accuracy as group size increases (green solid line), a hallmark of collective wisdom (). In contrast, exclusive use of a low correlation cue shows no increase in collective accuracy with group size (black solid line; ). A mixed strategy, whereby individuals probabilistically choose one of the cues, may lead to collective accuracy greater than that obtained from using either of the cues exclusively when .</p

How the Spatial Position of Individuals Affects Their Influence on Swarms: A Numerical Comparison of Two Popular Swarm Dynamics Models

<div><p>Schools of fish and flocks of birds are examples of self-organized animal groups that arise through social interactions among individuals. We numerically study two individual-based models, which recent empirical studies have suggested to explain self-organized group animal behavior: (i) a zone-based model where the group communication topology is determined by finite interacting zones of repulsion, attraction, and orientation among individuals; and (ii) a model where the communication topology is described by Delaunay triangulation, which is defined by each individual's Voronoi neighbors. The models include a tunable parameter that controls an individual's relative weighting of attraction and alignment. We perform computational experiments to investigate how effectively simulated groups transfer information in the form of velocity when an individual is perturbed. A cross-correlation function is used to measure the sensitivity of groups to sudden perturbations in the heading of individual members. The results show how relative weighting of attraction and alignment, location of the perturbed individual, population size, and the communication topology affect group structure and response to perturbation. We find that in the Delaunay-based model an individual who is perturbed is capable of triggering a cascade of responses, ultimately leading to the group changing direction. This phenomenon has been seen in self-organized animal groups in both experiments and nature.</p> </div