Designing systems for autonomous transport of groups of living agents has received a lot of attention in recent years due to a wealth of important potential applications. Biomimetic approaches are often sought, and a range of herding algorithms, inspired by how dogs herd sheep, as well as leadership algorithms mimicking leader-follower systems, have been introduced. However, they suffer from a common problem: shepherding algorithms require that agents evade the shepherd, and leading algorithms require that agents follow. This can cause problems in real-world applications where the behavioral responses of the agents to a transporter are likely to be heterogeneous over both long and short timescales. Here, we introduce an algorithm that adaptively switches between leading and herding depending on the response it receives from the agents to mitigate this problem. We show via simulation that this mixed algorithm can transport groups with any follower and evader composition, and we compare its performance with lead-only and herd-only algorithms. We also show that the mixed algorithm can deal with groups where individual agents randomly switch their strategy over time, as long as sufficient time is provided to complete the task relative to the switching rate. Given that our algorithm overcomes issues associated with herd-only and lead-only algorithms and might also, as a side effect, mitigate the issue of habituation to robotic transporters, it takes us one step closer to realizing many of the proposed applications for these types of algorithms.

The spotted lanternfly is rapidly establishing itself as a major insect pest with global implications. Despite substantial management efforts, its spread continues in invaded regions, highlighting the need for refined strategies. A recent model generalized results of empirical control studies by incorporating population dynamics and incomplete delivery, introducing a formula for the minimum proportion of a population that must be treated to induce decline. However, that model cannot address the more practical question of how to allocate control efforts. Here, we extend the model to identify effective deployment strategies. When control effects scale linearly with effort, we show that sequential application of stage-specific controls, ordered by efficacy, is optimal. When effects exhibit diminishing returns, we derive a switching criterion between controls that accommodates variable or uncertain resources. For fixed resources, we employ global optimization to obtain deployment strategies. Both approaches consistently outperform random deployment, which we show can lead to catastrophic outcomes. Our findings demonstrate the importance of adopting effective control strategies for the lanternfly. Despite this need, we found no prior studies addressing deployment strategies in the lanternfly literature. The methods developed here provide a foundation for ensuring that limited management resources are used effectively.

The spotted lanternfly is an emerging global invasive insect pest. Due to a lack of natural enemies where it is invasive, human intervention is required. Extensive management has been applied but the spread continues. Recently, the idea of bird-based biological controls has re-emerged and shown effective in studies. However, it is questionable, if birds are able to effectively control unfamiliar and occasionally toxic invasive pests in short timeframes. Unless, perhaps, the birds are effective social learners and toxicity of the invaders is rare. Here, we introduce a mathematical model for social learning in a great tit-like bird to investigate conditions for the emergence of a collective biological control of a pest that is occasionally toxic, like the lanternfly. We find that the social observation rate relative to the proportion of toxic lanternfly dictate when collective biological controls will emerge. We also implement the social learning model into a model of collective motion in bird-like animals, and find that it produces results consistent with the mathematical model. Our work suggests that social birds may be useful in managing the spotted lanternfly, and that removing the toxicity-inducing preferred host of the lanternfly should be a priority to facilitate this.

The spotted lanternfly (Lycorma delicatula) has recently spread from its native range to several other countries and forecasts predict that it may become a global invasive pest. In particular, since its confirmed presence in the United States in 2014 it has established itself as a major invasive pest in the Mid-Atlantic region where it is damaging both naturally occurring and commercially important farmed plants. Quarantine zones have been introduced to contain the infestation, but the spread to new areas continues. At present the pathways and drivers of spread are not well-understood. In particular, several human activity related factors have been proposed to contribute to the spread; however, which features of the current spread can be attributed to these factors remains unclear. Here we collect county level data on infestation status and four specific human activity related factors and use statistical methods to determine whether there is evidence for an association between the factors and infestation. Then we construct a network model based on the factors found to be associated with infestation and use it to simulate local spread. We find that the model reproduces key features of the spread 2014 to 2021. In particular, the growth of the main infestation region and the opening of spread corridors in the westward and southwestern directions is consistent with data and the model accurately forecasts the correct infestation status at the county level in 2021 with 81% accuracy. We then use the model to forecast the spread up to 2025 in a larger region. Given that this model is based on a few human activity related factors that can be targeted, it may prove useful to incorporate it into more elaborate predictive forecasting models and in informing management efforts focused on interstate highway transport and garden centers in the US and potentially for current and future invasions elsewhere globally.

Many models developed to forecast and attempt to understand the COVID-19 pandemic are highly complex, and few take collective behavior into account. As the pandemic progressed individual recurrent infection was observed and simpler susceptible-infected type models were introduced. However, these do not include mechanisms to model collective behavior. Here, we introduce an extension of the SIS model that accounts for collective behavior and show that it has four equilibria. Two of the equilibria are the standard SIS model equilibria, a third is always unstable, and a fourth where collective behavior and infection prevalence interact to produce either node-like or oscillatory dynamics. We then parameterized the model using estimates of the transmission and recovery rates for COVID-19 and present phase diagrams for fixed recovery rate and free transmission rate, and both rates fixed. We observe that regions of oscillatory dynamics exist in both cases and that the collective behavior parameter regulates their extent. Finally, we show that the system exhibits hysteresis when the collective behavior parameter varies over time. This model provides a minimal framework for explaining oscillatory phenomena such as recurring waves of infection and hysteresis effects observed in COVID-19, and other SIS-type epidemics, in terms of collective behavior.

Moving animal groups consist of many distinct individuals but can operate and function as one unit when performing different tasks. Effectively evading unexpected predator attacks is one primary task for many moving groups. The current explanation for predator evasion responses in moving animal groups require the individuals in the groups to interact via (velocity) alignment. However, experiments have shown that some animals do not use alignment. This suggests that another explanation for the predator evasion capacity in at least these species is needed. Here we establish that effective collective predator evasion does not require alignment, it can be induced via attraction and repulsion alone. We also show that speed differences between individuals that have directly observed the predator and those that have not influence evasion success and the speed of the collective evasion process, but are not required to induce the phenomenon. Our work here adds collective predator evasion to a number of phenomena previously thought to require alignment interactions that have recently been shown to emerge from attraction and repulsion alone. Based on our findings we suggest experiments and make predictions that may lead to a deeper understanding of not only collective predator evasion but also collective motion in general.

A single sheepdog can bring together and manoeuvre hundreds of sheep from one location to another. Engineers and ecologists are fascinated by this sheepdog herding because of the potential it provides for 'bio-herding': a biologically inspired herding of animal groups by robots. Although many herding algorithms have been proposed, most are studied via simulation. There are a variety of ecological problems where management of wild animal groups is currently impossible, dangerous and/or costly for humans to manage directly, and which may benefit from bio-herding solutions. Unmanned aerial vehicles (UAVs) now deliver significant benefits to the economy and society. Here, we suggest the use of UAVs for bio-herding. Given their mobility and speed, UAVs can be used in a wide range of environments and interact with animal groups at sea, over the land and in the air. We present a potential roadmap for achieving bio-herding using a pair of UAVs. In our framework, one UAV performs 'surveillance' of animal groups, informing the movement of a second UAV that herds them. We highlight the promise and flexibility of a paired UAV approach while emphasising its practical and ethical challenges. We start by describing the types of experiments and data required to understand individual and collective responses to UAVs. Next, we describe how to develop appropriate herding algorithms. Finally, we describe the integration of bio-herding algorithms into software and hardware architecture.

Moving animal groups exhibit a range of fascinating behaviors. The standard explanation for how these groups form and function is that the individual animals interact via attraction, repulsion, and alignment, where alignment is proposed to drive the collective motion. However, it has been shown both experimentally and theoretically that alignment interactions are not required to induce group level alignment. In particular, via the use of self-propelled particle models it has been established that several other mechanisms induce group level alignment (aka polarization) in combination with attraction alone. However, no systematic comparison of these mechanisms among themselves, or with explicit alignment, has been presented and it remains unclear how, or even if, they can be distinguished at the collective level. Here, we introduce two previously unreported mechanisms, burst-and-glide and burst-and-stop, and show via simulation that they also induce polarization in combination with attraction alone. Then, we compare the polarization inducing characteristics of six mechanisms; asymmetric interactions, asynchrony, anticipation, burst-and-glide, burst-and stop, and explicit alignment. We show that the mechanisms induce polarization in different parts of the attraction parameter space, that the route to polarization from uniformly random initial conditions, as well as repolarization following strong perturbations, is markedly different among the mechanisms. In particular, we find that alignment based and non-alignment based mechanisms can be distinguished via their polarization and repolarization processes. These findings further challenge the current alignment based theory of collective motion and may contribute to a more versatile theory of collective motion across scales.

Moving animal groups such as schools of fish and flocks of birds frequently switch between different group structures. Standard models of collective motion have been used successfully to explain how stable groups form via local interactions between individuals, but they are typically unable to produce groups that exhibit spontaneous switching. We are only aware of one model, constructed for barred flagtail fish that are known to rely on alignment and attraction to organize their collective motion, that has been shown to generate this type of behavior in 2D (or 3D). Interestingly, another species of fish, golden shiners, do exhibit switching but have been shown to use attraction and repulsion, not alignment, to coordinate themselves in schools. Suggesting that switching may be explained by attraction and repulsion alone, without an alignment interaction. Here we introduce a model based on attraction and repulsion only and show that groups exhibiting switching similar to that observed in experiments with golden shiners emerges. We also establish that switching occur in two boundary-free extensions of the model. Our work suggests that the bistability and switching behavior observed in golden shiners and other moving animal groups may be explained via attractive and repulsive interactions alone.

Moving animal groups often spontaneously change their group structure and dynamics, but standard models used to explain collective motion in animal groups are typically unable to generate changes of this type. Recently, a model based on attraction, repulsion and asymmetric interactions designed for specific fish experiments was shown capable of producing such changes. However, the origin of the model’s ability to generate them, and the range of this capacity, remains unknown. Here we modify and extend this model to address these questions. We establish that its ability to generate groups exhibiting changes depends on the size of the blind zone parameter β. Specifically, we show that for small β swarms and mills are generated, for larger β polarized groups forms, and for a region of intermediate β values there is a bistability region where continuous switching between milling and polarized groups occurs. We also show that the location of the bistability region depends on group size and the relative strength of velocity alignment when this interaction is added to the model. These findings may contribute to advance the use of self-propelled particle models to explain a range of disruptive phenomena previously thought to be beyond the capabilities of such models.

The spotted lanternfly (SLF) is an invasive pest that emerged in the US less than a decade ago. With few natural enemies and an ability to feed on a wide variety of readily available plants the population has grown rapidly. It is causing damage to a wide range of natural and economically important farmed plants and at present there is no known way to stop the growth and spread of the population. However, a number of control measures have been proposed to limit the growth and the effectiveness of some of these have been assessed via empirical studies. Studies to estimate the natural mortality rate of the lanternfly's different life stages and other properties of its life cycle are also available. However, no attempt to integrate this empirical information to estimate population level characteristics such as the population growth rate and the potential effects of proposed control measures can be found in the literature. Here, we introduce a simple population dynamics model parameterized using available information in the literature to obtain estimates of this type. Our model suggests that the annual growth rate of the SLF population in the US is 5.47, that only three out of six proposed control measures considered here have the potential to decrease the population even if we can find and treat each SLF in every stage, and that even with a combined strategy involving the most effective proposed control measures about 35% of all SLF in the relevant stages must be found and treated to turn the current population growth into decline. Suggesting that eradication of the spotted lanternfly over larger geographical areas in the US will be challenging, and we believe that the modeling framework presented here may be useful in providing estimates to inform feasibility assessment of proposed management efforts.

In most models of collective motion in animal groups each individual updates its heading based on the current positions and headings of its neighbors. Several authors have investigated the effects of including anticipation into models of this type, and have found that anticipation inhibits polarized collective motion in alignment based models and promotes milling and swarming in the one attraction-repulsion model studied. However, it was recently reported that polarized collective motion does emerge in an alignment based asynchronous lattice model with mutual anticipation. To our knowledge this is the only reported case where polarized collective motion has been observed in a model with anticipation. Here we show that including anticipation induces polarized collective motion in a synchronous, off lattice, attraction based model. This establishes that neither asynchrony, mutual anticipation nor motion restricted to a lattice environment are strict requirements for anticipation to promote polarized collective motion. In addition, unlike alignment based models the attraction based model used here does not produce any type of polarized collective motion in the absence of anticipation. Here anticipation is a direct polarization inducing mechanism. We believe that utilizing anticipation instead of frequently used alternatives such as explicit alignment terms, asynchronous updates and asymmetric interactions to generate polarized collective motion may be advantageous in some cases.

Animal groups frequently move in a highly organized manner, as represented by flocks of birds and schools of fish. Despite being an everyday occurrence, we do not fully understand how this works. In particular, what social interactions between animals give rise to the flock structures we observe? This question is often investigated using self-propelled particle models where particles represent the individual animals. These models differ in the social interactions used, individual particle properties, and various technical assumptions. One particular technical assumption relates to whether all particles update their headings and positions at exactly the same time (synchronous update) or not (asynchronous update). Here, we investigate the causal effects of this assumption in an attraction-only model and find that it has a dramatic impact. Polarized groups do not form when synchronous update is used, but are produced with asynchronous update, and this phenomenon is robust with respect to variation in particle displacements and inclusion of noise. Given that many important models have been implemented with synchronous update only, we speculate that our understanding of the social interactions on which they are based may be incomplete. Perhaps previously unobserved phenomena will emerge if other potentially more realistic update schemes are used.

Animals as diverse as ants and humans are faced with the tasks of collecting, transporting or herding objects. Sheepdogs do this daily when they collect, herd, and maneuver flocks of sheep. Here, we adapt a shepherding algorithm inspired by sheepdogs to collect and transport objects using a robot. Our approach produces an effective robot collection process that autonomously adapts to changing environmental conditions and is robust to noise from various sources. We suggest that this biomimetic process could be implemented into suitable robots to perform collection and transport tasks that might include – for example – cleaning up objects in the environment, keeping animals away from sensitive areas or collecting and herding animals to a specific location. Furthermore, the feedback controlled interactions between the robot and objects which we study can be used to interrogate and understand the local and global interactions of real animal groups, thus offering a novel methodology of value to researchers studying collective animal behavior.

Ants, termites and humans often form well-organized and highly efficient trails between different locations. Yet the microscopic traffic rules responsible for this organization and efficiency are not fully understood. In previous experimental studies with leaf-cutting ants (Atta colombica), a set of local priority rules were isolated and it was proposed that these rules govern the temporal and spatial organization of the traffic on the trails. Here we introduce a model based on these priority rules to investigate whether they are sufficient to produce traffic similar to that observed in the experiments on both a narrow and a wider trail. We establish that the model is able to reproduce key characteristics of the traffic on the trails. In particular, we show that the proposed priority rules induce de-synchronization into clusters of inbound and outbound ants on a narrow trail, and that priority-type dependent segregated traffic emerges on a wider trail. Due to the generic nature of the proposed priority rules we speculate that they may be used to model traffic organization in a variety of other ant species.

The costs and benefits of group living often depend on the spatial position of individuals within groups and the ability of individuals to occupy preferred positions. For example, models of predation events for moving prey groups predict higher mortality risk for individuals at the periphery and front of groups. We investigated these predictions in sardine (Sardinella aurita) schools under attack from group hunting sailfish (Istiophorus platypterus) in the open ocean. Sailfish approached sardine schools about equally often from the front and rear, but prior to attack there was a chasing period in which sardines attempted to swim away from the predator. Consequently, all sailfish attacks were directed at the rear and peripheral positions of the school, resulting in higher predation risk for individuals at these positions. During attacks, sailfish slash at sardines with their bill causing prey injury including scale removal and tissue damage. Sardines injured in previous attacks were more often found in the rear half of the school than in the front half. Moreover, injured fish had lower tail-beat frequencies and lagged behind uninjured fish. Injuries inflicted by sailfish bills may, therefore, hinder prey swimming speed and drive spatial sorting in prey schools through passive self-assortment. We found only partial support for the theoretical predictions from current predator–prey models, highlighting the importance of incorporating more realistic predator–prey dynamics into these models.This article is part of the themed issue ‘Physiological determinants of social behaviour in animals’.

Animal groups frequently move in a highly organized manner, as represented by flocks of birds and schools of fish. Despite being an everyday occurrence, we do not yet fully understand how this works. What type of social interactions between animals gives rise to the overall flock structure and behavior we observe? This question is often investigated using self-propelled particle models where particles represent the individual animals. These models differ in the social interactions used, individual particle properties, and various technical assumptions. One particular technical assumption relates to whether all particles update their headings and positions at exactly the same time (synchronous update) or not (asynchronous update). Here we investigate the causal effects of this assumption in a specific model and find that it has a dramatic impact. In particular, polarized groups do not form when synchronous update is used, but are always produced with asynchronous updates. We also show that full asynchrony is not required for polarized groups to form and quantify time to polarized group formation. Since many important models in the literature have been implemented with synchronous update only, we speculate that our understanding of these models, or rather the social interactions on which they are based, may be incomplete. Perhaps a range of previously unobserved dynamic phenomena will emerge if other potentially more realistic update schemes are chosen.

We present evidence of a novel form of group hunting. Individual sailfish ( Istiophorus platypterus ) alternate attacks with other group members on their schooling prey ( Sardinella aurita ). While only 24% of attacks result in prey capture, multiple prey are injured in 95% of attacks, resulting in an increase of injured fish in the school with the number of attacks. How quickly prey are captured is positively correlated with the level of injury of the school, suggesting that hunters can benefit from other conspecifics' attacks on the prey. To explore this, we built a mathematical model capturing the dynamics of the hunt. We show that group hunting provides major efficiency gains (prey caught per unit time) for individuals in groups of up to 70 members. We also demonstrate that a free riding strategy, where some individuals wait until the prey are sufficiently injured before attacking, is only beneficial if the cost of attacking is high, and only then when waiting times are short. Our findings provide evidence that cooperative benefits can be realized through the facilitative effects of individuals' hunting actions without spatial coordination of attacks. Such ‘proto-cooperation’ may be the pre-cursor to more complex group-hunting strategies.

In a situation with a limited common resource, cooperation between individuals sharing the resource is essential. However, people often act upon self-interest in irrational ways that threaten the long-term survival of the whole group. A lack of sustainable or environmentally responsible behavior is often observed. In this study, we examine how the maximization of benefits principle works in a wider social interactive context of personality preferences in order to gain a more realistic insight into the evolution of cooperation. We used time perspective (TP), a concept reflecting individual differences in orientation towards past, present, or future, and relevant for making sustainable choices. We developed a personality-driven agent-based model that explores the role of personality in the outcomes of social dilemmas and includes multiple facets of diversity: (1) The agents have different behavior strategies: individual differences derived by applying cluster analysis to survey data from 22 countries (N = 10,940) and resulting in 7 cross-cultural profiles of TP; (2) The non-uniform distribution of the types of agents across countries; (3) The diverse interactions between the agents; and (4) diverse responses to those interactions in a well-mixed population. As one of the results, we introduced an index of overall cooperation for each of the 22 countries, which was validated against cultural, economic, and sustainability indicators (HDI, dimensions of national culture, and Environment Performance Index). It was associated with higher human development, higher individualism, lower power distance, and better environmental performance. The findings illustrate how individual differences in TP can be simulated to predict the ways people in different countries solve the personal vs. common gain dilemma in the global limited-resource situation. This interdisciplinary approach to social simulation can be adopted to explain the possible causes of global environmental issues and to predict their possible outcomes.