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Group-level patterns emerge from individual speed as revealed by an extremely social robotic fish



Understanding the emergence of collective behaviour has long been a key research focus in the natural sciences. Besides the fundamental role of social interaction rules, a combination of theoretical and empirical work indicates individual speed may be a key process that drives the collective behaviour of animal groups. Socially induced changes in speed by interacting animals make it difficult to isolate the effects of individual speed on group-level behaviours. Here, we tackled this issue by pairing guppies with a biomimetic robot. We used a closed-loop tracking and feedback system to let a robotic fish naturally interact with a live partner in real time, and programmed it to strongly copy and follow its partner's movements while lacking any preferred movement speed or directionality of its own. We show that individual differences in guppies' movement speed were highly repeatable and in turn shaped key collective patterns: a higher individual speed resulted in stronger leadership, lower cohesion, higher alignment and better temporal coordination of the pairs. By combining the strengths of individual-based models and observational work with state-of-the-art robotics, we provide novel evidence that individual speed is a key, fundamental process in the emergence of collective behaviour.
Cite this article: Jolles JW, Weimar N,
Landgraf T, Romanczuk P, Krause J, Bierbach D.
2020 Group-level patterns emerge from
individual speed as revealed by an extremely
social robotic fish. Biol. Lett. 16: 20200436.
Received: 10 June 2020
Accepted: 26 August 2020
Subject Areas:
behaviour, biotechnology, systems biology
speed, collective behaviour, guppy, individual
differences, robot, social
Authors for correspondence:
Jolle W. Jolles
David Bierbach
Electronic supplementary material is available
online at
Animal behaviour
Group-level patterns emerge from
individual speed as revealed by an
extremely social robotic fish
Jolle W. Jolles1,2, Nils Weimar3, Tim Landgraf4,5, Pawel Romanczuk5,6,
Jens Krause3,5,6 and David Bierbach3,5,6
Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany
Zukunftskolleg, University of Konstanz, Konstanz, Germany
Department of Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries,
Berlin, Germany
Department of Mathematics and Computer Science, Institute for Computer Science, Freie Universität Berlin,
Berlin, Germany
Excellence Cluster Science of Intelligence, Technische Universität Berlin, Berlin, Germany
Faculty of Life Sciences, Thaer-Institute, Humboldt-Universität zu Berlin, Berlin, Germany
JWJ, 0000-0001-9905-2633; TL, 0000-0003-4951-5235; PR, 0000-0002-4733-998X;
JK, 0000-0002-1289-2857; DB, 0000-0001-7049-2299
Understanding the emergence of collective behaviour has long been a key
research focus in the natural sciences. Besides the fundamental role of
social interaction rules, a combination of theoretical and empirical work
indicates individual speed may be a key process that drives the collective
behaviour of animal groups. Socially induced changes in speed by interact-
ing animals make it difficult to isolate the effects of individual speed on
group-level behaviours. Here, we tackled this issue by pairing guppies
with a biomimetic robot. We used a closed-loop tracking and feedback
system to let a robotic fish naturally interact with a live partner in real
time, and programmed it to strongly copy and follow its partnersmove-
ments while lacking any preferred movement speed or directionality of its
own. We show that individual differences in guppiesmovement speed
were highly repeatable and in turn shaped key collective patterns: a
higher individual speed resulted in stronger leadership, lower cohesion,
higher alignment and better temporal coordination of the pairs. By combining
the strengths of individual-based models and observational work with state-
of-the-art robotics, we provide novel evidence that individual speed is a key,
fundamental process in the emergence of collective behaviour.
1. Introduction
Understanding the emergence of collective behavioural patterns has long been
a key research focus in the natural sciences. Considerable theoretical and exper-
imental work has accumulated that describes how complex collective patterns
may arise via relatively simple mechanisms [1,2], including the role of pheno-
typic heterogeneity within and among groups [3]. A fundamental insight is
that social interaction rules at the individual levelsuch as avoiding
others that are too near and approaching those far awaycan explain the
large-scale cohesion, coordination and decision-making of animal groups [2,4].
Most animals control their motion by modulating their speed and turning,
and this speed regulation has been shown to be crucial for the attraction and
avoidance behaviour when animals group and interact [57]. Hence, individual
speed may be an additional fundamental factor that underlies the emergence of
the global properties of groups. Indeed, both short-term changes and heterogen-
eity in speed have been linked to a range of group-level properties, such as group
© 2020 The Author(s) Published by the Royal Society. All rights reserved.
cohesion, structure, shape, coordination, and leadership by
theoretical analyses [8], computer simulations [1,4,9] and
empirical work [6,1014]. Importantly, grouping individuals
may differ in their preferred and optimal movement speeds
yet must also coordinate and adjust their behaviour to success-
fully group together [3,15]. Such socially induced changes in
speed by individuals interacting with one another make it dif-
ficult to empirically isolate the effects of individual speed for
group-level properties. And while with agent-based simu-
lations one can separately model such effects and thereby
make important predictions for collective behaviour, they are
no substitute for empirical data of real animal groups [2].
Due to recent advances in the field of robotics, robotic indi-
viduals now not only realistically look and behave like
conspecifics, but also interact with live animals in a natural
way [1619]. This sets the stage for manipulative experiments
where part of the group members can be controlled and pro-
grammed with theoretical models under investigation. Here,
we present results from such an experiment using live guppies
(Poecilia reticulata) swimming with an interactive biomimetic
fish-like robot (robofish) to examine the role of fishs individ-
ual movement speed on collective behavioural patterns. We
combined high-definition video tracking and a closed-loop
feedback system that used interaction rules from well-known
agent-based models [4] to steer the robot interactively in real
time [20,21]. We programmed the robot to always follow its
partner and copy its behaviour, while excluding any preferred
swimming speed or directionality, thus enabling us to deter-
mine how individual differences in the guppiesmovement
speed alone determined group-level properties, in terms of
leadership, cohesion, alignment and temporal coordination.
2. Methods
We used laboratory-reared descendants of wild-caught Trinida-
dian guppies that were housed in large, randomly out-bred
mixed-sex stock tanks under controlled laboratory conditions
(12 h : 12 h light : dark; 26°C). We randomly selected 20 naive
adult females (standard length BL: 31.7 ± 0.8 mm) and moved
them to individual holding tanks (40 × 20 × 25 cm). The follow-
ing week, we tested fish first without robofish to assess their
preferred movement speed (week 2) and then twice with the
robofish (week 3; trial 2 five days later). Throughout, fish were
fed twice daily ad libitum with TetraMin flake food.
The test arena consisted of a large white glass tank (88 cm ×
88 cm, water height 7.5 cm) that was illuminated from above and
enclosed to minimize potential external disturbances. Fish were
moved from their individual holding compartment to the exper-
imental tank where they were allowed to acclimatize for 1 min in
an opaque PVC cylinder in the corner of the tank. After the cylin-
der was raised, the fish filmed from above for 10 min, and its
movements automatically tracked at 30 fps using BioTracker
[22]. For the trials with the robotic fish, we used a three-dimen-
sional-printed fish replica resembling a female guppy that was
connected via magnets to a two-wheeled robot below the tank
(see electronic supplementary material, figure S1 and for details
[21]). The robot was controlled via a closed-loop system whereby
the movements of the fish were tracked and fed-back to the robot
control. The robot unit then adjusted its position and orientation
in real time (i.e. with 30 hertz) to result in natural response times.
Robofish was circling in front of the acclimatization cylinder and
as soon as the guppy was released from the cylinder started its
interactive behaviour.
Robofishs interactive behaviour was based on the zonal model
[4] and allowed the robot to copy the live fishs motions and follow
at a similar speed without a preferred speed or directional prefer-
ence of their own (figure 1 and electronic supplementary material,
figure S2). We programmed robofish to orientate towards the live
fishs position and stay at a distance between 1015 cm away (4
BL, optimal distance zone), reflecting spacing observed in wild
guppies [23]. This resulted in robofish following at the instan-
taneous speed exhibited by the live fish while it was in this
optimal distance zone. Robofish gradually decreased or increased
its speed when the focal fish got into the graduation zone (3
10 cm) or beyond the optimal distance zone, respectively. If the
focal fish was at a distance less than 3 cm away, robofish stopped
moving forward but kept turning at its location to focus on the live
fishs position. The maximum speed and acceleration of robofish
were set to reflect that observed for the guppies when alone
(25 cm s
and 2.5 cm s
respectively, see electronic supplemen-
tary material, figure S3), with its maximum turning rate being
greater than 360°/s.
Tracking data were checked for errors, processed to correct for
missing frames, and converted to millimetres. Subsequently, based
on the centroid of each individual (focal and robotic), we calcu-
lated speed and heading as well as inter-individual distance. For
each trial, we computed the fishs median speed, the median
inter-individual distance, median difference in heading angle,
and proportion of time the focal fish was in front (when moving
greater than 0.5 cm s
;leadership). In addition, we computed
their coordination by running temporal correlations of both indi-
vidualschange in speed and heading, with a higher correlation
indicating movement changes were better copied between them.
We used a linear-mixed modelling approach to investigate
relationships in the individual- and group-level metrics as well
as repeatability in behaviour. Further details of our methods
and statistics can be found in the electronic supplementary
3. Results
There were large and significant among-individual differ-
ences in guppiesmovement speed across the two robofish
trials (R= 0.70, 95% confidence interval (CI) = 0.380.88),
which correlated well with their movement speed when
tested alone (χ
= 9.70, p= 0.002, R2
mar ¼0:32, electronic sup-
plementary material, figure S4). Although the majority of
fish slowed down when tested with robofish (30/38 trials),
there was large among-individual variation in fishs speed
relative to that expressed in the solo assay (0.62± 0.06, 95%
robo speed (cm s–1)
10 cm
live guppy
Figure 1. Tracking data (approx. 1 min) of a randomly selected representative
pair, with the speed of the robofish coloured blue (low) to yellow (high), show-
ing how it followed the position and movements of its partner by natural
changes in speed (see further electronic supplementary material, figure S2).
Inset shows a photo of robofish following a guppy. Data were subsetted to
a region where the guppy made a number of turns and changes in speed. Biol. Lett. 16: 20200436
CI = 0.151.29; a value of 1 would indicate no change in
speed), which was itself not linked to their solo speed (χ
0.155, p= 0.212, R
= 0.06).
Robofish conformed extremely well to the speed of the
guppy partner (χ
= 87.04, p<0.001, R2
mar ¼0:92, electronic
supplementary material, figure S5) and consistently exhibited
a slightly slower speed (speed difference: 0.58 ± 0.07 cm s
As a consequence, robofish primarily occupied the following
position (38/38 trials, electronic supplementary material,
figure S6), an effect that strongly increased with the speed of
the focal fish (χ
= 40.77, p< 0.001, R2
mar ¼0:68), with the fastest
guppies leading more than 90% of the time (figure 2a,b).
Robofish was able to maintain good cohesion and align-
ment with its live partner, and naturally copied its changes
in speed and heading (median max correlation coefficients:
0.51 and 0.58, electronic supplementary material, figure S7).
As for leadership, we found that these group-level outcomes
were very well explained by the individual speed of the
guppy. Pairs in which the guppy had a high median speed
were considerably less cohesive (χ
=60.04, p<0.001,
mar ¼0:81), more aligned (χ
=13.66, p<0.001, R2
mar ¼0:25)
and more coordinated (χ
=41.33, p< 0.001, R2
mar ¼0:65)
than pairs with a much slower guppy (figure 2cf).
4. Discussion
Live guppies paired with an extremely social robotic fish
showed large and repeatable individual differences in move-
ment speeds that in turn strongly explained leadership, group
cohesion, alignment and movement coordination. By testing
all fish with the same robot using identical interaction rules
and lacked any preferred movement speed and directionality,
these results provide novel experimental evidence that suggest
that individual speed is a fundamental factor in the emergence
of collective behavioural patterns, in line with existing theoreti-
cal and empirical work [4,8,1214]. As individual differences in
speed are associated with a broad range of phenotypic traits
observed among grouping animals, this may also help provide
a mechanistic explanation for the effect of phenotypic heterogen-
eity of group-level patterns [3], as has for example been shown
for size, hunger and parasitism [24,25].
We observed a very strong, positive link between speed
and leadership, both in terms of clearer front-back position-
ing the faster fish were moving, as well as fish being
overall more in front the higher their median movement
speed. This result is in line with predictions from model
simulations [4,12] and previous empirical work [11,12]. By
testing fish with an extremely social partner that always
tried to follow, we found that at higher speeds fish led
almost all the time. This shows how leaders depend on the
responsiveness of their followers in order to express their
own preference (see also [26]) and more generally highlights
how both individual speeds and high levels of social respon-
siveness are important for the collective performance of
groups [27]. At lower speeds, leadership differences were
not as apparent, potentially because individuals have greater
turning freedom at lower speeds [8]. The absolute speed at
which a group is moving may therefore be just as important
as relative differences in speed between individuals in shap-
ing group structure. Our experiments show that the observed
effects of speed emerge from the self-organized dynamics of
the coupled leaderfollower system, i.e. that it does not
require any diverging preferences of the follower. An analogy
prop time in front
–20 –10
robofish relative y-position (cm) median speed (BL s–1)
speed threshold
full data
> 0.5 cm s–1
> 0.5 BL s–1
inter-individual distance (cm)
0 102030
median heading diff (deg)
inter-individual dist (cm)
median speed (BL s–1)median speed (BL s–1)
max temporal heading corr
median speed (BL s–1)
Figure 2. (a,c) Density plot of robofishs relative y-position and distance to its live partner, and (b,d,e,f) scatterplots of fishs median speed in relation to the leadership,
cohesion, alignment and temporal coordination of the pair. Plots aand cshow boththe full data (blue line), the data was subsetted to where the focal fish moved at >0.5 cm/s
(green line), and at >0.5 BL/s (yellow line). Colour scale indicates speed (blue = low; yellow= high) and solid lines show the polynomial functions fitted in our models. Biol. Lett. 16: 20200436
can be drawn to a physical system, where the follower can be
viewed as an active particlestrongly coupled to the leader
through a spring-like social force with friction.
Pairs consisting of a guppy with a high median speed were
considerably less cohesive than those with low median speeds,
in line with previous work on schooling sticklebacks [12]. To
avoid collisions, grouping animals may actively increase their
distance when moving at higher speeds. However, robofish
was not programmed with such a rule, suggesting that the
observed positive link between cohesion and speed is due to
speed mediating the use of social interaction rules: faster indi-
viduals moving farther before they can change their position
in response to their group mates. This suggests that a shift in
interaction rules, such as via changes in the environment, may
alter the relationship between group speed and cohesion (see
e.g. [28,29]). Individual speed also strongly drove the alignment
and temporal coordination of the pairs, in line with previous
empirical studies that found fast-moving groups tend to be
polarized and slow-moving groups to be disordered
[12,13,30]. Since in our study the robotic partner completely
lacked any alignment rules, our findings provide novel empiri-
cal evidence that individual speed is a key factor facilitating
group alignment and coordination. Although speed-mediated
changes in local interaction rules may help to explain these
effects [6,10], groups may be more likely to become disordered
at lower speeds because of larger potential angular fluctuations
at lower speeds, as is predicted from the theoretical analysis [8].
Given that rates of information flow have been shown to be
higher in faster-moving groups [13], our finding that faster
groups showed better coordination of movement changes can
therefore be explained by the higher (local) order that arises
with higher individual speeds.
The large individual differences in movement speed
during the robofish trials were highly repeatable (R = 0.70),
as compared to the average repeatability of 0.37 reported
by a large meta-analysis [31]. As robofish always copied its
live partners speed and movements, and always used the
same interaction rules, the large variability in movement
speed among the pairs must be attributable to the speed of
the live fish, which was in turn well explained by fishs
solo speed. Interestingly however, considerable speed vari-
ation among the fish remained. The reduction in movement
speed between the solo and robofish trials therefore likely
reflects socially mediated changes, with guppies that
slowed down more being more socially responsive and/or
less inclined to lead. This corroborates previous observational
work that found live fish pairs moved faster when led by fish
that were less socially plastic in their speeding changes [7].
Future work is needed to properly determine to what
extent this behavioural variation in social speedindicates
true individual differences in social responsiveness.
In summary, by closed-loop experiments of live guppies
swimming with a biomimetic robot that always followed and
naturally copied its partnersmovements, we bridged the rea-
lity gap between computer simulations and real-world
observations and provide novel evidence that individual
speed is a fundamental factor for the emergence of collective
behaviour. By programming the robotic fish without any of
its own movement preferences, we had the unique opportunity
to investigate how individual behaviour leads to group-level
patterns without the potential influence of individual hetero-
geneity in group mates. Exciting interdisciplinary work lies
ahead to further investigate the role that individuals play in
animal groups and how that depends on the social feedback
among heterogeneous group members.
Ethics. All reported experiments comply with the current German law
approved by LaGeSo (G0117/16 to D.B.).
Data accessibility. Data accompanying this paper can be found in the
electronic supplementary material.
Authorscontributions. J.W.J., N.W. and D.B. designed the study; T.L.,
D.B., P.R. and J.K. developed robofish; N.W. and D.B. conducted
the experiment with help from J.W.J. J.W.J. performed the analysis;
J.W.J. and D.B. wrote the manuscript with feedback from all other
authors. All authors agree accountability for the content in this
paper and approved the final version of the manuscript.
Competing interests. We declare we have no competing interests.
Funding. We acknowledge support from the Alexander von Humboldt-
Stiftung (postdoctoral fellowship to J.W.J.), the Zukunftskolleg, Uni-
versity of Konstanz (postdoctoral fellowship to J.W.J.), the German
Research Foundation (BI 1828/2-1, LA 3534/1-1, RO 4766/2-1) and
Germanys Excellence Strategy (EXC 2002/1 Science of Intelligence,
project number 390523135).
Acknowledgements. We thank two anonymous reviewers for their help-
ful feedback. We are also very grateful to Hauke Mönck and Hai
Nguyen for help with programming the robot.
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... One popular spatial organization algorithm, Boids, was created to mimic the flocking behaviors of birds and fish for computer simulations [7]. Since its creation, the Boids algorithm has expanded, and modified algorithms have also arisen [18], improving its performance and controlling robotic fish swimming with biological fish in a controlled environment [8,9]. Other algorithms, including PSO [10], Artificial Fish Swarm Algorithm [11], and a slew of other algorithms, have been designed to create flocking motions [12,13]. ...
... It should also be noted that while current models can respond to a predator, they cannot produce the same dynamic response of schooling fish due to the information being "diluted" as it moves through the school [60]. The Couzin model was also used in a study to control a bioinspired robotic guppy that swam with a school of biological guppies [8,9]. Another group of researchers combined birds, wolves, and fish behaviors to create a swarm that could school, circle an object, and produce the torus formation found in fish [61]. ...
... For both research groups, an overhead camera was placed above the pool to provide accurate positioning and headings of all agents and fish within the area. While Boids and the Couzin model have been around for many years, they continue to be the primary algorithms utilized for controlling robots intended for swimming alongside biological fish [8,9,62]. Nevertheless, conventional particle-based models face challenges in effectively being utilized by robots with limited perception and delays associated with sensor data [63]. ...
Full-text available
This study proposes a novel flocking algorithm for underwater robotics based on the collective behaviors of schooling fish. The algorithm, Fish-Inspired Robotic Algorithm (FIRA), integrates standard flocking behaviors such as Attraction, Alignment, and Repulsion, as well as predator avoidance, foraging, general obstacle avoidance, and wandering. Compared with traditional flocking algorithms, FIRA includes predictive elements to counteract processing delays from sensors and prioritizes the highest cluster of agents on a single side, leading to superior performance in collision avoidance, exploration, foraging, and the emergence of realistic behaviors. To address the challenges of underwater communication, FIRA is designed to work with high-latency, non-guaranteed communication methodology based on stigmergy methods found in nature, enabling practical, multi-agent, inexpensive, and tetherless communication. FIRA aims to provide a computational light control algorithm designed to work with low-cost, low-computing agents to further research using robotics to assimilate into a school of biological fish. To demonstrate the effectiveness of FIRA, it was simulated within Unity using a digital twin of a bio-inspired robotic fish, which incorporates the robot's motion and sensors in a realistic, real-time environment, with the algorithm used to direct the movements of individual agents. The performance of FIRA was compared against other collective motion algorithms to determine its effectiveness as a flocking algorithm. From the experiments, FIRA outperformed the other algorithms in both collision avoidance and exploration. These experiments establish FIRA as a viable flocking algorithm to mimic fish behavior in robotics.
... In previous works on birds [23], bats [25], and fish [16,17,24,[26][27][28][29], information flow was identified to naturally occur from influential individuals at the front of the group towards following individuals located further back. Leadership was also observed to depend on average individual speeds of previous solo flights for homing pigeons [30] and for schooling fish [28,31,32], but also on instantaneous individual speeds for schooling fish [29]. ...
... While previous research suggests that individual speeds play an important role in shaping collective movement, and that leader-follower interactions are mediated by speeds, it either considered average speeds [28,[30][31][32] or instantaneous absolute speeds of the neighbour [29]. Furthermore, the proposed mechanism is based on selforganization of individuals with different average speeds assuming homogeneous and constant social interactions [31,32]. ...
... While previous research suggests that individual speeds play an important role in shaping collective movement, and that leader-follower interactions are mediated by speeds, it either considered average speeds [28,[30][31][32] or instantaneous absolute speeds of the neighbour [29]. Furthermore, the proposed mechanism is based on selforganization of individuals with different average speeds assuming homogeneous and constant social interactions [31,32]. Our results reinforce the link between speed and leadership with completely different methods and by using a more restrictive concept, given by instantaneous speeds of the neighbour relative to the focal individual. ...
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Animals moving together in groups are believed to interact among each other with effective social forces, such as attraction, repulsion and alignment. Such forces can be inferred using 'force maps', i.e. by analysing the dependency of the acceleration of a focal individual on relevant variables. Here we introduce a force map technique for alignment depending on relative velocities between an individual and its neighbours. After the force map approach is validated with an agent-based model, we apply it to experimental data of schooling fish, where we observe signatures of an effective alignment force with faster neighbours, and an unexpected anti-alignment with slower neighbours. Instead of an explicit anti-alignment behaviour, we suggest that the observed pattern is a result of a selective attention mechanism, where fish pay less attention to slower neighbours. We present support for this hypothesis both from agent-based modeling, as well as from exploring leader-follower relationships between faster and slower neighbouring fish in the experimental data.
... Abdai et al. 2022a, b;Quinn et al. 2018); or they may be integrated into a group (e.g. Halloy et al. 2007;Landgraf et al. 2018;Jolles et al. 2020). Note that many of these situations can be envisaged as a specific Turing test mentioned above. ...
... The Robofish have been used successfully to investigate specific aspects of the behaviour of different fish species in experimental situations which are very difficult to arrange among living individuals. Examples include whether larger leaders are more likely to be followed irrespective of the follower's body size and risk-taking behaviour (Bierbach et al. 2020); how individual speed influences the collective behaviour without the confounding influence of potential socially induced changes due to interactions (Jolles et al. 2020); and the role of visual and non-visual cues on the social behaviours of two populations (surface-vs cave-dwelling) of the Atlantic molly (Poecilia mexicana) . ...
... 6. Application of these agents allows researchers to study those details of an interaction that would be impossible when using real animals. For example, one can study how individual speed influences the collective behaviour without the confounding influence of potential socially induced changes due to the interactions (Jolles et al. 2020). 7. Application of robots as interactive partners may contribute to animal welfare by reducing the number of animals needed for research. ...
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The Darwinian idea of mental continuity is about 150 years old. Although nobody has strongly denied this evolutionary link, both conceptually and practically, relative slow advance has been made by ethology and comparative psychology to quantify mental evolution. Debates on the mechanistic interpretation of cognition often struggle with the same old issues (e.g., associationism vs cognitivism), and in general, experimental methods have made also relative slow progress since the introduction of the puzzle box. In this paper, we illustrate the prevailing issues using examples on ‘mental state attribution’ and ‘perspective taking” and argue that the situation could be improved by the introduction of novel methodological inventions and insights. We suggest that focusing on problem-solving skills and constructing artificial agents that aim to correspond and interact with biological ones, may help to understand the functioning of the mind. We urge the establishment of a novel approach, synthetic ethology, in which researchers take on a practical stance and construct artificial embodied minds relying of specific computational architectures the performance of which can be compared directly to biological agents.
... However, our statistical analysis revealed that this effect could be accounted for by a reduction in swimming speed in turbid water, which then had the effect of reducing nearest neighbour distance. Empirical and theoretical studies of collective behaviour have established that changes in individuals' speeds are a key mechanism for changes to the collective behaviour of fish shoals (Herbert-Read et al., 2011;Jolles et al., 2020;Katz et al., 2011;Schaerf et al., 2017). It is unclear from our experiment whether fish reduce their swimming speed in turbid water as an adaptive response, for example to maintain nearest neighbour distance (and associated benefits for social information detection) in low visibility, or whether this change occurs as a by-product of other effects of the turbid environment, such as increases in the perception of predation risk or as a stress response (Ajemian et al., 2015;Chamberlain & Ioannou, 2019). ...
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Many fresh and coastal waters are becoming increasingly turbid because of human activities, which may disrupt the visually mediated behaviours of aquatic organisms. Shoaling fish typically depend on vision to maintain collective behaviour, which has a range of benefits including protection from predators, enhanced foraging efficiency and access to mates. Previous studies of the effects of turbidity on shoaling behaviour have focussed on changes to nearest neighbour distance and average group‐level behaviours. Here, we investigated whether and how experimental shoals of three‐spined sticklebacks ( Gasterosteus aculeatus ) in clear (<10 Nephelometric Turbidity Units [NTU]) and turbid (~35 NTU) conditions differed in five local‐level behaviours of individuals (nearest and furthest neighbour distance, heading difference with nearest neighbour, bearing angle to nearest neighbour and swimming speed). These variables are important for the emergent group‐level properties of shoaling behaviour. We found an indirect effect of turbidity on nearest neighbour distances driven by a reduction in swimming speed, and a direct effect of turbidity which increased variability in furthest neighbour distances. In contrast, the alignment and relative position of individuals was not significantly altered in turbid compared to clear conditions. Overall, our results suggest that the shoals were usually robust to adverse effects of turbidity on collective behaviour, but group cohesion was occasionally lost during periods of instability.
... Speed plays a prominent role in structuring fish schools, as individuals adjust their distance and alignment with neighbors through changes in speed 21,37,38 . Indeed, empirical studies across several species, as well as modeling approaches, have recently demonstrated a positive correlation between individual's speed and group polarization [38][39][40][41][42][43] . In our experiments, the presence of a novel object and a predator model prompted strong reductions in the median speed of individuals in the test. ...
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One of the most spectacular displays of social behavior is the synchronized movements that many animal groups perform to travel, forage and escape from predators. However, elucidating the neural mechanisms underlying the evolution of collective behaviors, as well as their fitness effects, remains challenging. Here, we study collective motion patterns with and without predation threat and predator inspection behavior in guppies experimentally selected for divergence in polarization, an important ecological driver of coordinated movement in fish. We find that groups from artificially selected lines remain more polarized than control groups in the presence of a threat. Neuroanatomical measurements of polarization-selected individuals indicate changes in brain regions previously suggested to be important regulators of perception, fear and attention, and motor response. Additional visual acuity and temporal resolution tests performed in polarization-selected and control individuals indicate that observed differences in predator inspection and schooling behavior should not be attributable to changes in visual perception, but rather are more likely the result of the more efficient relay of sensory input in the brain of polarization-selected fish. Our findings highlight that brain morphology may play a fundamental role in the evolution of coordinated movement and anti-predator behavior.
... Creating biohybrid colonies of animals and robots interacting each other represents an emergent context of bionics encompassing animal behavioural ecology and robotics (Romano et al. 2019). This relatively novel field of science and technology provides advanced engineered systems for studying assumptions on cognitive and ecological mechanisms in animals that can be generalized to humans, as well as to control intraspecific and interspecific interactions for applied purposes (Polverino et al. 2019;Jolles et al. 2020;Worm et al. 2021). A growing number of studies are using biomimetic robots to interact with many animal species, ranging from invertebrates to vertebrates. ...
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Mass-rearing procedures of insect species, often used in biological control and Sterile Insect Technique, can reduce the insects competitiveness in foraging, dispersal, and mating. The evocation of certain behaviours responsible to induce specific neuroendocrine products may restore or improve the competitiveness of mass-reared individuals. Herein, we used a mass-reared strain of Ceratitis capitata as model organism. C. capitata is a polyphagous pest exhibiting territorial displays that are closely related to its reproductive performance. We tested if the behaviour of C. capitata males could be altered by hybrid aggressive interactions with a conspecific-mimicking robotic fly, leading to more competitive individuals in subsequent mating events. Aggressive interactions with the robotic fly had a notable effect on subsequent courtship and mating sequences of males that performed longer courtship displays compared to naïve individuals. Furthermore, previous interactions with the robotic fly produced a higher mating success of males. Reproductive performances of C. capitata males may be improved by specific octopaminergic neurones activated during previous aggressive interactions with the robotic fly. This study adds fundamental knowledge on the potential role of specific neuro-behavioural processes in the ecology of tephritid species and paves the way to innovative biotechnological control methods based on robotics and bionics.
... In these experiments, the precise movements of robotic prey (or predators) can be programmed to provide controls that are impossible with real animals Landgraf et al., 2021). Recently, robotic fish have been used as prey to test how the predictability of the escape path affects predator pursuit behaviour (Szopa-Comley & Ioannou, 2022), and robots have also been used to assess differences in social responsiveness between social partners (Bierbach et al., 2018) and how movement variables such as speed drive patterns of collective movement (Jolles et al., 2020). ...
Group-hunting is ubiquitous across animal taxa and has received considerable attention in the context of its functions. By contrast much less is known about the mechanisms by which grouping predators hunt their prey. This is primarily due to a lack of experimental manipulation alongside logistical difficulties quantifying the behaviour of multiple predators at high spatiotemporal resolution as they search, select, and capture wild prey. However, the use of new remote-sensing technologies and a broadening of the focal taxa beyond apex predators provides researchers with a great opportunity to discern accurately how multiple predators hunt together and not just whether doing so provides hunters with a per capita benefit. We incorporate many ideas from collective behaviour and locomotion throughout this review to make testable predictions for future researchers and pay particular attention to the role that computer simulation can play in a feedback loop with empirical data collection. Our review of the literature showed that the breadth of predator:prey size ratios among the taxa that can be considered to hunt as a group is very large (<100 to >102 ). We therefore synthesised the literature with respect to these predator:prey ratios and found that they promoted different hunting mechanisms. Additionally, these different hunting mechanisms are also related to particular stages of the hunt (search, selection, capture) and thus we structure our review in accordance with these two factors (stage of the hunt and predator:prey size ratio). We identify several novel group-hunting mechanisms which are largely untested, particularly under field conditions, and we also highlight a range of potential study organisms that are amenable to experimental testing of these mechanisms in connection with tracking technology. We believe that a combination of new hypotheses, study systems and methodological approaches should help push the field of group-hunting in new directions.
... Expressing interactions as forces, it has been found that they depend on non-trivial combinations of the neighbour's position and velocity [41,42]. Experiments using robotic fish showed that the speed of the robot was important for the interactions with guppies [43]. Also, while most modelling work does not use speed as a relevant variable [44][45][46][47][48][49][50][51], the importance of speed has also been shown in the modelling work [52][53][54], including an impact in the collective emergent dynamics [55]. ...
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We studied how the interactions among animals in a collective allow for the transfer of information. We performed laboratory experiments to study how zebrafish in a collective follow a subset of trained animals that move towards a light when it turns on because they expect food at that location. We built some deep learning tools to distinguish from video which are the trained and the naïve animals and to detect when each animal reacts to the light turning on. These tools gave us the data to build a model of interactions that we designed to have a balance between transparency and accuracy. The model finds a low-dimensional function that describes how a naïve animal weights neighbours depending on focal and neighbour variables. According to this low-dimensional function, neighbour speed plays an important role in the interactions. Specifically, a naïve animal weights more a neighbour in front than to the sides or behind, and more so the faster the neighbour is moving; and if the neighbour moves fast enough, the differences coming from the neighbour’s relative position largely disappear. From the lens of decision-making, neighbour speed acts as confidence measure about where to go. This article is part of a discussion meeting issue ‘Collective behaviour through time’.
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Collective motion is commonly modeled with static interaction rules between agents. Substantial empirical evidence indicates, however, that animals may adapt their interaction rules depending on a variety of factors and social contexts. Here, we hypothesized that leadership performance is linked to the leader's responsiveness to the follower's actions and we predicted that a leader is followed longer if it adapts to the follower's avoidance movements. We tested this prediction with live guppies that interacted with a biomimetic robotic fish programmed to act as a "socially competent" leader. Fish that were avoiding the robot were approached more carefully in future approaches. In two separate experiments we then asked how the leadership performance of the socially competent robot leader differed to that of a robot leader that either approached all fish in the same, non-responsive, way or one that did change its approach behavior randomly, irrespective of the fish's actions. We found that 1) behavioral variability itself appears attractive and that socially competent robots are better leaders which 2) require fewer approach attempts to 3) elicit longer average following behavior than non-competent agents. This work provides evidence that social responsiveness to avoidance reactions plays a role in the social dynamics of guppies. We showcase how social responsiveness can be modeled and tested directly embedded in a living animal model using adaptive, interactive robots.
Group models based on simple rules are viewed as a bridge to clarifying animal group movements. The more similar a model to real-world observations, the closer it is to the essence of such movements. Inspired by the fish school, this study suggests a principle called fellow-following for group movements. More specifically, a simple-rules-based model was proposed and extended into a set of concrete rules, and two- and three-dimensional group models were established. The model results are intuitively similar to the fish school, and when the group size increases, the milling phase of both the model and fish school tends from unstable to stable. Further, we proposed a novel order parameter and a similarity measurement framework for group structures. The proposed model indicates the intuition similarity, consistency of dynamic characteristics, and static structure similarity with fish schools, which suggests that the principle of fellow-following may reveal the essence of fish school movements. Our work suggests a different approach for the self-organized formation of a swarm robotic system based on local information.
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Parasitism is ubiquitous in the animal kingdom. Although many fundamental aspects of host-parasite relationships have been unravelled, few studies have systematically investigated how parasites affect organismal movement. Here we combine behavioural experiments of Schistocephalus solidus infected sticklebacks with individual-based simulations to understand how parasitism affects individual movement ability and thereby shapes social interaction patterns. High-resolution tracking revealed that infected fish swam, accelerated, and turned more slowly than did non-infected fish, and tended to be more predictable in their movements. Importantly, the strength of these effects increased with increasing parasite load (proportion of body weight), with more heavily infected fish showing larger changes and impairments in behaviour. When grouped, pairs of infected fish moved more slowly, were less cohesive, less aligned, and less temporally coordinated than non-infected pairs, and mixed pairs were primarily led by the non-infected fish. These social patterns also emerged in simulations of self-organised groups composed of individuals differing similarly in speed and turning tendency, suggesting infection-induced changes in mobility and manoeuvrability may drive collective outcomes. Together, our results demonstrate how infection with a complex life-cycle parasite affects the movement ability of individuals and how this in turn shapes social interaction patterns, providing important mechanistic insights into the effects of parasites on host movement dynamics.
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What can interactive robots offer to the study of social behaviour? Philosophical reflections about the use of robotic models in animal research have focused so far on methods (including the so-called synthetic method) involving robots which do not interact with the target system. Yet, leading researchers have claimed that interactive robots may constitute powerful experimental tools to study collective behaviour. Can they live up to these epistemic expectations? This question is addressed here by focusing on a particular experimental methodology involving interactive robots which has been often adopted in animal research. This methodology is shown to differ from other robot-supported methods for the study of animal behaviour analysed in the philosophical literature, chiefly including the synthetic method. It is also discussed whether biomimicry (i.e., similarity between the robot and the target animal in behaviour, appearance, and internal mechanisms) and acceptability (i.e., whether or not the robot is accepted as a conspecific by the animal) are necessary for an interactive robot to be sensibly used in animal research according to this method.
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Social grouping is omnipresent in the animal kingdom. Considerable research has focused on understanding how animal groups form and function, including how collective behaviour emerges via self-organising mechanisms and how phenotypic variation drives the behaviour and functioning of animal groups. However, we still lack a mechanistic understanding of the role of phenotypic variation in collective animal behaviour. Here we present a common framework to quantify individual heterogeneity and synthesise the literature to systematically explain and predict its role in collective behaviour across species, contexts, and traits. We show that individual heterogeneity provides a key intermediary mechanism with broad consequences for sociality (e.g., group structure, functioning), ecology (e.g., response to environmental change), and evolution. We also outline a roadmap for future research.
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Collectively moving animals often display a high degree of synchronization and cohesive group-level formations, such as elongated schools of fish. These global patterns emerge as the result of localized rules of interactions. However, the exact relationship between speed, polarization, neighbour positioning and group structure has produced conflicting results and is largely limited to modelling approaches. This hinders our ability to understand how information spreads between individuals, which may determine the collective functioning of groups. We tested how speed interacts with polarization and positional composition to produce the elongation observed in moving groups of fish as well as how this impacts information flow between individuals. At the local level, we found that increases in speed led to increases in alignment and shifts from lateral to linear neighbour positioning. At the global level, these increases in linear neighbour positioning resulted in elongation of the group. Furthermore, mean pairwise transfer entropy increased with speed and alignment, implying an adaptive value to forming faster, more polarized and linear groups. Ultimately, this research provides vital insight into the mechanisms underlying the elongation of moving animal groups and highlights the functional significance of cohesive and coordinated movement.
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Living organisms are far superior to state-of-the-art robots as they have evolved a wide number of capabilities that far encompass our most advanced technologies. The merging of biological and artificial world, both physically and cognitively, represents a new trend in robotics that provides promising prospects to revolutionize the paradigms of conventional bio-inspired design as well as biological research. In this review, a comprehensive definition of animal–robot interactive technologies is given. They can be at animal level, by augmenting physical or mental capabilities through an integrated technology, or at group level, in which real animals interact with robotic conspecifics. Furthermore, an overview of the current state of the art and the recent trends in this novel context is provided. Bio-hybrid organisms represent a promising research area allowing us to understand how a biological apparatus (e.g. muscular and/or neural) works, thanks to the interaction with the integrated technologies. Furthermore, by using artificial agents, it is possible to shed light on social behaviours characterizing mixed societies. The robots can be used to manipulate groups of living organisms to understand self-organization and the evolution of cooperative behaviour and communication.
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A wide range of measurements can be made on the collective motion of groups, and the movement of individuals within them. These include, but are not limited to: group size, polarization, speed, turning speed, speed or directional correlations, and distances to near neighbours. From an ecological and evolutionary perspective, we would like to know which of these measurements capture biologically meaningful aspects of an animal's behaviour and contribute to its survival chances. Previous simulation studies have emphasized two main factors shaping individuals' behaviour in groups; attraction and alignment. Alignment responses appear to be important in transferring information between group members and providing synergistic benefits to group members. Likewise, attraction to conspecifics is thought to provide benefits through, for example, selfish herding. Here, we use a factor analysis on a wide range of simple measurements to identify two main axes of collective motion in guppies (Poecilia reticulata): (i) sociability, which corresponds to attraction (and to a lesser degree alignment) to neighbours, and (ii) activity, which combines alignment with directed movement. We show that for guppies, predation in a natural environment produces higher degrees of sociability and (in females) lower degrees of activity, while female guppies sorted for higher degrees of collective alignment have higher degrees of both sociability and activity. We suggest that the activity and sociability axes provide a useful framework for measuring the behaviour of animals in groups, allowing the comparison of individual and collective behaviours within and between species. This article is part of the theme issue ‘Collective movement ecology’.
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The study of animal behavior increasingly relies on (semi-) automatic methods for the extraction of relevant behavioral features from video or picture data. To date, several specialized software products exist to detect and track animals' positions in simple (laboratory) environments. Tracking animals in their natural environments, however, often requires substantial customization of the image processing algorithms to the problem-specific image characteristics. Here we introduce BioTracker, an open-source computer vision framework, that provides programmers with core functionalities that are essential parts of a tracking software, such as video I/O, graphics overlays and mouse and keyboard interfaces. BioTracker additionally provides a number of different tracking algorithms suitable for a variety of image recording conditions. The main feature of BioTracker is however the straightforward implementation of new problem-specific tracking modules and vision algorithms that can build upon BioTracker's core functionalities. With this open-source framework the scientific community can accelerate their research and focus on the development of new vision algorithms.
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Establishing how collective behaviour emerges is central to our understanding of animal societies. Previous research has highlighted how universal interaction rules shape collective behaviour, and that individual differences can drive group functioning. Groups themselves may also differ considerably in their collective behaviour, but little is known about the consistency of such group variation, especially across different ecological contexts that may alter individuals' behavioural responses. Here, we test if randomly composed groups of sticklebacks differ consistently from one another in both their structure and movement dynamics across an open environment, an environment with food, and an environment with food and shelter. Based on high-resolution tracking data of the free-swimming shoals, we found large context-associated changes in the average behaviour of the groups. But despite these changes and limited social familiarity among group members, substantial and predictable behavioural differences between the groups persisted both within and across the different contexts (group-level repeatability): some groups moved consistently faster, more cohesively, showed stronger alignment and/or clearer leadership than other groups. These results suggest that among-group heterogeneity could be a widespread feature in animal societies. Future work that considers group-level variation in collective behaviour may help understand the selective pressures that shape how animal collectives form and function.
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The ubiquity of consistent inter-individual differ- ences in behavior (‘‘animal personalities’’) [1, 2] sug- gests that they might play a fundamental role in driving the movements and functioning of animal groups [3, 4], including their collective decision-mak- ing, foraging performance, and predator avoidance. Despite increasing evidence that highlights their importance [5–16], we still lack a unified mechanistic framework to explain and to predict how consistent inter-individual differences may drive collective behavior. Here we investigate how the structure, leadership, movement dynamics, and foraging per- formance of groups can emerge from inter-individual differences by high-resolution tracking of known behavioral types in free-swimming stickleback (Gasterosteus aculeatus) shoals. We show that indi- vidual’s propensity to stay near others, measured by a classic ‘‘sociability’’ assay, was negatively linked to swim speed across a range of contexts, and predicted spatial positioning and leadership within groups as well as differences in structure and movement dynamics between groups. In turn, this trait, together with individual’s exploratory ten- dency, measured by a classic ‘‘boldness’’ assay, ex- plained individual and group foraging performance. These effects of consistent individual differences on group-level states emerged naturally from a generic model of self-organizing groups composed of individuals differing in speed and goal-oriented- ness. Our study provides experimental and theoret- ical evidence for a simple mechanism to explain the emergence of collective behavior from consistent individual differences, including variation in the structure, leadership, movement dynamics, and functional capabilities of groups, across social and ecological scales. In addition, we demonstrate indi- vidual performance is conditional on group composi- tion, indicating how social selection may drive behavioral differentiation between individuals.
The possibility of regulating the behavior of live animals using biologically-inspired robots has attracted the interest of biologists and engineers for over 25 years. From early work on insects to recent endeavors on mammals, we have witnessed fascinating applications that have pushed forward our understanding of animal behavior along new directions. Despite significant progress, most of the research has focused on open-loop control systems, in which robots execute predetermined actions, independent of the animal behavior. In this article, we integrate mathematical modeling of social behavior toward the design of realistic feedback laws for robots to interact with a live animal. In particular, we leverage recent advancements in data-driven modeling of zebrafish behavior. Ultimately, we establish a novel robotic platform that allows real-time actuation of a biologically-inspired three-dimensionally printed zebrafish replica to implement model-based control of animal behavior. We demonstrate our approach through a series of experiments, designed to elucidate the appraisal of the replica by live subjects with respect to conspecifics and to quantify the biological value of closed-loop control.