Ants find shortest paths using simple, local rules.

Garg et al. (2023, Proceedings of the National Academy of Sciences, 120[6], e2207959120) build simulation models to understand how turtle ants collectively find efficient paths through branched networks, highlighting the importance of bidirectional traffic, leakage of ants at junctions, and the ability to increase flow as key components for efficiency. Their findings provide new, biologically realistic mechanisms that could improve applications in our own engineered networks.

Weaver ants regulate the rate of prey delivery during collective vertical transport.

The collective transport of massive food items by ant teams is a striking example of biological cooperation, but it remains unclear how these decentralized teams coordinate to overcome the various challenges associated with transport. Previous research has focused on transport across horizontal surfaces and very shallow inclines, disregarding the complexity of natural foraging environments. In the ant Oecophylla smaragdina, prey are routinely carried up tree trunks to arboreal nests. Using this species, we induced collective transport over a variety of angled surfaces with varying prey masses to investigate how ants respond to inclines. We found that weight and incline pose qualitatively different challenges during transport. Prey were carried over vertical and inclined surfaces faster than across horizontal surfaces, even though inclines were associated with longer routes and a higher probability of dropping the load. This additional speed was associated with more transporters being allocated to loads on steeper inclines and not with the persistence of individual ants. Ant teams also regulated a stable prey delivery rate (rate of return per transporter) across all treatments. Our proposed constrained optimization model suggests a possible explanation for these results; theoretically, prey intake rate at the colony level is maximized when the allocation of transporters yields a similar prey delivery rate across loads.

A simple mechanism for collective decision-making in the absence of payoff information.

Animals are often faced with time-critical decisions without prior information about their actions' outcomes. In such scenarios, individuals budget their investment into the task to cut their losses in case of an adverse outcome. In animal groups, this may be challenging because group members can only access local information, and consensus can only be achieved through distributed interactions among individuals. Here, we combined experimental analyses with theoretical modeling to investigate how groups modulate their investment into tasks in uncertain conditions. Workers of the arboreal weaver ant Oecophylla smaragdina form three-dimensional chains using their own bodies to bridge vertical gaps between existing trails and new areas to explore. The cost of a chain increases with its length because ants participating in the structure are prevented from performing other tasks. The payoffs of chain formation, however, remain unknown to the ants until the chain is complete and they can explore the new area. We demonstrate that weaver ants cap their investment into chains, and do not form complete chains when the gap is taller than 90 mm. We show that individual ants budget the time they spend in chains depending on their distance to the ground, and propose a distance-based model of chain formation that explains the emergence of this tradeoff without the need to invoke complex cognition. Our study provides insights into the proximate mechanisms that lead individuals to engage (or not) in collective actions and furthers our knowledge of how decentralized groups make adaptive decisions in uncertain conditions.

Thoughts from the forest floor: a review of cognition in the slime mould Physarum polycephalum.

Sensing, communication, navigation, decision-making, memory and learning are key components in a standard cognitive tool-kit that enhance an animal's ability to successfully survive and reproduce. However, these tools are not only useful for, or accessible to, animals-they evolved long ago in simpler organisms using mechanisms which may be either unique or widely conserved across diverse taxa. In this article, I review the recent research that demonstrates these key cognitive abilities in the plasmodial slime mould Physarum polycephalum, which has emerged as a model for non-animal cognition. I discuss the benefits and limitations of comparisons drawn between neural and non-neural systems, and the implications of common mechanisms across wide taxonomic divisions. I conclude by discussing future avenues of research that will draw the most benefit from a closer integration of Physarum and animal cognition research.

Fish Avoid Visually Noisy Environments Where Prey Targeting Is Reduced.

AbstractThe environment contains different forms of ecological noise that can reduce the ability of animals to detect information. Here, we ask whether animals adapt their behavior to either exploit or avoid areas of their environment with increased dynamic visual noise. Three-spined sticklebacks (Gasterosteus aculeatus) were immersed in environments with a simulated form of naturally occurring visual noise-moving light bands that form on underwater substrates caused by the refraction of light through surface waves. We tested whether this form of visual noise affected fish's habitat selection, movements, and prey-targeting behavior. Fish avoided areas of the environment with increased visual noise and achieved this by increasing their activity as a function of the locally perceived noise level. Fish were less likely to respond to virtual prey in environments with increased visual noise, highlighting a potential impact that visual noise has on their perceptual abilities. Fish did not increase or decrease their refuge use in environments with increased visual noise, providing no evidence that visual noise increased either exploratory or risk-aversive behavior. Our results indicate that animals can use simple behavioral strategies to avoid visually noisy environments, thereby mitigating the impacts that these environments appear to have on their perceptual abilities.

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

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.

Dual ecosystem services of syrphid flies (Diptera: Syrphidae): pollinators and biological control agents.

With increasing worldwide pressure on bee pollinator populations and an increase in insecticide resistance amongst pest insects, there is a growing need for diversification of pollinator and pest control systems. Syrphid flies (Diptera: Syrphidae) contribute ecosystem services to agroecosystems through their supporting roles as crop pollinators and predators of pests. Adult syrphids are important pollinators with high floral visitation rates and pollen carrying capacity, while predatory syrphid larvae are natural biological control agents, reducing aphid populations in both field and laboratory conditions. The present challenge is to determine whether syrphid flies have the potential for application as pollinators and in integrated pest management schemes as biological control agents. Currently, there are gaps in research that are hindering the use of syrphids as dual service providers. Such gaps include a lack of knowledge of syrphid floral preferences, the role and viability of adult syrphids as pollinators in natural and agro-ecological pollinator networks, and the predatory efficiency of larvae in field and glasshouse conditions. By reviewing relevant literature, we demonstrate syrphid flies have the potential to be used as pollinators and biological control agents. © 2020 Society of Chemical Industry.

Clarity of objectives and working principles enhances the success of biomimetic programs.

Biomimetics, the transfer of functional principles from living systems into product designs, is increasingly being utilized by engineers. Nevertheless, recurring problems must be overcome if it is to avoid becoming a short-lived fad. Here we assess the efficiency and suitability of methods typically employed by examining three flagship examples of biomimetic design approaches from different disciplines: (1) the creation of gecko-inspired adhesives; (2) the synthesis of spider silk, and (3) the derivation of computer algorithms from natural self-organizing systems. We find that identification of the elemental working principles is the most crucial step in the biomimetic design process. It bears the highest risk of failure (e.g. losing the target function) due to false assumptions about the working principle. Common problems that hamper successful implementation are: (i) a discrepancy between biological functions and the desired properties of the product, (ii) uncertainty about objectives and applications, (iii) inherent limits in methodologies, and (iv) false assumptions about the biology of the models. Projects that aim for multi-functional products are particularly challenging to accomplish. We suggest a simplification, modularisation and specification of objectives, and a critical assessment of the suitability of the model. Comparative analyses, experimental manipulation, and numerical simulations followed by tests of artificial models have led to the successful extraction of working principles. A searchable database of biological systems would optimize the choice of a model system in top-down approaches that start at an engineering problem. Only when biomimetic projects become more predictable will there be wider acceptance of biomimetics as an innovative problem-solving tool among engineers and industry.

Decision-making without a brain: how an amoeboid organism solves the two-armed bandit.

Several recent studies hint at shared patterns in decision-making between taxonomically distant organisms, yet few studies demonstrate and dissect mechanisms of decision-making in simpler organisms. We examine decision-making in the unicellular slime mould Physarum polycephalum using a classical decision problem adapted from human and animal decision-making studies: the two-armed bandit problem. This problem has previously only been used to study organisms with brains, yet here we demonstrate that a brainless unicellular organism compares the relative qualities of multiple options, integrates over repeated samplings to perform well in random environments, and combines information on reward frequency and magnitude in order to make correct and adaptive decisions. We extend our inquiry by using Bayesian model selection to determine the most likely algorithm used by the cell when making decisions. We deduce that this algorithm centres around a tendency to exploit environments in proportion to their reward experienced through past sampling. The algorithm is intermediate in computational complexity between simple, reactionary heuristics and calculation-intensive optimal performance algorithms, yet it has very good relative performance. Our study provides insight into ancestral mechanisms of decision-making and suggests that fundamental principles of decision-making, information processing and even cognition are shared among diverse biological systems.

Collective behaviour and swarm intelligence in slime moulds.

The study of collective behaviour aims to understand how individual-level behaviours can lead to complex group-level patterns. Collective behaviour has primarily been studied in animal groups such as colonies of insects, flocks of birds and schools of fish. Although less studied, collective behaviour also occurs in microorganisms. Here, we argue that slime moulds are powerful model systems for solving several outstanding questions in collective behaviour. In particular, slime mould may hold the key to linking individual-level mechanisms to colony-level behaviours. Using well-established principles of collective animal behaviour as a framework, we discuss the extent to which slime mould collectives are comparable to animal groups, and we highlight some potentially fruitful areas for future research.

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

The ability of individual animals to create functional structures by joining together is rare and confined to the social insects. Army ants (Eciton) form collective assemblages out of their own bodies to perform a variety of functions that benefit the entire colony. Here we examine ?bridges" of linked individuals that are constructed to span gaps in the colony's foraging trail. How these living structures adjust themselves to varied and changing conditions remains poorly understood. Our field experiments show that the ants continuously modify their bridges, such that these structures lengthen, widen, and change position in response to traffic levels and environmental geometry. Ants initiate bridges where their path deviates from their incoming direction and move the bridges over time to create shortcuts over large gaps. The final position of the structure depended on the intensity of the traffic and the extent of path deviation and was influenced by a cost-benefit trade-off at the colony level, where the benefit of increased foraging trail efficiency was balanced by the cost of removing workers from the foraging pool to form the structure. To examine this trade-off, we quantified the geometric relationship between costs and benefits revealed by our experiments. We then constructed a model to determine the bridge location that maximized foraging rate, which qualitatively matched the observed movement of bridges. Our results highlight how animal self-assemblages can be dynamically modified in response to a group-level cost-benefit trade-off, without any individual unit's having information on global benefits or costs.

Solving the Towers of Hanoi - how an amoeboid organism efficiently constructs transport networks.

Many biological systems require extensive networks to transport resources and information. Biological networks must trade-off network efficiency with the risk of network failure. Yet, biological networks develop in the absence of centralised control from the interactions of many components. Moreover, many biological systems need to be able to adapt when conditions change and the network requires modification. We used the slime mould Physarum polycephalum (Schwein) to study how the organism adapts its network after disruption. To allow us to determine the efficiency of the constructed networks, we used a well-known shortest-path problem: the Towers of Hanoi maze. We first show that while P. polycephalum is capable of building networks with minimal length paths through the maze, most solutions are sub-optimal. We then disrupted the network by severing the main connecting path while opening a new path in the maze. In response to dynamic changes to the environment, P. polycephalum reconstructed more efficient solutions, with all replicates building networks with minimal length paths through the maze after network disruption. While P. polycephalum altered some of its existing network to accommodate changes in the environment, it also reconstructed large sections of the network from scratch. We compared the results obtained from P. polycephalum with those obtained using another distributed biological system: ant colonies. We hypothesise that network construction in ants hinges upon stronger positive feedback than for slime mould, ensuring that ants converge more accurately upon the shortest path but are more constrained by the history of their networks in dynamic environments.

Slime mold uses an externalized spatial "memory" to navigate in complex environments.

Spatial memory enhances an organism's navigational ability. Memory typically resides within the brain, but what if an organism has no brain? We show that the brainless slime mold Physarum polycephalum constructs a form of spatial memory by avoiding areas it has previously explored. This mechanism allows the slime mold to solve the U-shaped trap problem--a classic test of autonomous navigational ability commonly used in robotics--requiring the slime mold to reach a chemoattractive goal behind a U-shaped barrier. Drawn into the trap, the organism must rely on other methods than gradient-following to escape and reach the goal. Our data show that spatial memory enhances the organism's ability to navigate in complex environments. We provide a unique demonstration of a spatial memory system in a nonneuronal organism, supporting the theory that an externalized spatial memory may be the functional precursor to the internal memory of higher organisms.

A mathematical model of foraging in a dynamic environment by trail-laying Argentine ants.

Ants live in dynamically changing environments, where food sources become depleted and alternative sources appear. Yet most mathematical models of ant foraging assume that the ants' foraging environment is static. Here we describe a mathematical model of ant foraging in a dynamic environment. Our model attempts to explain recent empirical data on dynamic foraging in the Argentine ant Linepithema humile (Mayr). The ants are able to find the shortest path in a Towers of Hanoi maze, a complex network containing 32,768 alternative paths, even when the maze is altered dynamically. We modify existing models developed to explain ant foraging in static environments, to elucidate what possible mechanisms allow the ants to quickly adapt to changes in their foraging environment. Our results suggest that navigation of individual ants based on a combination of one pheromone deposited during foraging and directional information enables the ants to adapt their foraging trails and recreates the experimental results.

Optimisation in a natural system: Argentine ants solve the Towers of Hanoi.

Natural systems are a source of inspiration for computer algorithms designed to solve optimisation problems. Yet most 'nature-inspired' algorithms take only superficial inspiration from biology, and little is known about how real biological systems solve difficult problems. Moreover, ant algorithms, neural networks and similar methods are usually applied to static problems, whereas most biological systems have evolved to perform under dynamically changing conditions. We used the Towers of Hanoi puzzle to test whether Argentine ants can solve a potentially difficult optimisation problem. We also tested whether the ants can adapt to dynamic changes in the problem. We mapped all possible solutions to the Towers of Hanoi on a single graph and converted this into a maze for the ants to solve. We show that the ants are capable of solving the Towers of Hanoi, and are able to adapt when sections of the maze are blocked off and new sections installed. The presence of exploration pheromone increased the efficiency of the resulting network and increased the ants' ability to adapt to changing conditions. Contrary to previous studies, our study shows that mass-recruiting ant species such as the Argentine ant can forage effectively in a dynamic environment. Our results also suggest that novel optimisation algorithms can benefit from stronger biological mimicry.