Social context modulates idiosyncrasy of behaviour in the gregarious cockroach Blaberus discoidalis

Feedback control of automatic navigation for cyborg cockroach without external motion capture system

Due to their size and locomotion ability, cockroaches are favorable as hybrid robot platforms in search and rescue (SAR) missions. However, cockroaches most likely approach the corner area and stay for an uncertain time. This natural behavior will hinder the utilization of cyborg cockroaches in SAR missions under rubble, unstructured, and unknown areas. Therefore, we proposed onboard automatic obstacle avoidance and human detection that can run on the wireless backpack stimulator without an external motion capture system. A low-power and small-size Time of Flight (ToF) sensor was selected as a distance measurement sensor, while a low-resolution thermopile array sensor was applied for human presence detection. The implemented feedback control based on IMU and ToF sensors has successfully navigated the cyborg cockroach to avoid obstacles and escape from the sharp corners in the laboratory unstructured area without stopping or being trapped. It could also recognize the human presence when the human was in front of it in real-time. Due to its performance, the random forest classifier was implemented as an embedded human detection system. It could achieve the highest accuracy at a distance of around 25 cm (92.5%) and the lowest accuracy at about 100 cm (70%).

Social modulation of oogenesis and egg laying in Drosophila melanogaster

Being part of a group facilitates cooperation between group members but also creates competition for resources. This is a conundrum for gravid females, whose future offspring benefit from being in a group only if there are enough resources relative to group size. Females may therefore be expected to modulate reproductive output depending on social context. In the fruit fly Drosophila melanogaster, females actively attract conspecifics to lay eggs on the same resources, generating groups in which individuals may cooperate or compete. The genetic tractability of this species allows dissecting the mechanisms underlying physiological adaptation to social context. Here, we show that females produce eggs increasingly faster as group size increases. By laying eggs faster when grouped than when isolated, females reduce competition between offspring and increase offspring survival. In addition, grouped females lay eggs during the day, while isolated females lay them at night. We show that responses to the presence of others requires visual input and that flies from any sex, mating status, or species can trigger these responses. The mechanisms of this modulation of egg laying by group is connected to a lifting of the inhibition of light on oogenesis and egg laying, possibly mediated in part by an increase in juvenile hormone activity. Because modulation of reproduction by social context is a hallmark of animals with higher levels of sociality, our findings in a species considered solitary question the validity of this nomenclature and suggest a widespread and profound influence of social context on reproduction.

Movement Optimization for a Cyborg Cockroach in a Bounded Space Incorporating Machine Learning

Cockroaches can traverse unknown obstacle-terrain, self-right on the ground and climb above the obstacle. However, they have limited motion, such as less activity in light/bright areas and lower temperatures. Therefore, the movement of the cyborg cockroaches needs to be optimized for the utilization of the cockroach as a cyborg insect. This study aims to increase the search rate and distance traveled by cockroaches and reduce the stop time by utilizing automatic stimulation from machine learning. Multiple machine learning classifiers were applied to classify the offline binary classification of the cockroach movement based on the inertial measuring unit input signals. Ten time-domain features were chosen and applied as the classifier inputs. The highest performance of the classifiers was implemented for the online motion recognition and automatic stimulation provided to the cerci to trigger the free walking motion of the cockroach. A user interface was developed to run multiple computational processes simultaneously in real time such as computer vision, data acquisition, feature extraction, automatic stimulation, and machine learning using a multithreading algorithm. On the basis of the experiment results, we successfully demonstrated that the movement performance of cockroaches was importantly improved by applying machine learning classification and automatic stimulation. This system increased the search rate and traveled distance by 68% and 70%, respectively, while the stop time was reduced by 78%.

Social context mediates the expression of a personality trait in a gregarious lizard

The social environment is a key factor that influences behavioural traits across a wide array of species. Yet, when investigating individual differences in behaviour, studies tend to measure animals in isolation from other conspecifics—even in social species. Surprisingly, whether behavioural traits measured in isolation are predictive of individual-level behaviour when in social groups is still poorly understood. Here, we repeatedly measured risk-taking behaviour (i.e. boldness; 741 total trials) in both the presence and absence of conspecifics in a social lizard, the delicate skink (Lampropholis delicata). Further, we manipulated food availability during group trials to test whether the effect of the social environment on risk-taking behaviour was mediated by competition over resources. Using 105 lizards collected from three independent populations, we found that individual risk-taking behaviour was repeatable when measured in either social isolation or within groups both with and without food resources available. However, lizards that were bolder during individual trials were not also bolder when in groups, regardless of resource availability. This was largely driven by individual differences in social behavioural plasticity, whereby individual skinks responded differently to the presence of conspecifics. Together, this resulted in a rank order change of individual behavioural types across the social conditions. Our results highlight the importance of the social environment in mediating animal personality traits across varying levels of resource availability. Further, these findings suggest that behavioural traits when measured in isolation, may not reflect individual variation in behaviour when measured in more ecologically realistic social groups.

Extracting individual characteristics from population data reveals a negative social effect during honeybee defence

Honeybees protect their colony against vertebrates by mass stinging and they coordinate their actions during this crucial event thanks to an alarm pheromone carried directly on the stinger, which is therefore released upon stinging. The pheromone then recruits nearby bees so that more and more bees participate in the defence. However, a quantitative understanding of how an individual bee adapts its stinging response during the course of an attack is still a challenge: Typically, only the group behaviour is effectively measurable in experiment; Further, linking the observed group behaviour with individual responses requires a probabilistic model enumerating a combinatorial number of possible group contexts during the defence; Finally, extracting the individual characteristics from group observations requires novel methods for parameter inference.

We first experimentally observed the behaviour of groups of bees confronted with a fake predator inside an arena and quantified their defensive reaction by counting the number of stingers embedded in the dummy at the end of a trial. We propose a biologically plausible model of this phenomenon, which transparently links the choice of each individual bee to sting or not, to its group context at the time of the decision. Then, we propose an efficient method for inferring the parameters of the model from the experimental data. Finally, we use this methodology to investigate the effect of group size on stinging initiation and alarm pheromone recruitment.

Our findings shed light on how the social context influences stinging behaviour, by quantifying how the alarm pheromone concentration level affects the decision of each bee to sting or not in a given group size. We show that recruitment is curbed as group size grows, thus suggesting that the presence of nestmates is integrated as a negative cue by individual bees. Moreover, the unique integration of exact and statistical methods provides a quantitative characterisation of uncertainty associated to each of the inferred parameters.

In this paper, our interdisciplinary team has significantly improved the understanding of how honeybees coordinate their actions during defence. Our first step was to measure the output behaviour of groups of bees under controlled experimental conditions. We then developed a model and methodology that allow us to quantify how the responsiveness to the alarm pheromone evolves during a defensive event, for a given group size. We show that recruitment becomes less effective as group size increases, thus revealing the existence of a negative social effect that acts on top of alarm pheromone communication. Our contribution is thus two-fold: on the computational side, we provide new tools to extract individual characteristics from population data, which is a challenging issue in the study of collective behaviour. On the biological side, we provide evidence that bees weight in their social context when making the decision to sting. We hypothesize that this may be an important mechanism to prevent recruitment from spinning out of control, ultimately preserving the colony from workforce depletion.

Differential Gene Expression Correlates with Behavioural Polymorphism during Collective Behaviour in Cockroaches

Simple Summary

It is currently well accepted that animals differ from one another in their behaviour and tendency to perform actions, a property we refer to as animal personality. In group-living animals, variation in animal personality can be important to determine group survival, as it determines how individuals interact with each other and with their environment. However, we have little knowledge of the proximal mechanisms underlying personality, particularly in group-living organisms. Here, we investigate the relationship between gene expression and two behavioural types (bold and shy) in a gregarious species: the American cockroach. Our results show that bold individuals have upregulated genes with functions associated with sensory activity (phototaxis and odour detection) and aggressive/dominant behaviour, and suggest that social context can modulate gene expression related to bold/shy characteristics. This work could help identify genes important in the earliest stages of group living and social life, and provides a first step toward establishing cockroaches as a focal group for the study of the evolution of sociality.

Abstract

Consistent inter-individual variation in the propensity to perform different tasks (animal personality) can contribute significantly to the success of group-living organisms. The distribution of different personalities in a group influences collective actions and therefore how these organisms interact with their environment. However, we have little understanding of the proximate mechanisms underlying animal personality in animal groups, and research on this theme has often been biased towards organisms with advanced social systems. The goal of this study is to investigate the mechanistic basis for personality variation during collective behaviour in a species with rudimentary societies: the American cockroach. We thus use an approach which combines experimental classification of individuals into behavioural phenotypes (‘bold’ and ‘shy’ individuals) with comparative gene expression. Our analyses reveal differences in gene expression between behavioural phenotypes and suggest that social context may modulate gene expression related to bold/shy characteristics. We also discuss how cockroaches could be a valuable model for the study of genetic mechanisms underlying the early steps in the evolution of social behaviour and social complexity. This study provides a first step towards a better understanding of the molecular mechanisms associated with differences in boldness and behavioural plasticity in these organisms.

Learning and memory in the orange head cockroach (Eublaberus posticus)

This paper describes two experiments aimed at establishing the orange head cockroach (Eublaberus posticus) as a model organism for behavioral research. While many invertebrate models are available, cockroaches have several benefits over others that show impressive behavioral abilities. Most notably, cockroaches are long-lived generalists that can be maintained in controlled indoor laboratory conditions. While the most popular cockroaches in behavioral research, Periplaneta americana and Blattella germanica, have the potential to become domestic pests, our E. posticus is extremely unlikely to escape or infest a human environment, making it a very practical species. In our first experiment, we investigated the ability of E. posticus to associate novel odors with appetitive and aversive solutions. They quickly learned to approach odors associated with a dog food sucrose solution and learned to avoid odors associated with salt water. The second experiment repeated the methods of the first experiment, while also testing retained preferences for conditioned odors, from 15 to 1,215 minutes after the conditioning procedure ended. We found that preferences for odors associated with food were strongest 45 minutes after training, then decreased as a function of time. Our work is the first to show associative learning and memory in the orange head cockroach. Findings are discussed in comparison to other invertebrate models as well as to other cockroach research.

Precise Quantification of Behavioral Individuality From 80 Million Decisions Across 183,000 Flies

Individual animals behave differently from each other. This variability is a component of personality and arises even when genetics and environment are held constant. Discovering the biological mechanisms underlying behavioral variability depends on efficiently measuring individual behavioral bias, a requirement that is facilitated by automated, high-throughput experiments. We compiled a large data set of individual locomotor behavior measures, acquired from over 183,000 fruit flies walking in Y-shaped mazes. With this data set we first conducted a "computational ethology natural history" study to quantify the distribution of individual behavioral biases with unprecedented precision and examine correlations between behavioral measures with high power. We discovered a slight, but highly significant, left-bias in spontaneous locomotor decision-making. We then used the data to evaluate standing hypotheses about biological mechanisms affecting behavioral variability, specifically: the neuromodulator serotonin and its precursor transporter, heterogametic sex, and temperature. We found a variety of significant effects associated with each of these mechanisms that were behavior-dependent. This indicates that the relationship between biological mechanisms and behavioral variability may be highly context dependent. Going forward, automation of behavioral experiments will likely be essential in teasing out the complex causality of individuality.

Recent approaches to study the neural bases of complex insect behavior

Recent advances in biocompatible materials, miniaturized instrumentation, advanced computational algorithms, and genetic tools have enabled the development of novel methods and approaches to quantify the behavior of individuals or groups of animals. In conjunction with technologies that allow simultaneous monitoring of neural responses, quantitative studies of complex behaviors can reveal tighter links between the external sensory cues in the vicinity of the organism and neural responses they elicit, and how internal neural representations finally get mapped onto the behavior generated. In this review, we examine a few approaches that are beginning to be widely exploited for understanding neural-behavioral response transformations.

Controllable Positive/Negative Phototaxis of Millimeter-Sized Objects with Sensing Function

Phototaxis, which is the directional motion toward or away from light, is common in nature and inspires development of artificial light-steered active objects. Most of the light-steered objects developed so far exhibit either positive or negative phototaxis, and there are few examples of research on objects that exhibit both positive and negative phototaxis. Herein, small objects showing both positive and negative phototaxis on the water surface upon near-infrared (NIR) light irradiation, with the direction controlled by the position of light irradiation, are reported. The millimeter-sized tetrahedral liquid marble containing gelled water coated by one polymer plate with light-to-heat photothermal characteristic, which adsorbs onto the bottom of the liquid marble, and three polymer plates with highly transparent characteristic, which adsorb onto the upper part of the liquid marble, is utilized as a model small object. Light irradiation on the front side of the object induces negative phototaxis and that on the other side induces positive phototaxis, and the motion can be controlled to 360° arbitrary direction by precise control of the light irradiation position. Thermographic studies confirm that the motions are realized through Marangoni flow generated around the liquid marble, which is induced by position-selective NIR light irradiation. The object can move centimeter distances, and numerical analysis indicates that average velocity and acceleration are approximately 12 mm/s and 71 mm/s², respectively, which are independent of the direction of motions. The generated force is estimated to be approximately 0.4 μN based on Newton's equation. Furthermore, functional cargo can be loaded into the inner phase of the small objects, which can be delivered and released on demand and endows them with environmental sensing ability.

Collective motion as a distinct behavioral state of the individual

The collective motion of swarms depends on adaptations at the individual level. We explored these and their effects on swarm formation and maintenance in locusts. The walking kinematics of individual insects were monitored under laboratory settings, before, as well as during collective motion in a group, and again after separation from the group. It was found that taking part in collective motion induced in the individual unique behavioral kinematics, suggesting the existence of a distinct behavioral mode that we term a "collective-motion-state." This state, characterized by behavioral adaptation to the social context, is long lasting, not induced by crowding per se, but only by experiencing collective motion. Utilizing computational models, we show that this adaptability increases the robustness of the swarm. Overall, our findings suggest that collective motion is not only an emergent property of the group but also depends on a behavioral mode, rooted in endogenous mechanisms of the individual.

Personality variation improves collective decision-making in cockroaches

Many animals live in groups and engage in collective actions which can enhance their fitness. One common example is collective decision-making, which mainly arises from social interactions that modify the individual behaviour. Despite the widespread interest in animal personalities on the one hand and in social effects (such as social organisation, social learning or anti-predator behaviour) on the other, the question of how the amount of among-individual differences, coupled with social interactions, influence group cohesion has rarely been addressed. For this purpose, I used a modelling approach based on aggregation behaviour of cockroaches to explore the mechanisms underlying such context-dependent behaviour. The results of simulations considering different degrees (none, medium, high) of personality variation in a non-social and social context were compared to experimental patterns of aggregation dynamics in cockroaches. The comparison between the simulated and experimental data show that only a model that considers differences in individuals was able to reproduce the experimental patterns of individuals and groups. In addition, the comparison between models suggest that some individuals may play a keystone role during aggregation dynamics, influencing the behaviour of others and facilitating the collective decision. Finally, I show that personality variation amplifies the effects of social inter-attractions, thus increasing the speed of aggregation, shedding light on the mechanisms underpinning social modification of individual behaviour.

Individual, but not population asymmetries, are modulated by social environment and genotype in Drosophila melanogaster

Theory predicts that social interactions can induce an alignment of behavioral asymmetries between individuals (i.e., population-level lateralization), but evidence for this effect is mixed. To understand how interaction with other individuals affects behavioral asymmetries, we systematically manipulated the social environment of Drosophila melanogaster, testing individual flies and dyads (female-male, female-female and male-male pairs). In these social contexts we measured individual and population asymmetries in individual behaviors (circling asymmetry, wing use) and dyadic behaviors (relative position and orientation between two flies) in five different genotypes. We reasoned that if coordination between individuals drives alignment of behavioral asymmetries, greater alignment at the population-level should be observed in social contexts compared to solitary individuals. We observed that the presence of other individuals influenced the behavior and position of flies but had unexpected effects on individual and population asymmetries: individual-level asymmetries were strong and modulated by the social context but population-level asymmetries were mild or absent. Moreover, the strength of individual-level asymmetries differed between strains, but this was not the case for population-level asymmetries. These findings suggest that the degree of social interaction found in Drosophila is insufficient to drive population-level behavioral asymmetries.

Conflictual influence of humidity during shelter selection of the American cockroach (Periplaneta americana)

In collective decision-making, when confronted with different options, groups usually show a more marked preference for one of the options than do isolated individuals. This results from the amplification of individual preferences by social interactions within the group. We show, in an unusual counter-example, that when facing a binary choice between shelters with different relative humidities, isolated cockroaches of the species Periplaneta americana select the wettest shelter, while groups select the driest one. This inversion of selection results from a conflictual influence of humidity on the probabilities of entering and leaving each shelter. It is shown that the individual probability of entering the wettest shelter is higher than the group probability and is increased by previous entries and exits. The probability of leaving each shelter decreases in the population due to social interactions, but this decrease is less pronounced in the wettest shelter, suggesting weaker social interactions. A theoretical model is developed and highlights the existence of tipping points dependent on population size, beyond which an inversion of selection of a resting place is observed.

MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology

Fast object tracking in real time allows convenient tracking of very large numbers of animals and closed-loop experiments that control stimuli for many animals in parallel. We developed MARGO, a MATLAB-based, real-time animal tracking suite for custom behavioral experiments. We demonstrated that MARGO can rapidly and accurately track large numbers of animals in parallel over very long timescales, typically when spatially separated such as in multiwell plates. We incorporated control of peripheral hardware, and implemented a flexible software architecture for defining new experimental routines. These features enable closed-loop delivery of stimuli to many individuals simultaneously. We highlight MARGO's ability to coordinate tracking and hardware control with two custom behavioral assays (measuring phototaxis and optomotor response) and one optogenetic operant conditioning assay. There are currently several open source animal trackers. MARGO's strengths are 1) fast and accurate tracking, 2) high throughput, 3) an accessible interface and data output and 4) real-time closed-loop hardware control for for sensory and optogenetic stimuli, all of which are optimized for large-scale experiments.

MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology

Fast object tracking in real time allows convenient tracking of very large numbers of animals and closed-loop experiments that control stimuli for many animals in parallel. We developed MARGO, a MATLAB-based, real-time animal tracking suite for custom behavioral experiments. We demonstrated that MARGO can rapidly and accurately track large numbers of animals in parallel over very long timescales, typically when spatially separated such as in multiwell plates. We incorporated control of peripheral hardware, and implemented a flexible software architecture for defining new experimental routines. These features enable closed-loop delivery of stimuli to many individuals simultaneously. We highlight MARGO's ability to coordinate tracking and hardware control with two custom behavioral assays (measuring phototaxis and optomotor response) and one optogenetic operant conditioning assay. There are currently several open source animal trackers. MARGO's strengths are 1) fast and accurate tracking, 2) high throughput, 3) an accessible interface and data output and 4) real-time closed-loop hardware control for for sensory and optogenetic stimuli, all of which are optimized for large-scale experiments.

Alteration of the aggregation and spatial organization of the vector of Chagas disease, Triatoma infestans, by the parasite Trypanosoma cruzi

Triatominae insects are vectors of the parasite Trypanosoma cruzi, the etiological agent of Chagas disease affecting millions of people in Latin America. Some species, such as Triatoma infestans, live in the human neighborhood, aggregating in walls or roof cracks during the day and going out to feed blood at night. The comprehension of how sex and T. cruzi infection affect their aggregation and geotaxis is essential for understanding their spatial organization and the parasite dispersion. Experiments in laboratory-controlled conditions were carried out with groups of ten adults of T. infestans able to explore and aggregate on a vertical surface. The influence of the sex (male vs. female) and the proportion of infected insects in the group were tested (100% of infected insects vs. a small proportion of infected insects, named infected and potentially weakly infected groups, respectively). Therefore, four distinct groups of insects were tested: infected males, infected females, potentially weakly infected males, and potentially weakly infected females, with 12, 9, 15, and 16 replicates, respectively. The insects presented a high negative geotaxis and a strong aggregation behavior whatever the sex or their infection. After an exploration phase, these behaviors were stable in time. The insects exhibited a preferential vertical position, head toward the top of the setup. Males had a higher negative geotaxis and a higher aggregation level than females. Both behaviors were enhanced in groups of 100% infected insects, the difference between sexes being maintained. According to a comparison between experimental and theoretical results, geotaxis favors the aggregation that mainly results from the inter-attraction between individuals.

Plant behaviour in response to the environment: information processing in the solid state

Information processing and storage underpins many biological processes of vital importance to organism survival. Like animals, plants also acquire, store and process environmental information relevant to their fitness, and this is particularly evident in their decision-making. The control of plant organ growth and timing of their developmental transitions are carefully orchestrated by the collective action of many connected computing agents, the cells, in what could be addressed as distributed computation. Here, we discuss some examples of biological information processing in plants, with special interest in the connection to formal computational models drawn from theoretical frameworks. Research into biological processes with a computational perspective may yield new insights and provide a general framework for information processing across different substrates. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.

Intra- versus intergroup variance in collective behavior

Animal collective motion arises from the intricate interactions between the natural variability among individuals, and the homogenizing effect of the group, working to generate synchronization and maintain coherence. Here, these interactions were studied using marching locust nymphs under controlled laboratory settings. A novel experimental approach compared single animals, small groups, and virtual groups composed of randomly shuffled real members. We found that the locust groups developed unique, group-specific behavioral characteristics, reflected in large intergroup and small intragroup variance (compared with the shuffled groups). Behavioral features that differed between single animals and groups, but not between group types, were classified as essential for swarm formation. Comparison with Markov chain models showed that individual tendencies and the interaction network among animals dictate the group characteristics. Deciphering the bidirectional interactions between individual and group properties is essential for understanding the swarm phenomenon and predicting large-scale swarm behaviors.

The role of personality variation, plasticity and social facilitation in cockroach aggregation

Personality variation has been proven to affect ecology, evolution and group behaviour in many ways. Nevertheless, how social context influences behavioural strategies and individual personality variation has rarely been addressed. This study sheds light on the relationship between social interactions, personality variation and plasticity in a collective context. For this purpose, we used a binary setup (i.e. an arena with two identical shelters) to study the aggregation process of cockroaches. We tested the same individuals in isolated and social (groups of 16 individuals) conditions. We show that even if social interactions reduce the observation of personality variation, the behaviour in a group is correlated to individual preferences displayed in isolation. Furthermore, our results suggest that individuals show different levels of plasticity according to their shelter occupancy; individuals with high occupancy rates show low levels of plasticity and are less affected by social amplification in social conditions.

Behavioral flexibility promotes collective consistency in a social insect

Deciphering the mechanisms that integrate individuals and their behavior into a functional unit is crucial for our understanding of collective behaviors. We here present empirical evidence for the impressive strength of social processes in this integration. We investigated collective temperature homeostasis in bumblebee (Bombus terrestris) colonies and found that bees are less likely to engage in thermoregulatory fanning and do so with less time investment when confronted with heat stress in a group setting than when facing the same challenge alone and that this down-regulation of individual stimulus-response behavior resulted in a consistent proportion of workers in a group engaged in the task of fanning. Furthermore, the bees that comprised the subset of fanning individuals changed from trial to trial and participation in the task was predominately unpredictable based on previous response behavior. Our results challenge basic assumptions in the most commonly used class of models for task allocation and contrast numerous collective behavior studies that emphasize the importance of fixed inter-individual variation for the functioning of animal groups. We demonstrate that bumblebee colonies maintain within-group behavioral heterogeneity and a consistent collective response pattern based on social responsiveness and behavioral flexibility at the individual level.

Fitness benefits and emergent division of labour at the onset of group living

The initial fitness benefits of group-living are considered the greatest hurdle to the evolution of sociality¹, and theory predicts that they need to arise at very small group sizes². Such benefits are thought to emerge partly from scaling effects that increase efficiency as group size increases^3–5. In social insects and other taxa, they have been proposed to stem from division of labor (DOL)^5–8, which is characterized by between-individual variability and within-individual consistency (specialization) in task performance. At the onset of sociality, however, groups were likely small and composed of similar individuals with potentially redundant rather than complementary function¹. Theory suggests that DOL can emerge even in relatively small, simple groups^9,10. However, empirical data on the effects of group size on DOL and fitness remain equivocal⁶. Here, we use long-term automated behavioral tracking in clonal ant colonies, combined with mathematical modeling, to show that increases in social-group size can generate DOL among extremely similar workers, in groups as small as six individuals. These early effects on behavior were associated with large increases in homeostasis—the maintenance of stable conditions in the colony¹¹— and per capita fitness. Our model suggests that increases in homeostasis are primarily driven by increases in group size itself, and, to a smaller extent, by higher DOL. Overall, our results indicate that DOL, increased homeostasis, and higher fitness can naturally emerge in small, homogeneous social groups, and that scaling effects associated with increasing group size can thus promote social cohesion at incipient stages of group-living.

The interplay between personalities and social interactions affects the cohesion of the group and the speed of aggregation

Collective decision-making plays a central role in group-living animals and can be crucial to the survival of a group and the fitness of its members. As group-level properties emerge from individual decisions, personality variation can be a major determinant of collective behaviours. Here, we explore the relationship between personality and social interactions to explain the speed and cohesion of collective decision making during the aggregation process of the American cockroach (Periplaneta americana). We composed groups solely with shy individuals (spending a long time sheltered) or bold individuals (spending a short time sheltered) and tested them in a binary setup (arena with two shelters) for 3 consecutive days. We analysed the shelter use of individuals and groups to compare behavioural consistency among days and analyse the collective decision-making process. Contrary to the bold groups, shy groups had a faster aggregation process with more individuals sheltered mainly because shy individuals found the shelter more rapidly. Moreover, we show that personality is modulated by social interactions. We show high behavioural plasticity in bold groups, where some individuals act shy. This also suggests that learning and regulation mechanisms may take place. This study sheds some light on the implications of individual personality for collective decision making and the key role of shy individuals in gregarious species, such as P. americana.

Spatial fidelity of workers predicts collective response to disturbance in a social insect

Individuals in social insect colonies cooperate to perform collective work. While colonies often respond to changing environmental conditions by flexibly reallocating workers to different tasks, the factors determining which workers switch and why are not well understood. Here, we use an automated tracking system to continuously monitor nest behavior and foraging activity of uniquely identified workers from entire bumble bee (Bombus impatiens) colonies foraging in a natural outdoor environment. We show that most foraging is performed by a small number of workers and that the intensity and distribution of foraging is actively regulated at the colony level in response to forager removal. By analyzing worker nest behavior before and after forager removal, we show that spatial fidelity of workers within the nest generates uneven interaction with relevant localized information sources, and predicts which workers initiate foraging after disturbance. Our results highlight the importance of spatial fidelity for structuring information flow and regulating collective behavior in social insect colonies.

Emigration dynamics of cockroaches under different disturbance regimes do not depend on individual personalities

Group-level properties, such as collective movements or decisions, can be considered an outcome of the interplay between individual behavior and social interactions. However, the respective influences of individual preferences and social interactions are not evident. In this research, we study the implications of behavioral variability on the migration dynamics of a group of gregarious insects (Periplaneta americana) subjected to two different disturbance regimes (one without disturbances and another one with high frequency of disturbances). The results indicate that individuals presented consistent behavior during the nighttime (active phase of cockroaches) in both conditions. Moreover, we used a modeling approach to test the role of personality during the migration process. The model considers identical individuals (no personality) without memory and no direct inter-attraction between individuals. The agreement between theoretical and experimental results shows that behavioral variability play a secondary role during migration dynamics. Our results showing individual personality during the nighttime (spontaneous decision to forage) but not during the emigration process (induced by environmental disturbances) highlight the plasticity of personality traits.

Systematic exploration of unsupervised methods for mapping behavior

To fully understand the mechanisms giving rise to behavior, we need to be able to precisely measure it. When coupled with large behavioral data sets, unsupervised clustering methods offer the potential of unbiased mapping of behavioral spaces. However, unsupervised techniques to map behavioral spaces are in their infancy, and there have been few systematic considerations of all the methodological options. We compared the performance of seven distinct mapping methods in clustering a wavelet-transformed data set consisting of the x- and y-positions of the six legs of individual flies. Legs were automatically tracked by small pieces of fluorescent dye, while the fly was tethered and walking on an air-suspended ball. We find that there is considerable variation in the performance of these mapping methods, and that better performance is attained when clustering is done in higher dimensional spaces (which are otherwise less preferable because they are hard to visualize). High dimensionality means that some algorithms, including the non-parametric watershed cluster assignment algorithm, cannot be used. We developed an alternative watershed algorithm which can be used in high-dimensional spaces when a probability density estimate can be computed directly. With these tools in hand, we examined the behavioral space of fly leg postural dynamics and locomotion. We find a striking division of behavior into modes involving the fore legs and modes involving the hind legs, with few direct transitions between them. By computing behavioral clusters using the data from all flies simultaneously, we show that this division appears to be common to all flies. We also identify individual-to-individual differences in behavior and behavioral transitions. Lastly, we suggest a computational pipeline that can achieve satisfactory levels of performance without the taxing computational demands of a systematic combinatorial approach.