Using electric fields to control insects: current applications and future directions.

Chemical-based interventions are mostly used to control insects that are harmful to human health and agriculture or that simply cause a nuisance. An overreliance on these insecticides however raises concerns for the environment, human health, and the development of resistance, not only in the target species. As such, there is a critical need for the development of novel nonchemical technologies to control insects. Electrocution traps using UV light as an attractant are one classical nonchemical approach to insect control but lack the specificity necessary to target only pest insects and to avoid harmless or beneficial species. Here we review the fundamental physics behind electric fields (EFs) and place them in context with electromagnetic fields more broadly. We then focus on how novel uses of strong EFs, some of which are being piloted in the field and laboratory, have the potential to repel, capture, or kill (electrocute) insects without the negative side effects of other classical approaches. As EF-insect science remains in its infancy, we provide recommendations for future areas of research in EF-insect science.

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 modeling framework for adaptive collective defense: crisis response in social-insect colonies.

Living systems, from cells to superorganismic insect colonies, have an organizational boundary between inside and outside and allocate resources to defend it. Whereas the micro-scale dynamics of cell walls can be difficult to study, the adaptive allocation of workers to defense in social-insect colonies is more conspicuous. This is particularly the case for Tetragonisca angustula stingless bees, which combine different defensive mechanisms found across other colonial animals: (1) morphological specialization (distinct soldiers (majors) are produced over weeks); (2) age-based polyethism (young majors transition to guarding tasks over days); and (3) task switching (small workers (minors) replace soldiers within minutes under crisis). To better understand how these timescales of reproduction, development, and behavior integrate to balance defensive demands with other colony needs, we developed a demographic Filippov ODE system to study the effect of these processes on task allocation and colony size. Our results show that colony size peaks at low proportions of majors, but colonies die if minors are too plastic or defensive demands are too high or if there is a high proportion of quickly developing majors. For fast maturation, increasing major production may decrease defenses. This model elucidates the demographic factors constraining collective defense regulation in social insects while also suggesting new explanations for variation in defensive allocation at smaller scales where the mechanisms underlying defensive processes are not easily observable. Moreover, our work helps to establish social insects as model organisms for understanding other systems where the transaction costs for component turnover are nontrivial, as in manufacturing systems and just-in-time supply chains.

Decoding alarm signal propagation of seed-harvester ants using automated movement tracking and supervised machine learning.

Alarm signal propagation through ant colonies provides an empirically tractable context for analysing information flow through a natural system, with useful insights for network dynamics in other social animals. Here, we develop a methodological approach to track alarm spread within a group of harvester ants, Pogonomyrmex californicus. We initially alarmed three ants and tracked subsequent signal transmission through the colony. Because there was no actual standing threat, the false alarm allowed us to assess amplification and adaptive damping of the collective alarm response. We trained a random forest regression model to quantify alarm behaviour of individual workers from multiple movement features. Our approach translates subjective categorical alarm scores into a reliable, continuous variable. We combined these assessments with automatically tracked proximity data to construct an alarm propagation network. This method enables analyses of spatio-temporal patterns in alarm signal propagation in a group of ants and provides an opportunity to integrate individual and collective alarm response. Using this system, alarm propagation can be manipulated and assessed to ask and answer a wide range of questions related to information and misinformation flow in social networks.

Division of labour promotes the spread of information in colony emigrations by the ant Temnothorax rugatulus.

The fitness of group-living animals often depends on how well members share information needed for collective decision-making. Theoretical studies have shown that collective choices can emerge in a homogeneous group of individuals following identical rules, but real animals show much evidence for heterogeneity in the degree and nature of their contribution to group decisions. In social insects, for example, the transmission and processing of information is influenced by a well-organized division of labour. Studies that accurately quantify how this behavioural heterogeneity affects the spread of information among group members are still lacking. In this paper, we look at nest choices during colony emigrations of the ant Temnothorax rugatulus and quantify the degree of behavioural heterogeneity of workers. Using clustering methods and network analysis, we identify and characterize four behavioural castes of workers-primary, secondary, passive and wandering-covering distinct roles in the spread of information during an emigration. This detailed characterization of the contribution of each worker can improve models of collective decision-making in this species and promises a deeper understanding of behavioural variation at the colony level.

Habitat features and performance interact to determine the outcomes of terrestrial predator-prey pursuits.

Animals are responsive to predation risk, often seeking safer habitats at the cost of foraging rewards. Although previous research has examined how habitat features affect detection by predators, little is known about how the interaction of habitat features, sensory cues and physical performance capabilities affect prey escape performance once detected. To investigate how specific habitat features affect predation risk, we developed an individual-based model of terrestrial predator-prey pursuits in habitats with programmable features. We ran simulations varying the relative performance capabilities of predator and prey as well as the availability and abundance of refuges and obstacles in the habitat. Prey were more likely to avoid detection in complex habitats containing a higher abundance of obstacles; however, if detected, prey escape probability was dependent on both the abundance of refuges and obstacles and the predator's relative performance capabilities. Our model accurately predicted the relative escape success for impala escaping from cheetah in open savanna versus acacia thicket habitat, though escape success was consistently underestimated. Our model provides a mechanistic explanation for the differential effects of habitat on survival for different predator-prey pairs. Its flexible nature means that our model can be refined to simulate specific systems and could have applications towards management programmes for species threatened by habitat loss and predation.

Alternative model systems for cognitive variation: eusocial-insect colonies.

Understanding the origins and maintenance of cognitive variation in animal populations is central to the study of the evolution of cognition. However, the brain is itself a complex, hierarchical network of heterogeneous components, from diverse cell types to diverse neuropils, each of which may be of limited use to study in isolation or prohibitively challenging to manipulate in situ. Consequently, highly tractable alternative model systems may be valuable tools. Eusocial-insect colonies display emergent cognitive-like properties from relatively simple social interactions between diverse subunits that can be observed and manipulated while operating collectively. Here, we review the individual-scale mechanisms that cause group-level variation in how colonies solve problems analogous to cognitive challenges faced by brains, like decision-making, attention, and search.

Identifying the best strategy for soccer penalty success: A predictive model for optimising behavioural and biomechanical trade-offs.

Success in a soccer penalty can be the difference between winning and losing matches. The outcome is determined by a complex interaction between the shooter and goalkeeper, whose performances are constrained by biomechanical trade-offs. To overcome these performance constraints, each player has a range of available strategies. Shooters can kick at different speeds, affecting accuracy, while goalkeepers can move at various times (leave-times), affecting the time available to move and the probability they move in the correct direction. Previous models of penalty success ignore such trade-offs and how they interact to influence the outcome. Here, we present a model that accounts for shooting inaccuracy to predict the probability of success for all shooting strategies, defined as any combination of: shot speed, position where the shooter aims, shooter footedness, and kicking technique (side-foot or instep). To estimate the probability of success each shooting strategy is matched against all possible goalkeeper leave-times, considering the probability each leave-time is chosen. We test the model against an average goalkeeper and a goalkeeper who tends to move later. Against the average goalkeeper, aiming on the ground toward the centre of the goal is optimal; however, against a late moving goalkeeper, aiming on the ground to the extremities of the goal is effective, with the optimal target in the horizontal dimension dependent on shot speed, kick technique, and footedness. Coaches could use this model to identify their best penalty takers and each players' optimal shooting strategy against either the average goalkeeper or a specific goalkeeper.

Soldier neural architecture is temporarily modality specialized but poorly predicted by repertoire size in the stingless bee Tetragonisca angustula.

Individual heterogeneity within societies provides opportunities to test hypotheses about adaptive neural investment in the context of group cooperation. Here, we explore neural investment in defense specialist soldiers of the eusocial stingless bee (Tetragonisca angustula) which are age subspecialized on distinct defense tasks and have an overall higher lifetime task repertoire than other sterile workers within the colony. Consistent with predicted behavioral demands, soldiers had higher relative visual (optic lobe) investment than nonsoldiers but only during the period when they were performing the most visually demanding defense task (hovering guarding). As soldiers aged into the less visually demanding task of standing guarding this difference disappeared. Neural investment was otherwise similar across all colony members. Despite having larger task repertoires, soldiers had similar absolute brain size and the smaller relative brain size compared to other workers, meaning that lifetime task repertoire size was a poor predictor of brain size. Both high behavioral specialization in stable environmental conditions and reassignment across task groups during a crisis occur in T. angustula. The differences in neurobiology we report here are consistent with these specialized but flexible defense strategies. This work broadens our understanding of how neurobiology mediates age and morphological task specialization in highly cooperative societies.

A Minimal Framework for Describing Living Systems: A Multi-Dimensional View of Life Across Scales.

The almost limitless complexity of biology has led to two general approaches to understanding biological phenomena. One approach is dominated by reductionism in which high-level phenomena of whole systems are viewed as emerging from relatively simple and generally understood interactions at a substantially lower level. Although this approach is theoretically general, it can become intractable in practice when attempting to simultaneously explain a wide range of systems. A second approach is for specialists to investigate biological phenomena within one of many different hierarchical levels of description that are separated to decouple from concerns at other levels. Although this approach reduces the explanatory burden on specialists that operate within each level, it also reduces integration from insights gained at other levels. Thus, as beneficial as these approaches have been, they limit the scope and integration of knowledge across scales of biological organization to the detriment of a truly synoptic view of life. The challenge is to find a theoretical and experimental framework that facilitates a broader understanding of the hierarchy of life-providing permeability for the exchange of ideas among disciplinary specialists without discounting the peculiarities that have come to define those disciplines. For this purpose, coarse-grained, scale-invariant properties, and resources need to be identified that describe the characteristic features of a living system at all spatiotemporal scales. The approach will be aided by a common vernacular that underscores the realities of biological connections across a wide range of scales. Therefore, in this vision paper, we propose a conceptual approach based on four identified resources-energy, conductance, storage, and information (ECSI)-to reintegrate biological studies with the aim of unifying life sciences under resource limitations. We argue that no functional description of a living system is complete without accounting for at least all four of these resources. Thus, making these resources explicit will help to identify commonalities to aid in transdisciplinary discourse as well as opportunities for integrating among the differently scoped areas of specialized inquiry. The proposed conceptual framework for living systems should be valid across all scales and may uncover potential limitations of existing hypotheses and help researchers develop new hypotheses addressing fundamental processes of life without having to resort to reductionism.

The sunk-cost effect as an optimal rate-maximizing behavior.

Optimal foraging theory has been criticized for underestimating patch exploitation time. However, proper modeling of costs not only answers these criticisms, but it also explains apparently irrational behaviors like the sunk-cost effect. When a forager is sure to experience high initial costs repeatedly, the forager should devote more time to exploitation than searching in order to minimize the accumulation of said costs. Thus, increased recognition or reconnaissance costs lead to increased exploitation times in order to reduce the frequency of future costs, and this result can be used to explain paradoxical human preference for higher costs. In fact, this result also provides an explanation for how continuing a very costly task indefinitely provides the optimal long-term rate of gain; the entry cost of each new task is so great that the forager avoids ever returning to search. In general, apparently irrational decisions may be optimal when considering the lifetime of a forager within a larger system.

Naïve individuals promote collective exploration in homing pigeons.

Group-living animals that rely on stable foraging or migratory routes can develop behavioural traditions to pass route information down to inexperienced individuals. Striking a balance between exploitation of social information and exploration for better alternatives is essential to prevent the spread of maladaptive traditions. We investigated this balance during cumulative route development in the homing pigeon Columba livia. We quantified information transfer within pairs of birds in a transmission-chain experiment and determined how birds with different levels of experience contributed to the exploration-exploitation trade-off. Newly introduced naïve individuals were initially more likely to initiate exploration than experienced birds, but the pair soon settled into a pattern of alternating leadership with both birds contributing equally. Experimental pairs showed an oscillating pattern of exploration over generations that might facilitate the discovery of more efficient routes. Our results introduce a new perspective on the roles of leadership and information pooling in the context of collective learning.

Modeling escape success in terrestrial predator-prey interactions.

Prey species often modify their foraging and reproductive behaviors to avoid encounters with predators; yet once they are detected, survival depends on out-running, out-maneuvering, or fighting off the predator. Though predation attempts involve at least two individuals-namely, a predator and its prey-studies of escape performance typically measure a single trait (e.g., sprint speed) in the prey species only. Here, we develop a theoretical model in which the likelihood of escape is determined by the prey animal's tactics (i.e., path trajectory) and its acceleration, top speed, agility, and deceleration relative to the performance capabilities of a predator. The model shows that acceleration, top speed, and agility are all important determinants of escape performance, and because speed and agility are biomechanically related to size, smaller prey with higher agility should force larger predators to run along curved paths that do not allow them to use their superior speeds. Our simulations provide clear predictions for the path and speed a prey animal should choose when escaping from predators of different sizes (thus, biomechanical constraints) and could be used to explore the dynamics between predators and prey.

Revealing the structure of information flows discriminates similar animal social behaviors.

Behavioral correlations stretching over time are an essential but often neglected aspect of interactions among animals. These correlations pose a challenge to current behavioral-analysis methods that lack effective means to analyze complex series of interactions. Here we show that non-invasive information-theoretic tools can be used to reveal communication protocols that guide complex social interactions by measuring simultaneous flows of different types of information between subjects. We demonstrate this approach by showing that the tandem-running behavior of the ant Temnothorax rugatulus and that of the termites Coptotermes formosanus and Reticulitermes speratus are governed by different communication protocols. Our discovery reconciles the diverse ultimate causes of tandem running across these two taxa with their apparently similar signaling mechanisms. We show that bidirectional flow of information is present only in ants and is consistent with the use of acknowledgement signals to regulate the flow of directional information.

Social animals continuously influence each other's behavior. Most of these interactions simply consist in an individual immediately responding to the behavior of another in a predictable way. Still, when the same individuals interact over long periods, complex social interactions can arise. These can be difficult for scientists to study, because how animals behave at a given moment depends on their shared history. Certain species of ants and termites use smell and touch to do 'tandem runs' and move in pairs through the environment. Only ants, however, can learn a new route from their running partner. Understanding how this difference arises means examining how the animals interact and communicate over longer time scales. This requires new approaches to capture how information flows between the insects. Here, Valentini et al. used a scientific methodology known as information theory to study tandem running in one species of ants and two species of termites. Information theory provides a framework to quantify how information is shared, processed and stored. The flow of information between individuals was measured separately for different aspects of tandem running. At small time scales, ant and termite behavior appeared identical, but over longer periods, it was possible to distinguish between the two types of insects. In termites, only one individual in a pair sent information to the other to instruct the second termite where to go. By contrast, in ants, both members of the tandem communicated with each other in a way that was consistent with how humans acknowledge information they receive from other individuals. The approach used by Valentini et al. will be useful to researchers who study how complex and often cryptic social interactions develop over extended periods in social animals. This framework could also be applied in other systems such as groups of cells, or economic networks.

Distributed and cooperative task processing: Cournot oligopolies on a graph.

This paper introduces a novel framework for the design of distributed agents that must complete externally generated tasks but also can volunteer to process tasks encountered by other agents. To reduce the computational and communication burden of coordination between agents to perfectly balance load around the network, the agents adjust their volunteering propensity asynchronously within a fictitious trading economy. This economy provides incentives for nontrivial levels of volunteering for remote tasks, and thus load is shared. Moreover, the combined effects of diminishing marginal returns and network topology lead to competitive equilibria that have task reallocations that are qualitatively similar to what is expected in a load-balancing system with explicit coordination between nodes. In the paper, topological and algorithmic conditions are given that ensure the existence and uniqueness of a competitive equilibrium. Additionally, a decentralized distributed gradient-ascent algorithm is given that is guaranteed to converge to this equilibrium while not causing any node to over-volunteer beyond its maximum task-processing rate. The framework is applied to an autonomous-air-vehicle example, and connections are drawn to classic studies of the evolution of cooperation in nature.