Army ants dynamically adjust living bridges in response to a cost-benefit trade-off

Snail-inspired robotic swarms: a hybrid connector drives collective adaptation in unstructured outdoor environments

Terrestrial self-reconfigurable robot swarms offer adaptable solutions for various tasks. However, most existing swarms are limited to controlled indoor settings, and often compromise stability due to their freeform connections. To address these issues, we present a snail robotic swarm system inspired by land snails, tailored for unstructured environments. Our system also employs a two-mode connection mechanism, drawing from the adhesive capabilities of land snails. The free mode, mirroring a snail's natural locomotion, leverages magnet-embedded tracks for freeform mobility, thereby enhancing adaptability and efficiency. The strong mode, analogous to a snail's response to disturbance, employs a vacuum sucker with directional polymer stalks for robust adhesion. By assigning specific functions to each mode, our system achieves a balance between mobility and secure connections. Outdoor experiments demonstrate the capabilities of individual robots and the exceptional synergy within the swarm. This research advances the real-world applications of terrestrial robotic swarms in unstructured environments.

Nonlinear social evolution and the emergence of collective action

Organisms from microbes to humans engage in a variety of social behaviors, which affect fitness in complex, often nonlinear ways. The question of how these behaviors evolve has consequences ranging from antibiotic resistance to human origins. However, evolution with nonlinear social interactions is challenging to model mathematically, especially in combination with spatial, group, and/or kin assortment. We derive a mathematical condition for natural selection with synergistic interactions among any number of individuals. This result applies to populations with arbitrary (but fixed) spatial or network structure, group subdivision, and/or mating patterns. In this condition, nonlinear fitness effects are ascribed to collectives, and weighted by a new measure of collective relatedness. For weak selection, this condition can be systematically evaluated by computing branch lengths of ancestral trees. We apply this condition to pairwise games between diploid relatives, and to dilemmas of collective help or harm among siblings and on spatial networks. Our work provides a rigorous basis for extending the notion of "actor", in the study of social evolution, from individuals to collectives.

Motile Living Biobots Self-Construct from Adult Human Somatic Progenitor Seed Cells

Fundamental knowledge gaps exist about the plasticity of cells from adult soma and the potential diversity of body shape and behavior in living constructs derived from genetically wild-type cells. Here anthrobots are introduced, a spheroid-shaped multicellular biological robot (biobot) platform with diameters ranging from 30 to 500 microns and cilia-powered locomotive abilities. Each Anthrobot begins as a single cell, derived from the adult human lung, and self-constructs into a multicellular motile biobot after being cultured in extra cellular matrix for 2 weeks and transferred into a minimally viscous habitat. Anthrobots exhibit diverse behaviors with motility patterns ranging from tight loops to straight lines and speeds ranging from 5-50 microns s-1 . The anatomical investigations reveal that this behavioral diversity is significantly correlated with their morphological diversity. Anthrobots can assume morphologies with fully polarized or wholly ciliated bodies and spherical or ellipsoidal shapes, each related to a distinct movement type. Anthrobots are found to be capable of traversing, and inducing rapid repair of scratches in, cultured human neural cell sheets in vitro. By controlling microenvironmental cues in bulk, novel structures, with new and unexpected behavior and biomedically-relevant capabilities, can be discovered in morphogenetic processes without direct genetic editing or manual sculpting.

Sensory collectives in natural systems

Groups of animals inhabit vastly different sensory worlds, or umwelten, which shape fundamental aspects of their behaviour. Yet the sensory ecology of species is rarely incorporated into the emerging field of collective behaviour, which studies the movements, population-level behaviours, and emergent properties of animal groups. Here, we review the contributions of sensory ecology and collective behaviour to understanding how animals move and interact within the context of their social and physical environments. Our goal is to advance and bridge these two areas of inquiry and highlight the potential for their creative integration. To achieve this goal, we organise our review around the following themes: (1) identifying the promise of integrating collective behaviour and sensory ecology; (2) defining and exploring the concept of a 'sensory collective'; (3) considering the potential for sensory collectives to shape the evolution of sensory systems; (4) exploring examples from diverse taxa to illustrate neural circuits involved in sensing and collective behaviour; and (5) suggesting the need for creative conceptual and methodological advances to quantify 'sensescapes'. In the final section, (6) applications to biological conservation, we argue that these topics are timely, given the ongoing anthropogenic changes to sensory stimuli (e.g. via light, sound, and chemical pollution) which are anticipated to impact animal collectives and group-level behaviour and, in turn, ecosystem composition and function. Our synthesis seeks to provide a forward-looking perspective on how sensory ecologists and collective behaviourists can both learn from and inspire one another to advance our understanding of animal behaviour, ecology, adaptation, and evolution.

Collective Behaviors of Isotropic Micromotors: From Assembly to Reconstruction and Motion Control under External Fields

Swarms of self-propelled micromotors can mimic the processes of natural systems and construct artificial intelligent materials to perform complex collective behaviors. Compared to self-propelled Janus micromotors, the isotropic colloid motors, also called micromotors or microswimmers, have advantages in self-assembly to form micromotor swarms, which are efficient in resistance to external disturbance and the delivery of large quantity of cargos. In this minireview, we summarize the fundamental principles and interactions for the assembly of isotropic active particles to generate micromotor swarms. Recent discoveries based on either catalytic or external physical field-stimulated micromotor swarms are also presented. Then, the strategy for the reconstruction and motion control of micromotor swarms in complex environments, including narrow channels, maze, raised obstacles, and high steps/low gaps, is summarized. Finally, we outline the future directions of micromotor swarms and the remaining challenges and opportunities.

Emergence of intelligent collective motion in a group of agents with memory

Intelligent agents collect and process information from their dynamically evolving neighborhood to efficiently navigate through it. However, agent-level intelligence does not guarantee that at the level of a collective; a common example is the jamming we observe in traffic flows. In this study, we ask: how and when do the interactions between intelligent agents translate to desirable or intelligent collective outcomes? To explore this question, we choose a collective consisting of two kinds of agents with opposing desired directions of movement. Agents in this collective are minimally intelligent: they possess only a single facet of intelligence, viz., memory, where the agents remember how well they were able to travel in their desired directions and make up for their non-optimal past. We find that dynamics due to the agent's memory influences the collective, giving rise to diverse outcomes at the level of the group: from those that are undesirable to those that can be called "intelligent." When memory is short term, local rearrangement of agents leads to the formation of symmetrically jammed arrangements that take longer to unjam. However, when agents remember across longer time-scales, their dynamics become sensitive to small differences in their movement history. This gives rise to heterogeneity in the movement that causes agents to unjam more readily and form lanes.

Beyond Nature Versus Nurture: the Emergence of Emotion

Affective science is stuck in a version of the nature-versus-nurture debate, with theorists arguing whether emotions are evolved adaptations or psychological constructions. We do not see these as mutually exclusive options. Many adaptive behaviors that humans have evolved to be good at, such as walking, emerge during development - not according to a genetically dictated program, but through interactions between the affordances of the body, brain, and environment. We suggest emotions are the same. As developing humans acquire increasingly complex goals and learn optimal strategies for pursuing those goals, they are inevitably pulled to particular brain-body-behavior states that maximize outcomes and self-reinforce via positive feedback loops. We call these recurring, self-organized states emotions. Emotions display many of the hallmark features of self-organized attractor states, such as hysteresis (prior events influence the current state), degeneracy (many configurations of the underlying variables can produce the same global state), and stability. Because most bodily, neural, and environmental affordances are shared by all humans - we all have cardiovascular systems, cerebral cortices, and caregivers who raised us - similar emotion states emerge in all of us. This perspective helps reconcile ideas that, at first glance, seem contradictory, such as emotion universality and neural degeneracy.

Ontogeny of collective behaviour

During their lifetime, superorganisms, like unitary organisms, undergo transformations that change the machinery of their collective behaviour. Here, we suggest that these transformations are largely understudied and propose that more systematic research into the ontogeny of collective behaviours is needed if we hope to better understand the link between proximate behavioural mechanisms and the development of collective adaptive functions. In particular, certain social insects engage in self-assemblage, forming dynamic and physically connected architectures with striking similarities to developing multicellular organisms, making them good model systems for ontogenetic studies of collective behaviour. However, exhaustive time series and three-dimensional data are required to thoroughly characterize the different life stages of the collective structures and the transitions between these stages. The well-established fields of embryology and developmental biology offer practical tools and theoretical frameworks that could speed up the acquisition of new knowledge about the formation, development, maturity and dissolution of social insect self-assemblages and, by extension, other superorganismal behaviours. We hope that this review will encourage an expansion of the ontogenetic perspective in the field of collective behaviour and, in particular, in self-assemblage research, which has far-reaching applications in robotics, computer science and regenerative medicine. This article is part of a discussion meeting issue 'Collective behaviour through time'.

Distributed algorithms from arboreal ants for the shortest path problem

Colonies of the arboreal turtle ant create networks of trails that link nests and food sources on the graph formed by branches and vines in the canopy of the tropical forest. Ants put down a volatile pheromone on the edges as they traverse them. At each vertex, the next edge to traverse is chosen using a decision rule based on the current pheromone level. There is a bidirectional flow of ants around the network. In a previous field study, it was observed that the trail networks approximately minimize the number of vertices, thus solving a variant of the popular shortest path problem without any central control and with minimal computational resources. We propose a biologically plausible model, based on a variant of the reinforced random walk on a graph, which explains this observation and suggests surprising algorithms for the shortest path problem and its variants. Through simulations and analysis, we show that when the rate of flow of ants does not change, the dynamics converges to the path with the minimum number of vertices, as observed in the field. The dynamics converges to the shortest path when the rate of flow increases with time, so the colony can solve the shortest path problem merely by increasing the flow rate. We also show that to guarantee convergence to the shortest path, bidirectional flow and a decision rule dividing the flow in proportion to the pheromone level are necessary, but convergence to approximately short paths is possible with other decision rules.

The Forces behind Directed Cell Migration

Directed cell migration is an essential building block of life, present when an embryo develops, a dendritic cell migrates toward a lymphatic vessel, or a fibrotic organ fails to restore its normal parenchyma. Directed cell migration is often guided by spatial gradients in a physicochemical property of the cell microenvironment, such as a gradient in chemical factors dissolved in the medium or a gradient in the mechanical properties of the substrate. Single cells and tissues sense these gradients, establish a back-to-front polarity, and coordinate the migration machinery accordingly. Central to these steps we find physical forces. In some cases, these forces are integrated into the gradient sensing mechanism. Other times, they transmit information through cells and tissues to coordinate a collective response. At any time, they participate in the cellular migratory system. In this review, we explore the role of physical forces in gradient sensing, polarization, and coordinating movement from single cells to multicellular collectives. We use the framework proposed by the molecular clutch model and explore to what extent asymmetries in the different elements of the clutch can lead to directional migration.

Strength-mass scaling law governs mass distribution inside honey bee swarms

To survive during colony reproduction, bees create dense clusters of thousands of suspended individuals. How does this swarm, which is orders of magnitude larger than the size of an individual, maintain mechanical stability? We hypothesize that the internal structure in the bulk of the swarm, about which there is little prior information, plays a key role in mechanical stability. Here, we provide the first-ever 3D reconstructions of the positions of the bees in the bulk of the swarm using x-ray computed tomography. We find that the mass of bees in a layer decreases with distance from the attachment surface. By quantifying the distribution of bees within swarms varying in size (made up of 4000-10,000 bees), we find that the same power law governs the smallest and largest swarms, with the weight supported by each layer scaling with the mass of each layer to the [Formula: see text] power. This arrangement ensures that each layer exerts the same fraction of its total strength, and on average a bee supports a lower weight than its maximum grip strength. This illustrates the extension of the scaling law relating weight to strength of single organisms to the weight distribution within a superorganism made up of thousands of individuals.

Ordering and topological defects in social wasps' nests

Social insects have evolved a variety of architectural formations. Bees and wasps are well known for their ability to achieve compact structures by building hexagonal cells. Polistes wattii, an open nesting paper wasp species, builds planar hexagonal structures. Here, using the pair correlation function approach, we show that their nests exhibit short-range hexagonal order (no long-range order) akin to amorphous materials. Hexagonal orientational order was well preserved globally. We also show the presence of topological defects such as dislocations (pentagon-heptagon disclination pairs) and Stone-Wales quadrupoles, and discuss how these defects were organised in the nest, thereby restoring order. Furthermore, we suggest the possible role of such defects in shaping nesting architectures of other social insect species.

Design and Experiments of a Compact Self-Assembling Mobile Modular Robot with Joint Actuation and Onboard Visual-Based Perception

Modular robots have the advantage of self-assembling into a large and complex structure to travel through territories beyond an individual robot’s capacity. A swarm of mobile robots is combined through mechanical interconnection and joint actuation to achieve a linked or articular configuration. In this paper, to enhance the perception, actuation and docking capacity of modular robots, a parallel mechanism-based docking system and onboard visual perception system are proposed in the design of a novel compact self-assembling mobile modular robot (SMMRob). Each module is self-contained, with a sensing or joint function. The robot modules can dock with each other based on relative positioning, which employs the visual perception of passive markers or active infrared signals in different localizations. Performance experiments were conducted to evaluate the robot module. Docking experiments were performed, along with an analysis of the success and failure results. The self-assembly of snake-like and quadruped robots was achieved in response to different environments, including an obstacle, gap or stair, and experiments were performed on self-assembly into a snake-like structure.

Hysteresis stabilizes dynamic control of self-assembled army ant constructions

Biological systems must adjust to changing external conditions, and their resilience depends on their control mechanisms. How is dynamic control implemented in noisy, decentralized systems? Army ants’ self-assembled bridges are built on unstable features, like leaves, which frequently move. Using field experiments and simulations, we characterize the bridges’ response as the gaps they span change in size, identify the control mechanism, and explore how this emerges from individuals’ decisions. For a given gap size, bridges were larger after the gap increased rather than decreased. This hysteresis was best explained by an accumulator model, in which individual decisions to join or leave a bridge depend on the difference between its current and equilibrium state. This produces robust collective structures that adjust to lasting perturbations while ignoring small, momentary shifts. Our field data support separate joining and leaving cues; joining is prompted by high bridge performance and leaving by an excess of ants. This leads to stabilizing hysteresis, an important feature of many biological and engineered systems.

Army ant bridges are a remarkable example of self-assembled living structures. Here, the authors investigate experimentally how army ant bridges respond to unstable ground, revealing how responses emerge from the decentralized actions of individuals.

Thermoregulatory morphodynamics of honeybee swarm clusters

During reproductive swarming, honeybees clusters of more than 10,000 individuals that hang from structures in the environment (e.g., tree branches) are exposed to diurnal variations in ambient temperature for up to a week. Swarm clusters collectively modulate their morphology in response to these variations (i.e., expanding/contracting in response to heating/cooling) to maintain their internal temperature within a tolerable range and to avoid exhausting their honey stores prematurely. To understand the spatiotemporal aspects of thermoregulatory morphing, we measured the change in size, shape and internal temperature profiles of swarm clusters in response to dynamic temperature ramp perturbations. We see that swarm clusters show a two-fold variation in their volume/density when heated from 15°C to 30°C. However, they do not reach an equilibrium size or shape when held at 30°C for 5 hours, long after the core temperature of the cluster has stabilized. Furthermore, the changes in cluster shape and size are hysteretic, contracting in response to cooling faster than expanding in response to heating. Although the base contact diameter of the cluster increased continuously when the swarm is heated, the change in length of the swarm (base totip) over time is non-monotonic. Consequently, the aspect ratio of the swarm fluctuated continuously even when held at a constant temperature. Taken together, our results quantify the hysteretic and anisotropic morphological responses of swarm clusters to ambient temperature variations while suggesting that both mechanical constraints and heat transfer govern their thermoregulatory morphodynamics.

IDH knockdown alters foraging behavior in the termite Odontotermes formosanus in different social contexts

Foraging, as an energy-consuming behavior, is very important for colony survival in termites. How energy metabolism related to glucose decomposition and adenosine triphosphate (ATP) production influences foraging behavior in termites is still unclear. Here, we analyzed the change in energy metabolism in the whole organism and brain after silencing the key metabolic gene isocitrate dehydrogenase (IDH) and then investigated its impact on foraging behavior in the subterranean termite Odontotermes formosanus in different social contexts. The IDH gene exhibited higher expression in the abdomen and head of O. formosanus. The knockdown of IDH resulted in metabolic disorders in the whole organism. The dsIDH-injected workers showed significantly reduced walking activity but increased foraging success. Interestingly, IDH knockdown altered brain energy metabolism, resulting in a decline in ATP levels and an increase in IDH activity. Additionally, the social context affected brain energy metabolism and, thus, altered foraging behavior in O. formosanus. We found that the presence of predator ants increased the negative influence on the foraging behavior of dsIDH-injected workers, including a decrease in foraging success. However, an increase in the number of nestmate soldiers could provide social buffering to relieve the adverse effect of predator ants on worker foraging behavior. Our orthogonal experiments further verified that the role of the IDH gene as an inherent factor was dominant in manipulating termite foraging behavior compared with external social contexts, suggesting that energy metabolism, especially brain energy metabolism, plays a crucial role in regulating termite foraging behavior.

Animal architecture

Many animals shape and modify their physical environment, thereby creating a diversity of structures, from underground burrows to constructed nests to towering above-ground edifices, all of which are referred to as 'animal architecture'. Examples of animal architecture are found everywhere on Earth: beneath the sea and on land, below and above ground, and hanging into the air off trees and precipices. Fossils suggest that animals have been acting as architects by constructing shelters and other built structures for hundreds of millions of years. Animal architects are widespread taxonomically, spanning invertebrates (Figure 1) and vertebrates (Figure 2). Their architectural creations are diverse, including: the fortress-like mounds of termites, the housing markets of architecturally remodeled shells of social hermit crabs, the subterranean tunnel systems of naked mole rats, the intricately decorated bowers of bowerbirds or the engineered dams of beavers. Even the tallest of human architecture is rivaled by animal architecture: termite mounds exceed skyscrapers in their size relative to that of the architects. Animal architecture raises many fascinating questions at the interface of behavior, ecology and evolution: How is this architecture built? What instinctive 'blueprints' or cognitive mechanisms underlie its creation? What functions does the architecture serve? And why did it evolve? Notably, because architecture changes the world, it may have far-reaching impacts on collective behavior and social life, interactions among communities of species and whole ecosystems. Architecture may even have altered the very course of evolution.

When and Why Did Human Brains Decrease in Size? A New Change-Point Analysis and Insights From Brain Evolution in Ants

Human brain size nearly quadrupled in the six million years since Homo last shared a common ancestor with chimpanzees, but human brains are thought to have decreased in volume since the end of the last Ice Age. The timing and reason for this decrease is enigmatic. Here we use change-point analysis to estimate the timing of changes in the rate of hominin brain evolution. We find that hominin brains experienced positive rate changes at 2.1 and 1.5 million years ago, coincident with the early evolution of Homo and technological innovations evident in the archeological record. But we also find that human brain size reduction was surprisingly recent, occurring in the last 3,000 years. Our dating does not support hypotheses concerning brain size reduction as a by-product of body size reduction, a result of a shift to an agricultural diet, or a consequence of self-domestication. We suggest our analysis supports the hypothesis that the recent decrease in brain size may instead result from the externalization of knowledge and advantages of group-level decision-making due in part to the advent of social systems of distributed cognition and the storage and sharing of information. Humans live in social groups in which multiple brains contribute to the emergence of collective intelligence. Although difficult to study in the deep history of Homo, the impacts of group size, social organization, collective intelligence and other potential selective forces on brain evolution can be elucidated using ants as models. The remarkable ecological diversity of ants and their species richness encompasses forms convergent in aspects of human sociality, including large group size, agrarian life histories, division of labor, and collective cognition. Ants provide a wide range of social systems to generate and test hypotheses concerning brain size enlargement or reduction and aid in interpreting patterns of brain evolution identified in humans. Although humans and ants represent very different routes in social and cognitive evolution, the insights ants offer can broadly inform us of the selective forces that influence brain size.

Better tired than lost: Turtle ant trail networks favor coherence over short edges

Creating a routing backbone is a fundamental problem in both biology and engineering. The routing backbone of the trail networks of arboreal turtle ants (Cephalotes goniodontus) connects many nests and food sources using trail pheromone deposited by ants as they walk. Unlike species that forage on the ground, the trail networks of arboreal ants are constrained by the vegetation. We examined what objectives the trail networks meet by comparing the observed ant trail networks with networks of random, hypothetical trail networks in the same surrounding vegetation and with trails optimized for four objectives: minimizing path length, minimizing average edge length, minimizing number of nodes, and minimizing opportunities to get lost. The ants’ trails minimized path length by minimizing the number of nodes traversed rather than choosing short edges. In addition, the ants’ trails reduced the opportunity for ants to get lost at each node, favoring nodes with 3D configurations most likely to be reinforced by pheromone. Thus, rather than finding the shortest edges, turtle ant trail networks take advantage of natural variation in the environment to favor coherence, keeping the ants together on the trails.

We investigated the trail networks of arboreal turtle ants in the canopy of the tropical forest, to ask what characterizes the colony’s choice of foraging paths within the vegetation. We monitored day to day changes in the junctions and edges of trail networks of colonies in the dry forest of western Mexico. We compared the paths used by the ants to simulated random paths in the surrounding vegetation. We found that the paths of turtle ants prioritize coherence, keeping ants together on the trail, over minimizing the average edge length. The choice of paths reduces the number of junctions in the trail where ants could get lost, and favors junctions with a physical configuration that makes it likely that successive ants will reinforce the same path. Our work suggests that design principles that emphasize keeping information flow constrained to streamlined, coherent trails may be useful in human-designed distributed routing and transport networks or robot swarms.

Movement, Encounter Rate, and Collective Behavior in Ant Colonies

Spatial patterns of movement regulate many aspects of social insect behavior, because how workers move around, and how many are there, determines how often they meet and interact. Interactions are usually olfactory; for example, in ants, by means of antennal contact in which one worker assesses the cuticular hydrocarbons of another. Encounter rates may be a simple outcome of local density: a worker experiences more encounters, the more other workers there are around it. This means that encounter rate can be used as a cue for overall density even though no individual can assess global density. Encounter rate as a cue for local density regulates many aspects of social insect behavior, including collective search, task allocation, nest choice, and traffic flow. As colonies grow older and larger, encounter rates change, which leads to changes in task allocation. Nest size affects local density and movement patterns, which influences encounter rate, so that nest size and connectivity influence colony behavior. However, encounter rate is not a simple function of local density when individuals change their movement in response to encounters, thus influencing further encounter rates. Natural selection on the regulation of collective behavior can draw on variation within and among colonies in the relation of movement patterns, encounter rate, and response to encounters.

Self-reconfigurable multilegged robot swarms collectively accomplish challenging terradynamic tasks

Swarms of ground-based robots are presently limited to relatively simple environments, which we attribute in part to the lack of locomotor capabilities needed to traverse complex terrain. To advance the field of terradynamically capable swarming robotics, inspired by the capabilities of multilegged organisms, we hypothesize that legged robots consisting of reversibly chainable modular units with appropriate passive perturbation management mechanisms can perform diverse tasks in variable terrain without complex control and sensing. Here, we report a reconfigurable swarm of identical low-cost quadruped robots (with directionally flexible legs and tail) that can be linked on demand and autonomously. When tasks become terradynamically challenging for individuals to perform alone, the individuals suffer performance degradation. A systematic study of performance of linked units leads to new discoveries of the emergent obstacle navigation capabilities of multilegged robots. We also demonstrate the swarm capabilities through multirobot object transport. In summary, we argue that improvement capabilities of terrestrial swarms of robots can be achieved via the judicious interaction of relatively simple units.

Hierarchical networks of food exchange in the black garden ant Lasius niger

In most eusocial insects, the division of labor results in relatively few individuals foraging for the entire colony. Thus, the survival of the colony depends on its efficiency in meeting the nutritional needs of all its members. Here, we characterize the network topology of a eusocial insect to understand the role and centrality of each caste in this network during the process of food dissemination. We constructed trophallaxis networks from 34 food-exchange experiments in black garden ants (Lasius niger). We tested the influence of brood and colony size on (i) global indices at the network level (i.e., efficiency, resilience, centralization, and modularity) and (ii) individual values (i.e., degree, strength, betweenness, and the clustering coefficient). Network resilience, the ratio between global efficiency and centralization, was stable with colony size but increased in the presence of broods, presumably in response to the nutritional needs of larvae. Individual metrics highlighted the major role of foragers in food dissemination. In addition, a hierarchical clustering analysis suggested that some domestics acted as intermediaries between foragers and other domestics. Networks appeared to be hierarchical rather than random or centralized exclusively around foragers. Finally, our results suggested that networks emerging from social insect interactions can improve group performance and thus colony fitness.

Individual error correction drives responsive self-assembly of army ant scaffolds

Human-designed infrastructures and networks relying on centralized or hierarchical control are susceptible to single-point catastrophic failure when disrupted. By contrast, most complex biological systems employ distributed control and can be more robust to perturbations. In field experiments with Eciton burchellii army ants, we show that scaffold structures, self-assembled by living ants, emerge in response to disrupted traffic on inclines, facilitating traffic flow and stemming losses of foragers and prey. Informed by our observations, we present a theoretical model based on proportional control and negative feedback, which may be relevant to many distributed systems in which group-level properties can be modified through individual error sensing and correction. The mechanism is simple, and ants only require information about their individual state.

An inherent strength of evolved collective systems is their ability to rapidly adapt to dynamic environmental conditions, offering resilience in the face of disruption. This is thought to arise when individual sensory inputs are filtered through local interactions, producing an adaptive response at the group level. To understand how simple rules encoded at the individual level can lead to the emergence of robust group-level (or distributed) control, we examined structures we call “scaffolds,” self-assembled by Eciton burchellii army ants on inclined surfaces that aid travel during foraging and migration. We conducted field experiments with wild E. burchellii colonies, manipulating the slope over which ants traversed, to examine the formation of scaffolds and their effects on foraging traffic. Our results show that scaffolds regularly form on inclined surfaces and that they reduce losses of foragers and prey, by reducing slipping and/or falling of ants, thus facilitating traffic flow. We describe the relative effects of environmental geometry and traffic on their growth and present a theoretical model to examine how the individual behaviors underlying scaffold formation drive group-level effects. Our model describes scaffold growth as a control response at the collective level that can emerge from individual error correction, requiring no complex communication among ants. We show that this model captures the dynamics observed in our experiments and is able to predict the growth—and final size—of scaffolds, and we show how the analytical solution allows for estimation of these dynamics.

Activity effects on the nonlinear mechanical properties of fire-ant aggregations

Individual fire ants are inherently active as they are living organisms that convert stored chemical energy into motion. However, each individual ant is not equally disposed to motion at any given time. In an active aggregation, most of the constituent ants are active, and vice versa for an inactive aggregation. Here we look at the role activity plays on the nonlinear mechanical behavior of the aggregation through large amplitude oscillatory shear measurements. We find that the level of viscous nonlinearity can be decreased by increasing the activity or by increasing the volume fraction. In contrast, the level of elastic nonlinearity is not affected by either activity or volume fraction. We interpret this in terms of a transient network with equal rates of linking and unlinking but with varying number of linking and unlinking events.

Searching for structure in collective systems

From fish schools and bird flocks to biofilms and neural networks, collective systems in nature are made up of many mutually influencing individuals that interact locally to produce large-scale coordinated behavior. Although coordination is central to what it means to behave collectively, measures of large-scale coordination in these systems are ad hoc and system specific. The lack of a common quantitative scale makes broad cross-system comparisons difficult. Here we identify a system-independent measure of coordination based on an information-theoretic measure of multivariate dependence and show it can be used in practice to give a new view of even classic, well-studied collective systems. Moreover, we use this measure to derive a novel method for finding the most coordinated components within a system and demonstrate how this can be used in practice to reveal intrasystem organizational structure.

Attraction, Dynamics, and Phase Transitions in Fire Ant Tower-Building

Many insect species, and even some vertebrates, assemble their bodies to form multi-functional materials that combine sensing, computation, and actuation. The tower-building behavior of red imported fire ants, Solenopsis invicta, presents a key example of this phenomenon of collective construction. While biological studies of collective construction focus on behavioral assays to measure the dynamics of formation and studies of swarm robotics focus on developing hardware that can assemble and interact, algorithms for designing such collective aggregations have been mostly overlooked. We address this gap by formulating an agent-based model for collective tower-building with a set of behavioral rules that incorporate local sensing of neighboring agents. We find that an attractive force makes tower building possible. Next, we explore the trade-offs between attraction and random motion to characterize the dynamics and phase transition of the tower building process. Lastly, we provide an optimization tool that may be used to design towers of specific shapes, mechanical loads, and dynamical properties, such as mechanical stability and mobility of the center of mass.

How Approaches to Animal Swarm Intelligence Can Improve the Study of Collective Intelligence in Human Teams

Researchers of team behavior have long been interested in the essential components of effective teamwork. Much existing research focuses on examining correlations between team member traits, team processes, and team outcomes, such as collective intelligence or team performance. However, these approaches are insufficient for providing insight into the dynamic, causal mechanisms through which the components of teamwork interact with one another and impact the emergence of team outcomes. Advances in the field of animal behavior have enabled a precise understanding of the behavioral mechanisms that enable groups to perform feats that surpass the capabilities of the individuals that comprise them. In this manuscript, we highlight how studies of animal swarm intelligence can inform research on collective intelligence in human teams. By improving the ability to obtain precise, time-varying measurements of team behaviors and outcomes and building upon approaches used in studies of swarm intelligence to analyze and model individual and group-level behaviors, researchers can gain insight into the mechanisms underlying the emergence of collective intelligence. Such understanding could inspire targeted interventions to improve team effectiveness and support the development of a comparative framework of group-level intelligence in animal and human groups.

The roles of RNA in DNA double-strand break repair

Effective DNA repair is essential for cell survival: a failure to correctly repair damage leads to the accumulation of mutations and is the driving force for carcinogenesis. Multiple pathways have evolved to protect against both intrinsic and extrinsic genotoxic events, and recent developments have highlighted an unforeseen critical role for RNA in ensuring genome stability. It is currently unclear exactly how RNA molecules participate in the repair pathways, although many models have been proposed and it is possible that RNA acts in diverse ways to facilitate DNA repair. A number of well-documented DNA repair factors have been described to have RNA-binding capacities and, moreover, screens investigating DNA-damage repair mechanisms have identified RNA-binding proteins as a major group of novel factors involved in DNA repair. In this review, we integrate some of these datasets to identify commonalities that might highlight novel and interesting factors for future investigations. This emerging role for RNA opens up a new dimension in the field of DNA repair; we discuss its impact on our current understanding of DNA repair processes and consider how it might influence cancer progression.

Infrastructure construction without information exchange: the trail clearing mechanism in Atta leafcutter ants

A wide range of group-living animals construct tangible infrastructure networks, often of remarkable size and complexity. In ant colonies, infrastructure construction may require tens of thousands of work hours distributed among many thousand individuals. What are the individual behaviours involved in the construction and what level of complexity in inter-individual interaction is required to organize this effort? We investigate this question in one of the most sophisticated trail builders in the animal world: the leafcutter ants, which remove leaf litter, cut through overhangs and shift soil to level the path of trail networks that may cumulatively extend for kilometres. Based on obstruction experiments in the field and the laboratory, we identify and quantify different individual trail clearing behaviours. Via a computational model, we further investigate the presence of recruitment, which-through direct or indirect information transfer between individuals-is one of the main organizing mechanisms of many collective behaviours in ants. We show that large-scale transport networks can emerge purely from the stochastic process of workers encountering obstructions and subsequently engaging in removal behaviour with a fixed probability. In addition to such incidental removal, we describe a dedicated clearing behaviour in which workers remove additional obstructions independent of chance encounters. We show that to explain the dynamics observed in the experiments, no information exchange (e.g. via recruitment) is required, and propose that large-scale infrastructure construction of this type can be achieved without coordination between individuals.

Interactions between worker ants may influence the growth of ant cemeteries

When an ant dies within a nest, a worker ant carries its corpse away from the nest and drops it onto a pile known as an ant cemetery. These ant cemeteries form cluster patterns, and the dynamics of the corpse piles have been studied experimentally. The aim of the present study was to investigate how sensitivity to the presence of nest-mates would influence the corpse-carrying behaviour of ants, and how this would impact the dynamics of corpse pile clustering. This was achieved by developing an agent-based computational model in which simulated ‘ants’ (the agents) carry and drop ‘corpses’, resulting in the growth of the corpse pile. In the model, the probability of an ant dropping a corpse was tuned according to the presence or absence of nest-mates. The pile dynamics of the resulting model showed a partial match with the time series evolution of corpse piles observed with real ants in previous experimental studies. Although the switch of probabilities is a thought experiment, our results suggest that the corpse-carrying behaviour of worker ants might be influenced by interactions with their nest-mates because there is evidence that ant behaviour can be influenced by encounter rates.

How cognitive and environmental constraints influence the reliability of simulated animats in groups

Evolving in groups can either enhance or reduce an individual’s task performance. Still, we know little about the factors underlying group performance, which may be reduced to three major dimensions: (a) the individual’s ability to perform a task, (b) the dependency on environmental conditions, and (c) the perception of, and the reaction to, other group members. In our research, we investigated how these dimensions interrelate in simulated evolution experiments using adaptive agents equipped with Markov brains (“animats”). We evolved the animats to perform a spatial-navigation task under various evolutionary setups. The last generation of each evolution simulation was tested across modified conditions to evaluate and compare the animats’ reliability when faced with change. Moreover, the complexity of the evolved Markov brains was assessed based on measures of information integration. We found that, under the right conditions, specialized animats could be as reliable as animats already evolved for the modified tasks, and that reliability across varying group sizes correlated with evolved fitness in most tested evolutionary setups. Our results moreover suggest that balancing the number of individuals in a group may lead to higher reliability but also lower individual performance. Besides, high brain complexity was associated with balanced group sizes and, thus, high reliability under limited sensory capacity. However, additional sensors allowed for even higher reliability across modified environments without a need for complex, integrated Markov brains. Despite complex dependencies between the individual, the group, and the environment, our computational approach provides a way to study reliability in group behavior under controlled conditions. In all, our study revealed that balancing the group size and individual cognitive abilities prevents over-specialization and can help to evolve better reliability under unknown environmental situations.

Meat ants cut more trail shortcuts when facing long detours

Engineered paths increase efficiency and safety but also incur construction and maintenance costs, leading to a trade-off between investment and gain. Such a trade-off is faced by Australian meat ants, which create and maintain vegetation-free trails between nests and food sources, and thus their trails are expected to be constructed selectively. To test this, we placed an artificial obstacle consisting of 300 paper grass blades between a sucrose feeder and the colony, flanked by walls either 10 cm or 80 cm long. To exploit the feeder, ants could detour around the walls or take a direct route by traversing through the obstacle. We found that, when confronted with a long alternative detour, 76% of colonies removed more grass blades and ants were also 60% more likely to traverse the obstacle instead of detouring, with clearing activity favouring higher ant flow or vice versa. An analysis of cut patterns revealed that ants did not cut randomly, but instead concentrated on creating a trail to the food source. Meat ants were thus able to collectively deploy their trail-clearing efforts in a directed manner when detour costs were high, and rapidly established cleared trails to the food source by focusing on completing a central, vertically aligned trail which was then followed by the ants.

Optimal foraging and the information theory of gambling

At a macroscopic level, part of the ant colony life cycle is simple: a colony collects resources; these resources are converted into more ants, and these ants in turn collect more resources. Because more ants collect more resources, this is a multiplicative process, and the expected logarithm of the amount of resources determines how successful the colony will be in the long run. Over 60 years ago, Kelly showed, using information theoretic techniques, that the rate of growth of resources for such a situation is optimized by a strategy of betting in proportion to the probability of pay-off. Thus, in the case of ants, the fraction of the colony foraging at a given location should be proportional to the probability that resources will be found there, a result widely applied in the mathematics of gambling. This theoretical optimum leads to predictions as to which collective ant movement strategies might have evolved. Here, we show how colony-level optimal foraging behaviour can be achieved by mapping movement to Markov chain Monte Carlo (MCMC) methods, specifically Hamiltonian Monte Carlo (HMC). This can be done by the ants following a (noisy) local measurement of the (logarithm of) resource probability gradient (possibly supplemented with momentum, i.e. a propensity to move in the same direction). This maps the problem of foraging (via the information theory of gambling, stochastic dynamics and techniques employed within Bayesian statistics to efficiently sample from probability distributions) to simple models of ant foraging behaviour. This identification has broad applicability, facilitates the application of information theory approaches to understand movement ecology and unifies insights from existing biomechanical, cognitive, random and optimality movement paradigms. At the cost of requiring ants to obtain (noisy) resource gradient information, we show that this model is both efficient and matches a number of characteristics of real ant exploration.

How vision governs the collective behaviour of dense cycling pelotons

In densely packed groups demonstrating collective behaviour, such as bird flocks, fish schools or packs of bicycle racers (cycling pelotons), information propagates over a network, with individuals sensing and reacting to stimuli over relatively short space and time scales. What remains elusive is a robust, mechanistic understanding of how sensory system properties affect interactions, information propagation and emergent behaviour. Here, we show through direct observation how the spatio-temporal limits of the human visual sensory system govern local interactions and set the network structure in large, dense collections of cyclists. We found that cyclists align in patterns within a ± 30° arc corresponding to the human near-peripheral visual field, in order to safely accommodate motion perturbations. Furthermore, the group structure changes near the end of the race, suggesting a narrowing of the used field of vision. This change is consistent with established theory in psychology linking increased physical exertion to the decreased field of perception. Our results show how vision, modulated by arousal-dependent neurological effects, sets the local arrangement of cyclists, the mechanisms of interaction and the implicit communication across the group. We furthermore describe information propagation phenomena with an analogous elastic solid mechanics model. We anticipate our mechanistic description will enable a more detailed understanding of the interaction principles for collective behaviour in a variety of animals.

Organismal aggregations exhibit fluidic behaviors: a review

Groups of organisms such as flocks, swarms, herds, and schools form for a variety of motivations linked to survival and proliferation. Their size, locomotive domain, population, and the environmental stimuli guiding motion make challenging the study of member interactions and global behaviors. In this review, we borrow principles and analogies from fluids to describe the characteristics of organismal aggregations, which may inspire new tools for the analysis of collective motion. Examples of fluid resemblance include open channel flow, droplet formation, and particle-laden flow. We show how the properties of density, viscosity, and surface tension have strong parallels in the structure and behavior of aggregations of contrasting scale and domain. In certain cases, aggregations are sufficiently fluid-like that values can be assigned to such properties. We highlight how organisms engaging in collective motion can flow, roll, and change phase. Finally, we present limitations and exceptions for the application of fluidic principles to the motion of living groups.

Conflicting emergences. Weak vs. strong emergence for the modelling of brain function

The concept of "emergence" has become commonplace in the modelling of complex systems, both natural and man-made; a functional property" emerges" from a system when it cannot be readily explained by the properties of the system's sub-units. A bewildering array of adaptive and sophisticated behaviours can be observed from large ensembles of elementary agents such as ant colonies, bird flocks or by the interactions of elementary material units such as molecules or weather elements. Ultimately, emergence has been adopted as the ontological support of a number of attempts to model brain function. This manuscript aims to clarify the ontology of emergence and delve into its many facets, particularly into its "strong" and "weak" versions that underpin two different approaches to the modelling of behaviour. The first group of models is here represented by the "free energy" principle of brain function and the "integrated information theory" of consciousness. The second group is instead represented by computational models such as oscillatory networks that use mathematical scalable representations to generate emergent behaviours and are then able to bridge neurobiology with higher mental functions. Drawing on the epistemological literature, we observe that due to their loose mechanistic links with the underlying biology, models based on strong forms of emergence are at risk of metaphysical implausibility. This, in practical terms, translates into the over determination that occurs when the proposed model becomes only one of a large set of possible explanations for the observable phenomena. On the other hand, computational models that start from biologically plausible elementary units, hence are weakly emergent, are not limited by ontological faults and, if scalable and able to realistically simulate the hierarchies of brain output, represent a powerful vehicle for future neuroscientific research programmes.

Directional raids by army ants as an adaption to patchily distributed food: a simulation model

A typical colony of Neotropical army ants (subfamily Ecitoninae) regularly raids a large area around their bivouac by forming a narrow directional column that can reach up to one hundred meters in length. The raid is finished and then relaunched 12–17 times, each time toward different orientation. After completing all bouts the colony relocates to a new area. A hypothetical alternative to this foraging mode is raiding radially and symmetrically by expanding the search front in every direction like a circular bubble. Using an existing agent-based modeling software that simulates army ants’ behavior, we compared the two possible modes of foraging in different food distributions. Regardless of the food patch abundance, the radial raiding was superior to the directional raiding when food patches had low quality, and the directional raiding was favorable when the patches were rich. In terms of energy efficiency, the radial raiding was the better strategy in a wide range of conditions. In contrast, the directional raiding tended to yield more food per coverage area. Based on our model, we suggest that the directional raiding by army ants is an adaptation to the habitats with abundance of high-quality food patches. This conclusion fits well with the ecology of army ants.

Hydrodynamic Interactions, Hidden Order, and Emergent Collective Behavior in an Active Bacterial Suspension

Spontaneous self-organization (clustering) in magnetically oriented bacteria arises from attractive pairwise hydrodynamics, which are directly determined through experiment and corroborated by a simple analytical model. Lossless compression algorithms are used to identify the onset of many-body self-organization as a function of experimental tuning parameters. Cluster growth is governed by the interplay between hydrodynamic attraction and magnetic dipole repulsion, leading to logarithmic time dependence of the cluster size. The dynamics of these complex, far-from-equilibrium structures are relevant to broader phenomena in condensed matter, statistical mechanics, and biology.

Recent Advances in Behavioral (Epi)Genetics in Eusocial Insects

Eusocial insects live in societies in which distinct family members serve specific roles in maintaining the colony and advancing the reproductive ability of a few select individuals. Given the genetic similarity of all colony members, the diversity of morphologies and behaviors is surprising. Social communication relies on pheromones and olfaction, as shown by mutants of orco, the universal odorant receptor coreceptor, and through electrophysiological analysis of neuronal responses to pheromones. Additionally, neurohormonal factors and epigenetic regulators play a key role in caste-specific behavior, such as foraging and caste switching. These studies start to allow an understanding of the molecular mechanisms underlying social behavior and provide a technological foundation for future studies of eusocial insects. In this review, we highlight recent findings in eusocial insects that advance our understanding of genetic and epigenetic regulations of social behavior and provide perspectives on future studies using cutting-edge technologies.

Collective clog control: Optimizing traffic flow in confined biological and robophysical excavation

Groups of interacting active particles, insects, or humans can form clusters that hinder the goals of the collective; therefore, development of robust strategies for control of such clogs is essential, particularly in confined environments. Our biological and robophysical excavation experiments, supported by computational and theoretical models, reveal that digging performance can be robustly optimized within the constraints of narrow tunnels by individual idleness and retreating. Tools from the study of dense particulate ensembles elucidate how idleness reduces the frequency of flow-stopping clogs and how selective retreating reduces cluster dissolution time for the rare clusters that still occur. Our results point to strategies by which dense active matter and swarms can become task capable without sophisticated sensing, planning, and global control of the collective.

Architecture, space and information in constructions built by humans and social insects: a conceptual review

The similarities between the structures built by social insects and by humans have led to a convergence of interests between biologists and architects. This new, de facto interdisciplinary community of scholars needs a common terminology and theoretical framework in which to ground its work. In this conceptually oriented review paper, we review the terms 'information', 'space' and 'architecture' to provide definitions that span biology and architecture. A framework is proposed on which interdisciplinary exchange may be better served, with the view that this will aid better cross-fertilization between disciplines, working in the areas of collective behaviour and analysis of the structures and edifices constructed by non-humans; and to facilitate how this area of study may better contribute to the field of architecture. We then use these definitions to discuss the informational content of constructions built by organisms and the influence these have on behaviour, and vice versa. We review how spatial constraints inform and influence interaction between an organism and its environment, and examine the reciprocity of space and information on construction and the behaviour of humans and social insects.This article is part of the theme issue 'Interdisciplinary approaches for uncovering the impacts of architecture on collective behaviour'.

The Drivers of Heuristic Optimization in Insect Object Manufacture and Use

Insects have small brains and heuristics or 'rules of thumb' are proposed here to be a good model for how insects optimize the objects they make and use. Generally, heuristics are thought to increase the speed of decision making by reducing the computational resources needed for making decisions. By corollary, heuristic decisions are also deemed to impose a compromise in decision accuracy. Using examples from object optimization behavior in insects, we will argue that heuristics do not inevitably imply a lower computational burden or lower decision accuracy. We also show that heuristic optimization may be driven by certain features of the optimization problem itself: the properties of the object being optimized, the biology of the insect, and the properties of the function being optimized. We also delineate the structural conditions under which heuristic optimization may achieve accuracy equivalent to or better than more fine-grained and onerous optimization methods.

A distributed algorithm to maintain and repair the trail networks of arboreal ants

We study how the arboreal turtle ant (Cephalotes goniodontus) solves a fundamental computing problem: maintaining a trail network and finding alternative paths to route around broken links in the network. Turtle ants form a routing backbone of foraging trails linking several nests and temporary food sources. This species travels only in the trees, so their foraging trails are constrained to lie on a natural graph formed by overlapping branches and vines in the tangled canopy. Links between branches, however, can be ephemeral, easily destroyed by wind, rain, or animal movements. Here we report a biologically feasible distributed algorithm, parameterized using field data, that can plausibly describe how turtle ants maintain the routing backbone and find alternative paths to circumvent broken links in the backbone. We validate the ability of this probabilistic algorithm to circumvent simulated breaks in synthetic and real-world networks, and we derive an analytic explanation for why certain features are crucial to improve the algorithm's success. Our proposed algorithm uses fewer computational resources than common distributed graph search algorithms, and thus may be useful in other domains, such as for swarm computing or for coordinating molecular robots.

Ant Droplet Dynamics Evolve via Individual Decision-Making

The droplets of a set of ants were studied while they constructed a bridge. A droplet is a group of ants derived from a larger group. Several experimental studies have revealed the droplet dynamics of ants that resemble the self-organising characteristics that are displayed in their physico-chemical systems. However, little is known regarding how these typical behaviours emerge from individual decision-making. In this study, I developed an agent-based model where artificial ants aggregated, thereby resulting in chain and droplet growth. In my proposed model, the agents tuned their weight thresholds according to the local pattern stability and propagation of negative information. As a result, it was revealed that the droplet dynamics of my proposed model partly matched the time series of droplets of real ants, as demonstrated in previous experimental studies that included the fluctuation function and interdrop increments that followed a scale-free distribution.

Origins of Aminergic Regulation of Behavior in Complex Insect Social Systems

Neuromodulators are conserved across insect taxa, but how biogenic amines and their receptors in ancestral solitary forms have been co-opted to control behaviors in derived socially complex species is largely unknown. Here we explore patterns associated with the functions of octopamine (OA), serotonin (5-HT) and dopamine (DA) in solitary ancestral insects and their derived functions in eusocial ants, bees, wasps and termites. Synthesizing current findings that reveal potential ancestral roles of monoamines in insects, we identify physiological processes and conserved behaviors under aminergic control, consider how biogenic amines may have evolved to modulate complex social behavior, and present focal research areas that warrant further study.