Differentiated Anti-Predation Responses in a Superorganism

Insect societies are complex systems, displaying emergent properties much greater than the sum of their individual parts. As such, the concept of these societies as single 'superorganisms' is widely applied to describe their organisation and biology. Here, we test the applicability of this concept to the response of social insect colonies to predation during a vulnerable period of their life history. We used the model system of house-hunting behaviour in the ant Temnothorax albipennis. We show that removing individuals from directly within the nest causes an evacuation response, while removing ants at the periphery of scouting activity causes the colony to withdraw back into the nest. This suggests that colonies react differentially, but in a coordinated fashion, to these differing types of predation. Our findings lend support to the superorganism concept, as the whole society reacts much like a single organism would in response to attacks on different parts of its body. The implication of this is that a collective reaction to the location of worker loss within insect colonies is key to avoiding further harm, much in the same way that the nervous systems of individuals facilitate the avoidance of localised damage

A social mechanism facilitates ant colony emigrations overdifferent distances

Behavioural responses enable animals to react rapidly to fluctuating environments. In eusocial organisms, such changes are often enacted at the group level, but may be organised in a decentralised fashion by the actions of individuals. However, the contributions of different group members are rarely homogeneous, and there is evidence to suggest that certain ‘keystone’ individuals are important in shaping collective responses. Accordingly, investigations of the dynamics and structuring of behavioural changes at both the group and individual level are crucial for evaluating the relative influence of different individuals. Here, we examined the composition of tandem running behaviour during colony emigrations in the ant species Temnothorax albipennis. Tandem running is modulated in response to emigration distance, with more runs being conducted when a more distant nest site must be reached. We show that certain individuals are highly active in the tandem running process, attempting significantly more work in thetask. Contrary to expectations, however, such individuals are in fact no more successful at conducting tandem runs than their less active nest mates. Instead, it seems that when more tandem runs are required, colonies rely on greater recruitment of workers into the process. The implications of our study are that in some cases, even when apparently ‘key’ individuals exist within a group, their relative contribution to task performance may be far from decisive

Reconstructing the intrinsic statistical properties of intermittent locomotion through corrections for boundary effects

Locomotion characteristics are often recorded within bounded spaces, a constraint which introduces geometry-specific biases and potentially complicates the inference of behavioural features from empirical observations. We describe how statistical properties of an uncorrelated random walk, namely the steady-state stopping location probability density and the empirical step probability density, are affected by enclosure in a bounded space. The random walk here is considered as a null model for an organism moving intermittently in such a space, that is, the points represent stopping locations and the step is the displacement between them. Closed-form expressions are derived for motion in one dimension and simple two-dimensional geometries, in addition to an implicit expression for arbitrary (convex) geometries. For the particular choice of no-go boundary conditions, we demonstrate that the empirical step distribution is related to the intrinsic step distribution, i.e. the one we would observe in unbounded space, via a multiplicative transformation dependent solely on the boundary geometry. This conclusion allows in practice for the compensation of boundary effects and the reconstruction of the intrinsic step distribution from empirical observations

Social behaviour and collective motion in plant-animal worms

© 2016 The Author(s) Published by the Royal Society. All rights reserved. Social behaviour may enable organisms to occupy ecological niches that would otherwise be unavailable to them. Here, we test this major evolutionary prin- ciple by demonstrating self-organizing social behaviour in the plant-animal, Symsagittifera roscoffensis. These marine aceol flat worms rely for all of their nutrition on the algae within their bodies: hence their common name. We show that individual worms interact with one another to coordinate their movements so that even at low densities they begin to swim in small polarized groups and at increasing densities such flotillas turn into circular mills. We use computer simulations to: (i) determine if real worms interact socially by com- paring them with virtual worms that do not interact and (ii) show that the social phase transitions of the real worms can occur based only on local inter- actions between and among them. We hypothesize that such social behaviour helps the worms to form the dense biofilms or mats observed on certain sun- exposed sandy beaches in the upper intertidal of the East Atlantic and to become in effect a super-organismic seaweed in a habitat where macro-algal seaweeds cannot anchor themselves. Symsagittifera roscoffensis, a model organ- ism in many other areas in biology (including stem cell regeneration), also seems to be an ideal model for understanding how individual behaviours can lead, through collective movement, to social assemblages

Migration control: A distance compensation strategy in ants

©The Author(s) 2016. This article is published with open access at Springerlink.com. Migratory behaviour forms an intrinsic part of the life histories of many organisms but is often a high-risk process. Consequently, varied strategies have evolved to negate such risks, but empirical data relating to their functioning are limited. In this study, we use the model system of the househunting ant Temnothorax albipennis to demonstrate a key strategy that can shorten migration exposure times in a group of social insects. Colonies of these ants frequently migrate to new nest sites, and due to the nature of their habitat, the distances over which they do so are variable, leading to fluctuating potential costs dependent on migration parameters. We show that colonies of this species facultatively alter the dynamics of a migration and so compensate for the distance over which a given migration occurs. Specifically, they achieve this by modulating the rate of ‘tandem running’, in which workers teach each other the route to a new nest site. Using this method, colonies are able to engage a larger number of individuals in the migration process when the distance to be traversed is greater, and furthermore, the system appears to be based on perceived encounter rate at the individual level. This form of decentralised control highlights the adaptive nature of a behaviour of ecological importance, and indicates that the key to its robustness lies in the use of simple rules. Additionally, our results suggest that such coordinated group reactions are central to achieving the high levels of ecological success seen in many eusocial organisms

Time-Ordered Networks Reveal Limitations to Information Flow in Ant Colonies

BACKGROUND: An important function of many complex networks is to inhibit or promote the transmission of disease, resources, or information between individuals. However, little is known about how the temporal dynamics of individual-level interactions affect these networks and constrain their function. Ant colonies are a model comparative system for understanding general principles linking individual-level interactions to network-level functions because interactions among individuals enable integration of multiple sources of information to collectively make decisions, and allocate tasks and resources. METHODOLOGY/FINDINGS: Here we show how the temporal and spatial dynamics of such individual interactions provide upper bounds to rates of colony-level information flow in the ant Temnothorax rugatulus. We develop a general framework for analyzing dynamic networks and a mathematical model that predicts how information flow scales with individual mobility and group size. CONCLUSIONS/SIGNIFICANCE: Using thousands of time-stamped interactions between uniquely marked ants in four colonies of a range of sizes, we demonstrate that observed maximum rates of information flow are always slower than predicted, and are constrained by regulation of individual mobility and contact rate. By accounting for the ordering and timing of interactions, we can resolve important difficulties with network sampling frequency and duration, enabling a broader understanding of interaction network functioning across systems and scales

Spatial effects, sampling errors, and task specialization in the honey bee

Task allocation patterns should depend on the spatial distribution of work within the nest, variation in task demand, and the movement patterns of workers, however, relatively little research has focused on these topics. This study uses a spatially explicit agent based model to determine whether such factors alone can generate biases in task performance at the individual level in the honey bees, Apis mellifera. Specialization (bias in task performance) is shown to result from strong sampling error due to localized task demand, relatively slow moving workers relative to nest size, and strong spatial variation in task demand. To date, specialization has been primarily interpreted with the response threshold concept, which is focused on intrinsic (typically genotypic) differences between workers. Response threshold variation and sampling error due to spatial effects are not mutually exclusive, however, and this study suggests that both contribute to patterns of task bias at the individual level. While spatial effects are strong enough to explain some documented cases of specialization; they are relatively short term and not explanatory for long term cases of specialization. In general, this study suggests that the spatial layout of tasks and fluctuations in their demand must be explicitly controlled for in studies focused on identifying genotypic specialists

Ants in a Labyrinth: A Statistical Mechanics Approach to the Division of Labour

Division of labour (DoL) is a fundamental organisational principle in human societies, within virtual and robotic swarms and at all levels of biological organisation. DoL reaches a pinnacle in the insect societies where the most widely used model is based on variation in response thresholds among individuals, and the assumption that individuals and stimuli are well-mixed. Here, we present a spatially explicit model of DoL. Our model is inspired by Pierre de Gennes' 'Ant in a Labyrinth' which laid the foundations of an entire new field in statistical mechanics. We demonstrate the emergence, even in a simplified one-dimensional model, of a spatial patterning of individuals and a right-skewed activity distribution, both of which are characteristics of division of labour in animal societies. We then show using a two-dimensional model that the work done by an individual within an activity bout is a sigmoidal function of its response threshold. Furthermore, there is an inverse relationship between the overall stimulus level and the skewness of the activity distribution. Therefore, the difference in the amount of work done by two individuals with different thresholds increases as the overall stimulus level decreases. Indeed, spatial fluctuations of task stimuli are minimised at these low stimulus levels. Hence, the more unequally labour is divided amongst individuals, the greater the ability of the colony to maintain homeostasis. Finally, we show that the non-random spatial distribution of individuals within biological and social systems could be caused by indirect (stigmergic) interactions, rather than direct agent-to-agent interactions. Our model links the principle of DoL with principles in the statistical mechanics and provides testable hypotheses for future experiments

Evolution of self-organized division of labor in a response threshold model

Division of labor in social insects is determinant to their ecological success. Recent models emphasize that division of labor is an emergent property of the interactions among nestmates obeying to simple behavioral rules. However, the role of evolution in shaping these rules has been largely neglected. Here, we investigate a model that integrates the perspectives of self-organization and evolution. Our point of departure is the response threshold model, where we allow thresholds to evolve. We ask whether the thresholds will evolve to a state where division of labor emerges in a form that fits the needs of the colony. We find that division of labor can indeed evolve through the evolutionary branching of thresholds, leading to workers that differ in their tendency to take on a given task. However, the conditions under which division of labor evolves depend on the strength of selection on the two fitness components considered: amount of work performed and on worker distribution over tasks. When selection is strongest on the amount of work performed, division of labor evolves if switching tasks is costly. When selection is strongest on worker distribution, division of labor is less likely to evolve. Furthermore, we show that a biased distribution (like 3:1) of workers over tasks is not easily achievable by a threshold mechanism, even under strong selection. Contrary to expectation, multiple matings of colony foundresses impede the evolution of specialization. Overall, our model sheds light on the importance of considering the interaction between specific mechanisms and ecological requirements to better understand the evolutionary scenarios that lead to division of labor in complex systems

Dynamic social networks in guppies (Poecilia reticulata)

One of the main challenges in the study of social networks in vertebrates is to close the gap between group patterns and dynamics. Usually scan samples or transect data are recorded to provide information about social patterns of animals, but these techniques themselves do not shed much light on the underlying dynamics of such groups. Here we show an approach which captures the fission-fusion dynamics of a fish population in the wild and demonstrates how the gap between pattern and dynamics may be closed. Our analysis revealed that guppies have complex association patterns that are characterised by close strong connections between individuals of similar behavioural type. Intriguingly, the preference for particular social partners is not expressed in the length of associations but in their frequency. Finally, we show that the observed association preferences could have important consequences for transmission processes in animal social networks, thus moving the emphasis of network research from descriptive mechanistic studies to functional and predictive ones. © 2014 Springer-Verlag Berlin Heidelberg