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.

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.

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.

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.

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.

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.

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 sub-specialized 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 non-soldiers 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 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. This article is protected by copyright. All rights reserved.

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.

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.

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.

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.

Changing of the guard: mixed specialization and flexibility in nest defense (Tetragonisca angustula)

Task allocation is a central challenge of collective behavior in a variety of group-living species, and this is particularly the case for the allocation of social insect workers for group defense. In social insects, both benefits and considerable costs are associated with the production of specialized soldiers. We asked whether colonies mitigate costs of production of specialized soldiers by simultaneously employing behavioral flexibility in nonspecialist workers that can augment defense capabilities at short time scales. We studied colonies of the stingless bee Tetragonisca angustula, a species that has 2 discrete nest-guarding tasks typically performed by majors: hovering guarding and standing guarding. Majors showed age polyethism across nest-guarding tasks, first hovering and then changing to the task of standing guarding after 1 week. Colonies were also able to reassign minors to guarding tasks when majors were experimentally removed. Replacement guards persisted in nest defense tasks until colonies produced enough majors to return to their initial state. Tetragonisca angustula colonies thus employed a coordinated set of specialization strategies in nest defense: morphologically specialized soldiers, age polyethism among soldiers within specific guarding tasks, and rapid flexible reallocation of nonspecialists to guarding during soldier loss. This mixed strategy achieves the benefits of a highly specialized defensive force while maintaining the potential for rapid reinforcement when soldiers are lost or colonies face unexpectedly intense attack.

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.

Generalizing foraging theory for analysis and design

Foraging theory has been the inspiration for several decision-making algorithms for task-processing agents facing random environments. As nature selects for foraging behaviors that maximize lifetime calorie gain or minimize starvation probability, engineering designs are favored that maximize returned value (e.g. profit) or minimize the probability of not reaching performance targets. Prior foraging-inspired designs are direct applications of classical optimal foraging theory (OFT). Here, we describe a generalized optimization framework that encompasses the classical OFT model, a popular competitor, and several new models introduced here that are better suited for some task-processing applications in engineering. These new models merge features of rate maximization, efficiency maximization, and risk-sensitive foraging while not sacrificing the intuitive character of classical OFT. However, the central contributions of this paper are analytical and graphical methods for designing decision-making algorithms guaranteed to be optimal within the framework. Thus, we provide a general modeling framework for solitary agent behavior, several new and classic examples that apply to it, and generic methods for design and analysis of optimal task-processing behaviors that fit within the framework. Our results extend the key mathematical features of optimal foraging theory to a wide range of other optimization objectives in biological, anthropological, and technological contexts.